





"""simple docstring"""




import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

UpperCamelCase_	 =logging.get_logger(__name__)





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       F"""{sampling_rate}"""
						_UpperCamelCase     :   int      =       '''1'''
						_UpperCamelCase     :   List[str]      =       '''f32le'''
						_UpperCamelCase     :   Optional[int]      =       [
						    '''ffmpeg''',
						    '''-i''',
						    '''pipe:0''',
						    '''-ac''',
						    ac,
						    '''-ar''',
						    ar,
						    '''-f''',
						    format_for_conversion,
						    '''-hide_banner''',
						    '''-loglevel''',
						    '''quiet''',
						    '''pipe:1''',
						]

						try:
												_UpperCamelCase     :   Dict      =       subprocess.Popen(_lowercase			,		stdin=subprocess.PIPE			,		stdout=subprocess.PIPE  )
						except FileNotFoundError:
												raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''  )
						_UpperCamelCase     :   Tuple      =       ffmpeg_process.communicate(_lowercase  )
						_UpperCamelCase     :   int      =       output_stream[0]

						_UpperCamelCase     :   Tuple      =       np.frombuffer(_lowercase			,		np.floataa  )
						if audio.shape[0] == 0:
												raise ValueError('''Malformed soundfile'''  )
						return audio






@add_end_docstrings(_lowerCAmelCase							)
class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       Tuple,					*lowerCAmelCase__						:       int,					**lowerCAmelCase__						:       List[Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       5
										super().__init__(*lowerCAmelCase__,					**lowerCAmelCase__				)

										if self.framework != "pt":
																raise ValueError(f"""The {self.__class__} is only available in PyTorch."""				)

										self.check_model_type(lowerCAmelCase__				)






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




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Dict=None,					**lowerCAmelCase__						:       Dict				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       {}
										if top_k is not None:
																if top_k > self.model.config.num_labels:
																						_UpperCamelCase     :   Optional[int]      =       self.model.config.num_labels
																_UpperCamelCase     :   List[Any]      =       top_k
										return {}, {}, postprocess_params






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										if isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																if inputs.startswith('''http://'''				) or inputs.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     :   Dict      =       requests.get(lowerCAmelCase__				).content
																else:
																						with open(lowerCAmelCase__,					'''rb'''				) as f:
																												_UpperCamelCase     :   Optional[Any]      =       f.read()

										if isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																_UpperCamelCase     :   Tuple      =       ffmpeg_read(lowerCAmelCase__,					self.feature_extractor.sampling_rate				)

										if isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																# Accepting `"array"` which is the key defined in `datasets` for
																# better integration
																if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																						raise ValueError(
																						    '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
																						    '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
																						    '''containing the sampling_rate associated with that array'''				)

																_UpperCamelCase     :   Optional[Any]      =       inputs.pop('''raw''',					lowerCAmelCase__				)
																if _inputs is None:
																						# Remove path which will not be used from `datasets`.
																						inputs.pop('''path''',					lowerCAmelCase__				)
																						_UpperCamelCase     :   str      =       inputs.pop('''array''',					lowerCAmelCase__				)
																_UpperCamelCase     :   str      =       inputs.pop('''sampling_rate'''				)
																_UpperCamelCase     :   Dict      =       _inputs
																if in_sampling_rate != self.feature_extractor.sampling_rate:
																						import torch

																						if is_torchaudio_available():
																												from torchaudio import functional as F
																						else:
																												raise ImportError(
																												    '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
																												    '''The torchaudio package can be installed through: `pip install torchaudio`.'''				)

																						_UpperCamelCase     :   Any      =       F.resample(
																						    torch.from_numpy(lowerCAmelCase__				),					lowerCAmelCase__,					self.feature_extractor.sampling_rate				).numpy()

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

										_UpperCamelCase     :   Tuple      =       self.feature_extractor(
										    lowerCAmelCase__,					sampling_rate=self.feature_extractor.sampling_rate,					return_tensors='''pt'''				)
										return processed






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




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.model(**lowerCAmelCase__				)
										return model_outputs






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Dict=5				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       model_outputs.logits[0].softmax(-1				)
										_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       probs.topk(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       scores.tolist()
										_UpperCamelCase     :   List[Any]      =       ids.tolist()

