





import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch


SCREAMING_SNAKE_CASE_						       =	random.Random()



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=1.0			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=None     )			->       str:


						'''simple docstring'''


						if rng is None:
												SCREAMING_SNAKE_CASE		=						global_rng

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

						return values

@require_torch
class 		UpperCamelCase__			(				unittest.TestCase       ):



					'''simple docstring'''

					def __init__(							self						:   Union[str, Any]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   str=7						,lowerCamelCase__						:   List[Any]=400						,lowerCamelCase__						:   List[Any]=2000						,lowerCamelCase__						:   List[str]=1						,lowerCamelCase__						:   Dict=0.0						,lowerCamelCase__						:   Dict=16000						,lowerCamelCase__						:   Tuple=True						,lowerCamelCase__						:   Optional[Any]=80						,lowerCamelCase__						:   List[Any]=16						,lowerCamelCase__						:   List[str]=64						,lowerCamelCase__						:   Optional[int]="hann_window"						,lowerCamelCase__						:   List[Any]=80						,lowerCamelCase__						:   Union[str, Any]=7600						,lowerCamelCase__						:   Dict=1e-1_0						,lowerCamelCase__						:   List[str]=True						,)					->		str:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						parent
											SCREAMING_SNAKE_CASE		=						batch_size
											SCREAMING_SNAKE_CASE		=						min_seq_length
											SCREAMING_SNAKE_CASE		=						max_seq_length
											SCREAMING_SNAKE_CASE		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											SCREAMING_SNAKE_CASE		=						feature_size
											SCREAMING_SNAKE_CASE		=						padding_value
											SCREAMING_SNAKE_CASE		=						sampling_rate
											SCREAMING_SNAKE_CASE		=						do_normalize
											SCREAMING_SNAKE_CASE		=						num_mel_bins
											SCREAMING_SNAKE_CASE		=						hop_length
											SCREAMING_SNAKE_CASE		=						win_length
											SCREAMING_SNAKE_CASE		=						win_function
											SCREAMING_SNAKE_CASE		=						fmin
											SCREAMING_SNAKE_CASE		=						fmax
											SCREAMING_SNAKE_CASE		=						mel_floor
											SCREAMING_SNAKE_CASE		=						return_attention_mask






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Any:




											'''simple docstring'''


											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "do_normalize": self.do_normalize,
											    "num_mel_bins": self.num_mel_bins,
											    "hop_length": self.hop_length,
											    "win_length": self.win_length,
											    "win_function": self.win_function,
											    "fmin": self.fmin,
											    "fmax": self.fmax,
											    "mel_floor": self.mel_floor,
											    "return_attention_mask": self.return_attention_mask,
											}






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Tuple=False						,lowerCamelCase__						:   Union[str, Any]=False	)					->		Optional[Any]:




											'''simple docstring'''


											def _flatten(lowerCamelCase__						:   str	):
																	return list(itertools.chain(*lowerCamelCase__	)	)

											if equal_length:
																	SCREAMING_SNAKE_CASE		=						floats_list((self.batch_size, self.max_seq_length)	)
											else:
																	# make sure that inputs increase in size
																	SCREAMING_SNAKE_CASE		=						[
																	    _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		=						[np.asarray(lowerCamelCase__	) for x in speech_inputs]

											return speech_inputs






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   Tuple=False						,lowerCamelCase__						:   str=False	)					->		str:




											'''simple docstring'''


											if equal_length:
																	SCREAMING_SNAKE_CASE		=						[floats_list((self.max_seq_length, self.num_mel_bins)	) for _ in range(self.batch_size	)]
											else:
																	# make sure that inputs increase in size
																	SCREAMING_SNAKE_CASE		=						[
																	    floats_list((x, self.num_mel_bins)	)
																	    for x in range(self.min_seq_length						,self.max_seq_length						,self.seq_length_diff	)
																	]

											if numpify:
																	SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__	) for x in speech_inputs]

											return speech_inputs

@require_torch
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



					'''simple docstring'''

