



"""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

_lowercase				:				Union[str, Any]   		=		random.Random()






def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					str=1.0							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					List[str]=None			):
			"""simple docstring"""
			if rng is None:
						lowerCamelCase__	:	Optional[Any]				=global_rng

			lowerCamelCase__	:	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  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def __init__( self							:						str,						lowerCamelCase							:						List[str],						lowerCamelCase							:						str=7,						lowerCamelCase							:						Optional[int]=400,						lowerCamelCase							:						Any=2000,						lowerCamelCase							:						int=10,						lowerCamelCase							:						Optional[Any]=160,						lowerCamelCase							:						Dict=8,						lowerCamelCase							:						Dict=0.0,						lowerCamelCase							:						Any=4000,						lowerCamelCase							:						Dict=False,						lowerCamelCase							:						List[str]=True,						)->      Optional[int]:
								lowerCamelCase__	:	List[Any]				=parent
								lowerCamelCase__	:	Union[str, Any]				=batch_size
								lowerCamelCase__	:	List[str]				=min_seq_length
								lowerCamelCase__	:	List[str]				=max_seq_length
								lowerCamelCase__	:	Union[str, Any]				=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								lowerCamelCase__	:	Tuple				=padding_value
								lowerCamelCase__	:	List[Any]				=sampling_rate
								lowerCamelCase__	:	str				=return_attention_mask
								lowerCamelCase__	:	Optional[Any]				=do_normalize
								lowerCamelCase__	:	Optional[int]				=feature_size
								lowerCamelCase__	:	str				=chunk_length
								lowerCamelCase__	:	Optional[Any]				=hop_length



					def 	snake_case ( self							:						Tuple  )->      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							:						Any,						lowerCamelCase							:						Optional[int]=False,						lowerCamelCase							:						int=False  )->      Optional[Any]:
								def _flatten(lowerCamelCase							:						Any  ):
											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__	:	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:
											lowerCamelCase__	:	List[Any]				=[np.asarray(lowerCamelCase  ) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							WhisperFeatureExtractor if is_speech_available() else None



					def 	snake_case ( self							:						List[Any]  )->      Dict:
								lowerCamelCase__	:	Optional[int]				=WhisperFeatureExtractionTester(self  )



					def 	snake_case ( self							:						str  )->      List[str]:
								lowerCamelCase__	:	List[str]				=self.feature_extraction_class(**self.feat_extract_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCamelCase__	:	List[Any]				=feat_extract_first.save_pretrained(lowerCamelCase  )[0]
											check_json_file_has_correct_format(lowerCamelCase  )
											lowerCamelCase__	:	Optional[Any]				=self.feature_extraction_class.from_pretrained(lowerCamelCase  )

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



					def 	snake_case ( self							:						Union[str, Any]  )->      List[Any]:
								lowerCamelCase__	:	str				=self.feature_extraction_class(**self.feat_extract_dict  )

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

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



					def 	snake_case ( self							:						str  )->      List[str]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								lowerCamelCase__	:	Union[str, Any]				=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								# create three inputs of length 800, 1000, and 1200
								lowerCamelCase__	:	List[Any]				=[floats_list((1, x)  )[0] for x in range(800,						1400,						200  )]
								lowerCamelCase__	:	Dict				=[np.asarray(lowerCamelCase  ) for speech_input in speech_inputs]

								# Test feature size
								lowerCamelCase__	:	List[Any]				=feature_extractor(lowerCamelCase,						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
								lowerCamelCase__	:	Optional[Any]				=feature_extractor(speech_inputs[0],						return_tensors='''np'''  ).input_features
								lowerCamelCase__	:	List[Any]				=feature_extractor(np_speech_inputs[0],						return_tensors='''np'''  ).input_features
								self.assertTrue(np.allclose(lowerCamelCase,						lowerCamelCase,						atol=1E-3  )  )

