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   				=      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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=0     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   int					   =		(size, size) if isinstance(_UpperCamelCase,    _UpperCamelCase     ) else size
        lowercase   ,			lowercase			:   Dict					   =		img.size
        lowercase			:   List[str]					   =		size[1] - original_height if original_height < size[1] else 0
        lowercase			:   str					   =		size[0] - original_width if original_width < size[0] else 0
        lowercase			:   Optional[int]					   =		functional.pad(_UpperCamelCase,    (0, 0, pad_width, pad_height),    fill=_UpperCamelCase     )
        return img
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Tuple					   =		transforms
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              for t in self.transforms:
                    lowercase   ,			lowercase			:   Any					   =		t(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self		):
              pass
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		size
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Optional[int]					   =		functional.resize(SCREAMING_SNAKE_CASE__							,						self.size		)
              lowercase			:   List[str]					   =		functional.resize(SCREAMING_SNAKE_CASE__							,						self.size							,						interpolation=transforms.InterpolationMode.NEAREST		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=None		):
              lowercase			:   List[str]					   =		min_size
              if max_size is None:
                    lowercase			:   Dict					   =		min_size
              lowercase			:   int					   =		max_size
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Dict					   =		random.randint(self.min_size							,						self.max_size		)
              lowercase			:   Optional[Any]					   =		functional.resize(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
              lowercase			:   Tuple					   =		functional.resize(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						interpolation=transforms.InterpolationMode.NEAREST		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   List[str]					   =		size if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else (size, size)
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		pad_if_smaller(SCREAMING_SNAKE_CASE__							,						self.size		)
              lowercase			:   Union[str, Any]					   =		pad_if_smaller(SCREAMING_SNAKE_CASE__							,						self.size							,						fill=255		)
              lowercase			:   int					   =		transforms.RandomCrop.get_params(SCREAMING_SNAKE_CASE__							,						self.size		)
              lowercase			:   Union[str, Any]					   =		functional.crop(SCREAMING_SNAKE_CASE__							,						*SCREAMING_SNAKE_CASE__		)
              lowercase			:   str					   =		functional.crop(SCREAMING_SNAKE_CASE__							,						*SCREAMING_SNAKE_CASE__		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   str					   =		flip_prob
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              if random.random() < self.flip_prob:
                    lowercase			:   Tuple					   =		functional.hflip(SCREAMING_SNAKE_CASE__		)
                    lowercase			:   Dict					   =		functional.hflip(SCREAMING_SNAKE_CASE__		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   List[str]					   =		functional.pil_to_tensor(SCREAMING_SNAKE_CASE__		)
              lowercase			:   Optional[Any]					   =		torch.as_tensor(np.array(SCREAMING_SNAKE_CASE__		)							,						dtype=torch.intaa		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		dtype
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		functional.convert_image_dtype(SCREAMING_SNAKE_CASE__							,						self.dtype		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Tuple					   =		mean
              lowercase			:   Tuple					   =		std
  
  
  
  
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   str					   =		functional.normalize(SCREAMING_SNAKE_CASE__							,						mean=self.mean							,						std=self.std		)
              return image, target
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   :
  
  
        def __call__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              if not isinstance(SCREAMING_SNAKE_CASE__							,						np.ndarray		):
                    lowercase			:   Any					   =		np.array(SCREAMING_SNAKE_CASE__		).astype(np.uinta		)
              # avoid using underflow conversion
              lowercase			:   Tuple					   =		255
              lowercase			:   List[str]					   =		target - 1
              lowercase			:   str					   =		255
  
              lowercase			:   List[Any]					   =		Image.fromarray(SCREAMING_SNAKE_CASE__		)
              return image, target
  
  
  
  
  
  
  
