"""simple docstring"""







import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
	from transformers import WhisperFeatureExtractor

if is_torch_available():
	import torch

_a     :     List[Any]= random.Random()
def   __UpperCAmelCase				(    UpperCAmelCase_    :				List[str]			,							UpperCAmelCase_    :				str=1.0			,							UpperCAmelCase_    :				Union[str, Any]=None			,							UpperCAmelCase_    :				Optional[int]=None  )							->			List[Any]:

	'''simple docstring'''





	if rng is None:
		__snake_case			:							List[Any]      		= global_rng

	__snake_case			:							Union[str, Any]      		= []
	for batch_idx in range(shape[0]  ):
		values.append([]  )
		for _ in range(shape[1]  ):
			values[-1].append(rng.random() * scale  )

	return values

@require_torch
@require_torchaudio
class 		UpperCamelCase			(    unittest.TestCase						):



				def __init__(self				:      Optional[Any]	,  _A				:      Tuple	,  _A				:      Union[str, Any]=7	,  _A				:      str=4_00	,  _A				:      Union[str, Any]=20_00	,  _A				:      Tuple=10	,  _A				:      Dict=1_60	,  _A				:      List[Any]=8	,  _A				:      Tuple=0.0	,  _A				:      Dict=40_00	,  _A				:      List[str]=False	,  _A				:      Union[str, Any]=True	,  )    ->	Optional[Any]:
					__snake_case			:							Tuple      		= parent
					__snake_case			:							Dict      		= batch_size
					__snake_case			:							Tuple      		= min_seq_length
					__snake_case			:							List[Any]      		= max_seq_length
					__snake_case			:							List[str]      		= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__snake_case			:							Any      		= padding_value
					__snake_case			:							List[Any]      		= sampling_rate
					__snake_case			:							Optional[int]      		= return_attention_mask
					__snake_case			:							Union[str, Any]      		= do_normalize
					__snake_case			:							int      		= feature_size
					__snake_case			:							Dict      		= chunk_length
					__snake_case			:							str      		= hop_length



				def 					_lowercase     (self				:      Optional[Any])    ->	int:
					return {
					    "feature_size": self.feature_size,
					    "hop_length": self.hop_length,
					    "chunk_length": self.chunk_length,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}






				def 					_lowercase     (self				:      Dict	,  _A				:      List[str]=False	,  _A				:      Tuple=False)    ->	Any:
					def _flatten(_A				:      Dict):
						return list(itertools.chain(*lowercase_))

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







@require_torch
@require_torchaudio
class 		UpperCamelCase			(    a_      ,	unittest.TestCase						):
				UpperCAmelCase				:      Union[str, Any]    =		WhisperFeatureExtractor if is_speech_available() else None



				def 					_lowercase     (self				:      List[Any])    ->	Optional[int]:
					__snake_case			:							Union[str, Any]      		= WhisperFeatureExtractionTester(self)



				def 					_lowercase     (self				:      int)    ->	int:
					__snake_case			:							Union[str, Any]      		= self.feature_extraction_class(**self.feat_extract_dict)

					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case			:							Union[str, Any]      		= feat_extract_first.save_pretrained(lowercase_)[0]
						check_json_file_has_correct_format(lowercase_)
						__snake_case			:							List[str]      		= self.feature_extraction_class.from_pretrained(lowercase_)

					__snake_case			:							Optional[Any]      		= feat_extract_first.to_dict()
					__snake_case			:							int      		= feat_extract_second.to_dict()
					__snake_case			:							Tuple      		= feat_extract_first.mel_filters
					__snake_case			:							List[str]      		= feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowercase_	,  lowercase_))
					self.assertEqual(lowercase_	,  lowercase_)



				def 					_lowercase     (self				:      Union[str, Any])    ->	str:
					__snake_case			:							List[str]      		= self.feature_extraction_class(**self.feat_extract_dict)

					with tempfile.TemporaryDirectory() as tmpdirname:
						__snake_case			:							str      		= os.path.join(lowercase_	,  'feat_extract.json')
						feat_extract_first.to_json_file(lowercase_)
						__snake_case			:							List[Any]      		= self.feature_extraction_class.from_json_file(lowercase_)

