


"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


a__       :							Any        =  random.Random()




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_=1.0						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''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




class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	def __init__(				self    :       Dict    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       str=7    ,    UpperCAmelCase__    :       List[Any]=4_0_0    ,    UpperCAmelCase__    :       Tuple=2_0_0_0    ,    UpperCAmelCase__    :       List[str]=1    ,    UpperCAmelCase__    :       Dict=0.0    ,    UpperCAmelCase__    :       Union[str, Any]=1_6_0_0_0    ,    UpperCAmelCase__    :       List[str]=True    ,    UpperCAmelCase__    :       Optional[Any]=True    ,    ) ->			int:
						__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		     =       return_attention_mask
						__SCREAMING_SNAKE_CASE		     =       do_normalize




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			Union[str, 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 						UpperCAmelCase_   (				self    :       Optional[Any]    ,    UpperCAmelCase__    :       Dict=False    ,    UpperCAmelCase__    :       int=False     ) ->			Union[str, Any]:
						def _flatten(UpperCAmelCase__    :       int     ):
											return list(itertools.chain(*UpperCAmelCase__     )     )

						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(UpperCAmelCase__     ) for x in speech_inputs]

						return speech_inputs




class 				UpperCamelCase_			(				UpperCamelCase    ,						unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						Union[str, Any]					      =    WavaVecaFeatureExtractor




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       WavaVecaFeatureExtractionTester(self     )




	def 						UpperCAmelCase_   (				self    :       Dict    ,    UpperCAmelCase__    :       Tuple     ) ->			List[str]:
						self.assertTrue(np.all(np.mean(UpperCAmelCase__    ,    axis=0     ) < 1E-3     )     )
						self.assertTrue(np.all(np.abs(np.var(UpperCAmelCase__    ,    axis=0     ) - 1     ) < 1E-3     )     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[Any]:
						# Tests that all call wrap to encode_plus and batch_encode_plus
						__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(8_0_0    ,    1_4_0_0    ,    2_0_0     )]
						__SCREAMING_SNAKE_CASE		     =       [np.asarray(UpperCAmelCase__     ) 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(UpperCAmelCase__    ,    UpperCAmelCase__    ,    atol=1E-3     )     )

						# Test batched
						__SCREAMING_SNAKE_CASE		     =       feat_extract(UpperCAmelCase__    ,    return_tensors="np"     ).input_values
						__SCREAMING_SNAKE_CASE		     =       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     )     )

						# Test 2-D numpy arrays are batched.
						__SCREAMING_SNAKE_CASE		     =       [floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						__SCREAMING_SNAKE_CASE		     =       np.asarray(UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       feat_extract(UpperCAmelCase__    ,    return_tensors="np"     ).input_values
						__SCREAMING_SNAKE_CASE		     =       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     )     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Dict:
						__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(8_0_0    ,    1_4_0_0    ,    2_0_0     )]

						__SCREAMING_SNAKE_CASE		     =       ["longest", "max_length", "do_not_pad"]
						__SCREAMING_SNAKE_CASE		     =       [None, 1_6_0_0, None]
						for max_length, padding in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       feat_extract(UpperCAmelCase__    ,    padding=UpperCAmelCase__    ,    max_length=UpperCAmelCase__    ,    return_tensors="np"     )
											__SCREAMING_SNAKE_CASE		     =       processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
											self.assertTrue(input_values[0][8_0_0:].sum() < 1E-6     )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
											self.assertTrue(input_values[0][1_0_0_0:].sum() < 1E-6     )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )




	def 						UpperCAmelCase_   (				self    :       int     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
						__SCREAMING_SNAKE_CASE		     =       range(8_0_0    ,    1_4_0_0    ,    2_0_0     )
						__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, 1_6_0_0, None]

						for max_length, padding in zip(UpperCAmelCase__    ,    UpperCAmelCase__     ):
											__SCREAMING_SNAKE_CASE		     =       feat_extract(UpperCAmelCase__    ,    max_length=UpperCAmelCase__    ,    padding=UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]     )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]     )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]     )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Any:
						__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(8_0_0    ,    1_4_0_0    ,    2_0_0     )]
						__SCREAMING_SNAKE_CASE		     =       feat_extract(
						    UpperCAmelCase__    ,    truncation=UpperCAmelCase__    ,    max_length=1_0_0_0    ,    padding="max_length"    ,    return_tensors="np"     )
						__SCREAMING_SNAKE_CASE		     =       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
						self._check_zero_mean_unit_variance(input_values[1]     )
						self._check_zero_mean_unit_variance(input_values[2]     )




	def 						UpperCAmelCase_   (				self    :       str     ) ->			Any:
						__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(8_0_0    ,    1_4_0_0    ,    2_0_0     )]
						__SCREAMING_SNAKE_CASE		     =       feat_extract(
						    UpperCAmelCase__    ,    truncation=UpperCAmelCase__    ,    max_length=1_0_0_0    ,    padding="longest"    ,    return_tensors="np"     )
						__SCREAMING_SNAKE_CASE		     =       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
						self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
						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, 1_0_0_0)     )

						__SCREAMING_SNAKE_CASE		     =       [floats_list((1, x)     )[0] for x in range(8_0_0    ,    1_4_0_0    ,    2_0_0     )]
						__SCREAMING_SNAKE_CASE		     =       feat_extract(
						    UpperCAmelCase__    ,    truncation=UpperCAmelCase__    ,    max_length=2_0_0_0    ,    padding="longest"    ,    return_tensors="np"     )
						__SCREAMING_SNAKE_CASE		     =       processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :8_0_0]     )
						self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]     )
						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, 1_2_0_0)     )




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			int:
						import torch

						__SCREAMING_SNAKE_CASE		     =       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
						__SCREAMING_SNAKE_CASE		     =       np.random.rand(1_0_0     ).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     )




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			Optional[int]:
						# this test makes sure that models that are using
						# group norm don't have their feature extractor return the
						# attention_mask
						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
											__SCREAMING_SNAKE_CASE		     =       WavaVecaConfig.from_pretrained(UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__     )

											# only "layer" feature extraction norm should make use of
											# attention_mask
											self.assertEqual(feat_extract.return_attention_mask    ,    config.feat_extract_norm == "layer"     )




"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	@abstractmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						raise NotImplementedError()




	@abstractmethod
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			List[str]:
						raise NotImplementedError()
