
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

lowerCamelCase_  :	List[str] 				=					random.Random()







def     A__   (  lowerCamelCase       ,      lowerCamelCase=1.0       ,      lowerCamelCase=None       ,      lowerCamelCase=None  )					->		Tuple:
				if rng is None:
								UpperCamelCase_:							Optional[int]		=  global_rng

				UpperCamelCase_:							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     _UpperCamelCase			(   unittest.TestCase					):






			'''simple docstring'''

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

			def  lowerCAmelCase__		(    self	:    List[str] ):
							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  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    Optional[int]=False  ,		snake_case_	:    List[Any]=False ):
							def _flatten(snake_case_	:    int ):
											return list(itertools.chain(*snake_case_ ) )

							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_:							List[str]		=  [
											    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_:							Union[str, Any]		=  [np.asarray(snake_case_ ) for x in speech_inputs]
							return speech_inputs




@require_torch
@require_torchaudio
class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  str    =	WhisperFeatureExtractor if is_speech_available() else None

			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Any		=  WhisperFeatureExtractionTester(self )

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Any		=  self.feature_extraction_class(**self.feat_extract_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							Tuple		=  feat_extract_first.save_pretrained(snake_case_ )[0]
											check_json_file_has_correct_format(snake_case_ )
											UpperCamelCase_:							Union[str, Any]		=  self.feature_extraction_class.from_pretrained(snake_case_ )

							UpperCamelCase_:							str		=  feat_extract_first.to_dict()
							UpperCamelCase_:							Tuple		=  feat_extract_second.to_dict()
							UpperCamelCase_:							int		=  feat_extract_first.mel_filters
							UpperCamelCase_:							Any		=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(snake_case_  ,		snake_case_ ) )
							self.assertEqual(snake_case_  ,		snake_case_ )

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							Optional[int]		=  self.feature_extraction_class(**self.feat_extract_dict )

							with tempfile.TemporaryDirectory() as tmpdirname:
											UpperCamelCase_:							Dict		=  os.path.join(snake_case_  ,		"""feat_extract.json""" )
											feat_extract_first.to_json_file(snake_case_ )
											UpperCamelCase_:							Any		=  self.feature_extraction_class.from_json_file(snake_case_ )

							UpperCamelCase_:							Tuple		=  feat_extract_first.to_dict()
							UpperCamelCase_:							str		=  feat_extract_second.to_dict()
							UpperCamelCase_:							List[Any]		=  feat_extract_first.mel_filters
							UpperCamelCase_:							Optional[int]		=  feat_extract_second.mel_filters
							self.assertTrue(np.allclose(snake_case_  ,		snake_case_ ) )
							self.assertEqual(snake_case_  ,		snake_case_ )

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

							# Test feature size
							UpperCamelCase_:							Optional[Any]		=  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
							UpperCamelCase_:							str		=  feature_extractor(speech_inputs[0]  ,		return_tensors="""np""" ).input_features
							UpperCamelCase_:							Optional[Any]		=  feature_extractor(np_speech_inputs[0]  ,		return_tensors="""np""" ).input_features
							self.assertTrue(np.allclose(snake_case_  ,		snake_case_  ,		atol=1e-3 ) )

							# Test batched
							UpperCamelCase_:							int		=  feature_extractor(snake_case_  ,		return_tensors="""np""" ).input_features
							UpperCamelCase_:							List[Any]		=  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.
							UpperCamelCase_:							str		=  [floats_list((1, x) )[0] for x in (800, 800, 800)]
							UpperCamelCase_:							Union[str, Any]		=  np.asarray(snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  feature_extractor(snake_case_  ,		return_tensors="""np""" ).input_features
							UpperCamelCase_:							List[Any]		=  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
							UpperCamelCase_:							Optional[Any]		=  [floats_list((1, x) )[0] for x in range(200  ,		(feature_extractor.n_samples + 500)  ,		200 )]
							UpperCamelCase_:							int		=  [np.asarray(snake_case_ ) for speech_input in speech_inputs]

							UpperCamelCase_:							str		=  [x[: feature_extractor.n_samples] for x in speech_inputs]
							UpperCamelCase_:							Dict		=  [np.asarray(snake_case_ ) for speech_input in speech_inputs_truncated]

							UpperCamelCase_:							Optional[Any]		=  feature_extractor(snake_case_  ,		return_tensors="""np""" ).input_features
							UpperCamelCase_:							List[Any]		=  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  lowerCAmelCase__		(    self	:    Optional[int] ):
							import torch

							UpperCamelCase_:							Union[str, Any]		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							UpperCamelCase_:							List[str]		=  np.random.rand(100  ,		32 ).astype(np.floataa )
							UpperCamelCase_:							List[str]		=  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_:							Union[str, Any]		=  feature_extractor.pad([{"""input_features""": inputs}]  ,		return_tensors="""pt""" )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa )

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

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

			def  lowerCAmelCase__		(    self	:    Tuple ):
							# fmt: off
							UpperCamelCase_:							Dict		=  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_:							Tuple		=  self._load_datasamples(1 )
							UpperCamelCase_:							List[Any]		=  WhisperFeatureExtractor()
							UpperCamelCase_:							Optional[int]		=  feature_extractor(snake_case_  ,		return_tensors="""pt""" ).input_features
							self.assertEqual(input_features.shape  ,		(1, 80, 3000) )
							self.assertTrue(torch.allclose(input_features[0, 0, :30]  ,		snake_case_  ,		atol=1e-4 ) )




			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Union[str, Any]		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							UpperCamelCase_:							List[Any]		=  self._load_datasamples(1 )[0]
							UpperCamelCase_:							List[str]		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
							UpperCamelCase_:							Any		=  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 ) )








def     A__   (  lowerCamelCase       ,      lowerCamelCase  )					->		list:
				UpperCamelCase_:							Optional[int]		=  word.split()

				def justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  ) -> str:
								UpperCamelCase_:							Tuple		=  max_width - width
								UpperCamelCase_:							Optional[Any]		=  len(lowerCamelCase  )
								if len(lowerCamelCase  ) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												UpperCamelCase_:							List[Any]		=  words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												UpperCamelCase_:							Optional[Any]		=  spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												UpperCamelCase_:							List[str]		=  (
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowerCamelCase  ):
																num_spaces_between_words_list[i] += 1
												UpperCamelCase_:							Dict		=  []
												for i in range(lowerCamelCase  ):
																# add the word
																aligned_words_list.append(line[i]  )
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * """ """  )
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]  )
												# join the aligned words list to form a justified line
												return "".join(lowerCamelCase  )

				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							list[str]		=  []
				UpperCamelCase_:							List[str]		=  0
				for word in words:
								if width + len(lowerCamelCase  ) + len(lowerCamelCase  ) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowerCamelCase  )
												width += len(lowerCamelCase  )
								else:
												# justify the line and add it to result
												answer.append(justify(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )  )
												# reset new line and new width
												UpperCamelCase_,				UpperCamelCase_:							List[str]		=  [word], len(lowerCamelCase  )
				UpperCamelCase_:							List[str]		=  max_width - width - len(lowerCamelCase  )
				answer.append(""" """.join(lowerCamelCase  ) + (remaining_spaces + 1) * """ """  )
				return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()






