code: 
  
  
  '''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 							a_       (						lowercase							,						**lowercase    )			->Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
  
      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							,						**__lowerCAmelCase							,						)
  
  
  
  
  
  class _snake_case   ( A__   ):
         """simple docstring"""
  
  
  
  
  
         @staticmethod
         def 		__SCREAMING_SNAKE_CASE(   __A	)     ->Optional[Any]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      str											=					parser.add_parser("run_beam"			,				help="Run a Beam dataset processing pipeline"	)
             run_beam_parser.add_argument("dataset"			,				type=__A			,				help="Name of the dataset to download"	)
             run_beam_parser.add_argument("--name"			,				type=__A			,				default=__A			,				help="Dataset config name"	)
             run_beam_parser.add_argument(
                 "--cache_dir"			,				type=__A			,				default=__A			,				help="Cache directory where the datasets are stored"			,				)
             run_beam_parser.add_argument(
                 "--beam_pipeline_options"			,				type=__A			,				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=__A			,				default=__A			,				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=__A	)
  
  
  
  
  
  
  
         def __init__(   self			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A			,				**__A			,				)     ->List[str]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             __lowerCamelCase		:      int											=					dataset
             __lowerCamelCase		:      Dict											=					name
             __lowerCamelCase		:      Optional[Any]											=					cache_dir
             __lowerCamelCase		:      int											=					beam_pipeline_options
             __lowerCamelCase		:      Any											=					data_dir
             __lowerCamelCase		:      Union[str, Any]											=					all_configs
             __lowerCamelCase		:      List[Any]											=					save_infos
             __lowerCamelCase		:      Optional[Any]											=					ignore_verifications
             __lowerCamelCase		:      List[str]											=					force_redownload
             __lowerCamelCase		:      Optional[Any]											=					config_kwargs
  
  
  
  
  
  
  
         def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
             """simple docstring"""
  
  
  
  
  
  
  
             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	)
             __lowerCamelCase		:      Optional[int]											=					self._dataset, self._name
             __lowerCamelCase		:      Dict											=					dataset_module_factory(__A	)
             __lowerCamelCase		:      List[str]											=					import_main_class(dataset_module.module_path	)
             __lowerCamelCase		:      List[DatasetBuilder]											=					[]
             if self._beam_pipeline_options:
                 __lowerCamelCase		:      Optional[int]											=					beam.options.pipeline_options.PipelineOptions(
                     flags=[f'--{opt.strip()}' for opt in self._beam_pipeline_options.split(","	) if opt]	)
             else:
                 __lowerCamelCase		:      str											=					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=__A			,				cache_dir=self._cache_dir			,				base_path=dataset_module.builder_kwargs.get("base_path"	)			,				)	)
             else:
                 builders.append(
                     builder_cls(
                         config_name=__A			,				data_dir=self._data_dir			,				beam_options=__A			,				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=__A			,				)
                 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:
                 __lowerCamelCase		:      str											=					os.path.join(builder_cls.get_imported_module_dir()			,				config.DATASETDICT_INFOS_FILENAME	)
  
                 __lowerCamelCase		:      int											=					Path(__A	).name + ".py"
  
                 __lowerCamelCase		:      int											=					os.path.join(__A			,				__A	)
                 if os.path.isfile(__A	):
                     __lowerCamelCase		:      List[str]											=					os.path.dirname(__A	)
                 elif os.path.isfile(__A	):
                     __lowerCamelCase		:      Optional[int]											=					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
                 __lowerCamelCase		:      int											=					os.path.join(__A			,				config.DATASETDICT_INFOS_FILENAME	)
                 copyfile(__A			,				__A	)
                 print(f'Dataset Infos file saved at {user_dataset_infos_path}'	)
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import itertools
  import random
  import unittest
  
  import numpy as np
  
  from transformers import EncodecFeatureExtractor
  from transformers.testing_utils import require_torch
  from transformers.utils.import_utils import is_torch_available
  
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
  
  
  if is_torch_available():
  				import torch
  
  
  __lowercase  :      Any      		=				random.Random()
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase=1.0							,						lowercase=None							,						lowercase=None    )			->int:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if rng is None:
  								__lowerCamelCase		:      Any											=					global_rng
  
  				__lowerCamelCase		:      List[Any]											=					[]
  				for batch_idx in range(shape[0]    ):
  								values.append([]    )
  								for _ in range(shape[1]    ):
  												values[-1].append(rng.random() * scale    )
  
  				return values
  
  
  
  
  
  @require_torch
  class _snake_case   ( unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A=7			,				__A=400			,				__A=2000			,				__A=1			,				__A=0.0			,				__A=24000			,				__A=True			,				)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					parent
  											__lowerCamelCase		:      Tuple											=					batch_size
  											__lowerCamelCase		:      str											=					min_seq_length
  											__lowerCamelCase		:      Optional[Any]											=					max_seq_length
  											__lowerCamelCase		:      int											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  											__lowerCamelCase		:      List[Any]											=					feature_size
  											__lowerCamelCase		:      int											=					padding_value
  											__lowerCamelCase		:      Optional[Any]											=					sampling_rate
  											__lowerCamelCase		:      List[str]											=					return_attention_mask
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return {
  											    "feature_size": self.feature_size,
  											    "padding_value": self.padding_value,
  											    "sampling_rate": self.sampling_rate,
  											    "return_attention_mask": self.return_attention_mask,
  											}
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A=False			,				__A=False	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											def _flatten(__A	):
  															return list(itertools.chain(*__A	)	)
  
  											if equal_length:
  															__lowerCamelCase		:      Optional[int]											=					floats_list((self.batch_size, self.max_seq_length)	)
  											else:
  															# make sure that inputs increase in size
  															__lowerCamelCase		:      Optional[Any]											=					[
  															    _flatten(floats_list((x, self.feature_size)	)	)
  															    for x in range(self.min_seq_length			,				self.max_seq_length			,				self.seq_length_diff	)
  															]
  
  											if numpify:
  															__lowerCamelCase		:      int											=					[np.asarray(__A	) for x in audio_inputs]
  
  											return audio_inputs
  
  
  
