

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case							:     Optional[int] 						=	logging.get_logger(__name__)

__snake_case							:     List[Any] 						=	{
    """google/electra-small-generator""": """https://huggingface.co/google/electra-small-generator/resolve/main/config.json""",
    """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/config.json""",
    """google/electra-large-generator""": """https://huggingface.co/google/electra-large-generator/resolve/main/config.json""",
    """google/electra-small-discriminator""": (
        """https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json"""
    ),
    """google/electra-base-discriminator""": (
        """https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json"""
    ),
    """google/electra-large-discriminator""": (
        """https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json"""
    ),
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Optional[int]   			=	'''electra'''





						def __init__(  self       ,     _lowercase=30_522       ,     _lowercase=128       ,     _lowercase=256       ,     _lowercase=12       ,     _lowercase=4       ,     _lowercase=1_024       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0.1       ,     _lowercase=512       ,     _lowercase=2       ,     _lowercase=0.0_2       ,     _lowercase=1e-12       ,     _lowercase="first"       ,     _lowercase=True       ,     _lowercase="gelu"       ,     _lowercase=0.1       ,     _lowercase=0       ,     _lowercase="absolute"       ,     _lowercase=True       ,     _lowercase=None       ,     **_lowercase       ,     )      ->				Optional[int]:
									super().__init__(pad_token_id=_lowercase       ,     **_lowercase  )

									a_		:		Dict				    =				vocab_size
									a_		:		Tuple				    =				embedding_size
									a_		:		Dict				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		List[Any]				    =				num_attention_heads
									a_		:		Any				    =				intermediate_size
									a_		:		Union[str, Any]				    =				hidden_act
									a_		:		List[Any]				    =				hidden_dropout_prob
									a_		:		Optional[Any]				    =				attention_probs_dropout_prob
									a_		:		Dict				    =				max_position_embeddings
									a_		:		str				    =				type_vocab_size
									a_		:		Optional[Any]				    =				initializer_range
									a_		:		Any				    =				layer_norm_eps

									a_		:		Union[str, Any]				    =				summary_type
									a_		:		Dict				    =				summary_use_proj
									a_		:		Dict				    =				summary_activation
									a_		:		Dict				    =				summary_last_dropout
									a_		:		Tuple				    =				position_embedding_type
									a_		:		List[str]				    =				use_cache
									a_		:		Optional[int]				    =				classifier_dropout







class    A__(a_    ):
						"""simple docstring"""






						@property
						def 	UpperCamelCase__			(  self  )      ->				Mapping[str, Mapping[int, str]]:
									if self.task == "multiple-choice":
												a_		:		Union[str, Any]				    =				{0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
												a_		:		str				    =				{0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""input_ids""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									        ("""token_type_ids""", dynamic_axis),
									    ]  )





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
	for line in f_p:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	len(tokenizer.tokenize(token))

		# Token contains strange control characters like \x96 or \x95
		# Just filter out the complete line
		if current_subwords_len == 0:
			continue

		if (subword_len_counter + current_subwords_len) > max_len:
			print("""""")
			print(line)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


