

"""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


__UpperCamelCase  :      Dict							      =		random.Random()

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=1.0						,     A_=None						,     A_=None ):
	if rng is None:
		lowerCAmelCase__	:						Tuple  				=	global_rng

	lowerCAmelCase__	:						int  				=	[]
	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       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Tuple   ,lowercase_			:				List[str]=7   ,lowercase_			:				List[str]=4_0_0   ,lowercase_			:				Tuple=2_0_0_0   ,lowercase_			:				int=1   ,lowercase_			:				List[Any]=0.0   ,lowercase_			:				List[str]=1_6_0_0_0   ,lowercase_			:				Tuple=True   ,lowercase_			:				Dict=True   ,):
						lowerCAmelCase__	:						Any  				=	parent
						lowerCAmelCase__	:						Any  				=	batch_size
						lowerCAmelCase__	:						Dict  				=	min_seq_length
						lowerCAmelCase__	:						Union[str, Any]  				=	max_seq_length
						lowerCAmelCase__	:						Any  				=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						lowerCAmelCase__	:						List[Any]  				=	feature_size
						lowerCAmelCase__	:						int  				=	padding_value
						lowerCAmelCase__	:						List[Any]  				=	sampling_rate
						lowerCAmelCase__	:						List[Any]  				=	return_attention_mask
						lowerCAmelCase__	:						Any  				=	do_normalize







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						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   ,lowercase_			:				List[Any]=False   ,lowercase_			:				int=False		):
						def _flatten(lowercase_			:				int		):
							return list(itertools.chain(*lowercase_		)		)

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

						return speech_inputs







class 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       WavaVecaFeatureExtractor







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						int  				=	WavaVecaFeatureExtractionTester(self		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				Tuple		):
						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			:				List[str]		):
						# Tests that all call wrap to encode_plus and batch_encode_plus
						lowerCAmelCase__	:						Any  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						# create three inputs of length 800, 1000, and 1200
						lowerCAmelCase__	:						str  				=	[floats_list((1, x)		)[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0		)]
						lowerCAmelCase__	:						Union[str, Any]  				=	[np.asarray(lowercase_		) for speech_input in speech_inputs]

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

						# Test batched
						lowerCAmelCase__	:						Union[str, Any]  				=	feat_extract(lowercase_   ,return_tensors='''np'''		).input_values
						lowerCAmelCase__	:						Optional[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		)		)

						# Test 2-D numpy arrays are batched.
						lowerCAmelCase__	:						Union[str, Any]  				=	[floats_list((1, x)		)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						lowerCAmelCase__	:						int  				=	np.asarray(lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	feat_extract(lowercase_   ,return_tensors='''np'''		).input_values
						lowerCAmelCase__	:						Tuple  				=	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			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						str  				=	[floats_list((1, x)		)[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0		)]

						lowerCAmelCase__	:						Dict  				=	['''longest''', '''max_length''', '''do_not_pad''']
						lowerCAmelCase__	:						Dict  				=	[None, 1_6_0_0, None]
						for max_length, padding in zip(lowercase_   ,lowercase_		):
							lowerCAmelCase__	:						Tuple  				=	feat_extract(lowercase_   ,padding=lowercase_   ,max_length=lowercase_   ,return_tensors='''np'''		)
							lowerCAmelCase__	:						Tuple  				=	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			:				Optional[int]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						Union[str, Any]  				=	range(8_0_0   ,1_4_0_0   ,2_0_0		)
						lowerCAmelCase__	:						Optional[Any]  				=	[floats_list((1, x)		)[0] for x in lengths]

						lowerCAmelCase__	:						List[str]  				=	['''longest''', '''max_length''', '''do_not_pad''']
						lowerCAmelCase__	:						Dict  				=	[None, 1_6_0_0, None]

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

						lowerCAmelCase__	:						Any  				=	[floats_list((1, x)		)[0] for x in range(8_0_0   ,1_4_0_0   ,2_0_0		)]
						lowerCAmelCase__	:						List[Any]  				=	feat_extract(
						    lowercase_   ,truncation=lowercase_   ,max_length=2_0_0_0   ,padding='''longest'''   ,return_tensors='''np'''		)
						lowerCAmelCase__	:						Tuple  				=	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			:				Union[str, Any]		):
						import torch

						lowerCAmelCase__	:						Optional[int]  				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						lowerCAmelCase__	:						Dict  				=	np.random.rand(1_0_0		).astype(np.floataa		)
						lowerCAmelCase__	:						Dict  				=	np_speech_inputs.tolist()

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







					@slow
					@require_torch
					def        __lowerCAmelCase (       self			:				str		):
						# 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:
							lowerCAmelCase__	:						str  				=	WavaVecaConfig.from_pretrained(lowercase_		)
							lowerCAmelCase__	:						Union[str, Any]  				=	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'''		)







"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__UpperCamelCase  :      Any							      =		logging.getLogger(__name__)

if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		argparse.ArgumentParser(
							    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
							)
							parser.add_argument(
							    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
							)
							parser.add_argument(
							    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
							)
							parser.add_argument('''--vocab_size''', default=3_0_5_2_2, type=int)
							__UpperCamelCase  :      Any							      =		parser.parse_args()

							logger.info(F'''Loading data from {args.data_file}''')
							with open(args.data_file, '''rb''') as fp:
														__UpperCamelCase  :      List[Any]							      =		pickle.load(fp)

							logger.info('''Counting occurrences for MLM.''')
							__UpperCamelCase  :      Dict							      =		Counter()
							for tk_ids in data:
														counter.update(tk_ids)
							__UpperCamelCase  :      Optional[Any]							      =		[0] * args.vocab_size
							for k, v in counter.items():
														__UpperCamelCase  :      Dict							      =		v

							logger.info(F'''Dump to {args.token_counts_dump}''')
							with open(args.token_counts_dump, '''wb''') as handle:
														pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




