


'''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


__snake_case											=					logging.get_logger(__name__)
@add_end_docstrings(A__ )
class        lowercase     (							A__ ):

		"""simple docstring"""

		def __init__(		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)

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




		def __call__(		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			{}
							if "candidate_labels" in kwargs:
												UpperCamelCase__       :List[str]							 =			kwargs['''candidate_labels''']
							if "hypothesis_template" in kwargs:
												UpperCamelCase__       :Union[str, Any]							 =			kwargs['''hypothesis_template''']

							return preprocess_params, {}, {}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=None   ,       UpperCamelCase_="This is a sound of {}."							):

							'''simple docstring'''


							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												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
																	UpperCamelCase__       :Union[str, Any]							 =			requests.get(UpperCamelCase_							).content
												else:
																	with open(UpperCamelCase_   ,       '''rb'''							) as f:
																						UpperCamelCase__       :Optional[int]							 =			f.read()

							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												UpperCamelCase__       :Optional[Any]							 =			ffmpeg_read(UpperCamelCase_   ,       self.feature_extractor.sampling_rate							)

							if not isinstance(UpperCamelCase_   ,       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'''							)

							UpperCamelCase__       :Optional[int]							 =			self.feature_extractor(
							    [audio]   ,       sampling_rate=self.feature_extractor.sampling_rate   ,       return_tensors='''pt'''							)
							UpperCamelCase__       :Any							 =			candidate_labels
							UpperCamelCase__       :Union[str, Any]							 =			[hypothesis_template.format(UpperCamelCase_							) for x in candidate_labels]
							UpperCamelCase__       :List[Any]							 =			self.tokenizer(UpperCamelCase_   ,       return_tensors=self.framework   ,       padding=UpperCamelCase_							)
							UpperCamelCase__       :str							 =			[text_inputs]
							return inputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			model_inputs.pop('''candidate_labels'''							)
							UpperCamelCase__       :List[Any]							 =			model_inputs.pop('''text_inputs'''							)
							if isinstance(text_inputs[0]   ,       UpperCamelCase_							):
												UpperCamelCase__       :Any							 =			text_inputs[0]
							else:
												# Batching case.
												UpperCamelCase__       :int							 =			text_inputs[0][0]

							UpperCamelCase__       :Tuple							 =			self.model(**UpperCamelCase_   ,       **UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			{
							    '''candidate_labels''': candidate_labels,
							    '''logits''': outputs.logits_per_audio,
							}
							return model_outputs




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			model_outputs.pop('''candidate_labels'''							)
							UpperCamelCase__       :Any							 =			model_outputs['''logits'''][0]

							if self.framework == "pt":
												UpperCamelCase__       :Optional[Any]							 =			logits.softmax(dim=0							)
												UpperCamelCase__       :Tuple							 =			probs.tolist()
							else:
												raise ValueError('''`tf` framework not supported.'''							)

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



'''simple docstring'''
import argparse
from collections import defaultdict

import yaml


__snake_case											=					'''docs/source/en/_toctree.yml'''


def       a				(      __a       )  ->     int:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			defaultdict(__a       )
					UpperCamelCase__       :int							 =			[]
					UpperCamelCase__       :int							 =			[]
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}       )
										else:
															new_doc_list.append(__a       )

					UpperCamelCase__       :Union[str, Any]							 =			new_doc_list
					UpperCamelCase__       :Tuple							 =			[key for key, value in counts.items() if value > 1]

					UpperCamelCase__       :Union[str, Any]							 =			[]
					for duplicate_key in duplicates:
										UpperCamelCase__       :Dict							 =			list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key}       )
										if len(__a       ) > 1:
															raise ValueError(
															    f'''{duplicate_key} is present several times in the documentation table of content at '''
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''       )
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}       )

					# Add none duplicate-keys
					new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]       )
					UpperCamelCase__       :Union[str, Any]							 =			sorted(__a			, key=lambda __a       : s["title"].lower()       )

					# "overview" gets special treatment and is always first
					if len(__a       ) > 1:
										raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''       )

					overview_doc.extend(__a       )

					# Sort
					return overview_doc


def       a				(      __a=False       )  ->     Any:



					'''simple docstring'''






					with open(__a			, encoding='''utf-8'''       ) as f:
										UpperCamelCase__       :Any							 =			yaml.safe_load(f.read()       )

					# Get to the API doc
					UpperCamelCase__       :str							 =			0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					UpperCamelCase__       :str							 =			content[api_idx]['''sections''']

					# Then to the model doc
					UpperCamelCase__       :Optional[Any]							 =			0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					UpperCamelCase__       :List[Any]							 =			api_doc[scheduler_idx]['''sections''']
					UpperCamelCase__       :Union[str, Any]							 =			clean_doc_toc(__a       )

					UpperCamelCase__       :List[Any]							 =			False
					if new_scheduler_doc != scheduler_doc:
										UpperCamelCase__       :Optional[int]							 =			True
										if overwrite:
															UpperCamelCase__       :Dict							 =			new_scheduler_doc

					if diff:
										if overwrite:
															UpperCamelCase__       :Any							 =			api_doc
															with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
																				f.write(yaml.dump(__a			, allow_unicode=__a       )       )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''       )


def       a				(      __a=False       )  ->     Optional[Any]:



					'''simple docstring'''






					with open(__a			, encoding='''utf-8'''       ) as f:
										UpperCamelCase__       :str							 =			yaml.safe_load(f.read()       )

					# Get to the API doc
					UpperCamelCase__       :Optional[Any]							 =			0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					UpperCamelCase__       :Any							 =			content[api_idx]['''sections''']

					# Then to the model doc
					UpperCamelCase__       :str							 =			0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					UpperCamelCase__       :Any							 =			False
					UpperCamelCase__       :Union[str, Any]							 =			api_doc[pipeline_idx]['''sections''']
					UpperCamelCase__       :Tuple							 =			[]

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															UpperCamelCase__       :Dict							 =			pipeline_doc['''section''']
															UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )
															if overwrite:
																				UpperCamelCase__       :Optional[int]							 =			new_sub_pipeline_doc
										new_pipeline_docs.append(__a       )

					# sort overall pipeline doc
					UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )

					if new_pipeline_docs != pipeline_docs:
										UpperCamelCase__       :int							 =			True
										if overwrite:
															UpperCamelCase__       :Union[str, Any]							 =			new_pipeline_docs

					if diff:
										if overwrite:
															UpperCamelCase__       :Dict							 =			api_doc
															with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
																				f.write(yaml.dump(__a			, allow_unicode=__a       )       )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''       )


if __name__ == "__main__":
		__snake_case											=					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		__snake_case											=					parser.parse_args()

		check_scheduler_doc(args.fix_and_overwrite)
		check_pipeline_doc(args.fix_and_overwrite)