
'''simple docstring'''






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


UpperCAmelCase_							: Dict =							logging.get_logger(__name__)

UpperCAmelCase_							: Any =							{
    '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 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'trocr'
    lowerCAmelCase_					     =		['past_key_values']
    lowerCAmelCase_					     =		{
        'num_attention_heads': 'decoder_attention_heads',
        'hidden_size': 'd_model',
        'num_hidden_layers': 'decoder_layers',
    }



    def __init__(						self   :							List[Any],__A   :							int=5_0_2_6_5,__A   :							Union[str, Any]=1_0_2_4,__A   :							Any=1_2,__A   :							Any=1_6,__A   :							List[str]=4_0_9_6,__A   :							Tuple="gelu",__A   :							List[str]=5_1_2,__A   :							Tuple=0.1,__A   :							str=0.0,__A   :							Tuple=0.0,__A   :							List[Any]=2,__A   :							List[Any]=0.02,__A   :							Union[str, Any]=0.0,__A   :							Optional[int]=True,__A   :							List[Any]=False,__A   :							int=True,__A   :							Optional[int]=True,__A   :							List[str]=1,__A   :							Tuple=0,__A   :							Tuple=2,**__A   :							List[Any],):
          _lowerCamelCase					: Optional[Any]		    = vocab_size
          _lowerCamelCase					: str		    = d_model
          _lowerCamelCase					: Dict		    = decoder_layers
          _lowerCamelCase					: Dict		    = decoder_attention_heads
          _lowerCamelCase					: Union[str, Any]		    = decoder_ffn_dim
          _lowerCamelCase					: int		    = activation_function
          _lowerCamelCase					: Union[str, Any]		    = max_position_embeddings
          _lowerCamelCase					: Optional[int]		    = dropout
          _lowerCamelCase					: Any		    = attention_dropout
          _lowerCamelCase					: Tuple		    = activation_dropout
          _lowerCamelCase					: Dict		    = init_std
          _lowerCamelCase					: str		    = decoder_layerdrop
          _lowerCamelCase					: Optional[int]		    = use_cache
          _lowerCamelCase					: Any		    = scale_embedding
          _lowerCamelCase					: int		    = use_learned_position_embeddings
          _lowerCamelCase					: Optional[int]		    = layernorm_embedding

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






'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