										_UpperCamelCase     :   Dict      =       [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__,					lowerCAmelCase__				)]

										return labels





'''simple docstring'''

import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[str]   ,     **snake_case_				:	str							)       ->	Optional[int]:
 return RunBeamCommand(
     args.dataset   ,     args.name   ,     args.cache_dir   ,     args.beam_pipeline_options   ,     args.data_dir   ,     args.all_configs   ,     args.save_info or args.save_infos   ,     args.ignore_verifications   ,     args.force_redownload   ,     **snake_case_   ,     )





class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   @staticmethod
   def 					__a			(       __SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		parser.add_parser('run_beam'    ,help='Run a Beam dataset processing pipeline'      )
    run_beam_parser.add_argument('dataset'    ,type=__SCREAMING_SNAKE_CASE    ,help='Name of the dataset to download'      )
    run_beam_parser.add_argument('--name'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Dataset config name'      )
    run_beam_parser.add_argument(
        '--cache_dir'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Cache directory where the datasets are stored'    ,)
    run_beam_parser.add_argument(
        '--beam_pipeline_options'    ,type=__SCREAMING_SNAKE_CASE    ,default=''    ,help='Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'    ,)
    run_beam_parser.add_argument(
        '--data_dir'    ,type=__SCREAMING_SNAKE_CASE    ,default=__SCREAMING_SNAKE_CASE    ,help='Can be used to specify a manual directory to get the files from'    ,)
    run_beam_parser.add_argument('--all_configs'    ,action='store_true'    ,help='Test all dataset configurations'      )
    run_beam_parser.add_argument('--save_info'    ,action='store_true'    ,help='Save the dataset infos file'      )
    run_beam_parser.add_argument(
        '--ignore_verifications'    ,action='store_true'    ,help='Run the test without checksums and splits checks'      )
    run_beam_parser.add_argument('--force_redownload'    ,action='store_true'    ,help='Force dataset redownload'      )
    # aliases
    run_beam_parser.add_argument('--save_infos'    ,action='store_true'    ,help='alias for save_info'      )
    run_beam_parser.set_defaults(func=__SCREAMING_SNAKE_CASE      )






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		dataset
    SCREAMING_SNAKE_CASE  :  List[Any]								=		name
    SCREAMING_SNAKE_CASE  :  int								=		cache_dir
    SCREAMING_SNAKE_CASE  :  str								=		beam_pipeline_options
    SCREAMING_SNAKE_CASE  :  List[str]								=		data_dir
    SCREAMING_SNAKE_CASE  :  int								=		all_configs
    SCREAMING_SNAKE_CASE  :  Any								=		save_infos
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ignore_verifications
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		force_redownload
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		config_kwargs






   def 					__a			(       self      ):
    import apache_beam as beam

    if self._name is not None and self._all_configs:
     print('Both parameters `name` and `all_configs` can\'t be used at once.'      )
     exit(1      )
    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		self._dataset, self._name
    SCREAMING_SNAKE_CASE  :  str								=		dataset_module_factory(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  str								=		import_main_class(dataset_module.module_path      )
    SCREAMING_SNAKE_CASE  :  List[DatasetBuilder]								=		[]
    if self._beam_pipeline_options:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		beam.options.pipeline_options.PipelineOptions(
         flags=[f"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(','      ) if opt]      )
    else:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		None
    if self._all_configs and len(builder_cls.BUILDER_CONFIGS      ) > 0:
     for builder_config in builder_cls.BUILDER_CONFIGS:
      builders.append(
          builder_cls(
              config_name=builder_config.name    ,data_dir=self._data_dir    ,hash=dataset_module.hash    ,beam_options=__SCREAMING_SNAKE_CASE    ,cache_dir=self._cache_dir    ,base_path=dataset_module.builder_kwargs.get('base_path'      )    ,)      )
    else:
     builders.append(
         builder_cls(
             config_name=__SCREAMING_SNAKE_CASE    ,data_dir=self._data_dir    ,beam_options=__SCREAMING_SNAKE_CASE    ,cache_dir=self._cache_dir    ,base_path=dataset_module.builder_kwargs.get('base_path'      )    ,**self._config_kwargs    ,)      )

    for builder in builders:
     builder.download_and_prepare(
         download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
         if not self._force_redownload
         else DownloadMode.FORCE_REDOWNLOAD    ,download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH      )    ,verification_mode=VerificationMode.NO_CHECKS
         if self._ignore_verifications
         else VerificationMode.ALL_CHECKS    ,try_from_hf_gcs=__SCREAMING_SNAKE_CASE    ,)
     if self._save_infos:
      builder._save_infos()

    print('Apache beam run successful.'      )

    # If save_infos=True, the dataset infos file is created next to the loaded module file.
    # Let's move it to the original directory of the dataset script, to allow the user to
    # upload them on S3 at the same time afterwards.
    if self._save_infos:
     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(builder_cls.get_imported_module_dir()    ,config.DATASETDICT_INFOS_FILENAME      )

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		Path(__SCREAMING_SNAKE_CASE      ).name + '.py'

     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     if os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Tuple								=		os.path.dirname(__SCREAMING_SNAKE_CASE      )
     elif os.path.isfile(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Optional[Any]								=		path
     else:  # in case of a remote dataset
      print(f"""Dataset Infos file saved at {dataset_infos_path}"""      )
      exit(1      )

     # Move datasetinfo back to the user
     SCREAMING_SNAKE_CASE  :  List[Any]								=		os.path.join(__SCREAMING_SNAKE_CASE    ,config.DATASETDICT_INFOS_FILENAME      )
     copyfile(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )
     print(f"""Dataset Infos file saved at {user_dataset_infos_path}"""      )


