





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
    from transformers import SpeechaTextFeatureExtractor

_UpperCAmelCase				:       Union[str, Any]	   =				random.Random()


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=1.0						,	_UpperCAmelCase=None						,	_UpperCAmelCase=None )			->     str:
    if rng is None:
        lowerCamelCase__    :      Optional[Any]					 =		global_rng

    lowerCamelCase__    :      Tuple					 =		[]
    for batch_idx in range(shape[0] ):
        values.append([] )
        for _ in range(shape[1] ):
            values[-1].append(rng.random() * scale )

    return values

@require_torch
@require_torchaudio
class    lowerCAmelCase							(					unittest.TestCase			):
 def __init__(      self  :      int			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Union[str, Any]=7			,     UpperCAmelCase  :      Dict=400			,     UpperCAmelCase  :      Tuple=2000			,     UpperCAmelCase  :      Optional[Any]=24			,     UpperCAmelCase  :      List[Any]=24			,     UpperCAmelCase  :      Optional[Any]=0.0			,     UpperCAmelCase  :      Union[str, Any]=16000			,     UpperCAmelCase  :      Union[str, Any]=True			,     UpperCAmelCase  :      Dict=True			,     )			->	int:
     lowerCamelCase__    :      Union[str, Any]					 =		parent
     lowerCamelCase__    :      Tuple					 =		batch_size
     lowerCamelCase__    :      Any					 =		min_seq_length
     lowerCamelCase__    :      List[str]					 =		max_seq_length
     lowerCamelCase__    :      Dict					 =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCamelCase__    :      Optional[Any]					 =		feature_size
     lowerCamelCase__    :      Dict					 =		num_mel_bins
     lowerCamelCase__    :      Union[str, Any]					 =		padding_value
     lowerCamelCase__    :      str					 =		sampling_rate
     lowerCamelCase__    :      Tuple					 =		return_attention_mask
     lowerCamelCase__    :      Tuple					 =		do_normalize
 def      A_	(      self  :      List[str]	)			->	List[Any]:
     return {
         "feature_size": self.feature_size,
         "num_mel_bins": self.num_mel_bins,
         "padding_value": self.padding_value,
         "sampling_rate": self.sampling_rate,
         "return_attention_mask": self.return_attention_mask,
         "do_normalize": self.do_normalize,
     }







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[int]=False			,     UpperCAmelCase  :      List[Any]=False	)			->	str:
     def _flatten(UpperCAmelCase  :      Tuple	):
         return list(itertools.chain(*UpperCAmelCase	)	)

     if equal_length:
         lowerCamelCase__    :      List[str]					 =		[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
     else:
         # make sure that inputs increase in size
         lowerCamelCase__    :      List[Any]					 =		[
             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__    :      Union[str, Any]					 =		[np.asarray(UpperCAmelCase	) for x in speech_inputs]
     return speech_inputs


@require_torch
@require_torchaudio
class    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		SpeechaTextFeatureExtractor if is_speech_available() else None
 def      A_	(      self  :      Tuple	)			->	str:
     lowerCamelCase__    :      Any					 =		SpeechaTextFeatureExtractionTester(self	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      Optional[Any]	)			->	List[str]:
     self.assertTrue(np.all(np.mean(UpperCAmelCase			,     axis=0	) < 1e-3	)	)
     self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase			,     axis=0	) - 1	) < 1e-3	)	)
 def      A_	(      self  :      List[str]	)			->	Dict:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     lowerCamelCase__    :      List[Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     # create three inputs of length 800, 1000, and 1200
     lowerCamelCase__    :      str					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]
     lowerCamelCase__    :      Any					 =		[np.asarray(UpperCAmelCase	) for speech_input in speech_inputs]

     # Test feature size
     lowerCamelCase__    :      Optional[int]					 =		feature_extractor(UpperCAmelCase			,     padding=UpperCAmelCase			,     return_tensors='np'	).input_features
     self.assertTrue(input_features.ndim == 3	)
     self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

     # Test not batched input
     lowerCamelCase__    :      Optional[Any]					 =		feature_extractor(speech_inputs[0]			,     return_tensors='np'	).input_features
     lowerCamelCase__    :      List[Any]					 =		feature_extractor(np_speech_inputs[0]			,     return_tensors='np'	).input_features
     self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)