								# Test batched
								lowerCamelCase__	:	Dict				=feature_extractor(lowerCamelCase,						return_tensors='''np'''  ).input_features
								lowerCamelCase__	:	List[Any]				=feature_extractor(lowerCamelCase,						return_tensors='''np'''  ).input_features
								for enc_seq_a, enc_seq_a in zip(lowerCamelCase,						lowerCamelCase  ):
											self.assertTrue(np.allclose(lowerCamelCase,						lowerCamelCase,						atol=1E-3  )  )

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

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

								lowerCamelCase__	:	List[Any]				=[x[: feature_extractor.n_samples] for x in speech_inputs]
								lowerCamelCase__	:	Optional[int]				=[np.asarray(lowerCamelCase  ) for speech_input in speech_inputs_truncated]

								lowerCamelCase__	:	List[Any]				=feature_extractor(lowerCamelCase,						return_tensors='''np'''  ).input_features
								lowerCamelCase__	:	int				=feature_extractor(lowerCamelCase,						return_tensors='''np'''  ).input_features
								for enc_seq_a, enc_seq_a in zip(lowerCamelCase,						lowerCamelCase  ):
											self.assertTrue(np.allclose(lowerCamelCase,						lowerCamelCase,						atol=1E-3  )  )



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

								lowerCamelCase__	:	List[str]				=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								lowerCamelCase__	:	Union[str, Any]				=np.random.rand(100,						32  ).astype(np.floataa  )
								lowerCamelCase__	:	Optional[Any]				=np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
											lowerCamelCase__	:	int				=feature_extractor.pad([{'''input_features''': inputs}],						return_tensors='''np'''  )
											self.assertTrue(np_processed.input_features.dtype == np.floataa  )
											lowerCamelCase__	:	Optional[int]				=feature_extractor.pad([{'''input_features''': inputs}],						return_tensors='''pt'''  )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	Tuple				=load_dataset('''hf-internal-testing/librispeech_asr_dummy''',						'''clean''',						split='''validation'''  )
								# automatic decoding with librispeech
								lowerCamelCase__	:	str				=ds.sort('''id'''  ).select(range(lowerCamelCase  )  )[:num_samples]['''audio''']

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



					def 	snake_case ( self							:						Tuple  )->      Dict:
								# fmt: off
								lowerCamelCase__	:	str				=torch.tensor(
								    [
								        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
								        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
								        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
								        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
								    ]  )
								# fmt: on

								lowerCamelCase__	:	Union[str, Any]				=self._load_datasamples(1  )
								lowerCamelCase__	:	Optional[int]				=WhisperFeatureExtractor()
								lowerCamelCase__	:	Any				=feature_extractor(lowerCamelCase,						return_tensors='''pt'''  ).input_features
								self.assertEqual(input_features.shape,						(1, 80, 3000)  )
								self.assertTrue(torch.allclose(input_features[0, 0, :30],						lowerCamelCase,						atol=1E-4  )  )




					def 	snake_case ( self							:						Optional[Any]  )->      str:
								lowerCamelCase__	:	Optional[Any]				=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								lowerCamelCase__	:	Optional[int]				=self._load_datasamples(1  )[0]
								lowerCamelCase__	:	Any				=((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
								lowerCamelCase__	:	Union[str, Any]				=feat_extract.zero_mean_unit_var_norm([audio],						attention_mask=lowerCamelCase  )[0]

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









"""simple docstring"""






from __future__ import annotations

from collections.abc import Iterator

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						str,						lowerCamelCase							:						int  )->      None:
								lowerCamelCase__	:	str				=value
								lowerCamelCase__	:	Node | None				=None
								lowerCamelCase__	:	Node | None				=None





class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						int,						lowerCamelCase							:						Node  )->      None:
								lowerCamelCase__	:	Any				=tree



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Node | None  )->      int:
								if node is None:
											return 0
								return node.value + (
								    self.depth_first_search(node.left  ) + self.depth_first_search(node.right  )
								)




					def __iter__( self							:						Dict  )->      Iterator[int]:
								yield self.depth_first_search(self.tree  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




