code: 
  
  """simple docstring"""
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  @require_vision
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						tempfile.mkdtemp()
  
          UpperCAmelCase__ :       List[Any]						=						CLIPImageProcessor()
          UpperCAmelCase__ :       Dict						=						BertTokenizer.from_pretrained(
              """hf-internal-testing/tiny-random-BertModel"""       ,					model_input_names=["""input_ids""", """attention_mask"""]  )
  
          UpperCAmelCase__ :       List[Any]						=						GitProcessor(_lowerCamelCase       ,					_lowerCamelCase  )
  
          processor.save_pretrained(self.tmpdirname  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  ).tokenizer
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  ).image_processor
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          shutil.rmtree(self.tmpdirname  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						[np.random.randint(255       ,					size=(3, 30, 400)       ,					dtype=np.uinta  )]
  
          UpperCAmelCase__ :       Union[str, Any]						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,					0       ,					-1  )  ) for x in image_inputs]
  
          return image_inputs
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						GitProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()  )
          processor.save_pretrained(self.tmpdirname  )
  
          UpperCAmelCase__ :       str						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
          UpperCAmelCase__ :       List[str]						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          UpperCAmelCase__ :       Any						=						GitProcessor.from_pretrained(
              self.tmpdirname       ,					bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""       ,					do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          self.assertEqual(processor.tokenizer.get_vocab()       ,					tokenizer_add_kwargs.get_vocab()  )
          self.assertIsInstance(processor.tokenizer       ,					_lowerCamelCase  )
  
          self.assertEqual(processor.image_processor.to_json_string()       ,					image_processor_add_kwargs.to_json_string()  )
          self.assertIsInstance(processor.image_processor       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.get_image_processor()
          UpperCAmelCase__ :       List[str]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Union[str, Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       str						=						image_processor(_lowerCamelCase       ,					return_tensors="""np"""  )
          UpperCAmelCase__ :       Union[str, Any]						=						processor(images=_lowerCamelCase       ,					return_tensors="""np"""  )
  
          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,					input_processor[key].sum()       ,					delta=1e-2  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
          UpperCAmelCase__ :       int						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Any						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						"""lower newer"""
  
          UpperCAmelCase__ :       Dict						=						processor(text=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						tokenizer(_lowerCamelCase       ,					return_token_type_ids=_lowerCamelCase  )
  
          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,					encoded_processor[key]  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
          UpperCAmelCase__ :       List[str]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       str						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						"""lower newer"""
          UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       Union[str, Any]						=						processor(text=_lowerCamelCase       ,					images=_lowerCamelCase  )
  
          self.assertListEqual(list(inputs.keys()  )       ,					["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
          # test if it raises when no input is passed
          with pytest.raises(_lowerCamelCase  ):
                processor()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						self.get_image_processor()
          UpperCAmelCase__ :       Any						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
          UpperCAmelCase__ :       Union[str, Any]						=						processor.batch_decode(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						tokenizer.batch_decode(_lowerCamelCase  )
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.get_image_processor()
          UpperCAmelCase__ :       Tuple						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Union[str, Any]						=						GitProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[int]						=						"""lower newer"""
          UpperCAmelCase__ :       List[str]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       str						=						processor(text=_lowerCamelCase       ,					images=_lowerCamelCase  )
  
          # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
          self.assertListEqual(list(inputs.keys()  )       ,					["""input_ids""", """attention_mask""", """pixel_values"""]  )
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  