



'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
   from transformers import WhisperFeatureExtractor

if is_torch_available():
   import torch

A_							: int						 =	random.Random()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_=1.0       ,   lowerCAmelCase_=None       ,   lowerCAmelCase_=None		)->		Optional[Any]:





       '''simple docstring'''


       if rng is None:
              _UpperCAmelCase  :						int			     = global_rng

       _UpperCAmelCase  :						List[Any]			     = []
       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 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_=7  ,a_=400  ,a_=2_000  ,a_=10  ,a_=160  ,a_=8  ,a_=0.0  ,a_=4_000  ,a_=False  ,a_=True  ,)		->   Dict:
        _UpperCAmelCase  :						Optional[Any]			     = parent
        _UpperCAmelCase  :						int			     = batch_size
        _UpperCAmelCase  :						int			     = min_seq_length
        _UpperCAmelCase  :						Tuple			     = max_seq_length
        _UpperCAmelCase  :						Optional[int]			     = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _UpperCAmelCase  :						Dict			     = padding_value
        _UpperCAmelCase  :						int			     = sampling_rate
        _UpperCAmelCase  :						List[str]			     = return_attention_mask
        _UpperCAmelCase  :						List[Any]			     = do_normalize
        _UpperCAmelCase  :						List[Any]			     = feature_size
        _UpperCAmelCase  :						Dict			     = chunk_length
        _UpperCAmelCase  :						List[Any]			     = hop_length







 def _snake_case (							self							)		->   List[Any]:
        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }







 def _snake_case (							self  ,a_=False  ,a_=False							)		->   Dict:
        def _flatten(a_							):
               return list(itertools.chain(*a_							)							)

        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  :						Dict			     = [
                   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  :						int			     = [np.asarray(a_							) for x in speech_inputs]
        return speech_inputs







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


 """simple docstring"""


 UpperCAmelCase							=  WhisperFeatureExtractor if is_speech_available() else None







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						List[str]			     = WhisperFeatureExtractionTester(self							)







 def _snake_case (							self							)		->   int:
        _UpperCAmelCase  :						str			     = self.feature_extraction_class(**self.feat_extract_dict							)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCAmelCase  :						List[Any]			     = feat_extract_first.save_pretrained(a_							)[0]
               check_json_file_has_correct_format(a_							)
               _UpperCAmelCase  :						Tuple			     = self.feature_extraction_class.from_pretrained(a_							)

        _UpperCAmelCase  :						Any			     = feat_extract_first.to_dict()
        _UpperCAmelCase  :						Optional[Any]			     = feat_extract_second.to_dict()
        _UpperCAmelCase  :						Tuple			     = feat_extract_first.mel_filters
        _UpperCAmelCase  :						Optional[int]			     = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(a_  ,a_							)							)
        self.assertEqual(a_  ,a_							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						int			     = self.feature_extraction_class(**self.feat_extract_dict							)

        with tempfile.TemporaryDirectory() as tmpdirname:
               _UpperCAmelCase  :						Union[str, Any]			     = os.path.join(a_  ,"""feat_extract.json"""							)
               feat_extract_first.to_json_file(a_							)
               _UpperCAmelCase  :						str			     = self.feature_extraction_class.from_json_file(a_							)

        _UpperCAmelCase  :						List[str]			     = feat_extract_first.to_dict()
        _UpperCAmelCase  :						Union[str, Any]			     = feat_extract_second.to_dict()
        _UpperCAmelCase  :						str			     = feat_extract_first.mel_filters
        _UpperCAmelCase  :						List[str]			     = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(a_  ,a_							)							)
        self.assertEqual(a_  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _UpperCAmelCase  :						List[str]			     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        # create three inputs of length 800, 1000, and 1200
        _UpperCAmelCase  :						Optional[Any]			     = [floats_list((1, x)							)[0] for x in range(800  ,1_400  ,200							)]
        _UpperCAmelCase  :						str			     = [np.asarray(a_							) for speech_input in speech_inputs]

        # Test feature size
        _UpperCAmelCase  :						str			     = feature_extractor(a_  ,padding="""max_length"""  ,return_tensors="""np"""							).input_features
        self.assertTrue(input_features.ndim == 3							)
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

        # Test not batched input
        _UpperCAmelCase  :						str			     = feature_extractor(speech_inputs[0]  ,return_tensors="""np"""							).input_features
        _UpperCAmelCase  :						Union[str, Any]			     = feature_extractor(np_speech_inputs[0]  ,return_tensors="""np"""							).input_features
        self.assertTrue(np.allclose(a_  ,a_  ,atol=1E-3							)							)

        # Test batched
        _UpperCAmelCase  :						str			     = feature_extractor(a_  ,return_tensors="""np"""							).input_features
        _UpperCAmelCase  :						List[Any]			     = feature_extractor(a_  ,return_tensors="""np"""							).input_features
        for enc_seq_a, enc_seq_a in zip(a_  ,a_							):
               self.assertTrue(np.allclose(a_  ,a_  ,atol=1E-3							)							)

