



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 	_UpperCAmelCase       (   snake_case			,		**snake_case    ):





					"""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			,		**snake_case			,		)





class       __lowerCAmelCase				(  lowerCamelCase__ ):




				@staticmethod
				def 							snake_case     (							_snake_case	):



									"""simple docstring"""





									_lowerCAmelCase      			=					parser.add_parser("""run_beam"""		,       help="""Run a Beam dataset processing pipeline"""	)
									run_beam_parser.add_argument("""dataset"""		,       type=_snake_case		,       help="""Name of the dataset to download"""	)
									run_beam_parser.add_argument("""--name"""		,       type=_snake_case		,       default=_snake_case		,       help="""Dataset config name"""	)
									run_beam_parser.add_argument(
									    """--cache_dir"""		,       type=_snake_case		,       default=_snake_case		,       help="""Cache directory where the datasets are stored"""		,       )
									run_beam_parser.add_argument(
									    """--beam_pipeline_options"""		,       type=_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=_snake_case		,       default=_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=_snake_case	)




				def __init__(							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       **_snake_case		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					dataset
									_lowerCAmelCase      			=					name
									_lowerCAmelCase      			=					cache_dir
									_lowerCAmelCase      			=					beam_pipeline_options
									_lowerCAmelCase      			=					data_dir
									_lowerCAmelCase      			=					all_configs
									_lowerCAmelCase      			=					save_infos
									_lowerCAmelCase      			=					ignore_verifications
									_lowerCAmelCase      			=					force_redownload
									_lowerCAmelCase      			=					config_kwargs




				def 							snake_case     (							self	):



									"""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			,			_lowerCAmelCase      			=					self._dataset, self._name
									_lowerCAmelCase      			=					dataset_module_factory(_snake_case	)
									_lowerCAmelCase      			=					import_main_class(dataset_module.module_path	)
									_lowerCAmelCase      			=					[]
									if self._beam_pipeline_options:
														_lowerCAmelCase      			=					beam.options.pipeline_options.PipelineOptions(
														    flags=[F'--{opt.strip()}' for opt in self._beam_pipeline_options.split(""","""	) if opt]	)
									else:
														_lowerCAmelCase      			=					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=_snake_case		,       cache_dir=self._cache_dir		,       base_path=dataset_module.builder_kwargs.get("""base_path"""	)		,       )	)
									else:
														builders.append(
														    builder_cls(
														        config_name=_snake_case		,       data_dir=self._data_dir		,       beam_options=_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=_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:
														_lowerCAmelCase      			=					os.path.join(builder_cls.get_imported_module_dir()		,       config.DATASETDICT_INFOS_FILENAME	)

														_lowerCAmelCase      			=					Path(_snake_case	).name + """.py"""

														_lowerCAmelCase      			=					os.path.join(_snake_case		,       _snake_case	)
														if os.path.isfile(_snake_case	):
																			_lowerCAmelCase      			=					os.path.dirname(_snake_case	)
														elif os.path.isfile(_snake_case	):
																			_lowerCAmelCase      			=					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      			=					os.path.join(_snake_case		,       config.DATASETDICT_INFOS_FILENAME	)
														copyfile(_snake_case		,       _snake_case	)
														print(F'Dataset Infos file saved at {user_dataset_infos_path}'	)








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


A__			   = random.Random()


def 	_UpperCAmelCase       (   snake_case			,		snake_case=1.0			,		snake_case=None			,		snake_case=None    ):





					"""simple docstring"""

					if rng is None:
										_lowerCAmelCase      			=					global_rng

					_lowerCAmelCase      			=					[]
					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       __lowerCAmelCase				(  unittest.TestCase ):




				def __init__(							self		,       _snake_case		,       _snake_case=7		,       _snake_case=400		,       _snake_case=2000		,       _snake_case=1		,       _snake_case=0.0		,       _snake_case=24000		,       _snake_case=True		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					min_seq_length
									_lowerCAmelCase      			=					max_seq_length
									_lowerCAmelCase      			=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									_lowerCAmelCase      			=					feature_size
									_lowerCAmelCase      			=					padding_value
									_lowerCAmelCase      			=					sampling_rate
									_lowerCAmelCase      			=					return_attention_mask




				def 							snake_case     (							self	):



									"""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 							snake_case     (							self		,       _snake_case=False		,       _snake_case=False	):



									"""simple docstring"""





									def _flatten(_snake_case	):
														return list(itertools.chain(*_snake_case	)	)

									if equal_length:
														_lowerCAmelCase      			=					floats_list((self.batch_size, self.max_seq_length)	)
									else:
														# make sure that inputs increase in size
														_lowerCAmelCase      			=					[
														    _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      			=					[np.asarray(_snake_case	) for x in audio_inputs]

									return audio_inputs





@require_torch
class       __lowerCAmelCase				(  lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       EncodecFeatureExtractor




