
'''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 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=1.0 ,		UpperCamelCase__=None ,		UpperCamelCase__=None		):
      if rng is None:
            UpperCamelCase__    :   Optional[Any]					=				global_rng

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

      return values




class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=7						,						__SCREAMING_SNAKE_CASE=4_0_0						,						__SCREAMING_SNAKE_CASE=2_0_0_0						,						__SCREAMING_SNAKE_CASE=2_0_4_8						,						__SCREAMING_SNAKE_CASE=1_2_8						,						__SCREAMING_SNAKE_CASE=1						,						__SCREAMING_SNAKE_CASE=5_1_2						,						__SCREAMING_SNAKE_CASE=3_0						,						__SCREAMING_SNAKE_CASE=4_4_1_0_0						,						)  ->   Optional[Any]:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=False						,						__SCREAMING_SNAKE_CASE=False  )  ->   Optional[int]:




           """simple docstring"""







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

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




@require_torch
@require_torchaudio
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  TvltFeatureExtractor






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				TvltFeatureExtractionTester(self  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				self.feature_extraction_class(**self.feat_extract_dict  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''spectrogram_length'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''feature_size'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''num_audio_channels'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''hop_length'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''chunk_length'''  )  )
           self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE						,						'''sampling_rate'''  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.feature_extraction_class(**self.feat_extract_dict  )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 UpperCamelCase__    :   Optional[Any]					=				feat_extract_first.save_pretrained(__SCREAMING_SNAKE_CASE  )[0]
                 check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Tuple					=				self.feature_extraction_class.from_pretrained(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				feat_extract_first.to_dict()
           UpperCamelCase__    :   List[str]					=				feat_extract_second.to_dict()
           UpperCamelCase__    :   Optional[int]					=				dict_first.pop('''mel_filters'''  )
           UpperCamelCase__    :   List[Any]					=				dict_second.pop('''mel_filters'''  )
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )
           self.assertEqual(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.feature_extraction_class(**self.feat_extract_dict  )

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

           UpperCamelCase__    :   str					=				feat_extract_first.to_dict()
           UpperCamelCase__    :   Optional[int]					=				feat_extract_second.to_dict()
           UpperCamelCase__    :   Optional[int]					=				dict_first.pop('''mel_filters'''  )
           UpperCamelCase__    :   Dict					=				dict_second.pop('''mel_filters'''  )
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )
           self.assertEqual(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.feature_extraction_class(**self.feat_extract_dict  )

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

           # Test not batched input
           UpperCamelCase__    :   Optional[Any]					=				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__    :   Union[str, Any]					=				feature_extractor(__SCREAMING_SNAKE_CASE						,						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__    :   List[Any]					=				feature_extractor(
               __SCREAMING_SNAKE_CASE						,						return_tensors='''np'''						,						sampling_rate=4_4_1_0_0						,						mask_audio=__SCREAMING_SNAKE_CASE  ).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__    :   Union[str, Any]					=				[floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
           UpperCamelCase__    :   Optional[Any]					=				np.asarray(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				feature_extractor(__SCREAMING_SNAKE_CASE						,						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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







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

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self._load_datasamples(1  )
           UpperCamelCase__    :   Optional[int]					=				TvltFeatureExtractor()
           UpperCamelCase__    :   Any					=				feature_extractor(__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).audio_values

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

           UpperCamelCase__    :   Tuple					=				torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]  )
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]						,						__SCREAMING_SNAKE_CASE						,						atol=1e-4  )  )




lowerCamelCase      ={"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowerCamelCase      =["a", "b", "c", "d", "e"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				start
      # add current to visited
      visited.append(UpperCamelCase__		)
      UpperCamelCase__    :   int					=				edges[current]
      for neighbor in neighbors:
            # if neighbor not in visited, visit
            if neighbor not in visited:
                  UpperCamelCase__    :   int					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # if all neighbors visited add current to sort
      sort.append(UpperCamelCase__		)
      # if all vertices haven't been visited select a new one to visit
      if len(UpperCamelCase__		) != len(UpperCamelCase__		):
            for vertice in vertices:
                  if vertice not in visited:
                        UpperCamelCase__    :   Optional[int]					=				topological_sort(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
    # return sort
      return sort


if __name__ == "__main__":
    lowerCamelCase      =topological_sort("a", [], [])
    print(sort)


