



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   			=random.Random()

def        a						(      _UpperCAmelCase							:					Dict ,   _UpperCAmelCase							:					int=1.0 ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=None						):







		'''simple docstring'''





		if rng is None:
				__UpperCAmelCase    :     Tuple													=	global_rng

		__UpperCAmelCase    :     Dict													=	[]
		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 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			def __init__(   self							: Dict       ,       a_							: Optional[Any]       ,       a_							: Optional[Any]=7       ,       a_							: Tuple=4_00       ,       a_							: int=20_00       ,       a_							: int=10       ,       a_							: Any=1_60       ,       a_							: Optional[Any]=8       ,       a_							: Any=0.0       ,       a_							: int=40_00       ,       a_							: str=False       ,       a_							: Union[str, Any]=True       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	parent
					__UpperCAmelCase    :     Union[str, Any]													=	batch_size
					__UpperCAmelCase    :     Optional[Any]													=	min_seq_length
					__UpperCAmelCase    :     str													=	max_seq_length
					__UpperCAmelCase    :     int													=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__UpperCAmelCase    :     Union[str, Any]													=	padding_value
					__UpperCAmelCase    :     Union[str, Any]													=	sampling_rate
					__UpperCAmelCase    :     List[str]													=	return_attention_mask
					__UpperCAmelCase    :     Optional[int]													=	do_normalize
					__UpperCAmelCase    :     int													=	feature_size
					__UpperCAmelCase    :     Union[str, Any]													=	chunk_length
					__UpperCAmelCase    :     Optional[Any]													=	hop_length




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					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							: Union[str, Any]       ,       a_							: Optional[int]=False       ,       a_							: Dict=False ):
					'''simple docstring'''





					def _flatten(a_							: int ):
							return list(itertools.chain(*lowerCAmelCase__ ) )

					if equal_length:
							__UpperCAmelCase    :     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
							__UpperCAmelCase    :     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:
							__UpperCAmelCase    :     Tuple													=	[np.asarray(lowerCAmelCase__ ) for x in speech_inputs]
					return speech_inputs

@require_torch
@require_torchaudio
class 		UpperCAmelCase__			(			UpperCAmelCase_						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       WhisperFeatureExtractor if is_speech_available() else None




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	WhisperFeatureExtractionTester(self )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





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

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     str													=	feat_extract_first.save_pretrained(lowerCAmelCase__ )[0]
							check_json_file_has_correct_format(lowerCAmelCase__ )
							__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class.from_pretrained(lowerCAmelCase__ )

					__UpperCAmelCase    :     Dict													=	feat_extract_first.to_dict()
					__UpperCAmelCase    :     List[Any]													=	feat_extract_second.to_dict()
					__UpperCAmelCase    :     Dict													=	feat_extract_first.mel_filters
					__UpperCAmelCase    :     Any													=	feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCAmelCase__       ,       lowerCAmelCase__ ) )
					self.assertEqual(lowerCAmelCase__       ,       lowerCAmelCase__ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.feature_extraction_class(**self.feat_extract_dict )

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

					__UpperCAmelCase    :     Dict													=	feat_extract_first.to_dict()
					__UpperCAmelCase    :     Dict													=	feat_extract_second.to_dict()
					__UpperCAmelCase    :     Any													=	feat_extract_first.mel_filters
					__UpperCAmelCase    :     Union[str, Any]													=	feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowerCAmelCase__       ,       lowerCAmelCase__ ) )
					self.assertEqual(lowerCAmelCase__       ,       lowerCAmelCase__ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__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    :     Union[str, Any]													=	[floats_list((1, x) )[0] for x in range(8_00       ,       14_00       ,       2_00 )]
					__UpperCAmelCase    :     Dict													=	[np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs]

					# Test feature size
					__UpperCAmelCase    :     List[str]													=	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
					__UpperCAmelCase    :     Optional[Any]													=	feature_extractor(speech_inputs[0]       ,       return_tensors='''np''' ).input_features
					__UpperCAmelCase    :     Dict													=	feature_extractor(np_speech_inputs[0]       ,       return_tensors='''np''' ).input_features
					self.assertTrue(np.allclose(lowerCAmelCase__       ,       lowerCAmelCase__       ,       atol=1e-3 ) )

					# Test batched
					__UpperCAmelCase    :     int													=	feature_extractor(lowerCAmelCase__       ,       return_tensors='''np''' ).input_features
					__UpperCAmelCase    :     Optional[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 ) )

