code: 
  
  import json
  import logging
  import os
  import random
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  
  import evaluate
  import numpy as np
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoImageProcessor,
      AutoModelForSemanticSegmentation,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
  )
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
  __A   :     Any      =			logging.getLogger(__name__)
  
  # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
  check_min_version('''4.31.0''')
  
  require_version('''datasets>=2.0.0''', '''To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt''')
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=0				)					-> Tuple:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      (size, size) if isinstance(__lowerCAmelCase,      __lowerCAmelCase				) else size
      lowerCAmelCase				:			List[str]    			=      img.size
      lowerCAmelCase				:			int    			=      size[1] - original_height if original_height < size[1] else 0
      lowerCAmelCase				:			str    			=      size[0] - original_width if original_width < size[0] else 0
      lowerCAmelCase				:			int    			=      functional.pad(__lowerCAmelCase,      (0, 0, pad_width, pad_height),      fill=__lowerCAmelCase				)
      return img
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			Dict    			=      transforms
  
  
  
  
  
  
      def __call__(	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ):
  
  
  
  
  
  
          for t in self.transforms:
              lowerCAmelCase				:			Optional[int]    			=      t(UpperCAmelCase_    ,				UpperCAmelCase_    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Optional[int]    ):
          pass
  
  
  
  
  
  
      def __call__(	self			:    Any    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    List[str]    ):
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    List[Any]    ):
          lowerCAmelCase				:			Dict    			=      size
  
  
  
  
  
  
      def __call__(	self			:    List[str]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			Any    			=      functional.resize(UpperCAmelCase_    ,				self.size    )
          lowerCAmelCase				:			str    			=      functional.resize(UpperCAmelCase_    ,				self.size    ,				interpolation=transforms.InterpolationMode.NEAREST    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Tuple    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[Any]=None    ):
          lowerCAmelCase				:			Optional[int]    			=      min_size
          if max_size is None:
              lowerCAmelCase				:			Optional[Any]    			=      min_size
          lowerCAmelCase				:			str    			=      max_size
  
  
  
  
  
  
      def __call__(	self			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ):
          lowerCAmelCase				:			Optional[int]    			=      random.randint(self.min_size    ,				self.max_size    )
          lowerCAmelCase				:			Optional[Any]    			=      functional.resize(UpperCAmelCase_    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      functional.resize(UpperCAmelCase_    ,				UpperCAmelCase_    ,				interpolation=transforms.InterpolationMode.NEAREST    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    str    ,				UpperCAmelCase_			:    Any    ):
          lowerCAmelCase				:			Optional[int]    			=      size if isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ) else (size, size)
  
  
  
  
  
  
      def __call__(	self			:    List[str]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			Optional[Any]    			=      pad_if_smaller(UpperCAmelCase_    ,				self.size    )
          lowerCAmelCase				:			Dict    			=      pad_if_smaller(UpperCAmelCase_    ,				self.size    ,				fill=255    )
          lowerCAmelCase				:			Optional[Any]    			=      transforms.RandomCrop.get_params(UpperCAmelCase_    ,				self.size    )
          lowerCAmelCase				:			int    			=      functional.crop(UpperCAmelCase_    ,				*UpperCAmelCase_    )
          lowerCAmelCase				:			List[Any]    			=      functional.crop(UpperCAmelCase_    ,				*UpperCAmelCase_    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Any    ,				UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			int    			=      flip_prob
  
  
  
  
  
  
      def __call__(	self			:    str    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Any    ):
  
  
  
  
  
  
          if random.random() < self.flip_prob:
              lowerCAmelCase				:			Tuple    			=      functional.hflip(UpperCAmelCase_    )
              lowerCAmelCase				:			Tuple    			=      functional.hflip(UpperCAmelCase_    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __call__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    List[str]    ):
          lowerCAmelCase				:			Tuple    			=      functional.pil_to_tensor(UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      torch.as_tensor(np.array(UpperCAmelCase_    )    ,				dtype=torch.intaa    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    Tuple    ,				UpperCAmelCase_			:    Tuple    ):
          lowerCAmelCase				:			Dict    			=      dtype
  
  
  
  
  
  
      def __call__(	self			:    int    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    str    ):
          lowerCAmelCase				:			Optional[Any]    			=      functional.convert_image_dtype(UpperCAmelCase_    ,				self.dtype    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ):
          lowerCAmelCase				:			str    			=      mean
          lowerCAmelCase				:			List[str]    			=      std
  
  
  
  
  
  
      def __call__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			int    			=      functional.normalize(UpperCAmelCase_    ,				mean=self.mean    ,				std=self.std    )
          return image, target
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __call__(	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Any    ):
  
  
  
