





import logging
import os
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 PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
_UpperCAmelCase             =							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>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

_UpperCAmelCase             =							list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_UpperCAmelCase             =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   Tuple:
 with open(SCREAMING_SNAKE_CASE   ,   """rb"""   ) as f:
  __lowerCAmelCase					:	List[str]      =       Image.open(SCREAMING_SNAKE_CASE   )
  return im.convert("""RGB"""   )






@dataclass
class        snake_case_    :
  A_      							=   field(
      default=__lowercase					,metadata={
          'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
      }					,)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}						)
  A_      							=   field(default=__lowercase					,metadata={'help': 'A folder containing the training data.'}						)
  A_      							=   field(default=__lowercase					,metadata={'help': 'A folder containing the validation data.'}						)
  A_      							=   field(
      default=0.15					,metadata={'help': 'Percent to split off of train for validation.'}						)
  A_      							=   field(
      default=__lowercase					,metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }					,)
  A_      							=   field(
      default=__lowercase					,metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }					,)






  def 				UpperCAmelCase__						(							self			:					Dict      )->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_    :
  A_      							=   field(
      default='google/vit-base-patch16-224-in21k'					,metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					,)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__lowercase						)}					,)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}						)
  A_      							=   field(
      default='main'					,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}					,)
  A_      							=   field(default=__lowercase					,metadata={'help': 'Name or path of preprocessor config.'}						)
  A_      							=   field(
      default=__lowercase					,metadata={
          'help': (
              'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
              'with private models).'
          )
      }					,)
  A_      							=   field(
      default=__lowercase					,metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}					,)






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   Optional[Any]:
 __lowerCAmelCase					:	str      =       torch.stack([example["""pixel_values"""] for example in examples]   )
 __lowerCAmelCase					:	Dict      =       torch.tensor([example["""labels"""] for example in examples]   )
 return {"pixel_values": pixel_values, "labels": labels}






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Tuple:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 __lowerCAmelCase					:	Optional[int]      =       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 ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
 else:
  __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       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_image_classification"""   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 # 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					:	Union[str, Any]      =       training_args.get_process_log_level()
 logger.setLevel(SCREAMING_SNAKE_CASE   )
 transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE   )
 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					:	Tuple      =       None
 if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
  __lowerCAmelCase					:	Union[str, Any]      =       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."""   )

    # Set seed before initializing model.
 set_seed(training_args.seed   )

 # Initialize our dataset and prepare it for the 'image-classification' task.
 if data_args.dataset_name is not None:
  __lowerCAmelCase					:	Dict      =       load_dataset(
      data_args.dataset_name   ,   data_args.dataset_config_name   ,   cache_dir=model_args.cache_dir   ,   task="""image-classification"""   ,   use_auth_token=True if model_args.use_auth_token else None   ,   )
 else:
  __lowerCAmelCase					:	Optional[Any]      =       {}
  if data_args.train_dir is not None:
   __lowerCAmelCase					:	int      =       os.path.join(data_args.train_dir   ,   """**"""   )
  if data_args.validation_dir is not None:
   __lowerCAmelCase					:	Tuple      =       os.path.join(data_args.validation_dir   ,   """**"""   )
  __lowerCAmelCase					:	Union[str, Any]      =       load_dataset(
      """imagefolder"""   ,   data_files=SCREAMING_SNAKE_CASE   ,   cache_dir=model_args.cache_dir   ,   task="""image-classification"""   ,   )

 # If we don't have a validation split, split off a percentage of train as validation.
 __lowerCAmelCase					:	Optional[Any]      =       None if """validation""" in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split   ,   SCREAMING_SNAKE_CASE   ) and data_args.train_val_split > 0.0:
  __lowerCAmelCase					:	Any      =       dataset["""train"""].train_test_split(data_args.train_val_split   )
  __lowerCAmelCase					:	Union[str, Any]      =       split["""train"""]
  __lowerCAmelCase					:	Optional[int]      =       split["""test"""]

 # Prepare label mappings.
 # We'll include these in the model's config to get human readable labels in the Inference API.
 __lowerCAmelCase					:	str      =       dataset["""train"""].features["""labels"""].names
 __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       {}, {}
 for i, label in enumerate(SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	Tuple      =       str(SCREAMING_SNAKE_CASE   )
  __lowerCAmelCase					:	List[Any]      =       label

 # Load the accuracy metric from the datasets package
 __lowerCAmelCase					:	Optional[Any]      =       evaluate.load("""accuracy"""   )

