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
  lowerCamelCase										=  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        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase=0      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	(size, size) if isinstance(_lowercase,      _lowercase      ) else size
      _lowercase ,    _lowercase     =	img.size
      _lowercase     =	size[1] - original_height if original_height < size[1] else 0
      _lowercase     =	size[0] - original_width if original_width < size[0] else 0
      _lowercase     =	functional.pad(_lowercase,      (0, 0, pad_width, pad_height),      fill=_lowercase      )
      return img
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Tuple   ,       snake_case__:	str	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	transforms
  
     def __call__(    self:	List[str]   ,       snake_case__:	Optional[int]   ,       snake_case__:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         for t in self.transforms:
             _lowercase ,    _lowercase     =	t(snake_case__   ,       snake_case__	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	str	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def __call__(    self:	Dict   ,       snake_case__:	List[Any]   ,       snake_case__:	List[str]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[str]   ,       snake_case__:	Any	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	size
  
     def __call__(    self:	Optional[Any]   ,       snake_case__:	Optional[int]   ,       snake_case__:	List[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	functional.resize(snake_case__   ,       self.size	)
         _lowercase     =	functional.resize(snake_case__   ,       self.size   ,       interpolation=transforms.InterpolationMode.NEAREST	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Union[str, Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	Any=None	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	min_size
         if max_size is None:
             _lowercase     =	min_size
         _lowercase     =	max_size
  
     def __call__(    self:	Union[str, Any]   ,       snake_case__:	Any   ,       snake_case__:	Dict	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	random.randint(self.min_size   ,       self.max_size	)
         _lowercase     =	functional.resize(snake_case__   ,       snake_case__	)
         _lowercase     =	functional.resize(snake_case__   ,       snake_case__   ,       interpolation=transforms.InterpolationMode.NEAREST	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Tuple   ,       snake_case__:	Any	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	size if isinstance(snake_case__   ,       snake_case__	) else (size, size)
  
     def __call__(    self:	Optional[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Optional[Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	pad_if_smaller(snake_case__   ,       self.size	)
         _lowercase     =	pad_if_smaller(snake_case__   ,       self.size   ,       fill=255	)
         _lowercase     =	transforms.RandomCrop.get_params(snake_case__   ,       self.size	)
         _lowercase     =	functional.crop(snake_case__   ,       *snake_case__	)
         _lowercase     =	functional.crop(snake_case__   ,       *snake_case__	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[int]   ,       snake_case__:	Any	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	flip_prob
  
     def __call__(    self:	Any   ,       snake_case__:	Optional[Any]   ,       snake_case__:	Dict	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         if random.random() < self.flip_prob:
             _lowercase     =	functional.hflip(snake_case__	)
             _lowercase     =	functional.hflip(snake_case__	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __call__(    self:	Optional[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Tuple	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	functional.pil_to_tensor(snake_case__	)
         _lowercase     =	torch.as_tensor(np.array(snake_case__	)   ,       dtype=torch.intaa	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[str]   ,       snake_case__:	Optional[Any]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	dtype
  
     def __call__(    self:	List[Any]   ,       snake_case__:	List[str]   ,       snake_case__:	str	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	functional.convert_image_dtype(snake_case__   ,       self.dtype	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Any   ,       snake_case__:	List[Any]   ,       snake_case__:	List[Any]	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	mean
         _lowercase     =	std
  
     def __call__(    self:	Optional[Any]   ,       snake_case__:	Optional[Any]   ,       snake_case__:	str	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	functional.normalize(snake_case__   ,       mean=self.mean   ,       std=self.std	)
         return image, target
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __call__(    self:	Tuple   ,       snake_case__:	Optional[int]   ,       snake_case__:	str	)  ->		Any:
  
  
         """simple docstring"""
  
  
  
         if not isinstance(snake_case__   ,       np.ndarray	):
             _lowercase     =	np.array(snake_case__	).astype(np.uinta	)
         # avoid using underflow conversion
         _lowercase     =	255
         _lowercase     =	target - 1
         _lowercase     =	255
  
         _lowercase     =	Image.fromarray(snake_case__	)
         return image, target
  
  
  
  
  
