

import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A     :					List[str]     = logging.get_logger(__name__)

__A     :					Dict     = {
    '''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}


class        _SCREAMING_SNAKE_CASE	(   a__):
						_UpperCamelCase:Optional[int]     =						"""data2vec-audio"""







						def __init__(       self			,		_SCREAMING_SNAKE_CASE=32			,		_SCREAMING_SNAKE_CASE=768			,		_SCREAMING_SNAKE_CASE=12			,		_SCREAMING_SNAKE_CASE=12			,		_SCREAMING_SNAKE_CASE=3072			,		_SCREAMING_SNAKE_CASE="gelu"			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.1			,		_SCREAMING_SNAKE_CASE=0.0_2			,		_SCREAMING_SNAKE_CASE=1E-5			,		_SCREAMING_SNAKE_CASE="gelu"			,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)			,		_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)			,		_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=16			,		_SCREAMING_SNAKE_CASE=19			,		_SCREAMING_SNAKE_CASE=5			,		_SCREAMING_SNAKE_CASE=0.0_5			,		_SCREAMING_SNAKE_CASE=10			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=0.0			,		_SCREAMING_SNAKE_CASE=10			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE="sum"			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=256			,		_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1500)			,		_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)			,		_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)			,		_SCREAMING_SNAKE_CASE=512			,		_SCREAMING_SNAKE_CASE=0			,		_SCREAMING_SNAKE_CASE=1			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=False			,		_SCREAMING_SNAKE_CASE=3			,		_SCREAMING_SNAKE_CASE=2			,		_SCREAMING_SNAKE_CASE=3			,		_SCREAMING_SNAKE_CASE=None			,		**_SCREAMING_SNAKE_CASE			,		)->				Tuple:
													super().__init__(**lowerCAmelCase__			,		pad_token_id=lowerCAmelCase__			,		bos_token_id=lowerCAmelCase__			,		eos_token_id=lowerCAmelCase__				)
													lowerCamelCase_				     =hidden_size
													lowerCamelCase_				     =feat_extract_activation
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =conv_bias
													lowerCamelCase_				     =num_conv_pos_embeddings
													lowerCamelCase_				     =num_conv_pos_embedding_groups
													lowerCamelCase_				     =conv_pos_kernel_size
													lowerCamelCase_				     =len(self.conv_dim				)
													lowerCamelCase_				     =num_hidden_layers
													lowerCamelCase_				     =intermediate_size
													lowerCamelCase_				     =hidden_act
													lowerCamelCase_				     =num_attention_heads
													lowerCamelCase_				     =hidden_dropout
													lowerCamelCase_				     =attention_dropout
													lowerCamelCase_				     =activation_dropout
													lowerCamelCase_				     =feat_proj_dropout
													lowerCamelCase_				     =final_dropout
													lowerCamelCase_				     =layerdrop
													lowerCamelCase_				     =layer_norm_eps
													lowerCamelCase_				     =initializer_range
													lowerCamelCase_				     =vocab_size
													lowerCamelCase_				     =use_weighted_layer_sum

													if (
													    (len(self.conv_stride				) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
													    or (len(self.conv_dim				) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
																				    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
																				    f' {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,'
																				    f' `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'				)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													lowerCamelCase_				     =mask_time_prob
													lowerCamelCase_				     =mask_time_length
													lowerCamelCase_				     =mask_time_min_masks
													lowerCamelCase_				     =mask_feature_prob
													lowerCamelCase_				     =mask_feature_length
													lowerCamelCase_				     =mask_feature_min_masks

													# ctc loss
													lowerCamelCase_				     =ctc_loss_reduction
													lowerCamelCase_				     =ctc_zero_infinity

													# adapter
													lowerCamelCase_				     =add_adapter
													lowerCamelCase_				     =adapter_kernel_size
													lowerCamelCase_				     =adapter_stride
													lowerCamelCase_				     =num_adapter_layers
													lowerCamelCase_				     =output_hidden_size or hidden_size

													# SequenceClassification-specific parameter. Feel free to ignore for other classes.
													lowerCamelCase_				     =classifier_proj_size

													# XVector-specific parameters. Feel free to ignore for other classes.
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =list(lowerCAmelCase__				)
													lowerCamelCase_				     =xvector_output_dim







						@property
						def     _snake_case							(       self				)->				Union[str, Any]:
													return math.prod(self.conv_stride				)







"""simple docstring"""




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
)
a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






	a      :		Dict	=				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."					)
	a      :		Dict	=				parser.parse_args()

	logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""					)
	if args.tokenizer_type == "bert":
		a      :		Optional[Any]	=				BertTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		str	=				tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
		a      :		List[str]	=				tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
	elif args.tokenizer_type == "roberta":
		a      :		Tuple	=				RobertaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Union[str, Any]	=				tokenizer.special_tokens_map["cls_token"]  # `<s>`
		a      :		str	=				tokenizer.special_tokens_map["sep_token"]  # `</s>`
	elif args.tokenizer_type == "gpt2":
		a      :		List[Any]	=				GPTaTokenizer.from_pretrained(args.tokenizer_name					)
		a      :		Optional[int]	=				tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
		a      :		List[Any]	=				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:
		a      :		List[Any]	=				fp.readlines()

	logger.info("Start encoding"					)
	logger.info(F"""{len(_lowercase					)} examples to process."""					)

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

		iter += 1
		if iter % interval == 0:
			a      :		Optional[Any]	=				time.time()
			logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""					)
			a      :		Optional[Any]	=				time.time()
	logger.info("Finished binarization"					)
	logger.info(F"""{len(_lowercase					)} examples processed."""					)

	a      :		Optional[int]	=				F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
	a      :		Tuple	=				tokenizer.vocab_size
	if vocab_size < (1 << 16):
		a      :		Optional[int]	=				[np.uintaa(_lowercase					) for d in rslt]
	else:
		a      :		Optional[Any]	=				[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()