     # Test batched
     lowerCamelCase__    :      List[Any]					 =		feature_extractor(UpperCAmelCase			,     return_tensors='np'	).input_features
     lowerCamelCase__    :      Union[str, Any]					 =		feature_extractor(UpperCAmelCase			,     return_tensors='np'	).input_features
     for enc_seq_a, enc_seq_a in zip(UpperCAmelCase			,     UpperCAmelCase	):
         self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)

     # Test 2-D numpy arrays are batched.
     lowerCamelCase__    :      Union[str, Any]					 =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
     lowerCamelCase__    :      str					 =		np.asarray(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		feature_extractor(UpperCAmelCase			,     return_tensors='np'	).input_features
     lowerCamelCase__    :      Tuple					 =		feature_extractor(UpperCAmelCase			,     return_tensors='np'	).input_features
     for enc_seq_a, enc_seq_a in zip(UpperCAmelCase			,     UpperCAmelCase	):
         self.assertTrue(np.allclose(UpperCAmelCase			,     UpperCAmelCase			,     atol=1e-3	)	)
 def      A_	(      self  :      Any	)			->	List[Any]:
     lowerCamelCase__    :      Union[str, Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      str					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]

     lowerCamelCase__    :      str					 =		['longest', 'max_length', 'do_not_pad']
     lowerCamelCase__    :      Tuple					 =		[None, 16, None]
     for max_length, padding in zip(UpperCAmelCase			,     UpperCAmelCase	):
         lowerCamelCase__    :      Any					 =		feature_extractor(
             UpperCAmelCase			,     padding=UpperCAmelCase			,     max_length=UpperCAmelCase			,     return_attention_mask=UpperCAmelCase	)
         lowerCamelCase__    :      Optional[Any]					 =		inputs.input_features
         lowerCamelCase__    :      List[str]					 =		inputs.attention_mask
         lowerCamelCase__    :      Union[str, Any]					 =		[np.sum(UpperCAmelCase	) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)
 def      A_	(      self  :      int	)			->	Any:
     lowerCamelCase__    :      Optional[Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      Optional[Any]					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]

     lowerCamelCase__    :      Optional[Any]					 =		['longest', 'max_length', 'do_not_pad']
     lowerCamelCase__    :      str					 =		[None, 16, None]
     for max_length, padding in zip(UpperCAmelCase			,     UpperCAmelCase	):
         lowerCamelCase__    :      Any					 =		feature_extractor(
             UpperCAmelCase			,     max_length=UpperCAmelCase			,     padding=UpperCAmelCase			,     return_tensors='np'			,     return_attention_mask=UpperCAmelCase	)
         lowerCamelCase__    :      Optional[int]					 =		inputs.input_features
         lowerCamelCase__    :      List[Any]					 =		inputs.attention_mask
         lowerCamelCase__    :      Any					 =		[np.sum(UpperCAmelCase	) for x in attention_mask]

         self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
         self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6	)
         self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
         self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6	)
         self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)
 def      A_	(      self  :      int	)			->	Tuple:
     lowerCamelCase__    :      str					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      Union[str, Any]					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]
     lowerCamelCase__    :      Dict					 =		feature_extractor(
         UpperCAmelCase			,     padding='max_length'			,     max_length=4			,     truncation=UpperCAmelCase			,     return_tensors='np'			,     return_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      Optional[Any]					 =		inputs.input_features
     lowerCamelCase__    :      List[str]					 =		inputs.attention_mask
     lowerCamelCase__    :      Union[str, Any]					 =		np.sum(attention_mask == 1			,     axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1]	)
     self._check_zero_mean_unit_variance(input_features[2]	)
 def      A_	(      self  :      Optional[Any]	)			->	Optional[Any]:
     lowerCamelCase__    :      List[Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      Union[str, Any]					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]
     lowerCamelCase__    :      List[str]					 =		feature_extractor(
         UpperCAmelCase			,     padding='longest'			,     max_length=4			,     truncation=UpperCAmelCase			,     return_tensors='np'			,     return_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      int					 =		inputs.input_features
     lowerCamelCase__    :      List[str]					 =		inputs.attention_mask
     lowerCamelCase__    :      List[Any]					 =		np.sum(attention_mask == 1			,     axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
     self._check_zero_mean_unit_variance(input_features[2]	)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape			,     (3, 4, 24)	)