  
  
  
          if not isinstance(UpperCAmelCase_    ,				np.ndarray    ):
              lowerCAmelCase				:			Any    			=      np.array(UpperCAmelCase_    ).astype(np.uinta    )
          # avoid using underflow conversion
          lowerCAmelCase				:			int    			=      255
          lowerCAmelCase				:			Tuple    			=      target - 1
          lowerCAmelCase				:			List[Any]    			=      255
  
          lowerCAmelCase				:			Optional[Any]    			=      Image.fromarray(UpperCAmelCase_    )
          return image, target
  
  
  
  
  
  
  @dataclass
  class    __A				:
      lowerCAmelCase_							:		Optional[str]						=							field(
          default="segments/sidewalk-semantic"						,						metadata={
              "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
          }						,						)
      lowerCAmelCase_							:		Optional[str]						=							field(
          default=A__						,						metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
      lowerCAmelCase_							:		Optional[float]						=							field(
          default=0.1_5						,						metadata={"help": "Percent to split off of train for validation."} )
      lowerCAmelCase_							:		Optional[int]						=							field(
          default=A__						,						metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of training examples to this "
                  "value if set."
              )
          }						,						)
      lowerCAmelCase_							:		Optional[int]						=							field(
          default=A__						,						metadata={
              "help": (
                  "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                  "value if set."
              )
          }						,						)
      lowerCAmelCase_							:		Optional[bool]						=							field(
          default=A__						,						metadata={"help": "Whether or not to reduce all labels by 1 and replace background by 255."}						,						)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
  
  
  
  
  
  
          if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
              raise ValueError(
                  'You must specify either a dataset name from the hub or a train and/or validation directory.'    )
  
  
  
  
  
  
  @dataclass
  class    __A				:
      lowerCAmelCase_							:		str						=							field(
          default="nvidia/mit-b0"						,						metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						,						)
      lowerCAmelCase_							:		Optional[str]						=							field(
          default=A__						,						metadata={"help": "Pretrained config name or path if not the same as model_name"} )
      lowerCAmelCase_							:		Optional[str]						=							field(
          default=A__						,						metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} )
      lowerCAmelCase_							:		str						=							field(
          default="main"						,						metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,						)
      lowerCAmelCase_							:		str						=							field(default=A__						,						metadata={"help": "Name or path of preprocessor config."} )
      lowerCAmelCase_							:		bool						=							field(
          default=A__						,						metadata={
              "help": (
                  "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                  "with private models)."
              )
          }						,						)
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							)					-> Union[str, Any]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
      if len(sys.argv				) == 2 and sys.argv[1].endswith('.json'				):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          lowerCAmelCase				:			Optional[Any]    			=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
          lowerCAmelCase				:			List[str]    			=      parser.parse_args_into_dataclasses()
  
      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry('run_semantic_segmentation',      __lowerCAmelCase,      __lowerCAmelCase				)
  
      # Setup logging
      logging.basicConfig(
          format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',      datefmt='%m/%d/%Y %H:%M:%S',      handlers=[logging.StreamHandler(sys.stdout				)],      )
  
      if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()
  
      lowerCAmelCase				:			Any    			=      training_args.get_process_log_level()
      logger.setLevel(__lowerCAmelCase				)
      transformers.utils.logging.set_verbosity(__lowerCAmelCase				)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
          + f"distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}"				)
      logger.info(f"Training/evaluation parameters {training_args}"				)
  
      # Detecting last checkpoint.
      lowerCAmelCase				:			Optional[int]    			=      None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          lowerCAmelCase				:			List[str]    			=      get_last_checkpoint(training_args.output_dir				)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  'Use --overwrite_output_dir to overcome.'				)
          elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'				)
  
      # Load dataset
      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      # TODO support datasets from local folders
      lowerCAmelCase				:			Union[str, Any]    			=      load_dataset(data_args.dataset_name,      cache_dir=model_args.cache_dir				)
  
      # Rename column names to standardized names (only "image" and "label" need to be present)
      if "pixel_values" in dataset["train"].column_names:
          lowerCAmelCase				:			str    			=      dataset.rename_columns({'pixel_values': 'image'}				)
      if "annotation" in dataset["train"].column_names:
          lowerCAmelCase				:			int    			=      dataset.rename_columns({'annotation': 'label'}				)
  
      # If we don't have a validation split, split off a percentage of train as validation.
      lowerCAmelCase				:			List[str]    			=      None if "validation" in dataset.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split,      __lowerCAmelCase				) and data_args.train_val_split > 0.0:
          lowerCAmelCase				:			List[str]    			=      dataset["train"].train_test_split(data_args.train_val_split				)
          lowerCAmelCase				:			int    			=      split["train"]
          lowerCAmelCase				:			Tuple    			=      split["test"]
  
