




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
lowerCamelCase__					:  str      							=				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')

lowerCamelCase__					:  Optional[int]      							=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase__					:  str      							=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def    UpperCAmelCase_				( __UpperCAmelCase		:      str				)   ->					Optional[Any]:
      with open(__UpperCAmelCase     ,     'rb'				) as f:
            SCREAMING_SNAKE_CASE_      		=     Image.open(__UpperCAmelCase				)
            return im.convert('RGB'				)





@dataclass
class 			lowerCamelCase_				:


 '''simple docstring'''







 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={
         "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
     }			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "A folder containing the training data."}   )
 lowercase_							       =       field(default=_SCREAMING_SNAKE_CASE			,  metadata={"help": "A folder containing the validation data."}   )
 lowercase_							       =       field(
     default=0.1_5			,  metadata={"help": "Percent to split off of train for validation."}   )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,  )
 lowercase_							       =       field(
     default=_SCREAMING_SNAKE_CASE			,  metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     }			,  )



 def  lowerCAmelCase_    (				self    :						List[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 			lowerCamelCase_				:


 '''simple docstring'''







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


def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[int]				)   ->					List[str]:
      SCREAMING_SNAKE_CASE_      		=     torch.stack([example['pixel_values'] for example in examples]				)
      SCREAMING_SNAKE_CASE_      		=     torch.tensor([example['labels'] for example in examples]				)
      return {"pixel_values": pixel_values, "labels": labels}

def    UpperCAmelCase_				( )   ->					Union[str, Any]:
      # 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.

      SCREAMING_SNAKE_CASE_      		=     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.
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
      else:
            SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     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'     ,     __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()

      SCREAMING_SNAKE_CASE_      		=     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.
      SCREAMING_SNAKE_CASE_      		=     None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     {}
            if data_args.train_dir is not None:
                  SCREAMING_SNAKE_CASE_      		=     os.path.join(data_args.train_dir     ,     '**'				)
            if data_args.validation_dir is not None:
                  SCREAMING_SNAKE_CASE_      		=     os.path.join(data_args.validation_dir     ,     '**'				)
            SCREAMING_SNAKE_CASE_      		=     load_dataset(
                'imagefolder'     ,     data_files=__UpperCAmelCase     ,     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.
      SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     dataset['train'].train_test_split(data_args.train_val_split				)
            SCREAMING_SNAKE_CASE_      		=     split['train']
            SCREAMING_SNAKE_CASE_      		=     split['test']

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

      # Load the accuracy metric from the datasets package
      SCREAMING_SNAKE_CASE_      		=     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(__UpperCAmelCase		:      Any				):
            return metric.compute(predictions=np.argmax(p.predictions     ,     axis=1				)     ,     references=p.label_ids				)

      SCREAMING_SNAKE_CASE_      		=     AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path     ,     num_labels=len(__UpperCAmelCase				)     ,     labelaid=__UpperCAmelCase     ,     idalabel=__UpperCAmelCase     ,     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     ,     )
      SCREAMING_SNAKE_CASE_      		=     AutoModelForImageClassification.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     ,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes     ,     )
      SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     image_processor.size['shortest_edge']
      else:
            SCREAMING_SNAKE_CASE_      		=     (image_processor.size['height'], image_processor.size['width'])
      SCREAMING_SNAKE_CASE_      		=     Normalize(mean=image_processor.image_mean     ,     std=image_processor.image_std				)
      SCREAMING_SNAKE_CASE_      		=     Compose(
          [
              RandomResizedCrop(__UpperCAmelCase				),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]				)
      SCREAMING_SNAKE_CASE_      		=     Compose(
          [
              Resize(__UpperCAmelCase				),
              CenterCrop(__UpperCAmelCase				),
              ToTensor(),
              normalize,
          ]				)

      def train_transforms(__UpperCAmelCase		:      Dict				):
            SCREAMING_SNAKE_CASE_      		=     [
                _train_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['image']
            ]
            return example_batch

      def val_transforms(__UpperCAmelCase		:      Any				):
            SCREAMING_SNAKE_CASE_      		=     [_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:
                  SCREAMING_SNAKE_CASE_      		=     (
                      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:
                  SCREAMING_SNAKE_CASE_      		=     (
                      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
      SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_      		=     training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_      		=     last_checkpoint
            SCREAMING_SNAKE_CASE_      		=     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:
            SCREAMING_SNAKE_CASE_      		=     trainer.evaluate()
            trainer.log_metrics('eval'     ,     __UpperCAmelCase				)
            trainer.save_metrics('eval'     ,     __UpperCAmelCase				)

