

"""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

_lowerCAmelCase       						=		random.Random()

def 		lowerCamelCase__		(       _lowerCamelCase			, _lowerCamelCase=1.0			, _lowerCamelCase=None			, _lowerCamelCase=None ):

    '''simple docstring'''



    if rng is None:
        _lowerCAmelCase							: str	=     global_rng

    _lowerCAmelCase							: str	=     []
    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     ,_A     ,_A=7     ,_A=400     ,_A=2000     ,_A=2048     ,_A=128     ,_A=1     ,_A=512     ,_A=30     ,_A=4_4100     ,):
      '''simple docstring'''

      _lowerCAmelCase							: List[str]	=     parent
      _lowerCAmelCase							: List[Any]	=     batch_size
      _lowerCAmelCase							: Dict	=     min_seq_length
      _lowerCAmelCase							: Any	=     max_seq_length
      _lowerCAmelCase							: Union[str, Any]	=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
      _lowerCAmelCase							: List[Any]	=     spectrogram_length
      _lowerCAmelCase							: Tuple	=     feature_size
      _lowerCAmelCase							: str	=     num_audio_channels
      _lowerCAmelCase							: List[str]	=     hop_length
      _lowerCAmelCase							: Any	=     chunk_length
      _lowerCAmelCase							: List[Any]	=     sampling_rate

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      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 __lowerCamelCase		(		self     ,_A=False     ,_A=False   ):
      '''simple docstring'''

      def _flatten(_A   ):
          return list(itertools.chain(*__lowerCamelCase   )   )

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







@require_torch
@require_torchaudio
class 						__UpperCamelCase     (     UpperCAmelCase_				,				unittest.TestCase    ):
  _UpperCAmelCase        =		TvltFeatureExtractor

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: int	=     TvltFeatureExtractionTester(self   )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     self.feature_extraction_class(**self.feat_extract_dict   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'spectrogram_length'   )   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'feature_size'   )   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'num_audio_channels'   )   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'hop_length'   )   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'chunk_length'   )   )
      self.assertTrue(hasattr(__lowerCamelCase     ,'sampling_rate'   )   )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: List[Any]	=     self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
          _lowerCAmelCase							: str	=     feat_extract_first.save_pretrained(__lowerCamelCase   )[0]
          check_json_file_has_correct_format(__lowerCamelCase   )
          _lowerCAmelCase							: Dict	=     self.feature_extraction_class.from_pretrained(__lowerCamelCase   )

      _lowerCAmelCase							: List[Any]	=     feat_extract_first.to_dict()
      _lowerCAmelCase							: List[str]	=     feat_extract_second.to_dict()
      _lowerCAmelCase							: Any	=     dict_first.pop('mel_filters'   )
      _lowerCAmelCase							: Any	=     dict_second.pop('mel_filters'   )
      self.assertTrue(np.allclose(__lowerCamelCase     ,__lowerCamelCase   )   )
      self.assertEqual(__lowerCamelCase     ,__lowerCamelCase   )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: Optional[Any]	=     self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
          _lowerCAmelCase							: List[Any]	=     os.path.join(__lowerCamelCase     ,'feat_extract.json'   )
          feat_extract_first.to_json_file(__lowerCamelCase   )
          _lowerCAmelCase							: Optional[int]	=     self.feature_extraction_class.from_json_file(__lowerCamelCase   )

      _lowerCAmelCase							: List[Any]	=     feat_extract_first.to_dict()
      _lowerCAmelCase							: Dict	=     feat_extract_second.to_dict()
      _lowerCAmelCase							: str	=     dict_first.pop('mel_filters'   )
      _lowerCAmelCase							: Union[str, Any]	=     dict_second.pop('mel_filters'   )
      self.assertTrue(np.allclose(__lowerCamelCase     ,__lowerCamelCase   )   )
      self.assertEqual(__lowerCamelCase     ,__lowerCamelCase   )

  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: str	=     self.feature_extraction_class(**self.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							: List[Any]	=     [np.asarray(__lowerCamelCase   ) for speech_input in speech_inputs]

      # Test not batched input
      _lowerCAmelCase							: Optional[Any]	=     feature_extractor(np_speech_inputs[0]     ,return_tensors='np'     ,sampling_rate=4_4100   ).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
      _lowerCAmelCase							: int	=     feature_extractor(__lowerCamelCase     ,return_tensors='np'     ,sampling_rate=4_4100   ).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
      _lowerCAmelCase							: Tuple	=     feature_extractor(
          __lowerCamelCase     ,return_tensors='np'     ,sampling_rate=4_4100     ,mask_audio=__lowerCamelCase   ).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.
      _lowerCAmelCase							: Any	=     [floats_list((1, x)   )[0] for x in (800, 800, 800)]
      _lowerCAmelCase							: Union[str, Any]	=     np.asarray(__lowerCamelCase   )
      _lowerCAmelCase							: Dict	=     feature_extractor(__lowerCamelCase     ,return_tensors='np'     ,sampling_rate=4_4100   ).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 __lowerCamelCase		(		self     ,_A   ):
      '''simple docstring'''

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

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







  def __lowerCamelCase		(		self   ):
      '''simple docstring'''

      _lowerCAmelCase							: Dict	=     self._load_datasamples(1   )
      _lowerCAmelCase							: Tuple	=     TvltFeatureExtractor()
      _lowerCAmelCase							: Union[str, Any]	=     feature_extractor(__lowerCamelCase     ,return_tensors='pt'   ).audio_values

      self.assertEquals(audio_values.shape     ,(1, 1, 192, 128)   )

      _lowerCAmelCase							: List[str]	=     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]     ,__lowerCamelCase     ,atol=1E-4   )   )












def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:
		return not any(
		    neighbour == 1 and colored_vertices[i] == color
		    for i, neighbour in enumerate(snake_case							)							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    bool:

		# Base Case
		if index == len(snake_case							):
				return True

		# Recursive Step
		for i in range(snake_case							):
				if valid_coloring(graph[index]						,   snake_case						,   snake_case							):
						# Color current vertex
						__lowercase											=     i
						# Validate coloring
						if util_color(snake_case						,   snake_case						,   snake_case						,   index + 1							):
								return True
						# Backtrack
						__lowercase											=     -1
		return False



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[int]:
		__lowercase											=     [-1] * len(snake_case							)

		if util_color(snake_case						,   snake_case						,   snake_case						,   0							):
				return colored_vertices

		return []




