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     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[str]=1.0						,__lowercase						:			Optional[int]=None						,__lowercase						:			Optional[int]=None     ):
		'''simple docstring'''




		if rng is None:
				A_		:    Optional[Any]         =				global_rng

		A_		:    Tuple         =				[]
		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		,     lowercase		,     lowercase=7		,     lowercase=4_0_0		,     lowercase=2_0_0_0		,     lowercase=1		,     lowercase=0.0		,     lowercase=1_6_0_0_0		,     lowercase=True		,     lowercase=True		,     ):



						"""simple docstring"""





						A_		:    str         =				parent
						A_		:    int         =				batch_size
						A_		:    Tuple         =				min_seq_length
						A_		:    Dict         =				max_seq_length
						A_		:    List[str]         =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						A_		:    Optional[Any]         =				feature_size
						A_		:    Optional[Any]         =				padding_value
						A_		:    int         =				sampling_rate
						A_		:    Optional[Any]         =				return_attention_mask
						A_		:    int         =				do_normalize
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						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		,     lowercase=False		,     lowercase=False ):



						"""simple docstring"""





						def _flatten(lowercase ):
								return list(itertools.chain(*lowercase ) )

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

						return speech_inputs





class UpperCAmelCase						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				lowerCamelCase_    = WavaVecaFeatureExtractor
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				WavaVecaFeatureExtractionTester(self )
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						self.assertTrue(np.all(np.mean(lowercase		,     axis=0 ) < 1E-3 ) )
						self.assertTrue(np.all(np.abs(np.var(lowercase		,     axis=0 ) - 1 ) < 1E-3 ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						# create three inputs of length 800, 1000, and 1200
						A_		:    Dict         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    Dict         =				[np.asarray(lowercase ) for speech_input in speech_inputs]

						# Test not batched input
						A_		:    Dict         =				feat_extract(speech_inputs[0]		,     return_tensors='np' ).input_values
						A_		:    Optional[Any]         =				feat_extract(np_speech_inputs[0]		,     return_tensors='np' ).input_values
						self.assertTrue(np.allclose(lowercase		,     lowercase		,     atol=1E-3 ) )

						# Test batched
						A_		:    List[Any]         =				feat_extract(lowercase		,     return_tensors='np' ).input_values
						A_		:    Optional[int]         =				feat_extract(lowercase		,     return_tensors='np' ).input_values
						for enc_seq_a, enc_seq_a in zip(lowercase		,     lowercase ):
								self.assertTrue(np.allclose(lowercase		,     lowercase		,     atol=1E-3 ) )

						# Test 2-D numpy arrays are batched.
						A_		:    Union[str, Any]         =				[floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						A_		:    int         =				np.asarray(lowercase )
						A_		:    str         =				feat_extract(lowercase		,     return_tensors='np' ).input_values
						A_		:    List[Any]         =				feat_extract(lowercase		,     return_tensors='np' ).input_values
						for enc_seq_a, enc_seq_a in zip(lowercase		,     lowercase ):
								self.assertTrue(np.allclose(lowercase		,     lowercase		,     atol=1E-3 ) )
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    List[str]         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]

						A_		:    List[str]         =				['longest', 'max_length', 'do_not_pad']
						A_		:    Any         =				[None, 1_6_0_0, None]
						for max_length, padding in zip(lowercase		,     lowercase ):
								A_		:    Optional[Any]         =				feat_extract(lowercase		,     padding=lowercase		,     max_length=lowercase		,     return_tensors='np' )
								A_		:    int         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    Optional[Any]         =				range(8_0_0		,     1_4_0_0		,     2_0_0 )
						A_		:    str         =				[floats_list((1, x) )[0] for x in lengths]

						A_		:    Optional[Any]         =				['longest', 'max_length', 'do_not_pad']
						A_		:    List[Any]         =				[None, 1_6_0_0, None]

						for max_length, padding in zip(lowercase		,     lowercase ):
								A_		:    Union[str, Any]         =				feat_extract(lowercase		,     max_length=lowercase		,     padding=lowercase )
								A_		:    str         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    List[str]         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    List[str]         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     max_length=1_0_0_0		,     padding='max_length'		,     return_tensors='np' )
						A_		:    int         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    Union[str, Any]         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    str         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     max_length=1_0_0_0		,     padding='longest'		,     return_tensors='np' )
						A_		:    Optional[int]         =				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) )

						A_		:    Dict         =				[floats_list((1, x) )[0] for x in range(8_0_0		,     1_4_0_0		,     2_0_0 )]
						A_		:    Dict         =				feat_extract(
						    lowercase		,     truncation=lowercase		,     max_length=2_0_0_0		,     padding='longest'		,     return_tensors='np' )
						A_		:    List[str]         =				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   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						import torch

						A_		:    Tuple         =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
						A_		:    Optional[Any]         =				np.random.rand(1_0_0 ).astype(np.floataa )
						A_		:    Any         =				np_speech_inputs.tolist()

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







				@slow
				@require_torch
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
								A_		:    Optional[int]         =				WavaVecaConfig.from_pretrained(lowercase )
								A_		:    int         =				WavaVecaFeatureExtractor.from_pretrained(lowercase )

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







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_UpperCAmelCase            =		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			str     ):
		'''simple docstring'''




		return (abs(source - target     ) / target) < 0.01




@pytest.mark.integration
def     UpperCamelCase						(	__lowercase						:			Dict     ):
		'''simple docstring'''




		A_		:    Optional[Any]         =				_TestCommandArgs(dataset=__lowercase						,all_configs=__lowercase						,save_infos=__lowercase     )
		A_		:    List[Any]         =				TestCommand(*__lowercase     )
		test_command.run()
		A_		:    Any         =				os.path.join(__lowercase						,'README.md'     )
		assert os.path.exists(__lowercase     )
		A_		:    Tuple         =				DatasetInfosDict.from_directory(__lowercase     )
		A_		:    Any         =				DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'     )     ),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']     )     ),
		                    'langs': Sequence(Value('string'     )     ),
		                    'spans': Sequence(Value('string'     )     ),
		                }     )						,splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_35_15_63,
		                    'num_examples': 1_00_00,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 23_84_18,
		                    'num_examples': 10_00,
		                },
		            ]						,download_size=3_94_06_80						,dataset_size=2_58_99_81						,)
		    }     )
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				A_       ,      A_		:    Union[str, Any]         =				getattr(dataset_infos['default']						,__lowercase     ), getattr(expected_dataset_infos['default']						,__lowercase     )
				if key == "num_bytes":
						assert is_apercent_close(__lowercase						,__lowercase     )
				elif key == "splits":
						assert list(__lowercase     ) == list(__lowercase     )
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,expected[split].num_bytes     )
				else:
						result == expected