      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      if data_args.dataset_name == "scene_parse_150":
          lowerCAmelCase				:			Optional[int]    			=      "huggingface/label-files"
          lowerCAmelCase				:			Union[str, Any]    			=      "ade20k-id2label.json"
      else:
          lowerCAmelCase				:			List[str]    			=      data_args.dataset_name
          lowerCAmelCase				:			List[str]    			=      "id2label.json"
      lowerCAmelCase				:			str    			=      json.load(open(hf_hub_download(__lowerCAmelCase,      __lowerCAmelCase,      repo_type='dataset'				),      'r'				)				)
      lowerCAmelCase				:			Dict    			=      {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
      lowerCAmelCase				:			Tuple    			=      {v: str(__lowerCAmelCase				) for k, v in idalabel.items()}
  
      # Load the mean IoU metric from the datasets package
      lowerCAmelCase				:			List[str]    			=      evaluate.load('mean_iou'				)
  
      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      @torch.no_grad()
      def compute_metrics(_UpperCAmelCase				):
          lowerCAmelCase				:			List[Any]    			=      eval_pred
          lowerCAmelCase				:			Dict    			=      torch.from_numpy(__lowerCAmelCase				)
          # scale the logits to the size of the label
          lowerCAmelCase				:			List[str]    			=      nn.functional.interpolate(
              __lowerCAmelCase,      size=labels.shape[-2:],      mode='bilinear',      align_corners=__lowerCAmelCase,      ).argmax(dim=1				)
  
          lowerCAmelCase				:			Optional[Any]    			=      logits_tensor.detach().cpu().numpy()
          lowerCAmelCase				:			Any    			=      metric.compute(
              predictions=__lowerCAmelCase,      references=__lowerCAmelCase,      num_labels=len(__lowerCAmelCase				),      ignore_index=0,      reduce_labels=image_processor.do_reduce_labels,      )
          # add per category metrics as individual key-value pairs
          lowerCAmelCase				:			str    			=      metrics.pop('per_category_accuracy'				).tolist()
          lowerCAmelCase				:			List[str]    			=      metrics.pop('per_category_iou'				).tolist()
  
          metrics.update({f"accuracy_{idalabel[i]}": v for i, v in enumerate(__lowerCAmelCase				)}				)
          metrics.update({f"iou_{idalabel[i]}": v for i, v in enumerate(__lowerCAmelCase				)}				)
  
          return metrics
  
      lowerCAmelCase				:			int    			=      AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path,      labelaid=__lowerCAmelCase,      idalabel=__lowerCAmelCase,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
      lowerCAmelCase				:			str    			=      AutoModelForSemanticSegmentation.from_pretrained(
          model_args.model_name_or_path,      from_tf=bool('.ckpt' in model_args.model_name_or_path				),      config=__lowerCAmelCase,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
      lowerCAmelCase				:			List[str]    			=      AutoImageProcessor.from_pretrained(
          model_args.image_processor_name or model_args.model_name_or_path,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
  
      # Define torchvision transforms to be applied to each image + target.
      # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
      # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
      if "shortest_edge" in image_processor.size:
          # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
          lowerCAmelCase				:			Optional[int]    			=      (image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
      else:
          lowerCAmelCase				:			List[Any]    			=      (image_processor.size["height"], image_processor.size["width"])
      lowerCAmelCase				:			Any    			=      Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              RandomCrop(size=__lowerCAmelCase				),
              RandomHorizontalFlip(flip_prob=0.5				),
              PILToTensor(),
              ConvertImageDtype(torch.float				),
              Normalize(mean=image_processor.image_mean,      std=image_processor.image_std				),
          ]				)
      # Define torchvision transform to be applied to each image.
      # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
      lowerCAmelCase				:			Optional[int]    			=      Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              Resize(size=__lowerCAmelCase				),
              PILToTensor(),
              ConvertImageDtype(torch.float				),
              Normalize(mean=image_processor.image_mean,      std=image_processor.image_std				),
          ]				)
  
      def preprocess_train(_UpperCAmelCase				):
          lowerCAmelCase				:			Optional[int]    			=      []
          lowerCAmelCase				:			List[Any]    			=      []
          for image, target in zip(example_batch['image'],      example_batch['label']				):
              lowerCAmelCase				:			Union[str, Any]    			=      train_transforms(image.convert('RGB'				),      __lowerCAmelCase				)
              pixel_values.append(__lowerCAmelCase				)
              labels.append(__lowerCAmelCase				)
  
