




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

UpperCAmelCase_		:					Dict          =  random.Random()




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=1.0						,  lowerCamelCase=None						,  lowerCamelCase=None ):
				if rng is None:
								__magic_name__						:						Dict										=global_rng

				__magic_name__						:						int										=[]
				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 		__A   (      unittest.TestCase      ):

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

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

						def      A__    (		self :Any			):
										'''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      A__    (		self :str     ,			__snake_case :Dict=False     ,			__snake_case :Any=False			):
										'''simple docstring'''

										def _flatten(__snake_case :List[str]			):
														return list(itertools.chain(*__snake_case			)			)

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




@require_torch
@require_torchaudio
class 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       WhisperFeatureExtractor if is_speech_available() else None

						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=WhisperFeatureExtractionTester(self			)

						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
														__magic_name__						:						int										=feat_extract_first.save_pretrained(__snake_case			)[0]
														check_json_file_has_correct_format(__snake_case			)
														__magic_name__						:						Dict										=self.feature_extraction_class.from_pretrained(__snake_case			)

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

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

										__magic_name__						:						Optional[Any]										=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
														__magic_name__						:						str										=os.path.join(__snake_case     ,			"""feat_extract.json"""			)
														feat_extract_first.to_json_file(__snake_case			)
														__magic_name__						:						Tuple										=self.feature_extraction_class.from_json_file(__snake_case			)

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

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

										__magic_name__						:						Any										=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										# create three inputs of length 800, 1000, and 1200
										__magic_name__						:						Dict										=[floats_list((1, x)			)[0] for x in range(8_00     ,			14_00     ,			2_00			)]
										__magic_name__						:						List[str]										=[np.asarray(__snake_case			) for speech_input in speech_inputs]

										# Test feature size
										__magic_name__						:						Dict										=feature_extractor(__snake_case     ,			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
										__magic_name__						:						Optional[int]										=feature_extractor(speech_inputs[0]     ,			return_tensors="""np"""			).input_features
										__magic_name__						:						int										=feature_extractor(np_speech_inputs[0]     ,			return_tensors="""np"""			).input_features
										self.assertTrue(np.allclose(__snake_case     ,			__snake_case     ,			atol=1E-3			)			)

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

										# Test 2-D numpy arrays are batched.
										__magic_name__						:						Optional[Any]										=[floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
										__magic_name__						:						Tuple										=np.asarray(__snake_case			)
										__magic_name__						:						List[str]										=feature_extractor(__snake_case     ,			return_tensors="""np"""			).input_features
										__magic_name__						:						Dict										=feature_extractor(__snake_case     ,			return_tensors="""np"""			).input_features
										for enc_seq_a, enc_seq_a in zip(__snake_case     ,			__snake_case			):
														self.assertTrue(np.allclose(__snake_case     ,			__snake_case     ,			atol=1E-3			)			)

										# Test truncation required
										__magic_name__						:						Any										=[floats_list((1, x)			)[0] for x in range(2_00     ,			(feature_extractor.n_samples + 5_00)     ,			2_00			)]
										__magic_name__						:						int										=[np.asarray(__snake_case			) for speech_input in speech_inputs]

										__magic_name__						:						List[Any]										=[x[: feature_extractor.n_samples] for x in speech_inputs]
										__magic_name__						:						str										=[np.asarray(__snake_case			) for speech_input in speech_inputs_truncated]

										__magic_name__						:						Optional[Any]										=feature_extractor(__snake_case     ,			return_tensors="""np"""			).input_features
										__magic_name__						:						int										=feature_extractor(__snake_case     ,			return_tensors="""np"""			).input_features
										for enc_seq_a, enc_seq_a in zip(__snake_case     ,			__snake_case			):
														self.assertTrue(np.allclose(__snake_case     ,			__snake_case     ,			atol=1E-3			)			)

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

										import torch

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

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

						def      A__    (		self :Optional[int]     ,			__snake_case :Tuple			):
										'''simple docstring'''

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

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

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

										__magic_name__						:						int										=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

										__magic_name__						:						List[Any]										=self._load_datasamples(1			)
										__magic_name__						:						int										=WhisperFeatureExtractor()
										__magic_name__						:						Optional[Any]										=feature_extractor(__snake_case     ,			return_tensors="""pt"""			).input_features
										self.assertEqual(input_features.shape     ,			(1, 80, 30_00)			)
										self.assertTrue(torch.allclose(input_features[0, 0, :30]     ,			__snake_case     ,			atol=1E-4			)			)


						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
										__magic_name__						:						Tuple										=self._load_datasamples(1			)[0]
										__magic_name__						:						List[str]										=((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
										__magic_name__						:						List[str]										=feat_extract.zero_mean_unit_var_norm([audio]     ,			attention_mask=__snake_case			)[0]

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










UpperCAmelCase_		:					Tuple          =  0  # The first color of the flag.
UpperCAmelCase_		:					Any          =  1  # The second color of the flag.
UpperCAmelCase_		:					str          =  2  # The third color of the flag.
UpperCAmelCase_		:					Tuple          =  (red, white, blue)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not sequence:
								return []
				if len(lowerCamelCase ) == 1:
								return list(lowerCamelCase )
				__magic_name__						:						int										=0
				__magic_name__						:						str										=len(lowerCamelCase ) - 1
				__magic_name__						:						Optional[Any]										=0
				while mid <= high:
								if sequence[mid] == colors[0]:
												__magic_name__					,			__magic_name__						:						Tuple										=sequence[mid], sequence[low]
												low += 1
												mid += 1
								elif sequence[mid] == colors[1]:
												mid += 1
								elif sequence[mid] == colors[2]:
												__magic_name__					,			__magic_name__						:						Optional[Any]										=sequence[high], sequence[mid]
												high -= 1
								else:
												__magic_name__						:						Optional[int]										=F"The elements inside the sequence must contains only {colors} values"
												raise ValueError(lowerCamelCase )
				return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					List[Any]          =  input("Enter numbers separated by commas:\n").strip()
							UpperCAmelCase_		:					Optional[int]          =  [int(item.strip()) for item in user_input.split(",")]
							print(F"""{dutch_national_flag_sort(unsorted)}""")




