





'''simple docstring'''


import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class       a_   (				unittest.TestCase							):





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





        """simple docstring"""




        lowercase_			:Tuple    			= pipeline(
            task="zero-shot-audio-classification"	,      model="hf-internal-testing/tiny-clap-htsat-unfused"						)
        lowercase_			:Optional[Any]    			= load_dataset("ashraq/esc50"						)
        lowercase_			:Dict    			= dataset["train"]["audio"][-1]["array"]
        lowercase_			:List[str]    			= audio_classifier(lowercase	,      candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]						)
        self.assertEqual(
            nested_simplify(lowercase						)	,      [{"score": 0.5_01, "label": "Sound of a dog"}, {"score": 0.4_99, "label": "Sound of vaccum cleaner"}]	,      )





     @unittest.skip("No models are available in TF"						)
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        pass





     @slow
     @require_torch
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= pipeline(
            task="zero-shot-audio-classification"	,      model="laion/clap-htsat-unfused"	,      )
        # This is an audio of a dog
        lowercase_			:str    			= load_dataset("ashraq/esc50"						)
        lowercase_			:Union[str, Any]    			= dataset["train"]["audio"][-1]["array"]
        lowercase_			:Dict    			= audio_classifier(lowercase	,      candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]						)

        self.assertEqual(
            nested_simplify(lowercase						)	,      [
                {"score": 0.9_99, "label": "Sound of a dog"},
                {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
            ]	,      )

        lowercase_			:Dict    			= audio_classifier([audio] * 5	,      candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]						)
        self.assertEqual(
            nested_simplify(lowercase						)	,      [
                [
                    {"score": 0.9_99, "label": "Sound of a dog"},
                    {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
                ],
            ]
            * 5	,      )
        lowercase_			:Any    			= audio_classifier(
            [audio] * 5	,      candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"]	,      batch_size=5						)
        self.assertEqual(
            nested_simplify(lowercase						)	,      [
                [
                    {"score": 0.9_99, "label": "Sound of a dog"},
                    {"score": 0.0_01, "label": "Sound of vaccum cleaner"},
                ],
            ]
            * 5	,      )







     @unittest.skip("No models are available in TF"						)
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        pass









'''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						)