  @dataclass
  class 				__SCREAMING_SNAKE_CASE   :
        A				:							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).'
            }  ,						)
        A				:							Optional[str]    							=     field(
            default=A__  ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
        A				:							Optional[float]    							=     field(
            default=0.15  ,						metadata={'help': 'Percent to split off of train for validation.'}   )
        A				:							Optional[int]    							=     field(
            default=A__  ,						metadata={
                'help': (
                    'For debugging purposes or quicker training, truncate the number of training examples to this '
                    'value if set.'
                )
            }  ,						)
        A				:							Optional[int]    							=     field(
            default=A__  ,						metadata={
                'help': (
                    'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                    'value if set.'
                )
            }  ,						)
        A				:							Optional[bool]    							=     field(
            default=A__  ,						metadata={'help': 'Whether or not to reduce all labels by 1 and replace background by 255.'}  ,						)
  
  
        def        __lowerCamelCase			(							self		):
              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 				__SCREAMING_SNAKE_CASE   :
        A				:							str    							=     field(
            default='nvidia/mit-b0'  ,						metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  ,						)
        A				:							Optional[str]    							=     field(
            default=A__  ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
        A				:							Optional[str]    							=     field(
            default=A__  ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}   )
        A				:							str    							=     field(
            default='main'  ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}  ,						)
        A				:							str    							=     field(default=A__  ,						metadata={'help': 'Name or path of preprocessor config.'}   )
        A				:							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 			__lowercase	(						)    ->Union[str, Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		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			:   Dict					   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
        else:
              lowercase   ,			lowercase   ,			lowercase			:   int					   =		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''',    _UpperCamelCase,    _UpperCamelCase     )
  
        # 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			:   Optional[Any]					   =		training_args.get_process_log_level()
        logger.setLevel(_UpperCamelCase     )
        transformers.utils.logging.set_verbosity(_UpperCamelCase     )
        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			:   str					   =		None
        if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
              lowercase			:   Optional[int]					   =		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			:   Optional[int]					   =		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			:   int					   =		dataset.rename_columns({'''pixel_values''': '''image'''}     )
        if "annotation" in dataset["train"].column_names:
              lowercase			:   Optional[Any]					   =		dataset.rename_columns({'''annotation''': '''label'''}     )
  
        # If we don't have a validation split, split off a percentage of train as validation.
        lowercase			:   List[Any]					   =		None if '''validation''' in dataset.keys() else data_args.train_val_split
        if isinstance(data_args.train_val_split,    _UpperCamelCase     ) and data_args.train_val_split > 0.0:
              lowercase			:   Union[str, Any]					   =		dataset['''train'''].train_test_split(data_args.train_val_split     )
              lowercase			:   List[Any]					   =		split['''train''']
              lowercase			:   Dict					   =		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			:   Tuple					   =		'''huggingface/label-files'''
              lowercase			:   Any					   =		'''ade20k-id2label.json'''
        else:
              lowercase			:   str					   =		data_args.dataset_name
              lowercase			:   str					   =		'''id2label.json'''
        lowercase			:   Tuple					   =		json.load(open(hf_hub_download(_UpperCamelCase,    _UpperCamelCase,    repo_type='''dataset'''     ),    '''r'''     )     )
        lowercase			:   int					   =		{int(_UpperCamelCase     ): v for k, v in idalabel.items()}
        lowercase			:   int					   =		{v: str(_UpperCamelCase     ) for k, v in idalabel.items()}
  
        # Load the mean IoU metric from the datasets package
        lowercase			:   List[Any]					   =		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     ):
              lowercase   ,			lowercase			:   Optional[Any]					   =		eval_pred
              lowercase			:   List[Any]					   =		torch.from_numpy(_UpperCamelCase     )
              # scale the logits to the size of the label
              lowercase			:   Any					   =		nn.functional.interpolate(
                  _UpperCamelCase,    size=labels.shape[-2:],    mode='''bilinear''',    align_corners=_UpperCamelCase,    ).argmax(dim=1     )
  
              lowercase			:   Tuple					   =		logits_tensor.detach().cpu().numpy()
              lowercase			:   int					   =		metric.compute(
                  predictions=_UpperCamelCase,    references=_UpperCamelCase,    num_labels=len(_UpperCamelCase     ),    ignore_index=0,    reduce_labels=image_processor.do_reduce_labels,    )
              # add per category metrics as individual key-value pairs
              lowercase			:   Optional[Any]					   =		metrics.pop('''per_category_accuracy'''     ).tolist()
              lowercase			:   Tuple					   =		metrics.pop('''per_category_iou'''     ).tolist()
  
              metrics.update({f"""accuracy_{idalabel[i]}""": v for i, v in enumerate(_UpperCamelCase     )}     )
              metrics.update({f"""iou_{idalabel[i]}""": v for i, v in enumerate(_UpperCamelCase     )}     )
  