          lowerCAmelCase				:			List[str]    			=      {}
          lowerCAmelCase				:			List[str]    			=      torch.stack(__lowerCAmelCase				)
          lowerCAmelCase				:			List[str]    			=      torch.stack(__lowerCAmelCase				)
  
          return encoding
  
      def preprocess_val(_UpperCAmelCase				):
          lowerCAmelCase				:			List[Any]    			=      []
          lowerCAmelCase				:			List[Any]    			=      []
          for image, target in zip(example_batch['image'],      example_batch['label']				):
              lowerCAmelCase				:			Any    			=      val_transforms(image.convert('RGB'				),      __lowerCAmelCase				)
              pixel_values.append(__lowerCAmelCase				)
              labels.append(__lowerCAmelCase				)
  
          lowerCAmelCase				:			Optional[Any]    			=      {}
          lowerCAmelCase				:			str    			=      torch.stack(__lowerCAmelCase				)
          lowerCAmelCase				:			List[Any]    			=      torch.stack(__lowerCAmelCase				)
  
          return encoding
  
      if training_args.do_train:
          if "train" not in dataset:
              raise ValueError('--do_train requires a train dataset'				)
          if data_args.max_train_samples is not None:
              lowerCAmelCase				:			Optional[Any]    			=      (
                  dataset["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
              )
          # Set the training transforms
          dataset["train"].set_transform(__lowerCAmelCase				)
  
      if training_args.do_eval:
          if "validation" not in dataset:
              raise ValueError('--do_eval requires a validation dataset'				)
          if data_args.max_eval_samples is not None:
              lowerCAmelCase				:			Optional[int]    			=      (
                  dataset["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
              )
          # Set the validation transforms
          dataset["validation"].set_transform(__lowerCAmelCase				)
  
      # Initalize our trainer
      lowerCAmelCase				:			int    			=      Trainer(
          model=__lowerCAmelCase,      args=__lowerCAmelCase,      train_dataset=dataset['train'] if training_args.do_train else None,      eval_dataset=dataset['validation'] if training_args.do_eval else None,      compute_metrics=__lowerCAmelCase,      tokenizer=__lowerCAmelCase,      data_collator=__lowerCAmelCase,      )
  
      # Training
      if training_args.do_train:
          lowerCAmelCase				:			str    			=      None
          if training_args.resume_from_checkpoint is not None:
              lowerCAmelCase				:			List[Any]    			=      training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              lowerCAmelCase				:			List[Any]    			=      last_checkpoint
          lowerCAmelCase				:			List[str]    			=      trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
          trainer.save_model()
          trainer.log_metrics('train',      train_result.metrics				)
          trainer.save_metrics('train',      train_result.metrics				)
          trainer.save_state()
  
      # Evaluation
      if training_args.do_eval:
          lowerCAmelCase				:			Dict    			=      trainer.evaluate()
          trainer.log_metrics('eval',      __lowerCAmelCase				)
          trainer.save_metrics('eval',      __lowerCAmelCase				)
  
      # Write model card and (optionally) push to hub
      lowerCAmelCase				:			int    			=      {
          "finetuned_from": model_args.model_name_or_path,
          "dataset": data_args.dataset_name,
          "tags": ["image-segmentation", "vision"],
      }
      if training_args.push_to_hub:
          trainer.push_to_hub(**__lowerCAmelCase				)
      else:
          trainer.create_model_card(**__lowerCAmelCase				)
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  
  
style_context: 
  
  import argparse
  import logging
  import os
  import sys
  import tempfile
  from pathlib import Path
  
  import lightning_base
  import pytest
  import pytorch_lightning as pl
  import torch
  from convert_pl_checkpoint_to_hf import convert_pl_to_hf
  from distillation import distill_main
  from finetune import SummarizationModule, main
  from huggingface_hub import list_models
  from parameterized import parameterized
  from run_eval import generate_summaries_or_translations
  from torch import nn
  
  from transformers import AutoConfig, AutoModelForSeqaSeqLM
  from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
  from utils import label_smoothed_nll_loss, lmap, load_json
  
  
  logging.basicConfig(level=logging.DEBUG)
  
