

from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


lowerCamelCase      				=     logging.get_logger(__name__)





@add_end_docstrings(_a							)
class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						**_A							):
												super().__init__(**_A							)

												if self.framework != "pt":
																	raise ValueError(F"""The {self.__class__} is only available in PyTorch."""							)
												# No specific FOR_XXX available yet




							def __call__( self					,						_A					,						**_A							):
												return super().__call__(_A					,						**_A							)




							def        A__  ( self					,						**_A							):
												__lowerCAmelCase					=							{}
												if "candidate_labels" in kwargs:
																	__lowerCAmelCase					=							kwargs['candidate_labels']
												if "hypothesis_template" in kwargs:
																	__lowerCAmelCase					=							kwargs['hypothesis_template']

												return preprocess_params, {}, {}




							def        A__  ( self					,						_A					,						_A=None					,						_A="This is a sound of {}."							):
												if isinstance(_A					,						_A							):
																	if audio.startswith('http://'							) or audio.startswith('https://'							):
																						# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																						# like http_huggingface_co.png
																						__lowerCAmelCase					=							requests.get(_A							).content
																	else:
																						with open(_A					,						'rb'							) as f:
																											__lowerCAmelCase					=							f.read()

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							ffmpeg_read(_A					,						self.feature_extractor.sampling_rate							)

												if not isinstance(_A					,						np.ndarray							):
																	raise ValueError('We expect a numpy ndarray as input'							)
												if len(audio.shape							) != 1:
																	raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'							)

												__lowerCAmelCase					=							self.feature_extractor(
												    [audio]					,						sampling_rate=self.feature_extractor.sampling_rate					,						return_tensors='pt'							)
												__lowerCAmelCase					=							candidate_labels
												__lowerCAmelCase					=							[hypothesis_template.format(_A							) for x in candidate_labels]
												__lowerCAmelCase					=							self.tokenizer(_A					,						return_tensors=self.framework					,						padding=_A							)
												__lowerCAmelCase					=							[text_inputs]
												return inputs




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							model_inputs.pop('candidate_labels'							)
												__lowerCAmelCase					=							model_inputs.pop('text_inputs'							)
												if isinstance(text_inputs[0]					,						_A							):
																	__lowerCAmelCase					=							text_inputs[0]
												else:
																	# Batching case.
																	__lowerCAmelCase					=							text_inputs[0][0]

												__lowerCAmelCase					=							self.model(**_A					,						**_A							)

												__lowerCAmelCase					=							{
												    'candidate_labels': candidate_labels,
												    'logits': outputs.logits_per_audio,
												}
												return model_outputs




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							model_outputs.pop('candidate_labels'							)
												__lowerCAmelCase					=							model_outputs['logits'][0]

												if self.framework == "pt":
																	__lowerCAmelCase					=							logits.softmax(dim=0							)
																	__lowerCAmelCase					=							probs.tolist()
												else:
																	raise ValueError('`tf` framework not supported.'							)

												__lowerCAmelCase					=							[
												    {'score': score, 'label': candidate_label}
												    for score, candidate_label in sorted(zip(_A					,						_A							)					,						key=lambda _A							: -x[0]							)
												]
												return result









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






