'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
  from transformers import TvltImageProcessor

if is_speech_available():
  from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  int							      =    "ZinengTang/tvlt-base"
             _UpperCAmelCase     :  List[Any]							      =    tempfile.mkdtemp()




      def      _A	(       self						:				str						,					**A						:				Optional[int]       ):
             return TvltImageProcessor.from_pretrained(self.checkpoint						,					**A       )




      def      _A	(       self						:				Any						,					**A						:				Dict       ):
             return TvltFeatureExtractor.from_pretrained(self.checkpoint						,					**A       )




      def      _A	(       self						:				Union[str, Any]       ):
             shutil.rmtree(self.tmpdirname       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_image_processor()
             _UpperCAmelCase     :  Tuple							      =    self.get_feature_extractor()

             _UpperCAmelCase     :  int							      =    TvltProcessor(image_processor=A						,					feature_extractor=A       )
             processor.save_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  Union[str, Any]							      =    TvltProcessor.from_pretrained(self.tmpdirname       )

             self.assertIsInstance(processor.feature_extractor						,					A       )
             self.assertIsInstance(processor.image_processor						,					A       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.get_image_processor()
             _UpperCAmelCase     :  Tuple							      =    self.get_feature_extractor()

             _UpperCAmelCase     :  Tuple							      =    TvltProcessor(image_processor=A						,					feature_extractor=A       )

             _UpperCAmelCase     :  Tuple							      =    np.ones([12000]       )

             _UpperCAmelCase     :  Dict							      =    feature_extractor(A						,					return_tensors="np"       )
             _UpperCAmelCase     :  int							      =    processor(audio=A						,					return_tensors="np"       )

             for key in audio_dict.keys():
                    self.assertAlmostEqual(audio_dict[key].sum()						,					input_processor[key].sum()						,					delta=1E-2       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_image_processor()
             _UpperCAmelCase     :  str							      =    self.get_feature_extractor()

             _UpperCAmelCase     :  Any							      =    TvltProcessor(image_processor=A						,					feature_extractor=A       )

             _UpperCAmelCase     :  Any							      =    np.ones([3, 224, 224]       )

             _UpperCAmelCase     :  str							      =    image_processor(A						,					return_tensors="np"       )
             _UpperCAmelCase     :  Tuple							      =    processor(images=A						,					return_tensors="np"       )

             for key in image_dict.keys():
                    self.assertAlmostEqual(image_dict[key].sum()						,					input_processor[key].sum()						,					delta=1E-2       )




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Tuple							      =    self.get_image_processor()
             _UpperCAmelCase     :  str							      =    self.get_feature_extractor()

             _UpperCAmelCase     :  str							      =    TvltProcessor(image_processor=A						,					feature_extractor=A       )

             _UpperCAmelCase     :  Any							      =    np.ones([12000]       )
             _UpperCAmelCase     :  Optional[Any]							      =    np.ones([3, 224, 224]       )

             _UpperCAmelCase     :  Any							      =    processor(audio=A						,					images=A       )

             self.assertListEqual(list(inputs.keys()       )						,					["audio_values", "audio_mask", "pixel_values", "pixel_mask"]       )

             # test if it raises when no input is passed
             with pytest.raises(A       ):
                    processor()





      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.get_image_processor()
             _UpperCAmelCase     :  Tuple							      =    self.get_feature_extractor()

             _UpperCAmelCase     :  List[Any]							      =    TvltProcessor(image_processor=A						,					feature_extractor=A       )

             self.assertListEqual(
                 processor.model_input_names						,					image_processor.model_input_names + feature_extractor.model_input_names						,					msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"						,					)








'''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       a_   (				unittest.TestCase							):





     def __init__( self		:					List[str]	,      lowercase		:					str	,      lowercase		:					Union[str, Any]=13	,      lowercase		:					int=7	,      lowercase		:					List[str]=True	,      lowercase		:					int=True	,      lowercase		:					str=True	,      lowercase		:					Any=True	,      lowercase		:					List[str]=99	,      lowercase		:					Union[str, Any]=32	,      lowercase		:					Optional[Any]=5	,      lowercase		:					Dict=4	,      lowercase		:					Dict=37	,      lowercase		:					Dict="gelu"	,      lowercase		:					Optional[int]=0.1	,      lowercase		:					str=0.1	,      lowercase		:					List[Any]=512	,      lowercase		:					str=16	,      lowercase		:					Dict=2	,      lowercase		:					Any=0.02	,      lowercase		:					Any=4	,      ):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:int    			= ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

        lowercase_			:Union[str, Any]    			= None
        if self.use_attention_mask:
           lowercase_			:Dict    			= random_attention_mask([self.batch_size, self.seq_length]						)

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

        lowercase_			:Optional[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=lowercase	,      initializer_range=self.initializer_range	,      )

        return config, input_ids, token_type_ids, attention_mask





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Tuple    			= config_and_inputs
        lowercase_			:Tuple    			= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict







     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Any    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Union[str, Any]    			= config_and_inputs

        lowercase_			:Dict    			= True
        lowercase_			:Optional[int]    			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        lowercase_			:str    			= 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       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			True

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





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= FlaxBertModelTester(self						)







     @slow
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:List[str]    			= FlaxBertModel.from_pretrained("bert-base-cased"						)
        lowercase_			:str    			= model(np.ones((1, 1)						)						)
        self.assertIsNotNone(lowercase						)