      # Write model card and (optionally) push to hub
      SCREAMING_SNAKE_CASE_      		=     {
          '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(**__UpperCAmelCase				)
      else:
            trainer.create_model_card(**__UpperCAmelCase				)


if __name__ == "__main__":
   main()







import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			GPTaTokenizer
       lowerCamelCase				:					Optional[int]								=			GPTaTokenizerFast
       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					List[str]								=			{"add_prefix_space": True}
       lowerCamelCase				:					Optional[int]								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case      :Optional[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
                """<|endoftext|>""",
            ]
            __snake_case      :Dict 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :List[Any] 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :List[str] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Dict 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[Any] 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write(json.dumps(a__     ) + """\n"""     )
            with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(a__     )     )



       def        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :Any 							=    """lower newer"""
            return input_text, output_text



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






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    GPTaTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map     )
            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :int 							=    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __snake_case      :Any 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[Any] 							=    tokens + [tokenizer.unk_token]
            __snake_case      :List[Any] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()
            __snake_case      :Optional[Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )

            __snake_case      :Optional[int] 							=    """lower newer"""

            # Testing tokenization
            __snake_case      :List[Any] 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.tokenize(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids without special tokens
            __snake_case      :List[str] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.encode(a__      ,    add_special_tokens=a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids with special tokens
            __snake_case      :Union[str, Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    tokenizer.encode(a__      ,    add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    rust_tokenizer.encode(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing the unknown token
            __snake_case      :Dict 							=    tokens + [rust_tokenizer.unk_token]
            __snake_case      :Optional[int] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   Any:






            '''simple docstring'''





            pass



       def        __lowercase    (				self      ,    a__=15     )			->   Optional[Any]:






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Optional[int] 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )

                      # Simple input
                      __snake_case      :List[str] 							=    """This is a simple input"""
                      __snake_case      :List[str] 							=    ["""This is a simple input 1""", """This is a simple input 2"""]
                      __snake_case      :Tuple 							=    ("""This is a simple input""", """This is a pair""")
                      __snake_case      :Union[str, Any] 							=    [
                          ("""This is a simple input 1""", """This is a simple input 2"""),
                          ("""This is a simple pair 1""", """This is a simple pair 2"""),
                      ]

                      # Simple input tests
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Simple input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(a__      ,    tokenizer_r.encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""     )

                      # Pair input
                      self.assertRaises(
                          a__      ,    tokenizer_r.batch_encode_plus      ,    a__      ,    max_length=a__      ,    padding="""max_length"""      ,    )



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    pad_token="""<pad>"""     )

            # Simple input
            __snake_case      :Tuple 							=    """This is a simple input"""
            __snake_case      :Optional[int] 							=    ["""This is a simple input looooooooong""", """This is a simple input"""]
            __snake_case      :List[str] 							=    ("""This is a simple input""", """This is a pair""")
            __snake_case      :List[str] 							=    [
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            __snake_case      :Tuple 							=    tokenizer.pad_token_id

            __snake_case      :int 							=    tokenizer(a__      ,    padding="""max_length"""      ,    max_length=30      ,    return_tensors="""np"""     )
            __snake_case      :Optional[Any] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )
            __snake_case      :List[Any] 							=    tokenizer(*a__      ,    padding="""max_length"""      ,    max_length=60      ,    return_tensors="""np"""     )
            __snake_case      :Optional[int] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )

