
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
					import jax

					from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
					from transformers.models.bert.modeling_flax_bert import FlaxBertModel
					from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					for model_name in ["bert-base-cased", "bert-large-uncased"]:
									with self.subTest(A					):
													__A       =					AutoConfig.from_pretrained(A					)
													self.assertIsNotNone(A					)
													self.assertIsInstance(A				,A					)

													__A       =					FlaxAutoModel.from_pretrained(A					)
													self.assertIsNotNone(A					)
													self.assertIsInstance(A				,A					)






	@slow
	def 							UpperCamelCase_    (			self   :					Tuple					):
					for model_name in ["roberta-base", "roberta-large"]:
									with self.subTest(A					):
													__A       =					AutoConfig.from_pretrained(A					)
													self.assertIsNotNone(A					)
													self.assertIsInstance(A				,A					)

													__A       =					FlaxAutoModel.from_pretrained(A					)
													self.assertIsNotNone(A					)
													self.assertIsInstance(A				,A					)






	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					for model_name in ["bert-base-cased", "bert-large-uncased"]:
									__A       =					AutoTokenizer.from_pretrained(A					)
									__A       =					FlaxBertModel.from_pretrained(A					)
									__A       =					tokenizer("Do you support jax jitted function?"				,return_tensors=TensorType.JAX					)

									@jax.jit
									def eval(**A   :					Optional[int]					):
													return model(**A					)

									eval(**A					).block_until_ready()






	@slow
	def 							UpperCamelCase_    (			self   :					Any					):
					for model_name in ["roberta-base", "roberta-large"]:
									__A       =					AutoTokenizer.from_pretrained(A					)
									__A       =					FlaxRobertaModel.from_pretrained(A					)
									__A       =					tokenizer("Do you support jax jitted function?"				,return_tensors=TensorType.JAX					)

									@jax.jit
									def eval(**A   :					Optional[int]					):
													return model(**A					)

									eval(**A					).block_until_ready()






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					with self.assertRaisesRegex(
					    A				,"bert-base is not a local folder and is not a valid model identifier"					):
									__A       =					FlaxAutoModel.from_pretrained("bert-base"					)






	def 							UpperCamelCase_    (			self   :					List[str]					):
					with self.assertRaisesRegex(
					    A				,R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
									__A       =					FlaxAutoModel.from_pretrained(A				,revision="aaaaaa"					)






	def 							UpperCamelCase_    (			self   :					List[str]					):
					with self.assertRaisesRegex(
					    A				,"hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack"				,):
									__A       =					FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"					)







	def 							UpperCamelCase_    (			self   :					Any					):
					with self.assertRaisesRegex(A				,"Use `from_pt=True` to load this model"					):
									__A       =					FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"					)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["image_processor", "tokenizer"]
    __UpperCamelCase    ="CLIPImageProcessor"
    __UpperCamelCase    =("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(      self			:   int				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     **snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'				,     snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('feature_extractor'     )

     SCREAMING_SNAKE_CASE		    =      image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'     )
     if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'     )

     super().__init__(snake_case__				,     snake_case__     )

    def __call__(      self			:   List[Any]				,     snake_case__			:   Tuple=None				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   List[str]=None				,     **snake_case__			:   Dict     ):

     """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:
      SCREAMING_SNAKE_CASE		    =      self.tokenizer(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if images is not None:
      SCREAMING_SNAKE_CASE		    =      self.image_processor(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if text is not None and images is not None:
      SCREAMING_SNAKE_CASE		    =      image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case__     )				,     tensor_type=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Optional[int]     ):

     """simple docstring"""






     return self.tokenizer.batch_decode(*snake_case__				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Dict				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     return self.tokenizer.decode(*snake_case__				,     **snake_case__     )

    @property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE		    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     snake_case__				,     )
     return self.image_processor_class




    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     snake_case__				,     )
     return self.image_processor