  __A   :     Dict      =			logging.getLogger()
  __A   :     str      =			torch.cuda.is_available()
  __A   :     Union[str, Any]      =			{
      '''max_tokens_per_batch''': None,
      '''supervise_forward''': True,
      '''normalize_hidden''': True,
      '''label_smoothing''': 0.2,
      '''eval_max_gen_length''': None,
      '''eval_beams''': 1,
      '''val_metric''': '''loss''',
      '''save_top_k''': 1,
      '''adafactor''': True,
      '''early_stopping_patience''': 2,
      '''logger_name''': '''default''',
      '''length_penalty''': 0.5,
      '''cache_dir''': '''''',
      '''task''': '''summarization''',
      '''num_workers''': 2,
      '''alpha_hid''': 0,
      '''freeze_embeds''': True,
      '''enc_only''': False,
      '''tgt_suffix''': '''''',
      '''resume_from_checkpoint''': None,
      '''sortish_sampler''': True,
      '''student_decoder_layers''': 1,
      '''val_check_interval''': 1.0,
      '''output_dir''': '''''',
      '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      '''no_teacher''': False,
      '''fp16_opt_level''': '''O1''',
      '''gpus''': 1 if CUDA_AVAILABLE else 0,
      '''n_tpu_cores''': 0,
      '''max_grad_norm''': 1.0,
      '''do_train''': True,
      '''do_predict''': True,
      '''accumulate_grad_batches''': 1,
      '''server_ip''': '''''',
      '''server_port''': '''''',
      '''seed''': 42,
      '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
      '''config_name''': '''''',
      '''tokenizer_name''': '''facebook/bart-large''',
      '''do_lower_case''': False,
      '''learning_rate''': 0.3,
      '''lr_scheduler''': '''linear''',
      '''weight_decay''': 0.0,
      '''adam_epsilon''': 1E-08,
      '''warmup_steps''': 0,
      '''max_epochs''': 1,
      '''train_batch_size''': 2,
      '''eval_batch_size''': 2,
      '''max_source_length''': 12,
      '''max_target_length''': 12,
      '''val_max_target_length''': 12,
      '''test_max_target_length''': 12,
      '''fast_dev_run''': False,
      '''no_cache''': False,
      '''n_train''': -1,
      '''n_val''': -1,
      '''n_test''': -1,
      '''student_encoder_layers''': 1,
      '''freeze_encoder''': False,
      '''auto_scale_batch_size''': False,
      '''overwrite_output_dir''': False,
      '''student''': None,
  }
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      '\n'.join(_UpperCAmelCase				)
      Path(_UpperCAmelCase				).open('w'				).writelines(_UpperCAmelCase				)
  
  
  __A   :     Optional[int]      =			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
  __A   :     Dict      =			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
  __A   :     str      =			'''patrickvonplaten/t5-tiny-random'''
  __A   :     int      =			'''sshleifer/t5-tinier-random'''
  __A   :     Optional[int]      =			'''sshleifer/bart-tiny-random'''
  __A   :     List[str]      =			'''sshleifer/tiny-mbart'''
  __A   :     int      =			'''sshleifer/tiny-marian-en-de'''
  __A   :     Tuple      =			'''stas/tiny-wmt19-en-de'''
  
  
  __A   :     Any      =			logging.StreamHandler(sys.stdout)
  logger.addHandler(stream_handler)
  logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> Optional[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(_UpperCAmelCase,      f"{split}.source"				),      _UpperCAmelCase				)
          _dump_articles(os.path.join(_UpperCAmelCase,      f"{split}.target"				),      _UpperCAmelCase				)
      return tmp_dir
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @classmethod
      def 	lowercase__ (	cls			:    Optional[int]    ):
          logging.disable(logging.CRITICAL    )  # remove noisy download output from tracebacks
          return cls
  
  
  
  
  
  
      @slow
      @require_torch_gpu
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Union[str, Any]    			=      list_models()
          lowerCAmelCase				:			Optional[int]    			=      'sshleifer'
          lowerCAmelCase				:			List[str]    			=      [x.modelId for x in model_list if x.modelId.startswith(UpperCAmelCase_    )]
          lowerCAmelCase				:			Optional[int]    			=      ['sshleifer/blenderbot-3B', 'sshleifer/blenderbot-90M']
          lowerCAmelCase				:			int    			=      []
          for m in model_ids:
              if m in allowed_to_be_broken:
                  continue
              try:
                  AutoConfig.from_pretrained(UpperCAmelCase_    )
              except Exception:
                  failures.append(UpperCAmelCase_    )
          assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          lowerCAmelCase				:			List[Any]    			=      {'student_encoder_layers': 2, 'student_decoder_layers': 1, 'no_teacher': True}
          self._test_distiller_cli(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			List[str]    			=      {
              'student_encoder_layers': 2,
              'student_decoder_layers': 1,
              'max_epochs': 4,
              'val_check_interval': 0.25,
              'alpha_hid': 2.0,
              'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
          }
          lowerCAmelCase				:			Dict    			=      self._test_distiller_cli(UpperCAmelCase_    ,				check_contents=UpperCAmelCase_    )
  
