code: 
  
  
  
  
  """simple docstring"""
  
  import importlib.metadata
  import sys
  
  from transformers.testing_utils import TestCasePlus
  from transformers.utils.versions import require_version, require_version_core
  
  
  __A 		=			importlib.metadata.version("numpy")
  __A 		=			".".join([str(x) for x in sys.version_info[:3]])
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					require_version_core("numpy<1000.4.5"							)
  					require_version_core("numpy<1000.4"							)
  					require_version_core("numpy<1000"							)
  
  					# le
  					require_version_core("numpy<=1000.4.5"							)
  					require_version_core(F'''numpy<={numpy_ver}'''							)
  
  					# eq
  					require_version_core(F'''numpy=={numpy_ver}'''							)
  
  					# ne
  					require_version_core("numpy!=1000.4.5"							)
  
  					# ge
  					require_version_core("numpy>=1.0"							)
  					require_version_core("numpy>=1.0.0"							)
  					require_version_core(F'''numpy>={numpy_ver}'''							)
  
  					# gt
  					require_version_core("numpy>1.0.0"							)
  
  					# mix
  					require_version_core("numpy>1.0.0,<1000"							)
  
  					# requirement w/o version
  					require_version_core("numpy"							)
  
  					# unmet requirements due to version conflict
  					for req in ["numpy==1.0.0", "numpy>=1000.0.0", F'''numpy<{numpy_ver}''']:
  							try:
  									require_version_core(SCREAMING_SNAKE_CASE							)
  							except ImportError as e:
  									self.assertIn(F'''{req} is required'''				, str(SCREAMING_SNAKE_CASE							)							)
  									self.assertIn("but found"				, str(SCREAMING_SNAKE_CASE							)							)
  
          # unmet requirements due to missing module
  					for req in ["numpipypie>1", "numpipypie2"]:
  							try:
  									require_version_core(SCREAMING_SNAKE_CASE							)
  							except importlib.metadata.PackageNotFoundError as e:
  									self.assertIn(F'''The \'{req}\' distribution was not found and is required by this application'''				, str(SCREAMING_SNAKE_CASE							)							)
  									self.assertIn("Try: pip install transformers -U"				, str(SCREAMING_SNAKE_CASE							)							)
  
          # bogus requirements formats:
          # 1. whole thing
  					for req in ["numpy??1.0.0", "numpy1.0.0"]:
  							try:
  									require_version_core(SCREAMING_SNAKE_CASE							)
  							except ValueError as e:
  									self.assertIn("requirement needs to be in the pip package format"				, str(SCREAMING_SNAKE_CASE							)							)
          # 2. only operators
  					for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
  							try:
  									require_version_core(SCREAMING_SNAKE_CASE							)
  							except ValueError as e:
  									self.assertIn("need one of "				, str(SCREAMING_SNAKE_CASE							)							)
  
  
  
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					require_version("python>=3.6.0"							)
  
  					# not matching requirements
  					for req in ["python>9.9.9", "python<3.0.0"]:
  							try:
  									require_version_core(SCREAMING_SNAKE_CASE							)
  							except ImportError as e:
  									self.assertIn(F'''{req} is required'''				, str(SCREAMING_SNAKE_CASE							)							)
  									self.assertIn(F'''but found python=={python_ver}'''				, str(SCREAMING_SNAKE_CASE							)							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
  			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
  			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  
  
  			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if text is None and images is None:
  							raise ValueError("You have to specify either text or images. Both cannot be none."							)
  
  					if text is not None:
  							snake_case		:						Dict								=				self.tokenizer(
  							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if images is not None:
  							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is not None and images is not None:
  							snake_case		:						Dict								=				image_features.pixel_values
  							return encoding
  					elif text is not None:
  							return encoding
  					else:
  							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
  					snake_case		:						Any								=				self.image_processor.model_input_names
  					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
  
  