
import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

_lowerCamelCase	:    Optional[int]          =      random.Random()






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Any=1.0	, __lowercase       :     Dict=None	, __lowercase       :     Optional[int]=None			)			->  Union[str, Any]:
			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




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





	'''simple docstring'''



	def __init__(  self     :   Any      ,  lowercase     :   Optional[int]      ,  lowercase     :   Dict=7      ,  lowercase     :   str=400      ,  lowercase     :   Any=2_000      ,  lowercase     :   List[str]=24      ,  lowercase     :   Optional[Any]=24      ,  lowercase     :   Tuple=0.0      ,  lowercase     :   List[Any]=16_000      ,  lowercase     :   Optional[Any]=True      ,  lowercase     :   Optional[int]=True      ,  ):


				'''simple docstring'''


				_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          =      num_mel_bins
				_snake_case          =      padding_value
				_snake_case          =      sampling_rate
				_snake_case          =      return_attention_mask
				_snake_case          =      do_normalize



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				return {
				    "feature_size": self.feature_size,
				    "num_mel_bins": self.num_mel_bins,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				    "do_normalize": self.do_normalize,
				}





	def 			A							(  self     :   List[str]      ,  lowercase     :   str=False      ,  lowercase     :   Dict=False     ):


				'''simple docstring'''


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

				if equal_length:
							_snake_case          =      [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          =      [
							    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(lowercase     ) for x in speech_inputs]
				return speech_inputs




@require_torch
@require_torchaudio
class        SCREAMING_SNAKE_CASE__					(     _lowerCamelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Dict          = SpeechaTextFeatureExtractor if is_speech_available() else None



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      SpeechaTextFeatureExtractionTester(self     )



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   Optional[int]     ):


				'''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 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_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      ,  1_400      ,  200     )]
				_snake_case          =      [np.asarray(lowercase     ) for speech_input in speech_inputs]

				# Test feature size
				_snake_case          =      feature_extractor(lowercase      ,  padding=lowercase      ,  return_tensors='np'     ).input_features
				self.assertTrue(input_features.ndim == 3     )
				self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

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

				# Test batched
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				_snake_case          =      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          =      [floats_list((1, x)     )[0] for x in (800, 800, 800)]
				_snake_case          =      np.asarray(lowercase     )
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='np'     ).input_features
				_snake_case          =      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 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_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      ,  1_400      ,  200     )]

				_snake_case          =      ['longest', 'max_length', 'do_not_pad']
				_snake_case          =      [None, 16, None]
				for max_length, padding in zip(lowercase      ,  lowercase     ):
							_snake_case          =      feature_extractor(
							    lowercase      ,  padding=lowercase      ,  max_length=lowercase      ,  return_attention_mask=lowercase     )
							_snake_case          =      inputs.input_features
							_snake_case          =      inputs.attention_mask
							_snake_case          =      [np.sum(lowercase     ) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_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      ,  1_400      ,  200     )]

				_snake_case          =      ['longest', 'max_length', 'do_not_pad']
				_snake_case          =      [None, 16, None]
				for max_length, padding in zip(lowercase      ,  lowercase     ):
							_snake_case          =      feature_extractor(
							    lowercase      ,  max_length=lowercase      ,  padding=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase     )
							_snake_case          =      inputs.input_features
							_snake_case          =      inputs.attention_mask
							_snake_case          =      [np.sum(lowercase     ) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
							self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6     )
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
							self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6     )
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_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      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='max_length'      ,  max_length=4      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1]     )
				self._check_zero_mean_unit_variance(input_features[2]     )



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_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      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='longest'      ,  max_length=4      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
				self._check_zero_mean_unit_variance(input_features[2]     )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape      ,  (3, 4, 24)     )

				_snake_case          =      [floats_list((1, x)     )[0] for x in range(800      ,  1_400      ,  200     )]
				_snake_case          =      feature_extractor(
				    lowercase      ,  padding='longest'      ,  max_length=16      ,  truncation=lowercase      ,  return_tensors='np'      ,  return_attention_mask=lowercase      ,  )
				_snake_case          =      inputs.input_features
				_snake_case          =      inputs.attention_mask
				_snake_case          =      np.sum(attention_mask == 1      ,  axis=1     )

				self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
				self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
				self._check_zero_mean_unit_variance(input_features[2]     )

				# make sure that if max_length < longest -> then pad to max_length
				self.assertEqual(input_features.shape      ,  (3, 6, 24)     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				import torch

				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      np.random.rand(100      ,  32     ).astype(np.floataa     )
				_snake_case          =      np_speech_inputs.tolist()

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



	def 			A							(  self     :   Tuple      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				from datasets import load_dataset

				_snake_case          =      load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,  'clean'      ,  split='validation'     )
				# automatic decoding with librispeech
				_snake_case          =      ds.sort('id'     ).select(range(lowercase     )     )[:num_samples]['audio']

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





	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      np.array([
				    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
				    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
				    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
				]     )
				# fmt: on

				_snake_case          =      self._load_datasamples(1     )
				_snake_case          =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
				_snake_case          =      feature_extractor(lowercase      ,  return_tensors='pt'     ).input_features
				self.assertEquals(input_features.shape      ,  (1, 584, 24)     )
				self.assertTrue(np.allclose(input_features[0, 0, :30]      ,  lowercase      ,  atol=1E-4     )     )

import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


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






def 	a_		(	)			->  Any:
			_snake_case          =      argparse.ArgumentParser(
			    description='Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'			)
			parser.add_argument('--file_path'	, type=__lowercase	, default='data/dump.txt'	, help='The path to the data.'			)
			parser.add_argument('--tokenizer_type'	, type=__lowercase	, default='bert'	, choices=['bert', 'roberta', 'gpt2']			)
			parser.add_argument('--tokenizer_name'	, type=__lowercase	, default='bert-base-uncased'	, help='The tokenizer to use.'			)
			parser.add_argument('--dump_file'	, type=__lowercase	, default='data/dump'	, help='The dump file prefix.'			)
			_snake_case          =      parser.parse_args()

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''			)
			if args.tokenizer_type == "bert":
						_snake_case          =      BertTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_snake_case          =      RobertaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `<s>`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_snake_case          =      GPTaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_snake_case          =      tokenizer.special_tokens_map['eos_token']  # `<|endoftext|>`

			logger.info(f'''Loading text from {args.file_path}'''			)
			with open(args.file_path	, 'r'	, encoding='utf8'			) as fp:
						_snake_case          =      fp.readlines()

			logger.info('Start encoding'			)
			logger.info(f'''{len(__lowercase			)} examples to process.'''			)

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

						iter += 1
						if iter % interval == 0:
									_snake_case          =      time.time()
									logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''			)
									_snake_case          =      time.time()
			logger.info('Finished binarization'			)
			logger.info(f'''{len(__lowercase			)} examples processed.'''			)

			_snake_case          =      f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			_snake_case          =      tokenizer.vocab_size
			if vocab_size < (1 << 16):
						_snake_case          =      [np.uintaa(__lowercase			) for d in rslt]
			else:
						_snake_case          =      [np.intaa(__lowercase			) for d in rslt]
			random.shuffle(rslt_			)
			logger.info(f'''Dump to {dp_file}'''			)
			with open(__lowercase	, 'wb'			) as handle:
						pickle.dump(rslt_	, __lowercase	, protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
			main()