  
  
  @require_torch
  class _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     EncodecFeatureExtractor
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					EnCodecFeatureExtractionTester(self	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  											# create three inputs of length 800, 1000, and 1200
  											__lowerCamelCase		:      Optional[Any]											=					[floats_list((1, x)	)[0] for x in range(800			,				1400			,				200	)]
  											__lowerCamelCase		:      Optional[Any]											=					[np.asarray(__A	) for audio_input in audio_inputs]
  
  											# Test not batched input
  											__lowerCamelCase		:      List[str]											=					feat_extract(audio_inputs[0]			,				return_tensors="np"	).input_values
  											__lowerCamelCase		:      Union[str, Any]											=					feat_extract(np_audio_inputs[0]			,				return_tensors="np"	).input_values
  											self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)
  
  											# Test batched
  											__lowerCamelCase		:      Optional[int]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
  											__lowerCamelCase		:      List[str]											=					feat_extract(__A			,				padding=__A			,				return_tensors="np"	).input_values
  											for enc_seq_a, enc_seq_a in zip(__A			,				__A	):
  															self.assertTrue(np.allclose(__A			,				__A			,				atol=1E-3	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
  											__lowerCamelCase		:      Tuple											=					np.random.rand(100	).astype(np.floataa	)
  											__lowerCamelCase		:      Any											=					np_audio_inputs.tolist()
  
  											for inputs in [py_audio_inputs, np_audio_inputs]:
  															__lowerCamelCase		:      int											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="np"	)
  															self.assertTrue(np_processed.input_values.dtype == np.floataa	)
  															__lowerCamelCase		:      List[Any]											=					feature_extractor.pad([{"input_values": inputs}]			,				return_tensors="pt"	)
  															self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											from datasets import load_dataset
  
  											__lowerCamelCase		:      List[Any]											=					load_dataset("hf-internal-testing/librispeech_asr_dummy"			,				"clean"			,				split="validation"	)
  											# automatic decoding with librispeech
  											__lowerCamelCase		:      str											=					ds.sort("id"	).select(range(__A	)	)[:num_samples]["audio"]
  
  											return [x["array"] for x in audio_samples]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					torch.tensor(
  											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
  											# fmt: on
  											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
  											__lowerCamelCase		:      str											=					EncodecFeatureExtractor()
  											__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(input_values.shape			,				(1, 1, 93680)	)
  											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[Any]											=					torch.tensor(
  											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
  											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
  											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
  											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
  											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
  											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]	)
  											# fmt: on
  											__lowerCamelCase		:      Union[str, Any]											=					self._load_datasamples(1	)
  											__lowerCamelCase		:      str											=					[np.tile(input_audio[0][None]			,				reps=(2, 1)	)]
  											input_audio[0][1] *= 0.5
  											__lowerCamelCase		:      Optional[int]											=					EncodecFeatureExtractor(feature_size=2	)
  											__lowerCamelCase		:      str											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(input_values.shape			,				(1, 2, 93680)	)
  											self.assertTrue(torch.allclose(input_values[0, 0, :30]			,				__A			,				atol=1E-6	)	)
  											self.assertTrue(torch.allclose(input_values[0, 1, :30]			,				EXPECTED_INPUT_VALUES * 0.5			,				atol=1E-6	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self._load_datasamples(2	)
  											# would be easier if the stride was like
  											__lowerCamelCase		:      List[Any]											=					EncodecFeatureExtractor(feature_size=1			,				chunk_length_s=1			,				overlap=0.01	)
  
  											# pad and trunc raise an error ?
  											with self.assertRaisesRegex(
  											    __A			,				"^Both padding and truncation were set. Make sure you only set one.$"			,				):
  															__lowerCamelCase		:      Dict											=					feature_extractor(
  															    __A			,				padding="max_length"			,				truncation=__A			,				return_tensors="pt"	).input_values
  
  											# truncate to chunk
  											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				truncation=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 71520)	)  # 2 chunks
  
  											# force truncate to max_length
  											__lowerCamelCase		:      List[str]											=					feature_extractor(
  											    __A			,				truncation=__A			,				max_length=48000			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 48000)	)
  
  											# pad to chunk
  											__lowerCamelCase		:      List[str]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(padded_outputs.shape			,				(2, 1, 95280)	)
  
  											# pad to chunk
  											__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 95280)	)
  
  											# force pad to max length
  											__lowerCamelCase		:      int											=					feature_extractor(
  											    __A			,				padding="max_length"			,				max_length=100000			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(2, 1, 100000)	)
  
  											# force no pad
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      Optional[Any]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      int											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  											# no pad if no chunk_length_s
  											__lowerCamelCase		:      Any											=					None
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      int											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      Any											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  											# no pad if no overlap
  											__lowerCamelCase		:      List[Any]											=					2
  											__lowerCamelCase		:      Any											=					None
  											with self.assertRaisesRegex(
  											    __A			,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,				):
  															__lowerCamelCase		:      Optional[int]											=					feature_extractor(__A			,				padding=__A			,				return_tensors="pt"	).input_values
  
  											__lowerCamelCase		:      List[str]											=					feature_extractor(input_audio[0]			,				padding=__A			,				return_tensors="pt"	).input_values
  											self.assertEquals(truncated_outputs.shape			,				(1, 1, 93680)	)
  
  
  
  
  
  