  
  @dataclass
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						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      =						field(
         default=lowercase_      ,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}      )
     __lowerCAmelCase      =						field(
         default=0.15      ,			metadata={"""help""": """Percent to split off of train for validation."""}      )
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         }      ,			)
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                 """value if set."""
             )
         }      ,			)
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}      ,			)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         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 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						field(
         default="""nvidia/mit-b0"""      ,			metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      ,			)
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}      )
     __lowerCAmelCase      =						field(
         default="""main"""      ,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}      ,			)
     __lowerCAmelCase      =						field(default=lowercase_      ,			metadata={"""help""": """Name or path of preprocessor config."""}      )
     __lowerCAmelCase      =						field(
         default=lowercase_      ,			metadata={
             """help""": (
                 """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                 """with private models)."""
             )
         }      ,			)
  
  
  
  
  
  
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	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.
          _lowercase ,    _lowercase ,    _lowercase     =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
      else:
          _lowercase ,    _lowercase ,    _lowercase     =	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',      _lowercase,      _lowercase      )
  
      # 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()
  
      _lowercase     =	training_args.get_process_log_level()
      logger.setLevel(_lowercase      )
      transformers.utils.logging.set_verbosity(_lowercase      )
      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.
      _lowercase     =	None
      if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
          _lowercase     =	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
      _lowercase     =	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:
          _lowercase     =	dataset.rename_columns({'pixel_values': 'image'}      )
      if "annotation" in dataset["train"].column_names:
          _lowercase     =	dataset.rename_columns({'annotation': 'label'}      )
  
      # If we don't have a validation split, split off a percentage of train as validation.
      _lowercase     =	None if 'validation' in dataset.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split,      _lowercase      ) and data_args.train_val_split > 0.0:
          _lowercase     =	dataset['train'].train_test_split(data_args.train_val_split      )
          _lowercase     =	split['train']
          _lowercase     =	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":
          _lowercase     =	'huggingface/label-files'
          _lowercase     =	'ade20k-id2label.json'
      else:
          _lowercase     =	data_args.dataset_name
          _lowercase     =	'id2label.json'
      _lowercase     =	json.load(open(hf_hub_download(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
      _lowercase     =	{int(_lowercase      ): v for k, v in idalabel.items()}
      _lowercase     =	{v: str(_lowercase      ) for k, v in idalabel.items()}
  
      # Load the mean IoU metric from the datasets package
      _lowercase     =	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(_lowercase      ):
          _lowercase ,    _lowercase     =	eval_pred
          _lowercase     =	torch.from_numpy(_lowercase      )
          # scale the logits to the size of the label
          _lowercase     =	nn.functional.interpolate(
              _lowercase,      size=labels.shape[-2:],      mode='bilinear',      align_corners=_lowercase,      ).argmax(dim=1      )
  
          _lowercase     =	logits_tensor.detach().cpu().numpy()
          _lowercase     =	metric.compute(
              predictions=_lowercase,      references=_lowercase,      num_labels=len(_lowercase      ),      ignore_index=0,      reduce_labels=image_processor.do_reduce_labels,      )
          # add per category metrics as individual key-value pairs
          _lowercase     =	metrics.pop('per_category_accuracy'      ).tolist()
          _lowercase     =	metrics.pop('per_category_iou'      ).tolist()
  
          metrics.update({f'''accuracy_{idalabel[i]}''': v for i, v in enumerate(_lowercase      )}      )
          metrics.update({f'''iou_{idalabel[i]}''': v for i, v in enumerate(_lowercase      )}      )
  
          return metrics
  
      _lowercase     =	AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path,      labelaid=_lowercase,      idalabel=_lowercase,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
      _lowercase     =	AutoModelForSemanticSegmentation.from_pretrained(
          model_args.model_name_or_path,      from_tf=bool('.ckpt' in model_args.model_name_or_path      ),      config=_lowercase,      cache_dir=model_args.cache_dir,      revision=model_args.model_revision,      use_auth_token=True if model_args.use_auth_token else None,      )
      _lowercase     =	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.
          _lowercase     =	(image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
      else:
          _lowercase     =	(image_processor.size['height'], image_processor.size['width'])
      _lowercase     =	Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              RandomCrop(size=_lowercase      ),
              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)
      _lowercase     =	Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              Resize(size=_lowercase      ),
              PILToTensor(),
              ConvertImageDtype(torch.float      ),
              Normalize(mean=image_processor.image_mean,      std=image_processor.image_std      ),
          ]      )
  
      def preprocess_train(_lowercase      ):
          _lowercase     =	[]
          _lowercase     =	[]
          for image, target in zip(example_batch['image'],      example_batch['label']      ):
              _lowercase ,    _lowercase     =	train_transforms(image.convert('RGB'      ),      _lowercase      )
              pixel_values.append(_lowercase      )
              labels.append(_lowercase      )
  
