




'''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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					@require_torch
					def     __snake_case						(	self     :						str)    ->							Optional[int]:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='hf-internal-testing/tiny-clap-htsat-unfused')
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= 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     __snake_case						(	self     :						Tuple)    ->							str:
								pass



					@slow
					@require_torch
					def     __snake_case						(	self     :						Optional[Any])    ->							Tuple:
								A_									= pipeline(
								    task='zero-shot-audio-classification'      ,       model='laion/clap-htsat-unfused'      ,       )
								# This is an audio of a dog
								A_									= load_dataset('ashraq/esc50')
								A_									= dataset['train']['audio'][-1]['array']
								A_									= 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'},
								    ]      ,       )

								A_									= 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      ,       )
								A_									= 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     __snake_case						(	self     :						List[str])    ->							str:
								pass










'''simple docstring'''
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""mvp"""
					_UpperCamelCase							=		["""past_key_values"""]
					_UpperCamelCase							=		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



					def __init__(	self     :						List[str]      ,       _lowercase     :						int=50_267      ,       _lowercase     :						str=1_024      ,       _lowercase     :						int=12      ,       _lowercase     :						int=4_096      ,       _lowercase     :						Tuple=16      ,       _lowercase     :						Optional[Any]=12      ,       _lowercase     :						Union[str, Any]=4_096      ,       _lowercase     :						List[Any]=16      ,       _lowercase     :						str=0.0      ,       _lowercase     :						List[str]=0.0      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Union[str, Any]=1_024      ,       _lowercase     :						str=0.1      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						Any=0.0      ,       _lowercase     :						List[str]=0.02      ,       _lowercase     :						List[Any]=0.0      ,       _lowercase     :						List[str]=False      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						Any=1      ,       _lowercase     :						Optional[Any]=0      ,       _lowercase     :						Dict=2      ,       _lowercase     :						int=True      ,       _lowercase     :						Any=2      ,       _lowercase     :						Tuple=2      ,       _lowercase     :						Optional[Any]=False      ,       _lowercase     :						List[str]=100      ,       _lowercase     :						List[str]=800      ,       **_lowercase     :						Dict      ,       )    ->							Dict:
								A_									= vocab_size
								A_									= max_position_embeddings
								A_									= d_model
								A_									= encoder_ffn_dim
								A_									= encoder_layers
								A_									= encoder_attention_heads
								A_									= decoder_ffn_dim
								A_									= decoder_layers
								A_									= decoder_attention_heads
								A_									= dropout
								A_									= attention_dropout
								A_									= activation_dropout
								A_									= activation_function
								A_									= init_std
								A_									= encoder_layerdrop
								A_									= decoder_layerdrop
								A_									= classifier_dropout
								A_									= use_cache
								A_									= encoder_layers
								A_									= scale_embedding  # scale factor will be sqrt(d_model) if True
								A_									= use_prompt
								A_									= prompt_length
								A_									= prompt_mid_dim

								super().__init__(
								    pad_token_id=_lowercase      ,       bos_token_id=_lowercase      ,       eos_token_id=_lowercase      ,       is_encoder_decoder=_lowercase      ,       decoder_start_token_id=_lowercase      ,       forced_eos_token_id=_lowercase      ,       **_lowercase      ,       )

								if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'      ,       _lowercase):
											A_									= self.bos_token_id
											warnings.warn(
											    F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
											    'The config can simply be saved and uploaded again to be fixed.')