					# Test 2-D numpy arrays are batched.
					__UpperCAmelCase    :     int													=	[floats_list((1, x) )[0] for x in (8_00, 8_00, 8_00)]
					__UpperCAmelCase    :     Union[str, Any]													=	np.asarray(lowerCAmelCase__ )
					__UpperCAmelCase    :     str													=	feature_extractor(lowerCAmelCase__       ,       return_tensors='''np''' ).input_features
					__UpperCAmelCase    :     Union[str, 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
					__UpperCAmelCase    :     Optional[Any]													=	[floats_list((1, x) )[0] for x in range(2_00       ,       (feature_extractor.n_samples + 5_00)       ,       2_00 )]
					__UpperCAmelCase    :     List[Any]													=	[np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs]

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

					__UpperCAmelCase    :     Any													=	feature_extractor(lowerCAmelCase__       ,       return_tensors='''np''' ).input_features
					__UpperCAmelCase    :     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 ) )




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					import torch

					__UpperCAmelCase    :     Union[str, Any]													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
					__UpperCAmelCase    :     Union[str, Any]													=	np.random.rand(1_00       ,       32 ).astype(np.floataa )
					__UpperCAmelCase    :     Any													=	np_speech_inputs.tolist()

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




			def  snake_case__     (   self							: Dict       ,       a_							: Union[str, Any] ):
					'''simple docstring'''





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

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




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	torch.tensor(
					    [
					        0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
					        0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
					        0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
					        -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
					    ] )
					# fmt: on

					__UpperCAmelCase    :     Union[str, Any]													=	self._load_datasamples(1 )
					__UpperCAmelCase    :     Optional[int]													=	WhisperFeatureExtractor()
					__UpperCAmelCase    :     str													=	feature_extractor(lowerCAmelCase__       ,       return_tensors='''pt''' ).input_features
					self.assertEqual(input_features.shape       ,       (1, 80, 30_00) )
					self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,       lowerCAmelCase__       ,       atol=1e-4 ) )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
					__UpperCAmelCase    :     Dict													=	self._load_datasamples(1 )[0]
					__UpperCAmelCase    :     Union[str, Any]													=	((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
					__UpperCAmelCase    :     int													=	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 ) )








class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       name
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       val



			def __str__( self  :     Dict):
									return F"{self.__class__.__name__}({self.name}, {self.val})"




			def __lt__( self  :     List[str]   ,						lowerCAmelCase__  :     Any):
									return self.val < other.val







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: int  		=       {}
									SCREAMING_SNAKE_CASE_: Any  		=       self.build_heap(lowerCAmelCase__)



			def __getitem__( self  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.get_value(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return (idx - 1) // 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									return idx * 2 + 1



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return idx * 2 + 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									return self.heap_dict[key]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_parent_idx(lowerCAmelCase__)

									for idx, i in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       idx
															SCREAMING_SNAKE_CASE_: str  		=       i.val

									for i in range(lowerCAmelCase__   ,						-1   ,						-1):
															self.sift_down(lowerCAmelCase__   ,						lowerCAmelCase__)
									return array



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									while True:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_left_child_idx(lowerCAmelCase__)  # noqa: E741
															SCREAMING_SNAKE_CASE_: Dict  		=       self.get_right_child_idx(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       idx
															if l < len(lowerCAmelCase__) and array[l] < array[idx]:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       l
															if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
																					SCREAMING_SNAKE_CASE_: str  		=       r

															if smallest != idx:
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       array[smallest], array[idx]
																					(
																					    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    
																					): Optional[Any]  		=       (
																					    self.idx_of_element[array[smallest]],
																					    self.idx_of_element[array[idx]],
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       smallest
															else:
																					break



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_parent_idx(lowerCAmelCase__)
									while p >= 0 and self.heap[p] > self.heap[idx]:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.heap[idx], self.heap[p]
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       p
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_parent_idx(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.heap[0]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.heap[-1], self.heap[0]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

									SCREAMING_SNAKE_CASE_: int  		=       self.heap.pop()
									del self.idx_of_element[x]
									self.sift_down(0   ,						self.heap)
									return x



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									self.heap.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       len(self.heap) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       node.val
									self.sift_up(len(self.heap) - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return len(self.heap) == 0




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									SCREAMING_SNAKE_CASE_: Any  		=       new_value
									SCREAMING_SNAKE_CASE_: Tuple  		=       new_value
									self.sift_up(self.idx_of_element[node])







lowerCAmelCase							:  int   	=						Node("""R""", -1)
lowerCAmelCase							:  str   	=						Node("""B""", 6)
lowerCAmelCase							:  str   	=						Node("""A""", 3)
lowerCAmelCase							:  List[str]   	=						Node("""X""", 1)
lowerCAmelCase							:  Union[str, Any]   	=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase							:  Optional[Any]   	=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()






