






'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






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

																				_lowerCAmelCase										=FlaxAutoModel.from_pretrained(a_				)
																				self.assertIsNotNone(a_				)
																				self.assertIsInstance(a_		,						a_				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								for model_name in ["roberta-base", "roberta-large"]:
														with self.subTest(a_				):
																				_lowerCAmelCase										=AutoConfig.from_pretrained(a_				)
																				self.assertIsNotNone(a_				)
																				self.assertIsInstance(a_		,						a_				)

																				_lowerCAmelCase										=FlaxAutoModel.from_pretrained(a_				)
																				self.assertIsNotNone(a_				)
																				self.assertIsInstance(a_		,						a_				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Dict:
								for model_name in ["bert-base-cased", "bert-large-uncased"]:
														_lowerCAmelCase										=AutoTokenizer.from_pretrained(a_				)
														_lowerCAmelCase										=FlaxBertModel.from_pretrained(a_				)
														_lowerCAmelCase										=tokenizer('Do you support jax jitted function?'		,						return_tensors=TensorType.JAX				)

														@jax.jit
														def eval(**__A				):
																				return model(**a_				)

														eval(**a_				).block_until_ready()






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								for model_name in ["roberta-base", "roberta-large"]:
														_lowerCAmelCase										=AutoTokenizer.from_pretrained(a_				)
														_lowerCAmelCase										=FlaxRobertaModel.from_pretrained(a_				)
														_lowerCAmelCase										=tokenizer('Do you support jax jitted function?'		,						return_tensors=TensorType.JAX				)

														@jax.jit
														def eval(**__A				):
																				return model(**a_				)

														eval(**a_				).block_until_ready()






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								with self.assertRaisesRegex(
								    a_		,						'bert-base is not a local folder and is not a valid model identifier'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('bert-base'				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								with self.assertRaisesRegex(
								    a_		,						r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained(a_		,						revision='aaaaaa'				)






		def 		UpperCamelCase__       (   self				) ->     str:
								with self.assertRaisesRegex(
								    a_		,						'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'		,						):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								with self.assertRaisesRegex(a_		,						'Use `from_pt=True` to load this model'				):
														_lowerCAmelCase										=FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'				)






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=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__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								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:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

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






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





