


"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable
def _A						(						lowercase       ,    lowercase       ,    lowercase       ,    lowercase = 1_00       ,    ):







    """simple docstring"""
    a 		=x_start
    a 		=fnc(lowercase		)
    a 		=0.0

    for _ in range(lowercase		):
        # Approximates curve as a sequence of linear lines and sums their length
        a 		=(x_end - x_start) / steps + xa
        a 		=fnc(lowercase		)
        length += math.hypot(xa - xa       ,    fxa - fxa		)

        # Increment step
        a 		=xa
        a 		=fxa

    return length


if __name__ == "__main__":
       def _A						(						lowercase		):







           """simple docstring"""
           return math.sin(10 * x		)

       print("""f(x) = sin(10 * x)""")
       print("""The length of the curve from x = -10 to x = 10 is:""")
       lowerCamelCase_   :   Tuple									=  1_0
       while i <= 1_0_0_0_0_0:
              print(F'With {i} steps: {line_length(f, -1_0, 1_0, i)}')
              i *= 1_0






'''simple docstring'''







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


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    List[str]      	=			{
    '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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

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