
"""simple docstring"""

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand




def     lowercase__						(					lowerCAmelCase				:					Tuple ) ->      int:






    """simple docstring"""




    return EnvironmentCommand()




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ) ->      Tuple:






    """simple docstring"""




    return EnvironmentCommand(args.accelerate_config_file )



class      _UpperCAmelCase							(		a__							):




      @staticmethod
      def     a_      (							lowercase_							)						->					Tuple:
          UpperCAmelCase									=					parser.add_parser('env'							)
          download_parser.set_defaults(func=lowercase__							)
          download_parser.add_argument(
              '--accelerate-config_file'   ,       default=lowercase__   ,       help='The accelerate config file to use for the default values in the launching script.'   ,       )
          download_parser.set_defaults(func=lowercase__							)




      def __init__(							self   ,       lowercase_   ,       *lowercase_							)						->					None:
          UpperCAmelCase									=					accelerate_config_file




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					'''not installed'''
          if is_safetensors_available():
              import safetensors

              UpperCAmelCase									=					safetensors.__version__
          elif importlib.util.find_spec('safetensors'							) is not None:
              import safetensors

              UpperCAmelCase									=					F"{safetensors.__version__} but is ignored because of PyTorch version too old."

          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''not found'''
          if is_accelerate_available():
              import accelerate
              from accelerate.commands.config import default_config_file, load_config_from_file

              UpperCAmelCase									=					accelerate.__version__
              # Get the default from the config file.
              if self._accelerate_config_file is not None or os.path.isfile(lowercase__							):
                  UpperCAmelCase									=					load_config_from_file(self._accelerate_config_file							).to_dict()

              UpperCAmelCase									=					(
                  '''\n'''.join([F"\t- {prop}: {val}" for prop, val in accelerate_config.items()]							)
                  if isinstance(lowercase__   ,       lowercase__							)
                  else F"\t{accelerate_config}"
              )

          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''NA'''
          if is_torch_available():
              import torch

              UpperCAmelCase									=					torch.__version__
              UpperCAmelCase									=					torch.cuda.is_available()

          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''NA'''
          if is_tf_available():
              import tensorflow as tf

              UpperCAmelCase									=					tf.__version__
              try:
                  # deprecated in v2.1
                  UpperCAmelCase									=					tf.test.is_gpu_available()
              except AttributeError:
                  # returns list of devices, convert to bool
                  UpperCAmelCase									=					bool(tf.config.list_physical_devices('GPU'							)							)

          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''not installed'''
          UpperCAmelCase									=					'''NA'''
          if is_flax_available():
              import flax
              import jax
              import jaxlib

              UpperCAmelCase									=					flax.__version__
              UpperCAmelCase									=					jax.__version__
              UpperCAmelCase									=					jaxlib.__version__
              UpperCAmelCase									=					jax.lib.xla_bridge.get_backend().platform

          UpperCAmelCase									=					{
              '''`transformers` version''': version,
              '''Platform''': platform.platform(),
              '''Python version''': platform.python_version(),
              '''Huggingface_hub version''': huggingface_hub.__version__,
              '''Safetensors version''': F"{safetensors_version}",
              '''Accelerate version''': F"{accelerate_version}",
              '''Accelerate config''': F"{accelerate_config_str}",
              '''PyTorch version (GPU?)''': F"{pt_version} ({pt_cuda_available})",
              '''Tensorflow version (GPU?)''': F"{tf_version} ({tf_cuda_available})",
              '''Flax version (CPU?/GPU?/TPU?)''': F"{flax_version} ({jax_backend})",
              '''Jax version''': F"{jax_version}",
              '''JaxLib version''': F"{jaxlib_version}",
              '''Using GPU in script?''': '''<fill in>''',
              '''Using distributed or parallel set-up in script?''': '''<fill in>''',
          }

          print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'							)
          print(self.format_dict(lowercase__							)							)

          return info




      @staticmethod
      def     a_      (							lowercase_							)						->					Optional[int]:
          return "\n".join([F"- {prop}: {val}" for prop, val in d.items()]							) + "\n"





"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
   import torch

if is_datasets_available():
   from datasets import load_dataset

SCREAMING_SNAKE_CASE_    					=						random.Random()




def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple=1.0 ,				lowerCAmelCase				:					Tuple=None ,				lowerCAmelCase				:					List[str]=None ) ->      Dict:






    """simple docstring"""




    if rng is None:
        UpperCAmelCase									=					global_rng

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

    return values
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=4_0_0   ,       lowercase_=2_0_0_0   ,       lowercase_=2_0_4_8   ,       lowercase_=1_2_8   ,       lowercase_=1   ,       lowercase_=5_1_2   ,       lowercase_=3_0   ,       lowercase_=4_4_1_0_0   ,       )						->					str:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					min_seq_length
          UpperCAmelCase									=					max_seq_length
          UpperCAmelCase									=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          UpperCAmelCase									=					spectrogram_length
          UpperCAmelCase									=					feature_size
          UpperCAmelCase									=					num_audio_channels
          UpperCAmelCase									=					hop_length
          UpperCAmelCase									=					chunk_length
          UpperCAmelCase									=					sampling_rate




      def     a_      (							self							)						->					str:
          return {
              "spectrogram_length": self.spectrogram_length,
              "feature_size": self.feature_size,
              "num_audio_channels": self.num_audio_channels,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "sampling_rate": self.sampling_rate,
          }




      def     a_      (							self   ,       lowercase_=False   ,       lowercase_=False							)						->					Optional[Any]:
          def _flatten(lowercase_							):
              return list(itertools.chain(*lowercase_							)							)

          if equal_length:
              UpperCAmelCase									=					[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
          else:
              # make sure that inputs increase in size
              UpperCAmelCase									=					[
                  floats_list((x, self.feature_size)							)
                  for x in range(self.min_seq_length   ,       self.max_seq_length   ,       self.seq_length_diff							)
              ]
          if numpify:
              UpperCAmelCase									=					[np.asarray(lowercase_							) for x in speech_inputs]
          return speech_inputs

@require_torch
@require_torchaudio
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	TvltFeatureExtractor




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					TvltFeatureExtractionTester(self							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'spectrogram_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'feature_size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'num_audio_channels'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'hop_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'chunk_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'sampling_rate'							)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase									=					feat_extract_first.save_pretrained(lowercase_							)[0]
              check_json_file_has_correct_format(lowercase_							)
              UpperCAmelCase									=					self.feature_extraction_class.from_pretrained(lowercase_							)

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'feat_extract.json'							)
              feat_extract_first.to_json_file(lowercase_							)
              UpperCAmelCase									=					self.feature_extraction_class.from_json_file(lowercase_							)

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          # Initialize feature_extractor
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          # create three inputs of length 800, 1000, and 1200
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in range(8_0_0   ,       1_4_0_0   ,       2_0_0							)]
          UpperCAmelCase									=					[np.asarray(lowercase_							) for speech_input in speech_inputs]

          # Test not batched input
          UpperCAmelCase									=					feature_extractor(np_speech_inputs[0]   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test batched
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test audio masking
          UpperCAmelCase									=					feature_extractor(
              lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0   ,       mask_audio=lowercase_							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test 2-D numpy arrays are batched.
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          UpperCAmelCase									=					np.asarray(lowercase_							)
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values
          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)




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

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self._load_datasamples(1							)
          UpperCAmelCase									=					TvltFeatureExtractor()
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='pt'							).audio_values

          self.assertEquals(audio_values.shape   ,       (1, 1, 1_9_2, 1_2_8)							)

          UpperCAmelCase									=					torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]							)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,       lowercase_   ,       atol=1E-4							)							)