					__snake_case   :     int		     =      SpeechTaFeatureExtractor






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						SpeechTaFeatureExtractionTester(self	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]						,lowerCamelCase__						:   int	)					->		Tuple:




											'''simple docstring'''


											self.assertTrue(np.all(np.mean(lowerCamelCase__						,axis=0	) < 1e-3	)	)
											self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__						,axis=0	) - 1	) < 1e-3	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]
											SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__	) for speech_input in speech_inputs]

											# Test not batched input
											SCREAMING_SNAKE_CASE		=						feat_extract(speech_inputs[0]						,return_tensors="""np"""	).input_values
											SCREAMING_SNAKE_CASE		=						feat_extract(np_speech_inputs[0]						,return_tensors="""np"""	).input_values
											self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3	)	)

											# Test batched
											SCREAMING_SNAKE_CASE		=						feat_extract(lowerCamelCase__						,return_tensors="""np"""	).input_values
											SCREAMING_SNAKE_CASE		=						feat_extract(lowerCamelCase__						,return_tensors="""np"""	).input_values
											for enc_seq_a, enc_seq_a in zip(lowerCamelCase__						,lowerCamelCase__	):
																	self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Any:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]

											SCREAMING_SNAKE_CASE		=						["""longest""", """max_length""", """do_not_pad"""]
											SCREAMING_SNAKE_CASE		=						[None, 1600, None]
											for max_length, padding in zip(lowerCamelCase__						,lowerCamelCase__	):
																	SCREAMING_SNAKE_CASE		=						feat_extract(lowerCamelCase__						,padding=lowerCamelCase__						,max_length=lowerCamelCase__						,return_tensors="""np"""	)
																	SCREAMING_SNAKE_CASE		=						processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]	)
																	self.assertTrue(input_values[0][800:].sum() < 1e-6	)
																	self._check_zero_mean_unit_variance(input_values[1][:1000]	)
																	self.assertTrue(input_values[0][1000:].sum() < 1e-6	)
																	self._check_zero_mean_unit_variance(input_values[2][:1200]	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											SCREAMING_SNAKE_CASE		=						range(800						,1400						,200	)
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in lengths]

											SCREAMING_SNAKE_CASE		=						["""longest""", """max_length""", """do_not_pad"""]
											SCREAMING_SNAKE_CASE		=						[None, 1600, None]

											for max_length, padding in zip(lowerCamelCase__						,lowerCamelCase__	):
																	SCREAMING_SNAKE_CASE		=						feat_extract(lowerCamelCase__						,max_length=lowerCamelCase__						,padding=lowerCamelCase__	)
																	SCREAMING_SNAKE_CASE		=						processed.input_values

																	self._check_zero_mean_unit_variance(input_values[0][:800]	)
																	self._check_zero_mean_unit_variance(input_values[1][:1000]	)
																	self._check_zero_mean_unit_variance(input_values[2][:1200]	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]
											SCREAMING_SNAKE_CASE		=						feat_extract(
											    lowerCamelCase__						,truncation=lowerCamelCase__						,max_length=1000						,padding="""max_length"""						,return_tensors="""np"""	)
											SCREAMING_SNAKE_CASE		=						processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]	)
											self._check_zero_mean_unit_variance(input_values[1]	)
											self._check_zero_mean_unit_variance(input_values[2]	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]
											SCREAMING_SNAKE_CASE		=						feat_extract(
											    lowerCamelCase__						,truncation=lowerCamelCase__						,max_length=1000						,padding="""longest"""						,return_tensors="""np"""	)
											SCREAMING_SNAKE_CASE		=						processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]	)
											self._check_zero_mean_unit_variance(input_values[1, :1000]	)
											self._check_zero_mean_unit_variance(input_values[2]	)

											# make sure that if max_length < longest -> then pad to max_length
											self.assertTrue(input_values.shape == (3, 1000)	)