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					EnCodecFeatureExtractionTester(self	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
									# create three inputs of length 800, 1000, and 1200
									_lowerCAmelCase      			=					[floats_list((1, x)	)[0] for x in range(800		,       1400		,       200	)]
									_lowerCAmelCase      			=					[np.asarray(_snake_case	) for audio_input in audio_inputs]

									# Test not batched input
									_lowerCAmelCase      			=					feat_extract(audio_inputs[0]		,       return_tensors="""np"""	).input_values
									_lowerCAmelCase      			=					feat_extract(np_audio_inputs[0]		,       return_tensors="""np"""	).input_values
									self.assertTrue(np.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)	)

									# Test batched
									_lowerCAmelCase      			=					feat_extract(_snake_case		,       padding=_snake_case		,       return_tensors="""np"""	).input_values
									_lowerCAmelCase      			=					feat_extract(_snake_case		,       padding=_snake_case		,       return_tensors="""np"""	).input_values
									for enc_seq_a, enc_seq_a in zip(_snake_case		,       _snake_case	):
														self.assertTrue(np.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
									_lowerCAmelCase      			=					np.random.rand(100	).astype(np.floataa	)
									_lowerCAmelCase      			=					np_audio_inputs.tolist()

									for inputs in [py_audio_inputs, np_audio_inputs]:
														_lowerCAmelCase      			=					feature_extractor.pad([{"""input_values""": inputs}]		,       return_tensors="""np"""	)
														self.assertTrue(np_processed.input_values.dtype == np.floataa	)
														_lowerCAmelCase      			=					feature_extractor.pad([{"""input_values""": inputs}]		,       return_tensors="""pt"""	)
														self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





									from datasets import load_dataset

									_lowerCAmelCase      			=					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,       """clean"""		,       split="""validation"""	)
									# automatic decoding with librispeech
									_lowerCAmelCase      			=					ds.sort("""id"""	).select(range(_snake_case	)	)[:num_samples]["""audio"""]

									return [x["array"] for x in audio_samples]




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					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      			=					self._load_datasamples(1	)
									_lowerCAmelCase      			=					EncodecFeatureExtractor()
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(input_values.shape		,       (1, 1, 93680)	)
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,       _snake_case		,       atol=1e-6	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					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      			=					self._load_datasamples(1	)
									_lowerCAmelCase      			=					[np.tile(input_audio[0][None]		,       reps=(2, 1)	)]
									input_audio[0][1] *= 0.5
									_lowerCAmelCase      			=					EncodecFeatureExtractor(feature_size=2	)
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(input_values.shape		,       (1, 2, 93680)	)
									self.assertTrue(torch.allclose(input_values[0, 0, :30]		,       _snake_case		,       atol=1e-6	)	)
									self.assertTrue(torch.allclose(input_values[0, 1, :30]		,       EXPECTED_INPUT_VALUES * 0.5		,       atol=1e-6	)	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self._load_datasamples(2	)
									# would be easier if the stride was like
									_lowerCAmelCase      			=					EncodecFeatureExtractor(feature_size=1		,       chunk_length_s=1		,       overlap=0.01	)

									# pad and trunc raise an error ?
									with self.assertRaisesRegex(
									    _snake_case		,       """^Both padding and truncation were set. Make sure you only set one.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(
														    _snake_case		,       padding="""max_length"""		,       truncation=_snake_case		,       return_tensors="""pt"""	).input_values

									# truncate to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       truncation=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 71520)	)  # 2 chunks

									# force truncate to max_length
									_lowerCAmelCase      			=					feature_extractor(
									    _snake_case		,       truncation=_snake_case		,       max_length=48000		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 48000)	)

									# pad to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(padded_outputs.shape		,       (2, 1, 95280)	)

									# pad to chunk
									_lowerCAmelCase      			=					feature_extractor(_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (2, 1, 95280)	)

									# force pad to max length
									_lowerCAmelCase      			=					feature_extractor(
									    _snake_case		,       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(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)

									# no pad if no chunk_length_s
									_lowerCAmelCase      			=					None
									with self.assertRaisesRegex(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)

									# no pad if no overlap
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					None
									with self.assertRaisesRegex(
									    _snake_case		,       """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""		,       ):
														_lowerCAmelCase      			=					feature_extractor(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values

									_lowerCAmelCase      			=					feature_extractor(input_audio[0]		,       padding=_snake_case		,       return_tensors="""pt"""	).input_values
									self.assertEquals(truncated_outputs.shape		,       (1, 1, 93680)	)