          _lowercase     =	{}
          _lowercase     =	torch.stack(_lowercase      )
          _lowercase     =	torch.stack(_lowercase      )
  
          return encoding
  
      def preprocess_val(_lowercase      ):
          _lowercase     =	[]
          _lowercase     =	[]
          for image, target in zip(example_batch['image'],      example_batch['label']      ):
              _lowercase ,    _lowercase     =	val_transforms(image.convert('RGB'      ),      _lowercase      )
              pixel_values.append(_lowercase      )
              labels.append(_lowercase      )
  
          _lowercase     =	{}
          _lowercase     =	torch.stack(_lowercase      )
          _lowercase     =	torch.stack(_lowercase      )
  
          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:
              _lowercase     =	(
                  dataset['train'].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
              )
          # Set the training transforms
          dataset["train"].set_transform(_lowercase      )
  
      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:
              _lowercase     =	(
                  dataset['validation'].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
              )
          # Set the validation transforms
          dataset["validation"].set_transform(_lowercase      )
  
      # Initalize our trainer
      _lowercase     =	Trainer(
          model=_lowercase,      args=_lowercase,      train_dataset=dataset['train'] if training_args.do_train else None,      eval_dataset=dataset['validation'] if training_args.do_eval else None,      compute_metrics=_lowercase,      tokenizer=_lowercase,      data_collator=_lowercase,      )
  
      # Training
      if training_args.do_train:
          _lowercase     =	None
          if training_args.resume_from_checkpoint is not None:
              _lowercase     =	training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              _lowercase     =	last_checkpoint
          _lowercase     =	trainer.train(resume_from_checkpoint=_lowercase      )
          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:
          _lowercase     =	trainer.evaluate()
          trainer.log_metrics('eval',      _lowercase      )
          trainer.save_metrics('eval',      _lowercase      )
  
      # Write model card and (optionally) push to hub
      _lowercase     =	{
          '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(**_lowercase      )
      else:
          trainer.create_model_card(**_lowercase      )
  
  
  if __name__ == "__main__":
     main()
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import List, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  {
      '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
      # See all LED models at https://huggingface.co/models?filter=led
  }
  
  class 					snake_case_							(       lowercase_      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						"""led"""
     __lowerCAmelCase      =						{
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }
  
     def __init__(    self:	int   ,       snake_case__:	str=50_265   ,       snake_case__:	Dict=16_384   ,       snake_case__:	str=1_024   ,       snake_case__:	Tuple=12   ,       snake_case__:	int=4_096   ,       snake_case__:	Union[str, Any]=16   ,       snake_case__:	int=12   ,       snake_case__:	Union[str, Any]=4_096   ,       snake_case__:	Optional[Any]=16   ,       snake_case__:	str=0.0   ,       snake_case__:	Optional[Any]=0.0   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]=True   ,       snake_case__:	List[Any]="gelu"   ,       snake_case__:	Optional[int]=1_024   ,       snake_case__:	List[Any]=0.1   ,       snake_case__:	int=0.0   ,       snake_case__:	List[str]=0.0   ,       snake_case__:	List[str]=0.02   ,       snake_case__:	Dict=2   ,       snake_case__:	int=0.0   ,       snake_case__:	Optional[int]=1   ,       snake_case__:	Tuple=0   ,       snake_case__:	Union[str, Any]=2   ,       snake_case__:	Union[List[int], int] = 512   ,       **snake_case__:	int   ,       )  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	vocab_size
         _lowercase     =	max_encoder_position_embeddings
         _lowercase     =	max_decoder_position_embeddings
         _lowercase     =	d_model
         _lowercase     =	encoder_ffn_dim
         _lowercase     =	encoder_layers
         _lowercase     =	encoder_attention_heads
         _lowercase     =	decoder_ffn_dim
         _lowercase     =	decoder_layers
         _lowercase     =	decoder_attention_heads
         _lowercase     =	dropout
         _lowercase     =	attention_dropout
         _lowercase     =	activation_dropout
         _lowercase     =	activation_function
         _lowercase     =	init_std
         _lowercase     =	encoder_layerdrop
         _lowercase     =	decoder_layerdrop
         _lowercase     =	classifier_dropout
         _lowercase     =	use_cache
         _lowercase     =	encoder_layers
         _lowercase     =	attention_window
  
         super().__init__(
             pad_token_id=snake_case__   ,       bos_token_id=snake_case__   ,       eos_token_id=snake_case__   ,       is_encoder_decoder=snake_case__   ,       decoder_start_token_id=snake_case__   ,       **snake_case__   ,       )
  