


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





       if rng is None:
              _lowerCamelCase       :		Optional[int]		=global_rng

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

       return values




class A  (   unittest.TestCase     ):






      def __init__(					self						:     Optional[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[Any]=7			,   lowercase_						:     Tuple=400			,   lowercase_						:     Union[str, Any]=2000			,   lowercase_						:     int=2048			,   lowercase_						:     str=128			,   lowercase_						:     Optional[Any]=1			,   lowercase_						:     Dict=512			,   lowercase_						:     Union[str, Any]=30			,   lowercase_						:     str=4_4100			,   )					->						Dict:
             """simple docstring"""




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






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




             def _flatten(lowercase_						:     Any ):
                    return list(itertools.chain(*lowercase_ ) )

             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       :		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       :		Optional[int]		=[np.asarray(lowercase_ ) for x in speech_inputs]
             return speech_inputs




@require_torch
@require_torchaudio
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Dict      =TvltFeatureExtractor






      def        lowerCamelCase       (					self						:     str )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=TvltFeatureExtractionTester(self )






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




             _lowerCamelCase       :		Tuple		=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        lowerCamelCase       (					self						:     Tuple )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=self.feature_extraction_class(**self.feat_extract_dict )

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

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






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




             _lowerCamelCase       :		Optional[int]		=self.feature_extraction_class(**self.feat_extract_dict )

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

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






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




             _lowerCamelCase       :		Union[str, Any]		=self.feature_extraction_class(**self.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       :		List[str]		=[np.asarray(lowercase_ ) for speech_input in speech_inputs]

             # Test not batched input
             _lowerCamelCase       :		Tuple		=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       :		Optional[int]		=feature_extractor(lowercase_			,   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       :		int		=feature_extractor(
                 lowercase_			,   return_tensors='np'			,   sampling_rate=4_4100			,   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.
             _lowerCamelCase       :		Any		=[floats_list((1, x) )[0] for x in (800, 800, 800)]
             _lowerCamelCase       :		List[str]		=np.asarray(lowercase_ )
             _lowerCamelCase       :		Optional[Any]		=feature_extractor(lowercase_			,   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						:     Optional[int]			,   lowercase_						:     Optional[Any] )					->						str:
             """simple docstring"""




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

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






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




             _lowerCamelCase       :		str		=self._load_datasamples(1 )
             _lowerCamelCase       :		Optional[int]		=TvltFeatureExtractor()
             _lowerCamelCase       :		int		=feature_extractor(lowercase_			,   return_tensors='pt' ).audio_values

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

             _lowerCamelCase       :		str		=torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
             self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,   lowercase_			,   atol=1E-4 ) )






# using dfs for finding eulerian path traversal




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





       _lowerCamelCase       :		Optional[Any]		=(path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _lowerCamelCase	,     _lowerCamelCase       :		Dict		=True, True
                     _lowerCamelCase       :		Optional[Any]		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return path




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=0
       _lowerCamelCase       :		Union[str, Any]		=-1
       for i in range(SCREAMING_SNAKE_CASE__     ):
              if i not in graph.keys():
                     continue
              if len(graph[i]     ) % 2 == 1:
                     odd_degree_nodes += 1
                     _lowerCamelCase       :		Tuple		=i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Any     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[[False for _ in range(max_node + 1     )] for _ in range(max_node + 1     )]
       _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=check_circuit_or_path(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if check == 3:
              print('graph is not Eulerian'     )
              print('no path'     )
              return
       _lowerCamelCase       :		Any		=1
       if check == 2:
              _lowerCamelCase       :		Tuple		=odd_node
              print('graph has a Euler path'     )
       if check == 1:
              print('graph has a Euler cycle'     )
       _lowerCamelCase       :		int		=dfs(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       print(SCREAMING_SNAKE_CASE__     )




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





       _lowerCamelCase       :		Union[str, Any]		={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _lowerCamelCase       :		str		={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _lowerCamelCase       :		List[Any]		={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _lowerCamelCase       :		Any		={1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _lowerCamelCase       :		Dict		={
           1: [],
           2: []
           # all degree is zero
       }
       _lowerCamelCase       :		str		=10
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       check_euler(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()


