





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


UpperCAmelCase_      							=     random.Random()





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Optional[int]=1.0       ,    UpperCamelCase__			:       Any=None       ,    UpperCamelCase__			:       str=None						)			-> int:



					'''simple docstring'''




					if rng is None:
										_snake_case									=				global_rng

					_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					):




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=7				,						lowerCAmelCase_=400				,						lowerCAmelCase_=2000				,						lowerCAmelCase_=1				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=1_6000				,						lowerCAmelCase_=True				,						lowerCAmelCase_=True				,						)		->							int:
										_snake_case									=				parent
										_snake_case									=				batch_size
										_snake_case									=				min_seq_length
										_snake_case									=				max_seq_length
										_snake_case									=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										_snake_case									=				feature_size
										_snake_case									=				padding_value
										_snake_case									=				sampling_rate
										_snake_case									=				return_attention_mask
										_snake_case									=				do_normalize




					def 	lowerCAmelCase			(		self  )		->							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				,						lowerCAmelCase_=False				,						lowerCAmelCase_=False  )		->							Union[str, Any]:
										def _flatten(lowerCAmelCase_  ):
															return list(itertools.chain(*lowerCAmelCase_  )  )

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

										return speech_inputs





class 			UpperCamelCase_		(  _lowerCamelCase							,  unittest.TestCase					):
					lowerCAmelCase_							=	WavaVecaFeatureExtractor




					def 	lowerCAmelCase			(		self  )		->							Tuple:
										_snake_case									=				WavaVecaFeatureExtractionTester(self  )




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

										# Test not batched input
										_snake_case									=				feat_extract(speech_inputs[0]				,						return_tensors='np'  ).input_values
										_snake_case									=				feat_extract(np_speech_inputs[0]				,						return_tensors='np'  ).input_values
										self.assertTrue(np.allclose(lowerCAmelCase_				,						lowerCAmelCase_				,						atol=1E-3  )  )

										# Test batched
										_snake_case									=				feat_extract(lowerCAmelCase_				,						return_tensors='np'  ).input_values
										_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  )  )

										# Test 2-D numpy arrays are batched.
										_snake_case									=				[floats_list((1, x)  )[0] for x in (800, 800, 800)]
										_snake_case									=				np.asarray(lowerCAmelCase_  )
										_snake_case									=				feat_extract(lowerCAmelCase_				,						return_tensors='np'  ).input_values
										_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 	lowerCAmelCase			(		self  )		->							Any:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
										_snake_case									=				[floats_list((1, x)  )[0] for x in range(800				,						1400				,						200  )]

										_snake_case									=				['longest', 'max_length', 'do_not_pad']
										_snake_case									=				[None, 1600, None]
										for max_length, padding in zip(lowerCAmelCase_				,						lowerCAmelCase_  ):
															_snake_case									=				feat_extract(lowerCAmelCase_				,						padding=lowerCAmelCase_				,						max_length=lowerCAmelCase_				,						return_tensors='np'  )
															_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 	lowerCAmelCase			(		self  )		->							List[Any]:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
										_snake_case									=				range(800				,						1400				,						200  )
										_snake_case									=				[floats_list((1, x)  )[0] for x in lengths]

										_snake_case									=				['longest', 'max_length', 'do_not_pad']
										_snake_case									=				[None, 1600, None]

										for max_length, padding in zip(lowerCAmelCase_				,						lowerCAmelCase_  ):
															_snake_case									=				feat_extract(lowerCAmelCase_				,						max_length=lowerCAmelCase_				,						padding=lowerCAmelCase_  )
															_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 	lowerCAmelCase			(		self  )		->							List[str]:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
										_snake_case									=				[floats_list((1, x)  )[0] for x in range(800				,						1400				,						200  )]
										_snake_case									=				feat_extract(
										    lowerCAmelCase_				,						truncation=lowerCAmelCase_				,						max_length=1000				,						padding='max_length'				,						return_tensors='np'  )
										_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 	lowerCAmelCase			(		self  )		->							Optional[int]:
										_snake_case									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
										_snake_case									=				[floats_list((1, x)  )[0] for x in range(800				,						1400				,						200  )]
										_snake_case									=				feat_extract(
										    lowerCAmelCase_				,						truncation=lowerCAmelCase_				,						max_length=1000				,						padding='longest'				,						return_tensors='np'  )
										_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)  )

										_snake_case									=				[floats_list((1, x)  )[0] for x in range(800				,						1400				,						200  )]
										_snake_case									=				feat_extract(
										    lowerCAmelCase_				,						truncation=lowerCAmelCase_				,						max_length=2000				,						padding='longest'				,						return_tensors='np'  )
										_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)  )




					@require_torch
					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										import torch

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

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






					@slow
					@require_torch
					def 	lowerCAmelCase			(		self  )		->							Any:
										# 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:
															_snake_case									=				WavaVecaConfig.from_pretrained(lowerCAmelCase_  )
															_snake_case									=				WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase_  )

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










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_      							=     {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_byta import ByTaTokenizer
else:
					import sys

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