            # s
            # test single string max_length padding
            self.assertEqual(out_s["""input_ids"""].shape[-1]      ,    30     )
            self.assertTrue(pad_token_id in out_s["""input_ids"""]     )
            self.assertTrue(0 in out_s["""attention_mask"""]     )

            # s2
            # test automatic padding
            self.assertEqual(out_sa["""input_ids"""].shape[-1]      ,    33     )
            # long slice doesn't have padding
            self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]     )
            self.assertFalse(0 in out_sa["""attention_mask"""][0]     )
            # short slice does have padding
            self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]     )
            self.assertTrue(0 in out_sa["""attention_mask"""][1]     )

            # p
            # test single pair max_length padding
            self.assertEqual(out_p["""input_ids"""].shape[-1]      ,    60     )
            self.assertTrue(pad_token_id in out_p["""input_ids"""]     )
            self.assertTrue(0 in out_p["""attention_mask"""]     )

            # p2
            # test automatic padding pair
            self.assertEqual(out_pa["""input_ids"""].shape[-1]      ,    52     )
            # long slice pair doesn't have padding
            self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]     )
            self.assertFalse(0 in out_pa["""attention_mask"""][0]     )
            # short slice pair does have padding
            self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]     )
            self.assertTrue(0 in out_pa["""attention_mask"""][1]     )



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






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    """$$$"""
            __snake_case      :Optional[Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    bos_token=a__      ,    add_bos_token=a__     )

            __snake_case      :List[Any] 							=    """This is a simple input"""
            __snake_case      :Tuple 							=    ["""This is a simple input 1""", """This is a simple input 2"""]

            __snake_case      :Tuple 							=    tokenizer.bos_token_id

            __snake_case      :List[str] 							=    tokenizer(a__     )
            __snake_case      :Any 							=    tokenizer(a__     )

            self.assertEqual(out_s.input_ids[0]      ,    a__     )
            self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids     )     )

            __snake_case      :int 							=    tokenizer.decode(out_s.input_ids     )
            __snake_case      :Tuple 							=    tokenizer.batch_decode(out_sa.input_ids     )

            self.assertEqual(decode_s.split()[0]      ,    a__     )
            self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa     )     )



       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



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






            '''simple docstring'''





            __snake_case      :List[str] 							=    [self.get_tokenizer(do_lower_case=a__      ,    add_bos_token=a__     )]
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                      __snake_case      :Tuple 							=    """Encode this."""
                      __snake_case      :Tuple 							=    """This one too please."""
                      __snake_case      :Union[str, Any] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      encoded_sequence += tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      __snake_case      :List[str] 							=    tokenizer.encode_plus(
                          a__      ,    a__      ,    add_special_tokens=a__      ,    return_special_tokens_mask=a__      ,    )
                      __snake_case      :Union[str, Any] 							=    encoded_sequence_dict["""input_ids"""]
                      __snake_case      :Optional[Any] 							=    encoded_sequence_dict["""special_tokens_mask"""]
                      self.assertEqual(len(a__     )      ,    len(a__     )     )

                      __snake_case      :Optional[int] 							=    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__     )
                      ]
                      __snake_case      :str 							=    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(a__      ,    a__     )




@require_tokenizers
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )
            __snake_case      :Union[str, Any] 							=    """A photo of a cat"""

            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""test_opt"""     )

            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""./test_opt"""     )
            __snake_case      :Tuple 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    use_slow=a__     )
            __snake_case      :str 							=    """A photo of a cat"""

            __snake_case      :List[str] 							=    tokenizer.encode(
                a__      ,    )
            # Same as above
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""     )
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )

            __snake_case      :Optional[Any] 							=    """bos"""
            __snake_case      :int 							=    tokenizer.get_vocab()["""bos"""]

            __snake_case      :str 							=    """A photo of a cat"""
            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            # We changed the bos token
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""./tok"""     )
            __snake_case      :Dict 							=    AutoTokenizer.from_pretrained("""./tok"""     )
            self.assertTrue(tokenizer.is_fast     )
            __snake_case      :List[Any] 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