     lowerCamelCase__    :      int					 =		[floats_list((1, x)	)[0] for x in range(800			,     1400			,     200	)]
     lowerCamelCase__    :      str					 =		feature_extractor(
         UpperCAmelCase			,     padding='longest'			,     max_length=16			,     truncation=UpperCAmelCase			,     return_tensors='np'			,     return_attention_mask=UpperCAmelCase			,     )
     lowerCamelCase__    :      Optional[Any]					 =		inputs.input_features
     lowerCamelCase__    :      Optional[int]					 =		inputs.attention_mask
     lowerCamelCase__    :      Any					 =		np.sum(attention_mask == 1			,     axis=1	)

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
     self._check_zero_mean_unit_variance(input_features[2]	)

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape			,     (3, 6, 24)	)
 def      A_	(      self  :      Optional[Any]	)			->	Optional[int]:
     import torch

     lowerCamelCase__    :      Union[str, Any]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      Dict					 =		np.random.rand(100			,     32	).astype(np.floataa	)
     lowerCamelCase__    :      Optional[int]					 =		np_speech_inputs.tolist()

     for inputs in [py_speech_inputs, np_speech_inputs]:
         lowerCamelCase__    :      Union[str, Any]					 =		feature_extractor.pad([{'input_features': inputs}]			,     return_tensors='np'	)
         self.assertTrue(np_processed.input_features.dtype == np.floataa	)
         lowerCamelCase__    :      List[Any]					 =		feature_extractor.pad([{'input_features': inputs}]			,     return_tensors='pt'	)
         self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)
 def      A_	(      self  :      int			,     UpperCAmelCase  :      List[str]	)			->	Any:
     from datasets import load_dataset

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

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







 def      A_	(      self  :      str	)			->	Tuple:
     # fmt: off
     lowerCamelCase__    :      Optional[int]					 =		np.array([
         -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
         -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
         -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
     ]	)
     # fmt: on

     lowerCamelCase__    :      Any					 =		self._load_datasamples(1	)
     lowerCamelCase__    :      List[str]					 =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
     lowerCamelCase__    :      Dict					 =		feature_extractor(UpperCAmelCase			,     return_tensors='pt'	).input_features
     self.assertEquals(input_features.shape			,     (1, 584, 24)	)
     self.assertTrue(np.allclose(input_features[0, 0, :30]			,     UpperCAmelCase			,     atol=1e-4	)	)












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase=None )			->     Tuple:
    if subparsers is not None:
        lowerCamelCase__    :      Any					 =		subparsers.add_parser('test' )
    else:
        lowerCamelCase__    :      int					 =		argparse.ArgumentParser('Accelerate test command' )

    parser.add_argument(
        '--config_file'						,	default=_UpperCAmelCase						,	help=(
            'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
            'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
            'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
            'with \'huggingface\'.'
        )						,	)

    if subparsers is not None:
        parser.set_defaults(func=_UpperCAmelCase )
    return parser


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     Union[str, Any]:
    lowerCamelCase__    :      Tuple					 =		os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['test_utils', 'scripts', 'test_script.py'] )

    if args.config_file is None:
        lowerCamelCase__    :      List[str]					 =		script_name
    else:
        lowerCamelCase__    :      List[Any]					 =		F"""--config_file={args.config_file} {script_name}"""

    lowerCamelCase__    :      str					 =		['accelerate-launch'] + test_args.split()
    lowerCamelCase__    :      Dict					 =		execute_subprocess_async(_UpperCAmelCase						,	env=os.environ.copy() )
    if result.returncode == 0:
        print('Test is a success! You are ready for your distributed training!' )


def        SCREAMING_SNAKE_CASE      (       )			->     Any:
    lowerCamelCase__    :      Any					 =		test_command_parser()
    lowerCamelCase__    :      List[Any]					 =		parser.parse_args()
    test_command(_UpperCAmelCase )


if __name__ == "__main__":
    main()