          lowerCAmelCase				:			int    			=      list(Path(model.output_dir    ).glob('*.ckpt'    )    )
          self.assertEqual(1    ,				len(UpperCAmelCase_    )    )
          lowerCAmelCase				:			Any    			=      list(Path(model.output_dir    ).glob('**/*.bin'    )    )
          self.assertEqual(len(UpperCAmelCase_    )    ,				2    )
          lowerCAmelCase				:			Dict    			=      lmap(str.strip    ,				Path(model.hparams.data_dir    ).joinpath('test.source'    ).open().readlines()    )
          lowerCAmelCase				:			Optional[Any]    			=      tempfile.mktemp()  # XXX: not being cleaned up
          generate_summaries_or_translations(UpperCAmelCase_    ,				UpperCAmelCase_    ,				str(model.output_dir / 'best_tfmr'    )    )
          self.assertTrue(Path(UpperCAmelCase_    ).exists()    )
  
          lowerCAmelCase				:			Optional[int]    			=      self.get_auto_remove_tmp_dir()
          convert_pl_to_hf(ckpts[0]    ,				transformer_ckpts[0].parent    ,				UpperCAmelCase_    )
          assert os.path.exists(os.path.join(UpperCAmelCase_    ,				'pytorch_model.bin'    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			Optional[int]    			=      AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_    )
          lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      model.dummy_inputs['input_ids'], model.dummy_inputs['attention_mask']
          lowerCAmelCase				:			List[Any]    			=      torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]    ,				dtype=torch.long    ,				device=model.device    )
          lowerCAmelCase				:			List[str]    			=      target_ids[:, :-1].contiguous()  # Why this line?
          lowerCAmelCase				:			List[Any]    			=      target_ids[:, 1:].clone()  # why clone?
          lowerCAmelCase				:			Any    			=      model(
              UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				decoder_input_ids=UpperCAmelCase_    ,				labels=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ).loss
  
          lowerCAmelCase				:			Union[str, Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				decoder_input_ids=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    ).logits
  
          lowerCAmelCase				:			Optional[int]    			=      nn.functional.log_softmax(UpperCAmelCase_    ,				dim=-1    )
          lowerCAmelCase			,  lowerCAmelCase				:			Any    			=      label_smoothed_nll_loss(
              UpperCAmelCase_    ,				UpperCAmelCase_    ,				0.1    ,				ignore_index=model.config.pad_token_id    )
          with self.assertRaises(UpperCAmelCase_    ):
              # TODO: understand why this breaks
              self.assertEqual(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          lowerCAmelCase				:			Any    			=      {
              'student_encoder_layers': 2,
              'student_decoder_layers': 1,
              'num_train_epochs': 4,
              'val_check_interval': 0.25,
              'alpha_hid': 2.0,
              'task': 'translation',
              'model_name_or_path': 'IGNORE_THIS_IT_DOESNT_GET_USED',
              'tokenizer_name': MBART_TINY,
              'teacher': MBART_TINY,
              'src_lang': 'en_XX',
              'tgt_lang': 'ro_RO',
          }
          lowerCAmelCase				:			Dict    			=      self._test_distiller_cli(UpperCAmelCase_    ,				check_contents=UpperCAmelCase_    )
          assert model.model.config.model_type == "mbart"
  
          lowerCAmelCase				:			Any    			=      list(Path(model.output_dir    ).glob('*.ckpt'    )    )
          self.assertEqual(1    ,				len(UpperCAmelCase_    )    )
          lowerCAmelCase				:			int    			=      list(Path(model.output_dir    ).glob('**/*.bin'    )    )
          lowerCAmelCase				:			Dict    			=      list(Path(model.output_dir    ).glob('best_tfmr/*'    )    )
          assert len(UpperCAmelCase_    ) > 2
          self.assertEqual(len(UpperCAmelCase_    )    ,				2    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Union[str, Any]    			=      {
              'student_encoder_layers': 1,
              'student_decoder_layers': 1,
              'alpha_hid': 2.0,
              'teacher': T5_TINY,
              'model_name_or_path': T5_TINY,
              'tokenizer_name': T5_TINY,
          }
          self._test_distiller_cli(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Union[str, Any]    			=      {
              'teacher': T5_TINY,
              'student': T5_TINIER,
              'model_name_or_path': T5_TINIER,
              'tokenizer_name': T5_TINIER,
          }
          self._test_distiller_cli(UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    Dict=True    ):
          lowerCAmelCase				:			Optional[Any]    			=      {
              'label_smoothing': 0.0,
              'early_stopping_patience': -1,
              'train_batch_size': 1,
              'eval_batch_size': 2,
              'max_epochs': 2,
              'alpha_mlm': 0.2,
              'alpha_ce': 0.8,
              'do_predict': True,
              'model_name_or_path': 'sshleifer/tinier_bart',
              'teacher': CHEAP_ARGS['model_name_or_path'],
              'val_check_interval': 0.5,
          }
          default_updates.update(UpperCAmelCase_    )
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
          lowerCAmelCase				:			Optional[Any]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
          lowerCAmelCase				:			List[str]    			=      self.get_auto_remove_tmp_dir()
  
