



"""simple docstring"""







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


A    =			logging.get_logger(__name__)






@add_end_docstrings(UpperCamelCase     )
class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    int      ,							**snake_case  :    Optional[Any]				)			->						Optional[int]:






									'''simple docstring'''

									super().__init__(**snake_case				)

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



		def __call__( self  :    int      ,							snake_case  :    Union[np.ndarray, bytes, str]      ,							**snake_case  :    Dict				)			->						Union[str, Any]:






									'''simple docstring'''

									return super().__call__(snake_case      ,							**snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							**snake_case  :    Any				)			->						List[str]:






									'''simple docstring'''

									A         =					{}
									if "candidate_labels" in kwargs:
																A         =					kwargs['candidate_labels']
									if "hypothesis_template" in kwargs:
																A         =					kwargs['hypothesis_template']

									return preprocess_params, {}, {}



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    str=None      ,							snake_case  :    List[str]="This is a sound of {}."				)			->						Any:






									'''simple docstring'''

									if isinstance(snake_case      ,							snake_case				):
																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
																							A         =					requests.get(snake_case				).content
																else:
																							with open(snake_case      ,							'rb'				) as f:
																														A         =					f.read()

									if isinstance(snake_case      ,							snake_case				):
																A         =					ffmpeg_read(snake_case      ,							self.feature_extractor.sampling_rate				)

									if not isinstance(snake_case      ,							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'				)

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



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    Dict				)			->						List[str]:






									'''simple docstring'''

									A         =					model_inputs.pop('candidate_labels'				)
									A         =					model_inputs.pop('text_inputs'				)
									if isinstance(text_inputs[0]      ,							snake_case				):
																A         =					text_inputs[0]
									else:
																# Batching case.
																A         =					text_inputs[0][0]

									A         =					self.model(**snake_case      ,							**snake_case				)

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



		def 					A_		( self  :    Dict      ,							snake_case  :    str				)			->						str:






									'''simple docstring'''

									A         =					model_outputs.pop('candidate_labels'				)
									A         =					model_outputs['logits'][0]

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

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











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

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