					__snake_case			:							str      		= feat_extract_first.to_dict()
					__snake_case			:							Optional[Any]      		= feat_extract_second.to_dict()
					__snake_case			:							List[str]      		= feat_extract_first.mel_filters
					__snake_case			:							Tuple      		= feat_extract_second.mel_filters
					self.assertTrue(np.allclose(lowercase_	,  lowercase_))
					self.assertEqual(lowercase_	,  lowercase_)



				def 					_lowercase     (self				:      Optional[int])    ->	List[Any]:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					__snake_case			:							Dict      		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					# create three inputs of length 800, 1000, and 1200
					__snake_case			:							List[str]      		= [floats_list((1, x))[0] for x in range(8_00	,  14_00	,  2_00)]
					__snake_case			:							Tuple      		= [np.asarray(lowercase_) for speech_input in speech_inputs]

					# Test feature size
					__snake_case			:							List[str]      		= feature_extractor(lowercase_	,  padding='max_length'	,  return_tensors='np').input_features
					self.assertTrue(input_features.ndim == 3)
					self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames)
					self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size)

					# Test not batched input
					__snake_case			:							str      		= feature_extractor(speech_inputs[0]	,  return_tensors='np').input_features
					__snake_case			:							str      		= feature_extractor(np_speech_inputs[0]	,  return_tensors='np').input_features
					self.assertTrue(np.allclose(lowercase_	,  lowercase_	,  atol=1E-3))

					# Test batched
					__snake_case			:							str      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					__snake_case			:							List[Any]      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					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.
					__snake_case			:							str      		= [floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
					__snake_case			:							str      		= np.asarray(lowercase_)
					__snake_case			:							Any      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					__snake_case			:							List[str]      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					for enc_seq_a, enc_seq_a in zip(lowercase_	,  lowercase_):
						self.assertTrue(np.allclose(lowercase_	,  lowercase_	,  atol=1E-3))

					# Test truncation required
					__snake_case			:							Tuple      		= [floats_list((1, x))[0] for x in range(2_00	,  (feature_extractor.n_samples + 5_00)	,  2_00)]
					__snake_case			:							str      		= [np.asarray(lowercase_) for speech_input in speech_inputs]

					__snake_case			:							str      		= [x[: feature_extractor.n_samples] for x in speech_inputs]
					__snake_case			:							List[Any]      		= [np.asarray(lowercase_) for speech_input in speech_inputs_truncated]

					__snake_case			:							List[str]      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					__snake_case			:							Optional[Any]      		= feature_extractor(lowercase_	,  return_tensors='np').input_features
					for enc_seq_a, enc_seq_a in zip(lowercase_	,  lowercase_):
						self.assertTrue(np.allclose(lowercase_	,  lowercase_	,  atol=1E-3))



				def 					_lowercase     (self				:      Tuple)    ->	Optional[Any]:
					import torch

					__snake_case			:							str      		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					__snake_case			:							str      		= np.random.rand(1_00	,  32).astype(np.floataa)
					__snake_case			:							Optional[Any]      		= np_speech_inputs.tolist()

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



				def 					_lowercase     (self				:      List[str]	,  _A				:      Optional[Any])    ->	int:
					__snake_case			:							Optional[int]      		= load_dataset('hf-internal-testing/librispeech_asr_dummy'	,  'clean'	,  split='validation')
					# automatic decoding with librispeech
					__snake_case			:							int      		= ds.sort('id').select(range(lowercase_))[:num_samples]['''audio''']

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



				def 					_lowercase     (self				:      Optional[Any])    ->	Dict:
					# fmt: off
					__snake_case			:							List[Any]      		= torch.tensor(
					    [
					        0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
					        0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
					        0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
					        -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
					    ])
					# fmt: on

					__snake_case			:							Union[str, Any]      		= self._load_datasamples(1)
					__snake_case			:							List[Any]      		= WhisperFeatureExtractor()
					__snake_case			:							Union[str, Any]      		= feature_extractor(lowercase_	,  return_tensors='pt').input_features
					self.assertEqual(input_features.shape	,  (1, 80, 30_00))
					self.assertTrue(torch.allclose(input_features[0, 0, :30]	,  lowercase_	,  atol=1E-4))






				def 					_lowercase     (self				:      List[Any])    ->	str:
					__snake_case			:							int      		= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
					__snake_case			:							List[Any]      		= self._load_datasamples(1)[0]
					__snake_case			:							Union[str, Any]      		= ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
					__snake_case			:							List[str]      		= feat_extract.zero_mean_unit_var_norm([audio]	,  attention_mask=lowercase_)[0]

					self.assertTrue(np.all(np.mean(lowercase_) < 1E-3))
					self.assertTrue(np.all(np.abs(np.var(lowercase_) - 1) < 1E-3))








"""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)