          args_d.update(data_dir=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				**UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      distill_main(argparse.Namespace(**UpperCAmelCase_    )    )
          if not check_contents:
              return model
          lowerCAmelCase				:			Tuple    			=      os.listdir(UpperCAmelCase_    )
          lowerCAmelCase				:			Union[str, Any]    			=      {os.path.basename(UpperCAmelCase_    ) for p in contents}
          lowerCAmelCase				:			List[str]    			=      [p for p in contents if p.endswith('ckpt'    )]
          assert len(UpperCAmelCase_    ) > 0
  
          self.assertIn('test_generations.txt'    ,				UpperCAmelCase_    )
          self.assertIn('test_results.txt'    ,				UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[int]    			=      load_json(model.metrics_save_path    )
          lowerCAmelCase				:			Optional[int]    			=      metrics['val'][-1]
          self.assertGreaterEqual(last_step_stats['val_avg_gen_time']    ,				0.01    )
          self.assertGreaterEqual(1.0    ,				last_step_stats['val_avg_gen_time']    )
          self.assertIsInstance(last_step_stats[f"val_avg_{model.val_metric}"]    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[Any]    			=      int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) + 1    )
          self.assertEqual(len(metrics['val']    )    ,				UpperCAmelCase_    )
          self.assertEqual(len(metrics['test']    )    ,				1    )
          return model
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      @parameterized.expand(
          [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]    ,				)
      def 	lowercase__ (	self			:    int    ,				UpperCAmelCase_			:    int    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
          lowerCAmelCase				:			Any    			=      'translation' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else 'summarization'
          lowerCAmelCase				:			Union[str, Any]    			=      0.1 if task == 'translation' else 0
  
          lowerCAmelCase				:			Optional[int]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
          lowerCAmelCase				:			str    			=      self.get_auto_remove_tmp_dir()
          args_d.update(
              data_dir=UpperCAmelCase_    ,				model_name_or_path=UpperCAmelCase_    ,				tokenizer_name=UpperCAmelCase_    ,				train_batch_size=2    ,				eval_batch_size=2    ,				output_dir=UpperCAmelCase_    ,				do_predict=UpperCAmelCase_    ,				task=UpperCAmelCase_    ,				src_lang='en_XX'    ,				tgt_lang='ro_RO'    ,				freeze_encoder=UpperCAmelCase_    ,				freeze_embeds=UpperCAmelCase_    ,				)
          assert "n_train" in args_d
          lowerCAmelCase				:			List[str]    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      main(UpperCAmelCase_    )
  
          lowerCAmelCase				:			Tuple    			=      module.model.get_input_embeddings()
          assert not input_embeds.weight.requires_grad
          if model == T5_TINY:
              lowerCAmelCase				:			Union[str, Any]    			=      module.model.lm_head
              assert not lm_head.weight.requires_grad
              assert (lm_head.weight == input_embeds.weight).all().item()
          elif model == FSMT_TINY:
              lowerCAmelCase				:			Union[str, Any]    			=      module.model.model
              lowerCAmelCase				:			Any    			=      fsmt.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not fsmt.decoder.embed_tokens.weight.requires_grad
              # check that embeds are not the same
              assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
          else:
              lowerCAmelCase				:			Tuple    			=      module.model.model
              lowerCAmelCase				:			Dict    			=      bart.decoder.embed_positions
              assert not embed_pos.weight.requires_grad
              assert not bart.shared.weight.requires_grad
              # check that embeds are the same
              assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
              assert bart.decoder.embed_tokens == bart.shared
  
          lowerCAmelCase				:			Tuple    			=      load_json(module.output_dir / 'text_batch.json'    )
          assert isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    )
          assert len(UpperCAmelCase_    ) >= 4
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
  
          lowerCAmelCase				:			List[Any]    			=      'summarization'
          lowerCAmelCase				:			int    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  
          args_d.update(
              data_dir=UpperCAmelCase_    ,				tokenizer_name=UpperCAmelCase_    ,				train_batch_size=2    ,				eval_batch_size=2    ,				do_predict=UpperCAmelCase_    ,				task=UpperCAmelCase_    ,				src_lang='en_XX'    ,				tgt_lang='ro_RO'    ,				freeze_encoder=UpperCAmelCase_    ,				freeze_embeds=UpperCAmelCase_    ,				)
  