											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]
											SCREAMING_SNAKE_CASE		=						feat_extract(
											    lowerCamelCase__						,truncation=lowerCamelCase__						,max_length=2000						,padding="""longest"""						,return_tensors="""np"""	)
											SCREAMING_SNAKE_CASE		=						processed.input_values

											self._check_zero_mean_unit_variance(input_values[0, :800]	)
											self._check_zero_mean_unit_variance(input_values[1, :1000]	)
											self._check_zero_mean_unit_variance(input_values[2]	)

											# make sure that if max_length > longest -> then pad to longest
											self.assertTrue(input_values.shape == (3, 1200)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		Optional[int]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											SCREAMING_SNAKE_CASE		=						np.random.rand(100	).astype(np.floataa	)
											SCREAMING_SNAKE_CASE		=						np_speech_inputs.tolist()

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






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in range(800						,1400						,200	)]
											SCREAMING_SNAKE_CASE		=						[np.asarray(lowerCamelCase__	) for speech_input in speech_inputs]

											# Test feature size
											SCREAMING_SNAKE_CASE		=						feature_extractor(audio_target=lowerCamelCase__						,padding=lowerCamelCase__						,return_tensors="""np"""	).input_values
											self.assertTrue(input_values.ndim == 3	)
											self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins	)

											# Test not batched input
											SCREAMING_SNAKE_CASE		=						feature_extractor(speech_inputs[0]						,return_tensors="""np"""	).input_values
											SCREAMING_SNAKE_CASE		=						feature_extractor(np_speech_inputs[0]						,return_tensors="""np"""	).input_values
											self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3	)	)

											# Test batched
											SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_values
											SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_values
											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.
											SCREAMING_SNAKE_CASE		=						[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											SCREAMING_SNAKE_CASE		=						np.asarray(lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_values
											SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""np"""	).input_values
											for enc_seq_a, enc_seq_a in zip(lowerCamelCase__						,lowerCamelCase__	):
																	self.assertTrue(np.allclose(lowerCamelCase__						,lowerCamelCase__						,atol=1e-3	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target()
											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict	)
											SCREAMING_SNAKE_CASE		=						feat_extract.model_input_names[0]

											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}	)

											self.assertTrue(all(len(lowerCamelCase__	) == len(lowerCamelCase__	) for x, y in zip(lowerCamelCase__						,processed_features[input_name]	)	)	)

											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}						,tensor_type="""np"""	)

											SCREAMING_SNAKE_CASE		=						processed_features[input_name]

											if len(batch_features_input.shape	) < 3:
																	SCREAMING_SNAKE_CASE		=						batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)






					@require_torch
					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict	)
											SCREAMING_SNAKE_CASE		=						feat_extract.model_input_names[0]

											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}						,tensor_type="""pt"""	)

											SCREAMING_SNAKE_CASE		=						processed_features[input_name]

											if len(batch_features_input.shape	) < 3:
																	SCREAMING_SNAKE_CASE		=						batch_features_input[:, :, None]

											self.assertTrue(
											    batch_features_input.shape
											    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]	), self.feat_extract_tester.num_mel_bins)	)






					@require_torch
					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**self.feat_extract_dict	)
											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target()
											SCREAMING_SNAKE_CASE		=						feat_extract.model_input_names[0]

											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}	)

											SCREAMING_SNAKE_CASE		=						feat_extract.num_mel_bins  # hack!

											SCREAMING_SNAKE_CASE		=						feat_extract.pad(lowerCamelCase__						,padding="""longest"""						,return_tensors="""np"""	)[input_name]
											SCREAMING_SNAKE_CASE		=						feat_extract.pad(lowerCamelCase__						,padding="""longest"""						,return_tensors="""pt"""	)[input_name]

											self.assertTrue(abs(input_np.astype(np.floataa	).sum() - input_pt.numpy().astype(np.floataa	).sum()	) < 1e-2	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Optional[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feat_extract_dict
											SCREAMING_SNAKE_CASE		=						True
											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target()
											SCREAMING_SNAKE_CASE		=						[len(lowerCamelCase__	) for x in speech_inputs]
											SCREAMING_SNAKE_CASE		=						feat_extract.model_input_names[0]

											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}	)

											SCREAMING_SNAKE_CASE		=						feat_extract.num_mel_bins  # hack!

