


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

lowerCamelCase		   =			random.Random()




def       a_ (       SCREAMING_SNAKE_CASE__   : Dict						,     SCREAMING_SNAKE_CASE__   : Optional[int]=1.0						,     SCREAMING_SNAKE_CASE__   : Union[str, Any]=None						,     SCREAMING_SNAKE_CASE__   : List[Any]=None	):
       '''simple docstring'''





       if rng is None:
              _lowerCamelCase			:						int            =global_rng

       _lowerCamelCase			:						List[str]            =[]
       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 A    (  unittest.TestCase	):






      def __init__(   self		:	List[str]     ,  lowercase_		:	int     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Union[str, Any]=400     ,  lowercase_		:	int=2000     ,  lowercase_		:	int=24     ,  lowercase_		:	Optional[int]=24     ,  lowercase_		:	Any=0.0     ,  lowercase_		:	Any=1_6000     ,  lowercase_		:	str=True     ,  lowercase_		:	Union[str, Any]=True     ,  )   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						str            =parent
             _lowerCamelCase			:						List[Any]            =batch_size
             _lowerCamelCase			:						Optional[int]            =min_seq_length
             _lowerCamelCase			:						List[Any]            =max_seq_length
             _lowerCamelCase			:						Tuple            =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             _lowerCamelCase			:						List[Any]            =feature_size
             _lowerCamelCase			:						List[str]            =num_mel_bins
             _lowerCamelCase			:						Optional[Any]            =padding_value
             _lowerCamelCase			:						Union[str, Any]            =sampling_rate
             _lowerCamelCase			:						Dict            =return_attention_mask
             _lowerCamelCase			:						List[Any]            =do_normalize






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[Any]:
             """simple docstring"""




             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 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	List[Any]=False     ,  lowercase_		:	Union[str, Any]=False					)   ->		Dict:
             """simple docstring"""




             def _flatten(lowercase_		:	Optional[int]					):
                    return list(itertools.chain(*lowercase_					)					)

             if equal_length:
                    _lowerCamelCase			:						Dict            =[floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
             else:
                    # make sure that inputs increase in size
                    _lowerCamelCase			:						int            =[
                        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			:						List[str]            =[np.asarray(lowercase_					) for x in speech_inputs]
             return speech_inputs




@require_torch
@require_torchaudio
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    int =SpeechaTextFeatureExtractor if is_speech_available() else None






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =SpeechaTextFeatureExtractionTester(self					)






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	List[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             self.assertTrue(np.all(np.mean(lowercase_     ,  axis=0					) < 1E-3					)					)
             self.assertTrue(np.all(np.abs(np.var(lowercase_     ,  axis=0					) - 1					) < 1E-3					)					)






      def 			lowerCamelCase			(   self		:	str					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
             # create three inputs of length 800, 1000, and 1200
             _lowerCamelCase			:						Any            =[floats_list((1, x)					)[0] for x in range(800     ,  1400     ,  200					)]
             _lowerCamelCase			:						Tuple            =[np.asarray(lowercase_					) for speech_input in speech_inputs]

             # Test feature size
             _lowerCamelCase			:						Dict            =feature_extractor(lowercase_     ,  padding=lowercase_     ,  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			:						Tuple            =feature_extractor(speech_inputs[0]     ,  return_tensors='np'					).input_features
             _lowerCamelCase			:						Tuple            =feature_extractor(np_speech_inputs[0]     ,  return_tensors='np'					).input_features
             self.assertTrue(np.allclose(lowercase_     ,  lowercase_     ,  atol=1E-3					)					)

             # Test batched
             _lowerCamelCase			:						Optional[int]            =feature_extractor(lowercase_     ,  return_tensors='np'					).input_features
             _lowerCamelCase			:						Dict            =feature_extractor(lowercase_     ,  return_tensors='np'					).input_features
             for enc_seq_a, enc_seq_a in zip(lowercase_     ,  lowercase_					):
                    self.assertTrue(np.allclose(lowercase_     ,  lowercase_     ,  atol=1E-3					)					)

             # Test 2-D numpy arrays are batched.
             _lowerCamelCase			:						Tuple            =[floats_list((1, x)					)[0] for x in (800, 800, 800)]
             _lowerCamelCase			:						Union[str, Any]            =np.asarray(lowercase_					)
             _lowerCamelCase			:						Optional[int]            =feature_extractor(lowercase_     ,  return_tensors='np'					).input_features
             _lowerCamelCase			:						Union[str, Any]            =feature_extractor(lowercase_     ,  return_tensors='np'					).input_features
             for enc_seq_a, enc_seq_a in zip(lowercase_     ,  lowercase_					):
                    self.assertTrue(np.allclose(lowercase_     ,  lowercase_     ,  atol=1E-3					)					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =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[Any]            =['longest', 'max_length', 'do_not_pad']
             _lowerCamelCase			:						str            =[None, 16, None]
             for max_length, padding in zip(lowercase_     ,  lowercase_					):
                    _lowerCamelCase			:						Dict            =feature_extractor(
                        lowercase_     ,  padding=lowercase_     ,  max_length=lowercase_     ,  return_attention_mask=lowercase_					)
                    _lowerCamelCase			:						int            =inputs.input_features
                    _lowerCamelCase			:						Dict            =inputs.attention_mask
                    _lowerCamelCase			:						List[str]            =[np.sum(lowercase_					) 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 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
             _lowerCamelCase			:						Any            =[floats_list((1, x)					)[0] for x in range(800     ,  1400     ,  200					)]

