'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCamelCase_      	=	random.Random()

if is_torch_available():
		import torch


def    lowerCamelCase       (			UpperCAmelCase__				:  Any				,						UpperCAmelCase__				:  int=1.0				,						UpperCAmelCase__				:  Optional[Any]=None				,						UpperCAmelCase__				:  str=None		)			->	Any:


							'''simple docstring'''






							if rng is None:
														SCREAMING_SNAKE_CASE__     :List[str]						    = global_rng

							SCREAMING_SNAKE_CASE__     :Tuple						    = []
							for batch_idx in range(shape[0]		):
														values.append([]		)
														for _ in range(shape[1]		):
																					values[-1].append(rng.random() * scale		)

							return values




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Optional[int]							,							UpperCamelCase_ :       Dict							,							UpperCamelCase_ :       Dict=7							,							UpperCamelCase_ :       int=4_00							,							UpperCamelCase_ :       Any=20_00							,							UpperCamelCase_ :       Optional[Any]=1							,							UpperCamelCase_ :       str=0.0							,							UpperCamelCase_ :       Any=1_60_00							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       int=True							,							)     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Any						    = parent
													SCREAMING_SNAKE_CASE__     :Dict						    = batch_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = min_seq_length
													SCREAMING_SNAKE_CASE__     :List[Any]						    = max_seq_length
													SCREAMING_SNAKE_CASE__     :Any						    = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													SCREAMING_SNAKE_CASE__     :int						    = feature_size
													SCREAMING_SNAKE_CASE__     :Tuple						    = padding_value
													SCREAMING_SNAKE_CASE__     :Any						    = sampling_rate
													SCREAMING_SNAKE_CASE__     :str						    = return_attention_mask
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_normalize




						def      __lowerCamelCase       (    self :       int      )     ->				List[Any]:
													return {
													    "feature_size": self.feature_size,
													    "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							,							UpperCamelCase_ :       int=False							,							UpperCamelCase_ :       Optional[int]=False      )     ->				Union[str, Any]:
													def _flatten(UpperCamelCase_ :       List[str]      ):
																				return list(itertools.chain(*UpperCamelCase_      )      )

													if equal_length:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = floats_list((self.batch_size, self.max_seq_length)      )
													else:
																				# make sure that inputs increase in size
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [
																				    _flatten(floats_list((x, self.feature_size)      )      )
																				    for x in range(self.min_seq_length							,							self.max_seq_length							,							self.seq_length_diff      )
																				]

													if numpify:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = [np.asarray(UpperCamelCase_      ) for x in speech_inputs]

													return speech_inputs




@require_torch
@require_torchaudio
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Optional[Any]     		= ASTFeatureExtractor




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = ASTFeatureExtractionTester(self      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Tuple:
													# Tests that all call wrap to encode_plus and batch_encode_plus
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
													# create three inputs of length 800, 1000, and 1200
													SCREAMING_SNAKE_CASE__     :Dict						    = [floats_list((1, x)      )[0] for x in range(8_00							,							14_00							,							2_00      )]
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [np.asarray(UpperCamelCase_      ) for speech_input in speech_inputs]

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :int						    = feat_extract(speech_inputs[0]							,							return_tensors='np'      ).input_values
													SCREAMING_SNAKE_CASE__     :str						    = feat_extract(np_speech_inputs[0]							,							return_tensors='np'      ).input_values
													self.assertTrue(np.allclose(UpperCamelCase_							,							UpperCamelCase_							,							atol=1e-3      )      )

													# Test batched
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = feat_extract(UpperCamelCase_							,							padding=UpperCamelCase_							,							return_tensors='np'      ).input_values
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = feat_extract(UpperCamelCase_							,							padding=UpperCamelCase_							,							return_tensors='np'      ).input_values
													for enc_seq_a, enc_seq_a in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				self.assertTrue(np.allclose(UpperCamelCase_							,							UpperCamelCase_							,							atol=1e-3      )      )

													# Test 2-D numpy arrays are batched.
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
													SCREAMING_SNAKE_CASE__     :Any						    = np.asarray(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = feat_extract(UpperCamelCase_							,							return_tensors='np'      ).input_values
													SCREAMING_SNAKE_CASE__     :int						    = feat_extract(UpperCamelCase_							,							return_tensors='np'      ).input_values
													for enc_seq_a, enc_seq_a in zip(UpperCamelCase_							,							UpperCamelCase_      ):
																				self.assertTrue(np.allclose(UpperCamelCase_							,							UpperCamelCase_							,							atol=1e-3      )      )




						@require_torch
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				List[Any]:
													import torch

													SCREAMING_SNAKE_CASE__     :List[str]						    = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
													SCREAMING_SNAKE_CASE__     :str						    = np.random.rand(1_00      ).astype(np.floataa      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = np_speech_inputs.tolist()

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




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       Optional[int]      )     ->				List[str]:
													from datasets import load_dataset

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = load_dataset('hf-internal-testing/librispeech_asr_dummy'							,							'clean'							,							split='validation'      )
													# automatic decoding with librispeech
													SCREAMING_SNAKE_CASE__     :Any						    = ds.sort('id'      ).select(range(UpperCamelCase_      )      )[:num_samples]['audio']

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




						@require_torch
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Any:
													# fmt: off
													SCREAMING_SNAKE_CASE__     :str						    = torch.tensor(
													    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
													     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
													     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
													     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]      )
													# fmt: on

													SCREAMING_SNAKE_CASE__     :str						    = self._load_datasamples(1      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = ASTFeatureExtractor()
													SCREAMING_SNAKE_CASE__     :Any						    = feature_extractor(UpperCamelCase_							,							return_tensors='pt'      ).input_values
													self.assertEquals(input_values.shape							,							(1, 10_24, 1_28)      )
													self.assertTrue(torch.allclose(input_values[0, 0, :30]							,							UpperCamelCase_							,							atol=1e-4      )      )







'''simple docstring'''







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase_      	=	'''.'''


if __name__ == "__main__":
		UpperCamelCase_      	=	os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
		UpperCamelCase_      	=	[]
		UpperCamelCase_      	=	[]
		with open(doctest_file_path) as fp:
				for line in fp:
						UpperCamelCase_      	=	line.strip()
						UpperCamelCase_      	=	os.path.join(REPO_PATH, line)
						if not (os.path.isfile(path) or os.path.isdir(path)):
								non_existent_paths.append(line)
						all_paths.append(path)
		if len(non_existent_paths) > 0:
				UpperCamelCase_      	=	'''\n'''.join(non_existent_paths)
				raise ValueError(f"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
		if all_paths != sorted(all_paths):
				raise ValueError('''Files in `utils/documentation_tests.txt` are not in alphabetical order.''')






