





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


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

_UpperCAmelCase       : Tuple			     =       {
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}

class      lowerCAmelCase							(      snake_case__    ):
 UpperCAmelCase__									=	'''trocr'''
 UpperCAmelCase__									=	['''past_key_values''']
 UpperCAmelCase__									=	{
     '''num_attention_heads''': '''decoder_attention_heads''',
     '''hidden_size''': '''d_model''',
     '''num_hidden_layers''': '''decoder_layers''',
 }
 def __init__(   self						:   Any    ,  UpperCAmelCase						:   Any=50265    ,  UpperCAmelCase						:   int=1024    ,  UpperCAmelCase						:   Optional[int]=12    ,  UpperCAmelCase						:   List[Any]=16    ,  UpperCAmelCase						:   Dict=4096    ,  UpperCAmelCase						:   int="gelu"    ,  UpperCAmelCase						:   Optional[int]=512    ,  UpperCAmelCase						:   List[str]=0.1    ,  UpperCAmelCase						:   Tuple=0.0    ,  UpperCAmelCase						:   Any=0.0    ,  UpperCAmelCase						:   Dict=2    ,  UpperCAmelCase						:   Any=0.0_2    ,  UpperCAmelCase						:   List[Any]=0.0    ,  UpperCAmelCase						:   Union[str, Any]=True    ,  UpperCAmelCase						:   Dict=False    ,  UpperCAmelCase						:   Tuple=True    ,  UpperCAmelCase						:   Dict=True    ,  UpperCAmelCase						:   List[str]=1    ,  UpperCAmelCase						:   Any=0    ,  UpperCAmelCase						:   Dict=2    ,  **UpperCAmelCase						:   Dict    ,  )    ->			Any:
     lowerCamelCase__					:   str      				=	vocab_size
     lowerCamelCase__					:   Union[str, Any]      				=	d_model
     lowerCamelCase__					:   str      				=	decoder_layers
     lowerCamelCase__					:   Dict      				=	decoder_attention_heads
     lowerCamelCase__					:   Optional[int]      				=	decoder_ffn_dim
     lowerCamelCase__					:   Tuple      				=	activation_function
     lowerCamelCase__					:   Optional[int]      				=	max_position_embeddings
     lowerCamelCase__					:   Tuple      				=	dropout
     lowerCamelCase__					:   int      				=	attention_dropout
     lowerCamelCase__					:   Union[str, Any]      				=	activation_dropout
     lowerCamelCase__					:   str      				=	init_std
     lowerCamelCase__					:   List[Any]      				=	decoder_layerdrop
     lowerCamelCase__					:   List[Any]      				=	use_cache
     lowerCamelCase__					:   Any      				=	scale_embedding
     lowerCamelCase__					:   Optional[int]      				=	use_learned_position_embeddings
     lowerCamelCase__					:   Tuple      				=	layernorm_embedding

     super().__init__(
         pad_token_id=_A    ,  bos_token_id=_A    ,  eos_token_id=_A    ,  decoder_start_token_id=_A    ,  **_A    ,  )












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


if __name__ == "__main__":
    fire.Fire(minify)