             _lowerCamelCase			:						Optional[Any]            =['longest', 'max_length', 'do_not_pad']
             _lowerCamelCase			:						List[str]            =[None, 16, None]
             for max_length, padding in zip(lowercase_     ,  lowercase_					):
                    _lowerCamelCase			:						int            =feature_extractor(
                        lowercase_     ,  max_length=lowercase_     ,  padding=lowercase_     ,  return_tensors='np'     ,  return_attention_mask=lowercase_					)
                    _lowerCamelCase			:						int            =inputs.input_features
                    _lowerCamelCase			:						List[Any]            =inputs.attention_mask
                    _lowerCamelCase			:						int            =[np.sum(lowercase_					) 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 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
             _lowerCamelCase			:						Optional[int]            =[floats_list((1, x)					)[0] for x in range(800     ,  1400     ,  200					)]
             _lowerCamelCase			:						Dict            =feature_extractor(
                 lowercase_     ,  padding='max_length'     ,  max_length=4     ,  truncation=lowercase_     ,  return_tensors='np'     ,  return_attention_mask=lowercase_     ,  )
             _lowerCamelCase			:						Tuple            =inputs.input_features
             _lowerCamelCase			:						Optional[Any]            =inputs.attention_mask
             _lowerCamelCase			:						int            =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 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
             _lowerCamelCase			:						Any            =[floats_list((1, x)					)[0] for x in range(800     ,  1400     ,  200					)]
             _lowerCamelCase			:						List[Any]            =feature_extractor(
                 lowercase_     ,  padding='longest'     ,  max_length=4     ,  truncation=lowercase_     ,  return_tensors='np'     ,  return_attention_mask=lowercase_     ,  )
             _lowerCamelCase			:						List[str]            =inputs.input_features
             _lowerCamelCase			:						int            =inputs.attention_mask
             _lowerCamelCase			:						int            =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			:						Optional[Any]            =[floats_list((1, x)					)[0] for x in range(800     ,  1400     ,  200					)]
             _lowerCamelCase			:						int            =feature_extractor(
                 lowercase_     ,  padding='longest'     ,  max_length=16     ,  truncation=lowercase_     ,  return_tensors='np'     ,  return_attention_mask=lowercase_     ,  )
             _lowerCamelCase			:						Optional[int]            =inputs.input_features
             _lowerCamelCase			:						Optional[int]            =inputs.attention_mask
             _lowerCamelCase			:						List[str]            =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 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




             import torch

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

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    _lowerCamelCase			:						Dict            =feature_extractor.pad([{'input_features': inputs}]     ,  return_tensors='np'					)
                    self.assertTrue(np_processed.input_features.dtype == np.floataa					)
                    _lowerCamelCase			:						Any            =feature_extractor.pad([{'input_features': inputs}]     ,  return_tensors='pt'					)
                    self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	Dict					)   ->		int:
             """simple docstring"""




             from datasets import load_dataset

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

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






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						str            =np.array([
                 -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
                 -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
                 -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
             ]					)
             # fmt: on

             _lowerCamelCase			:						Optional[int]            =self._load_datasamples(1					)
             _lowerCamelCase			:						List[Any]            =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
             _lowerCamelCase			:						int            =feature_extractor(lowercase_     ,  return_tensors='pt'					).input_features
             self.assertEquals(input_features.shape     ,  (1, 584, 24)					)
             self.assertTrue(np.allclose(input_features[0, 0, :30]     ,  lowercase_     ,  atol=1E-4					)					)






# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available




def       a_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

       parser.add_argument(
           '--config_file'						,     default=SCREAMING_SNAKE_CASE__						,     help='The config file to use for the default values in the launching script.'	)

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




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`Accelerate` version': version,
           'Platform': platform.platform(),
           'Python version': platform.python_version(),
           'Numpy version': np.__version__,
           'PyTorch version (GPU?)': F'''{pt_version} ({pt_cuda_available})''',
           'PyTorch XPU available': str(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =torch.cuda.get_device_name()

       print('\nCopy-and-paste the text below in your GitHub issue\n'	)
       print('\n'.join([F'''- {prop}: {val}''' for prop, val in info.items()]	)	)

       print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


if __name__ == "__main__":
 raise SystemExit(main())