											SCREAMING_SNAKE_CASE		=						feat_extract.pad(lowerCamelCase__						,padding="""longest"""						,return_tensors="""np"""	)
											self.assertIn("""attention_mask"""						,lowerCamelCase__	)
											self.assertListEqual(list(processed.attention_mask.shape	)						,list(processed[input_name].shape[:2]	)	)
											self.assertListEqual(processed.attention_mask.sum(-1	).tolist()						,lowerCamelCase__	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Tuple:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						self.feat_extract_dict
											SCREAMING_SNAKE_CASE		=						True
											SCREAMING_SNAKE_CASE		=						self.feature_extraction_class(**lowerCamelCase__	)
											SCREAMING_SNAKE_CASE		=						self.feat_extract_tester.prepare_inputs_for_target()
											SCREAMING_SNAKE_CASE		=						[len(lowerCamelCase__	) for x in speech_inputs]
											SCREAMING_SNAKE_CASE		=						feat_extract.model_input_names[0]

											SCREAMING_SNAKE_CASE		=						BatchFeature({input_name: speech_inputs}	)
											SCREAMING_SNAKE_CASE		=						min(lowerCamelCase__	)

											SCREAMING_SNAKE_CASE		=						feat_extract.num_mel_bins  # hack!

											SCREAMING_SNAKE_CASE		=						feat_extract.pad(
											    lowerCamelCase__						,padding="""max_length"""						,max_length=lowerCamelCase__						,truncation=lowerCamelCase__						,return_tensors="""np"""	)
											self.assertIn("""attention_mask"""						,lowerCamelCase__	)
											self.assertListEqual(
											    list(processed_pad.attention_mask.shape	)						,[processed_pad[input_name].shape[0], max_length]	)
											self.assertListEqual(
											    processed_pad.attention_mask[:, :max_length].sum(-1	).tolist()						,[max_length for x in speech_inputs]	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   str	)					->		Optional[int]:




											'''simple docstring'''


											from datasets import load_dataset

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

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






					def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		int:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
											     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
											     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
											     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
											     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
											     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]	)
											# fmt: on

											SCREAMING_SNAKE_CASE		=						self._load_datasamples(1	)
											SCREAMING_SNAKE_CASE		=						SpeechTaFeatureExtractor()
											SCREAMING_SNAKE_CASE		=						feature_extractor(lowerCamelCase__						,return_tensors="""pt"""	).input_values
											self.assertEquals(input_values.shape						,(1, 93680)	)
											self.assertTrue(torch.allclose(input_values[0, :30]						,lowerCamelCase__						,atol=1e-6	)	)






					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Optional[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						torch.tensor(
											    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
											     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
											     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
											     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]	)
											# fmt: on

											SCREAMING_SNAKE_CASE		=						self._load_datasamples(1	)
											SCREAMING_SNAKE_CASE		=						SpeechTaFeatureExtractor()
											SCREAMING_SNAKE_CASE		=						feature_extractor(audio_target=lowerCamelCase__						,return_tensors="""pt"""	).input_values
											self.assertEquals(input_values.shape						,(1, 366, 80)	)
											self.assertTrue(torch.allclose(input_values[0, 0, :30]						,lowerCamelCase__						,atol=1e-4	)	)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_						       =	{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	["""ReformerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ReformerAttention""",
          """ReformerForMaskedLM""",
          """ReformerForQuestionAnswering""",
          """ReformerForSequenceClassification""",
          """ReformerLayer""",
          """ReformerModel""",
          """ReformerModelWithLMHead""",
          """ReformerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer import ReformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_reformer_fast import ReformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_reformer import (
                REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                ReformerAttention,
                ReformerForMaskedLM,
                ReformerForQuestionAnswering,
                ReformerForSequenceClassification,
                ReformerLayer,
                ReformerModel,
                ReformerModelWithLMHead,
                ReformerPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