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

        # Test truncation required
        _UpperCAmelCase  :						Optional[Any]			     = [floats_list((1, x)							)[0] for x in range(200  ,(feature_extractor.n_samples + 500)  ,200							)]
        _UpperCAmelCase  :						List[Any]			     = [np.asarray(a_							) for speech_input in speech_inputs]

        _UpperCAmelCase  :						Union[str, Any]			     = [x[: feature_extractor.n_samples] for x in speech_inputs]
        _UpperCAmelCase  :						str			     = [np.asarray(a_							) for speech_input in speech_inputs_truncated]

        _UpperCAmelCase  :						Dict			     = feature_extractor(a_  ,return_tensors="""np"""							).input_features
        _UpperCAmelCase  :						List[str]			     = feature_extractor(a_  ,return_tensors="""np"""							).input_features
        for enc_seq_a, enc_seq_a in zip(a_  ,a_							):
               self.assertTrue(np.allclose(a_  ,a_  ,atol=1E-3							)							)







 def _snake_case (							self							)		->   List[Any]:
        import torch

        _UpperCAmelCase  :						Optional[int]			     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _UpperCAmelCase  :						int			     = np.random.rand(100  ,32							).astype(np.floataa							)
        _UpperCAmelCase  :						int			     = np_speech_inputs.tolist()

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







 def _snake_case (							self  ,a_							)		->   Tuple:
        _UpperCAmelCase  :						List[str]			     = load_dataset("""hf-internal-testing/librispeech_asr_dummy"""  ,"""clean"""  ,split="""validation"""							)
        # automatic decoding with librispeech
        _UpperCAmelCase  :						Optional[int]			     = ds.sort("""id"""							).select(range(a_							)							)[:num_samples]["""audio"""]

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







 def _snake_case (							self							)		->   Union[str, Any]:
        # fmt: off
        _UpperCAmelCase  :						List[str]			     = torch.tensor(
            [
                0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
                0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
                0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
                -0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
            ]							)
        # fmt: on

        _UpperCAmelCase  :						str			     = self._load_datasamples(1							)
        _UpperCAmelCase  :						Any			     = WhisperFeatureExtractor()
        _UpperCAmelCase  :						Any			     = feature_extractor(a_  ,return_tensors="""pt"""							).input_features
        self.assertEqual(input_features.shape  ,(1, 80, 3_000)							)
        self.assertTrue(torch.allclose(input_features[0, 0, :30]  ,a_  ,atol=1E-4							)							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						Any			     = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
        _UpperCAmelCase  :						Optional[Any]			     = self._load_datasamples(1							)[0]
        _UpperCAmelCase  :						Any			     = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        _UpperCAmelCase  :						Dict			     = feat_extract.zero_mean_unit_var_norm([audio]  ,attention_mask=a_							)[0]

        self.assertTrue(np.all(np.mean(a_							) < 1E-3							)							)
        self.assertTrue(np.all(np.abs(np.var(a_							) - 1							) < 1E-3							)							)








'''simple docstring'''






# using dfs for finding eulerian path traversal




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = (path or []) + [u]
       for v in graph[u]:
              if visited_edge[u][v] is False:
                     _UpperCAmelCase     ,_UpperCAmelCase  :						Tuple			     = True, True
                     _UpperCAmelCase  :						List[Any]			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       return path




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = 0
       _UpperCAmelCase  :						Optional[int]			     = -1
       for i in range(lowerCAmelCase_		):
              if i not in graph.keys():
                     continue
              if len(graph[i]		) % 2 == 1:
                     odd_degree_nodes += 1
                     _UpperCAmelCase  :						Optional[int]			     = i
       if odd_degree_nodes == 0:
              return 1, odd_node
       if odd_degree_nodes == 2:
              return 2, odd_node
       return 3, odd_node




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = [[False for _ in range(max_node + 1		)] for _ in range(max_node + 1		)]
       _UpperCAmelCase     ,_UpperCAmelCase  :						int			     = check_circuit_or_path(lowerCAmelCase_       ,   lowerCAmelCase_		)
       if check == 3:
              print("""graph is not Eulerian"""		)
              print("""no path"""		)
              return
       _UpperCAmelCase  :						Dict			     = 1
       if check == 2:
              _UpperCAmelCase  :						Dict			     = odd_node
              print("""graph has a Euler path"""		)
       if check == 1:
              print("""graph has a Euler cycle"""		)
       _UpperCAmelCase  :						Dict			     = dfs(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)
       print(lowerCAmelCase_		)




def     snake_case_					(     )->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
       _UpperCAmelCase  :						int			     = {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
       _UpperCAmelCase  :						Tuple			     = {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
       _UpperCAmelCase  :						List[Any]			     = {1: [2, 3], 2: [1, 3], 3: [1, 2]}
       _UpperCAmelCase  :						List[str]			     = {
           1: [],
           2: []
           # all degree is zero
       }
       _UpperCAmelCase  :						Union[str, Any]			     = 10
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)
       check_euler(lowerCAmelCase_       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   main()



