code: 
  
  
  
  
  
  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        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		tempfile.mkdtemp()
  
  													a_					=		CLIPImageProcessor()
  													a_					=		BertTokenizer.from_pretrained(
  													    """hf-internal-testing/tiny-random-BertModel"""			,    model_input_names=["""input_ids""", """attention_mask"""]		)
  
  													a_					=		GitProcessor(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													processor.save_pretrained(self.tmpdirname		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).tokenizer
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return AutoProcessor.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		).image_processor
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													shutil.rmtree(self.tmpdirname		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[np.random.randint(255			,    size=(3, 30, 400)			,    dtype=np.uinta		)]
  
  													a_					=		[Image.fromarray(np.moveaxis(_UpperCAmelCase			,    0			,    -1		)		) for x in image_inputs]
  
  													return image_inputs
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		GitProcessor(tokenizer=self.get_tokenizer()			,    image_processor=self.get_image_processor()		)
  													processor.save_pretrained(self.tmpdirname		)
  
  													a_					=		self.get_tokenizer(bos_token="""(BOS)"""			,    eos_token="""(EOS)"""		)
  													a_					=		self.get_image_processor(do_normalize=_UpperCAmelCase			,    padding_value=1.0		)
  
  													a_					=		GitProcessor.from_pretrained(
  													    self.tmpdirname			,    bos_token="""(BOS)"""			,    eos_token="""(EOS)"""			,    do_normalize=_UpperCAmelCase			,    padding_value=1.0		)
  
  													self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab()		)
  													self.assertIsInstance(processor.tokenizer			,    _UpperCAmelCase		)
  
  													self.assertEqual(processor.image_processor.to_json_string()			,    image_processor_add_kwargs.to_json_string()		)
  													self.assertIsInstance(processor.image_processor			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_image_processor()
  													a_					=		self.get_tokenizer()
  
  													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)
  
  													a_					=		self.prepare_image_inputs()
  
  													a_					=		image_processor(_UpperCAmelCase			,    return_tensors="""np"""		)
  													a_					=		processor(images=_UpperCAmelCase			,    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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_image_processor()
  													a_					=		self.get_tokenizer()
  
  													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)
  
  													a_					=		"""lower newer"""
  
  													a_					=		processor(text=_UpperCAmelCase		)
  
  													a_					=		tokenizer(_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  													for key in encoded_tok.keys():
  																			self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_image_processor()
  													a_					=		self.get_tokenizer()
  
  													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)
  
  													a_					=		"""lower newer"""
  													a_					=		self.prepare_image_inputs()
  
  													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)
  
  													self.assertListEqual(list(inputs.keys()		)			,    ["""input_ids""", """attention_mask""", """pixel_values"""]		)
  
  													# test if it raises when no input is passed
  													with pytest.raises(_UpperCAmelCase		):
  																			processor()
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_image_processor()
  													a_					=		self.get_tokenizer()
  
  													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)
  
  													a_					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  													a_					=		processor.batch_decode(_UpperCAmelCase		)
  													a_					=		tokenizer.batch_decode(_UpperCAmelCase		)
  
  													self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.get_image_processor()
  													a_					=		self.get_tokenizer()
  
  													a_					=		GitProcessor(tokenizer=_UpperCAmelCase			,    image_processor=_UpperCAmelCase		)
  
  													a_					=		"""lower newer"""
  													a_					=		self.prepare_image_inputs()
  
  													a_					=		processor(text=_UpperCAmelCase			,    images=_UpperCAmelCase		)
  
  													# 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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  A_    :					Optional[Any]             ={
      """configuration_wav2vec2_conformer""": [
          """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """Wav2Vec2ConformerConfig""",
      ],
  }
  
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	A_    :					Union[str, Any]             =[
  	    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """Wav2Vec2ConformerForAudioFrameClassification""",
  	    """Wav2Vec2ConformerForCTC""",
  	    """Wav2Vec2ConformerForPreTraining""",
  	    """Wav2Vec2ConformerForSequenceClassification""",
  	    """Wav2Vec2ConformerForXVector""",
  	    """Wav2Vec2ConformerModel""",
  	    """Wav2Vec2ConformerPreTrainedModel""",
  	]
  
  if TYPE_CHECKING:
  	from .configuration_wavaveca_conformer import (
  	    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
  	    WavaVecaConformerConfig,
  	)
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_wavaveca_conformer import (
  		    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    WavaVecaConformerForAudioFrameClassification,
  		    WavaVecaConformerForCTC,
  		    WavaVecaConformerForPreTraining,
  		    WavaVecaConformerForSequenceClassification,
  		    WavaVecaConformerForXVector,
  		    WavaVecaConformerModel,
  		    WavaVecaConformerPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	A_    :					Union[str, Any]             =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)