




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        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 def __init__(							self  :		Dict				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Dict=13				,				_lowerCAmelCase  :		int=7				,				_lowerCAmelCase  :		str=True				,				_lowerCAmelCase  :		Union[str, Any]=True				,				_lowerCAmelCase  :		Union[str, Any]=True				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		Union[str, Any]=99				,				_lowerCAmelCase  :		Union[str, Any]=32				,				_lowerCAmelCase  :		List[Any]=5				,				_lowerCAmelCase  :		Optional[int]=4				,				_lowerCAmelCase  :		Dict=37				,				_lowerCAmelCase  :		Optional[Any]="gelu"				,				_lowerCAmelCase  :		int=0.1				,				_lowerCAmelCase  :		Dict=0.1				,				_lowerCAmelCase  :		Tuple=512				,				_lowerCAmelCase  :		Optional[Any]=16				,				_lowerCAmelCase  :		Optional[Any]=2				,				_lowerCAmelCase  :		Any=0.02				,				_lowerCAmelCase  :		List[str]=4				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			seq_length
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_attention_mask
       SCREAMING_SNAKE_CASE_							=			use_token_type_ids
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			max_position_embeddings
       SCREAMING_SNAKE_CASE_							=			type_vocab_size
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			num_choices



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_attention_mask:
             SCREAMING_SNAKE_CASE_							=			random_attention_mask([self.batch_size, self.seq_length]						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size						)

       SCREAMING_SNAKE_CASE_							=			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=_lowerCAmelCase				,				initializer_range=self.initializer_range				,				)

       return config, input_ids, token_type_ids, attention_mask



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
       return config, inputs_dict




 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			config_and_inputs

       SCREAMING_SNAKE_CASE_							=			True
       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       SCREAMING_SNAKE_CASE_							=			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        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    True

 lowercase_				    =    (
     (
         FlaxBertModel,
         FlaxBertForPreTraining,
         FlaxBertForMaskedLM,
         FlaxBertForMultipleChoice,
         FlaxBertForQuestionAnswering,
         FlaxBertForNextSentencePrediction,
         FlaxBertForSequenceClassification,
         FlaxBertForTokenClassification,
         FlaxBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			FlaxBertModelTester(self						)




 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       # Only check this for base model, not necessary for all model classes.
       # This will also help speed-up tests.
       SCREAMING_SNAKE_CASE_							=			FlaxBertModel.from_pretrained('bert-base-cased'						)
       SCREAMING_SNAKE_CASE_							=			model(np.ones((1, 1)						)						)
       self.assertIsNotNone(_lowerCAmelCase						)





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)