              return metrics
  
        lowercase			:   str					   =		AutoConfig.from_pretrained(
            model_args.config_name or model_args.model_name_or_path,    labelaid=_UpperCamelCase,    idalabel=_UpperCamelCase,    cache_dir=model_args.cache_dir,    revision=model_args.model_revision,    use_auth_token=True if model_args.use_auth_token else None,    )
        lowercase			:   Tuple					   =		AutoModelForSemanticSegmentation.from_pretrained(
            model_args.model_name_or_path,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path     ),    config=_UpperCamelCase,    cache_dir=model_args.cache_dir,    revision=model_args.model_revision,    use_auth_token=True if model_args.use_auth_token else None,    )
        lowercase			:   Any					   =		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			:   int					   =		(image_processor.size['''shortest_edge'''], image_processor.size['''shortest_edge'''])
        else:
              lowercase			:   Dict					   =		(image_processor.size['''height'''], image_processor.size['''width'''])
        lowercase			:   Optional[int]					   =		Compose(
            [
                ReduceLabels() if data_args.reduce_labels else Identity(),
                RandomCrop(size=_UpperCamelCase     ),
                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			:   int					   =		Compose(
            [
                ReduceLabels() if data_args.reduce_labels else Identity(),
                Resize(size=_UpperCamelCase     ),
                PILToTensor(),
                ConvertImageDtype(torch.float     ),
                Normalize(mean=image_processor.image_mean,    std=image_processor.image_std     ),
            ]     )
  
        def preprocess_train(_UpperCamelCase     ):
              lowercase			:   Any					   =		[]
              lowercase			:   Optional[Any]					   =		[]
              for image, target in zip(example_batch['''image'''],    example_batch['''label''']     ):
                    lowercase   ,			lowercase			:   str					   =		train_transforms(image.convert('''RGB'''     ),    _UpperCamelCase     )
                    pixel_values.append(_UpperCamelCase     )
                    labels.append(_UpperCamelCase     )
  
              lowercase			:   List[str]					   =		{}
              lowercase			:   List[str]					   =		torch.stack(_UpperCamelCase     )
              lowercase			:   Tuple					   =		torch.stack(_UpperCamelCase     )
  
              return encoding
  
        def preprocess_val(_UpperCamelCase     ):
              lowercase			:   str					   =		[]
              lowercase			:   str					   =		[]
              for image, target in zip(example_batch['''image'''],    example_batch['''label''']     ):
                    lowercase   ,			lowercase			:   Union[str, Any]					   =		val_transforms(image.convert('''RGB'''     ),    _UpperCamelCase     )
                    pixel_values.append(_UpperCamelCase     )
                    labels.append(_UpperCamelCase     )
  
              lowercase			:   List[str]					   =		{}
              lowercase			:   Any					   =		torch.stack(_UpperCamelCase     )
              lowercase			:   Optional[Any]					   =		torch.stack(_UpperCamelCase     )
  
              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			:   Union[str, Any]					   =		(
                        dataset['''train'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
                    )
              # Set the training transforms
              dataset["train"].set_transform(_UpperCamelCase     )
  
        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			:   int					   =		(
                        dataset['''validation'''].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
                    )
              # Set the validation transforms
              dataset["validation"].set_transform(_UpperCamelCase     )
  
        # Initalize our trainer
        lowercase			:   Any					   =		Trainer(
            model=_UpperCamelCase,    args=_UpperCamelCase,    train_dataset=dataset['''train'''] if training_args.do_train else None,    eval_dataset=dataset['''validation'''] if training_args.do_eval else None,    compute_metrics=_UpperCamelCase,    tokenizer=_UpperCamelCase,    data_collator=_UpperCamelCase,    )
  
        # Training
        if training_args.do_train:
              lowercase			:   Union[str, Any]					   =		None
              if training_args.resume_from_checkpoint is not None:
                    lowercase			:   Optional[Any]					   =		training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                    lowercase			:   Any					   =		last_checkpoint
              lowercase			:   List[Any]					   =		trainer.train(resume_from_checkpoint=_UpperCamelCase     )
              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			:   List[Any]					   =		trainer.evaluate()
              trainer.log_metrics('''eval''',    _UpperCamelCase     )
              trainer.save_metrics('''eval''',    _UpperCamelCase     )
  