          # test models whose config includes the extra_model_args
          lowerCAmelCase				:			Optional[int]    			=      BART_TINY
          lowerCAmelCase				:			int    			=      self.get_auto_remove_tmp_dir()
          lowerCAmelCase				:			int    			=      args_d.copy()
          args_da.update(
              model_name_or_path=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			List[Any]    			=      ('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
          for p in extra_model_params:
              lowerCAmelCase				:			str    			=      0.5
          lowerCAmelCase				:			Any    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			str    			=      main(UpperCAmelCase_    )
          for p in extra_model_params:
              assert getattr(model.config    ,				UpperCAmelCase_    ) == 0.5, f"failed to override the model config for param {p}"
  
          # test models whose config doesn't include the extra_model_args
          lowerCAmelCase				:			Any    			=      T5_TINY
          lowerCAmelCase				:			Any    			=      self.get_auto_remove_tmp_dir()
          lowerCAmelCase				:			Tuple    			=      args_d.copy()
          args_da.update(
              model_name_or_path=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				)
          lowerCAmelCase				:			Any    			=      'encoder_layerdrop'
          lowerCAmelCase				:			Dict    			=      0.5
          lowerCAmelCase				:			str    			=      argparse.Namespace(**UpperCAmelCase_    )
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              lowerCAmelCase				:			Optional[Any]    			=      main(UpperCAmelCase_    )
          assert str(excinfo.value    ) == f"model config doesn't have a `{unsupported_param}` attribute"
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Tuple    ):
          lowerCAmelCase				:			dict    			=      CHEAP_ARGS.copy()
  
          lowerCAmelCase				:			Dict    			=      'summarization'
          lowerCAmelCase				:			Union[str, Any]    			=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
  
          lowerCAmelCase				:			List[str]    			=      BART_TINY
          lowerCAmelCase				:			Dict    			=      self.get_auto_remove_tmp_dir()
  
          args_d.update(
              data_dir=UpperCAmelCase_    ,				model_name_or_path=UpperCAmelCase_    ,				output_dir=UpperCAmelCase_    ,				tokenizer_name=UpperCAmelCase_    ,				train_batch_size=2    ,				eval_batch_size=2    ,				do_predict=UpperCAmelCase_    ,				task=UpperCAmelCase_    ,				src_lang='en_XX'    ,				tgt_lang='ro_RO'    ,				freeze_encoder=UpperCAmelCase_    ,				freeze_embeds=UpperCAmelCase_    ,				)
  
          # emulate finetune.py
          lowerCAmelCase				:			Dict    			=      argparse.ArgumentParser()
          lowerCAmelCase				:			List[str]    			=      pl.Trainer.add_argparse_args(UpperCAmelCase_    )
          lowerCAmelCase				:			Tuple    			=      SummarizationModule.add_model_specific_args(UpperCAmelCase_    ,				os.getcwd()    )
          lowerCAmelCase				:			str    			=      {'--help': True}
  
          # --help test
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              with CaptureStdout() as cs:
                  lowerCAmelCase				:			List[str]    			=      parser.parse_args(UpperCAmelCase_    )
              assert False, "--help is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCAmelCase				:			Optional[int]    			=      lightning_base.arg_to_scheduler_metavar
          assert expected in cs.out, "--help is expected to list the supported schedulers"
  
          # --lr_scheduler=non_existing_scheduler test
          lowerCAmelCase				:			int    			=      'non_existing_scheduler'
          lowerCAmelCase				:			Optional[Any]    			=      {f"--lr_scheduler={unsupported_param}"}
          with pytest.raises(UpperCAmelCase_    ) as excinfo:
              with CaptureStderr() as cs:
                  lowerCAmelCase				:			int    			=      parser.parse_args(UpperCAmelCase_    )
              assert False, "invalid argument is expected to sys.exit"
          assert excinfo.type == SystemExit
          lowerCAmelCase				:			Dict    			=      f"invalid choice: '{unsupported_param}'"
          assert expected in cs.err, f"should have bailed on invalid choice of scheduler {unsupported_param}"
  
          # --lr_scheduler=existing_scheduler test
          lowerCAmelCase				:			Optional[int]    			=      'cosine'
          lowerCAmelCase				:			Tuple    			=      args_d.copy()
          lowerCAmelCase				:			str    			=      supported_param
          lowerCAmelCase				:			List[str]    			=      argparse.Namespace(**UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      main(UpperCAmelCase_    )
          assert (
              getattr(model.hparams    ,				'lr_scheduler'    ) == supported_param
          ), f"lr_scheduler={supported_param} shouldn't fail"
  
  
  
  
  
  