 # 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.
 def compute_metrics(SCREAMING_SNAKE_CASE		:Optional[Any]   ):
  return metric.compute(predictions=np.argmax(p.predictions   ,   axis=1   )   ,   references=p.label_ids   )

 __lowerCAmelCase					:	int      =       AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path   ,   num_labels=len(SCREAMING_SNAKE_CASE   )   ,   labelaid=SCREAMING_SNAKE_CASE   ,   idalabel=SCREAMING_SNAKE_CASE   ,   finetuning_task="""image-classification"""   ,   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[Any]      =       AutoModelForImageClassification.from_pretrained(
     model_args.model_name_or_path   ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )   ,   config=SCREAMING_SNAKE_CASE   ,   cache_dir=model_args.cache_dir   ,   revision=model_args.model_revision   ,   use_auth_token=True if model_args.use_auth_token else None   ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,   )
 __lowerCAmelCase					:	Optional[int]      =       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.
 if "shortest_edge" in image_processor.size:
  __lowerCAmelCase					:	Tuple      =       image_processor.size["""shortest_edge"""]
 else:
  __lowerCAmelCase					:	List[Any]      =       (image_processor.size["""height"""], image_processor.size["""width"""])
 __lowerCAmelCase					:	Optional[Any]      =       Normalize(mean=image_processor.image_mean   ,   std=image_processor.image_std   )
 __lowerCAmelCase					:	Dict      =       Compose(
     [
         RandomResizedCrop(SCREAMING_SNAKE_CASE   ),
         RandomHorizontalFlip(),
         ToTensor(),
         normalize,
     ]   )
 __lowerCAmelCase					:	Optional[Any]      =       Compose(
     [
         Resize(SCREAMING_SNAKE_CASE   ),
         CenterCrop(SCREAMING_SNAKE_CASE   ),
         ToTensor(),
         normalize,
     ]   )

 def train_transforms(SCREAMING_SNAKE_CASE		:Dict   ):
  __lowerCAmelCase					:	Optional[int]      =       [
      _train_transforms(pil_img.convert("""RGB"""   )   ) for pil_img in example_batch["""image"""]
  ]
  return example_batch

 def val_transforms(SCREAMING_SNAKE_CASE		:List[Any]   ):
  __lowerCAmelCase					:	Optional[Any]      =       [_val_transforms(pil_img.convert("""RGB"""   )   ) for pil_img in example_batch["""image"""]]
  return example_batch

 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					:	Dict      =       (
       dataset["""train"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_train_samples   )   )
   )
  # Set the training transforms
  dataset["train"].set_transform(SCREAMING_SNAKE_CASE   )

 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[Any]      =       (
       dataset["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
   )
  # Set the validation transforms
  dataset["validation"].set_transform(SCREAMING_SNAKE_CASE   )

 # Initalize our trainer
 __lowerCAmelCase					:	Tuple      =       Trainer(
     model=SCREAMING_SNAKE_CASE   ,   args=SCREAMING_SNAKE_CASE   ,   train_dataset=dataset["""train"""] if training_args.do_train else None   ,   eval_dataset=dataset["""validation"""] if training_args.do_eval else None   ,   compute_metrics=SCREAMING_SNAKE_CASE   ,   tokenizer=SCREAMING_SNAKE_CASE   ,   data_collator=SCREAMING_SNAKE_CASE   ,   )

 # Training
 if training_args.do_train:
  __lowerCAmelCase					:	List[str]      =       None
  if training_args.resume_from_checkpoint is not None:
   __lowerCAmelCase					:	int      =       training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __lowerCAmelCase					:	Optional[int]      =       last_checkpoint
  __lowerCAmelCase					:	List[str]      =       trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE   )
  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					:	Union[str, Any]      =       trainer.evaluate()
  trainer.log_metrics("""eval"""   ,   SCREAMING_SNAKE_CASE   )
  trainer.save_metrics("""eval"""   ,   SCREAMING_SNAKE_CASE   )

 # Write model card and (optionally) push to hub
 __lowerCAmelCase					:	int      =       {
     """finetuned_from""": model_args.model_name_or_path,
     """tasks""": """image-classification""",
     """dataset""": data_args.dataset_name,
     """tags""": ["""image-classification""", """vision"""],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**SCREAMING_SNAKE_CASE   )
 else:
  trainer.create_model_card(**SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase             =							get_tests_dir('fixtures/test_sentencepiece_no_bos.model')






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






  def 				UpperCAmelCase__						(							self			:					List[str]      )->Dict:






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Optional[int]      =       PegasusTokenizer(_snake_case      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					str      )->Dict:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/pegasus-large"""      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  **_snake_case			:					Tuple      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[Any]      )->Tuple:






   '''simple docstring'''

   return ("This is a test", "This is a test")






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       """</s>"""
   __lowerCAmelCase					:	int      =       1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case      )       ,  _snake_case      )
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case      )       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					int      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       list(self.get_tokenizer().get_vocab().keys()      )

   self.assertEqual(vocab_keys[0]       ,  """<pad>"""      )
   self.assertEqual(vocab_keys[1]       ,  """</s>"""      )
   self.assertEqual(vocab_keys[-1]       ,  """v"""      )
   self.assertEqual(len(_snake_case      )       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Optional[int]:






   '''simple docstring'''

   self.assertEqual(self.get_tokenizer().vocab_size       ,  1103      )






  def 				UpperCAmelCase__						(							self			:					Dict      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	Dict      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	str      =       (
       """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
       """ </s> <pad> <pad> <pad>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self._large_tokenizer
   # <mask_1> masks whole sentence while <mask_2> masks single word
   __lowerCAmelCase					:	Tuple      =       """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
   __lowerCAmelCase					:	List[str]      =       [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	str      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer
   # The tracebacks for the following asserts are **better** without messages or self.assertEqual
   assert tokenizer.vocab_size == 96103
   assert tokenizer.pad_token_id == 0
   assert tokenizer.eos_token_id == 1
   assert tokenizer.offset == 103
   assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
   assert tokenizer.unk_token == "<unk>"
   assert tokenizer.model_max_length == 1024
   __lowerCAmelCase					:	Tuple      =       """To ensure a smooth flow of bank resolutions."""
   __lowerCAmelCase					:	Optional[Any]      =       [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
   __lowerCAmelCase					:	int      =       tokenizer([raw_input_str]       ,  return_tensors=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )
   assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






  @require_torch
  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       ["""This is going to be way too long.""" * 150, """short example"""]
   __lowerCAmelCase					:	Union[str, Any]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	Dict      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	Tuple      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 1024)
   assert batch.attention_mask.shape == (2, 1024)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






  @slow
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=_snake_case       ,  model_name="""google/bigbird-pegasus-large-arxiv"""       ,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""       ,  )






@require_sentencepiece
@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PegasusTokenizer
  A_      							=   PegasusTokenizerFast
  A_      							=   True
  A_      							=   True






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

   super().setUp()

   # We have a SentencePiece fixture for testing
   __lowerCAmelCase					:	Any      =       PegasusTokenizer(_snake_case       ,  offset=0       ,  mask_token_sent=_snake_case       ,  mask_token="""[MASK]"""      )
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					Any      )->str:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  **_snake_case			:					Optional[Any]      )->PegasusTokenizer:






   '''simple docstring'''

   return PegasusTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   return ("This is a test", "This is a test")






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	List[str]      =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
   __lowerCAmelCase					:	int      =       (
       """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
       """ <pad> <pad> <pad>"""
   )
   __lowerCAmelCase					:	str      =       rust_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   __lowerCAmelCase					:	Tuple      =       py_tokenizer([raw_input_str]       ,  return_tensors=_snake_case       ,  add_special_tokens=_snake_case      ).input_ids[0]
   self.assertListEqual(_snake_case       ,  _snake_case      )






  @require_torch
  def 				UpperCAmelCase__						(							self			:					str      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       ["""This is going to be way too long.""" * 1000, """short example"""]
   __lowerCAmelCase					:	Optional[int]      =       ["""not super long but more than 5 tokens""", """tiny"""]
   __lowerCAmelCase					:	str      =       self._large_tokenizer(_snake_case       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )
   __lowerCAmelCase					:	List[Any]      =       self._large_tokenizer(
       text_target=_snake_case       ,  max_length=5       ,  padding=_snake_case       ,  truncation=_snake_case       ,  return_tensors="""pt"""      )

   assert batch.input_ids.shape == (2, 4096)
   assert batch.attention_mask.shape == (2, 4096)
   assert targets["input_ids"].shape == (2, 5)
   assert len(_snake_case      ) == 2  # input_ids, attention_mask.






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       (
       """This is an example string that is used to test the original TF implementation against the HF"""
       """ implementation"""
   )

   __lowerCAmelCase					:	Optional[Any]      =       self._large_tokenizer(_snake_case      ).input_ids

   self.assertListEqual(
       _snake_case       ,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]       ,  )