


"""simple docstring"""







import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     from transformers.models.bert.modeling_flax_bert import (
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForNextSentencePrediction,
         FlaxBertForPreTraining,
         FlaxBertForQuestionAnswering,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertModel,
     )


class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__=13		,     snake_case__=7		,     snake_case__=True		,     snake_case__=True		,     snake_case__=True		,     snake_case__=True		,     snake_case__=99		,     snake_case__=32		,     snake_case__=5		,     snake_case__=4		,     snake_case__=37		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=16		,     snake_case__=2		,     snake_case__=0.02		,     snake_case__=4		,     ):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							parent
         lowerCAmelCase     :     str								=							batch_size
         lowerCAmelCase     :     Dict								=							seq_length
         lowerCAmelCase     :     int								=							is_training
         lowerCAmelCase     :     int								=							use_attention_mask
         lowerCAmelCase     :     str								=							use_token_type_ids
         lowerCAmelCase     :     List[Any]								=							use_labels
         lowerCAmelCase     :     List[Any]								=							vocab_size
         lowerCAmelCase     :     Optional[int]								=							hidden_size
         lowerCAmelCase     :     int								=							num_hidden_layers
         lowerCAmelCase     :     Dict								=							num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							intermediate_size
         lowerCAmelCase     :     str								=							hidden_act
         lowerCAmelCase     :     Optional[int]								=							hidden_dropout_prob
         lowerCAmelCase     :     int								=							attention_probs_dropout_prob
         lowerCAmelCase     :     Optional[int]								=							max_position_embeddings
         lowerCAmelCase     :     Union[str, Any]								=							type_vocab_size
         lowerCAmelCase     :     Any								=							type_sequence_label_size
         lowerCAmelCase     :     Any								=							initializer_range
         lowerCAmelCase     :     Tuple								=							num_choices

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size						)

         lowerCAmelCase     :     Tuple								=							None
         if self.use_attention_mask:
           lowerCAmelCase     :     Any								=							random_attention_mask([self.batch_size, self.seq_length]						)

         lowerCAmelCase     :     Optional[Any]								=							None
         if self.use_token_type_ids:
           lowerCAmelCase     :     str								=							ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size						)

         lowerCAmelCase     :     Union[str, Any]								=							BertConfig(
             vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     is_decoder=__snake_case		,     initializer_range=self.initializer_range		,     )

         return config, input_ids, token_type_ids, attention_mask

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							self.prepare_config_and_inputs()
         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							config_and_inputs
         lowerCAmelCase     :     Optional[int]								=							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict







       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							self.prepare_config_and_inputs()
         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     Optional[Any]								=							config_and_inputs

         lowerCAmelCase     :     Tuple								=							True
         lowerCAmelCase     :     Tuple								=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
         lowerCAmelCase     :     Tuple								=							ids_tensor([self.batch_size, self.seq_length]		,     vocab_size=2						)

         return (
             config,
             input_ids,
             attention_mask,
             encoder_hidden_states,
             encoder_attention_mask,
         )


@require_flax
class       SCREAMING_SNAKE_CASE__				(		lowerCAmelCase_   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				int   =True

       a  :				Tuple   =(
           (
               FlaxBertModel,
               FlaxBertForPreTraining,
               FlaxBertForMaskedLM,
               FlaxBertForMultipleChoice,
               FlaxBertForQuestionAnswering,
               FlaxBertForNextSentencePrediction,
               FlaxBertForSequenceClassification,
               FlaxBertForTokenClassification,
               FlaxBertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							FlaxBertModelTester(self						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							FlaxBertModel.from_pretrained("bert-base-cased"						)
         lowerCAmelCase     :     Optional[Any]								=							model(np.ones((1, 1)						)						)
         self.assertIsNotNone(__snake_case						)








import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