        # Write model card and (optionally) push to hub
        lowercase			:   List[str]					   =		{
            '''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(**_UpperCamelCase     )
        else:
              trainer.create_model_card(**_UpperCamelCase     )
  
  
  if __name__ == "__main__":
    main()
  
  
style_context: 
  
  
  import argparse
  import logging
  
  import numpy as np
  import torch
  
  from transformers import AutoModelForCausalLM, AutoTokenizer
  
  
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
      datefmt='''%m/%d/%Y %H:%M:%S''',
      level=logging.INFO,
  )
  __a   				=      logging.getLogger(__name__)
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->Any:
  
  
  
  
  
  
  
        """simple docstring"""
  
        np.random.seed(args.seed     )
        torch.manual_seed(args.seed     )
        if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )
  
  
  
  
  def 			__lowercase	(						)    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		argparse.ArgumentParser()
        parser.add_argument(
            '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
        parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
        parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
        parser.add_argument('''--stop_token''',    type=_UpperCamelCase,    default=_UpperCamelCase,    help='''Token at which text generation is stopped'''     )
        parser.add_argument(
            '''--temperature''',    type=_UpperCamelCase,    default=1.0,    help='''temperature of 1.0 has no effect, lower tend toward greedy sampling''',    )
        parser.add_argument(
            '''--repetition_penalty''',    type=_UpperCamelCase,    default=1.0,    help='''primarily useful for CTRL model; in that case, use 1.2'''     )
        parser.add_argument('''--k''',    type=_UpperCamelCase,    default=0     )
        parser.add_argument('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
        parser.add_argument('''--p''',    type=_UpperCamelCase,    default=0.9     )
  
        parser.add_argument('''--prefix''',    type=_UpperCamelCase,    default='''''',    help='''Text added prior to input.'''     )
        parser.add_argument('''--padding_text''',    type=_UpperCamelCase,    default='''''',    help='''Deprecated, the use of `--prefix` is preferred.'''     )
        parser.add_argument('''--xlm_language''',    type=_UpperCamelCase,    default='''''',    help='''Optional language when used with the XLM model.'''     )
  
        parser.add_argument('''--seed''',    type=_UpperCamelCase,    default=42,    help='''random seed for initialization'''     )
        parser.add_argument('''--no_cuda''',    action='''store_true''',    help='''Avoid using CUDA when available'''     )
        parser.add_argument(
            '''--fp16''',    action='''store_true''',    help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',    )
        lowercase			:   Any					   =		parser.parse_args()
  
        lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
        lowercase			:   List[str]					   =		0 if args.no_cuda else torch.cuda.device_count()
  
        logger.warning(f"""device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}"""     )
  
        set_seed(_UpperCamelCase     )
  
        # Initialize the model and tokenizer
        lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
        lowercase			:   List[Any]					   =		AutoModelForCausalLM.from_pretrained(args.model_name_or_path     )
  
        # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
        # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
        model.to(args.device     )
  
        if args.fpaa:
              model.half()
  
        logger.info(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )
  
        lowercase			:   List[Any]					   =		tokenizer(_UpperCamelCase,    return_tensors='''pt''',    add_special_tokens=_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		{key: value.to(args.device     ) for key, value in inputs.items()}
  
        lowercase			:   Optional[int]					   =		model.generate(
            **_UpperCamelCase,    max_length=args.length + len(inputs['''input_ids'''][0]     ),    penalty_alpha=args.penalty_alpha,    top_k=args.k,    )
  
        lowercase			:   Union[str, Any]					   =		[]
        for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
              print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
              lowercase			:   Optional[Any]					   =		generated_sequence.tolist()
  
              # Decode text
              lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )
  
              # Remove all text after the stop token
              lowercase			:   Optional[int]					   =		text[: text.find(args.stop_token     ) if args.stop_token else None]
  
              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowercase			:   Optional[Any]					   =		(
                  prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
              )
  
              generated_sequences.append(_UpperCamelCase     )
              print(_UpperCamelCase     )
  
        return generated_sequences
  
  
  if __name__ == "__main__":
    main()
  
  