

import sys

from transformers import AutoTokenizer


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

__snake_case							:			str      		= 0

__snake_case							:			List[str]      		= 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							:			List[str]      		= line.rstrip()

		if not line:
			print(line)
			__snake_case							:			Union[str, Any]      		= 0
			continue

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

		__snake_case							:			Optional[int]      		= 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							:			Union[str, Any]      		= current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


__snake_case							:			int      		= logging.get_logger(__name__)

__snake_case							:			Dict      		= {
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



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






						_A						:							Dict									= '''vit-hybrid'''





						def __init__(						self     ,			_lowercase=None     ,			_lowercase=768     ,			_lowercase=12     ,			_lowercase=12     ,			_lowercase=3_072     ,			_lowercase="gelu"     ,			_lowercase=0.0     ,			_lowercase=0.0     ,			_lowercase=0.0_2     ,			_lowercase=1e-12     ,			_lowercase=224     ,			_lowercase=1     ,			_lowercase=3     ,			_lowercase=[1, 1_024, 24, 24]     ,			_lowercase=True     ,			**_lowercase     ,			)    ->   Tuple:
									super().__init__(**_lowercase						)

									if backbone_config is None:
												logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""						)
												a_	:	Union[str, Any]          =   {
												    """global_padding""": """same""",
												    """layer_type""": """bottleneck""",
												    """depths""": [3, 4, 9],
												    """out_features""": ["""stage3"""],
												    """embedding_dynamic_padding""": True,
												}

									if isinstance(_lowercase     ,			_lowercase						):
												if "model_type" in backbone_config:
															a_	:	Any          =   CONFIG_MAPPING[backbone_config["""model_type"""]]
												else:
															logger.info(
															    """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""						)
															a_	:	Optional[int]          =   BitConfig
												a_	:	Dict          =   backbone_config_class(**_lowercase						)

									a_	:	List[str]          =   backbone_featmap_shape
									a_	:	int          =   backbone_config
									a_	:	Union[str, Any]          =   hidden_size
									a_	:	Optional[int]          =   num_hidden_layers
									a_	:	Dict          =   num_attention_heads
									a_	:	Optional[int]          =   intermediate_size
									a_	:	Dict          =   hidden_act
									a_	:	Optional[Any]          =   hidden_dropout_prob
									a_	:	List[str]          =   attention_probs_dropout_prob
									a_	:	Tuple          =   initializer_range
									a_	:	Any          =   layer_norm_eps
									a_	:	List[Any]          =   image_size
									a_	:	Optional[Any]          =   patch_size
									a_	:	Optional[Any]          =   num_channels
									a_	:	str          =   qkv_bias





						def  UpperCamelCase__				(						self						)    ->   Dict[str, any]:
									a_	:	List[str]          =   copy.deepcopy(self.__dict__						)
									a_	:	Optional[int]          =   self.backbone_config.to_dict()
									a_	:	List[str]          =   self.__class__.model_type
									return output


