


'''simple docstring'''






import warnings

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


__UpperCAmelCase       =							logging.get_logger(__name__)

__UpperCAmelCase       =							{
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}




class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				"xlnet"
							lowercase__      :       Optional[Any]     						=				["mems"]
							lowercase__      :       Union[str, Any]     						=				{
							    "n_token": "vocab_size",  # Backward compatibility
							    "hidden_size": "d_model",
							    "num_attention_heads": "n_head",
							    "num_hidden_layers": "n_layer",
							}







							def __init__(		self ,   lowerCamelCase_=3_20_00 ,   lowerCamelCase_=10_24 ,   lowerCamelCase_=24 ,   lowerCamelCase_=16 ,   lowerCamelCase_=40_96 ,   lowerCamelCase_="gelu" ,   lowerCamelCase_=True ,   lowerCamelCase_="bi" ,   lowerCamelCase_=0.02 ,   lowerCamelCase_=1e-12 ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=5_12 ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_=False ,   lowerCamelCase_=False ,   lowerCamelCase_=-1 ,   lowerCamelCase_=False ,   lowerCamelCase_="last" ,   lowerCamelCase_=True ,   lowerCamelCase_="tanh" ,   lowerCamelCase_=0.1 ,   lowerCamelCase_=5 ,   lowerCamelCase_=5 ,   lowerCamelCase_=5 ,   lowerCamelCase_=1 ,   lowerCamelCase_=2 ,   **lowerCamelCase_ ,   )	->							List[str]:
														lowerCAmelCase__	       =     vocab_size
														lowerCAmelCase__	       =     d_model
														lowerCAmelCase__	       =     n_layer
														lowerCAmelCase__	       =     n_head
														if d_model % n_head != 0:
																					raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""       )
														if "d_head" in kwargs:
																					if kwargs["d_head"] != d_model // n_head:
																												raise ValueError(
																												    F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""       )
														lowerCAmelCase__	       =     d_model // n_head
														lowerCAmelCase__	       =     ff_activation
														lowerCAmelCase__	       =     d_inner
														lowerCAmelCase__	       =     untie_r
														lowerCAmelCase__	       =     attn_type

														lowerCAmelCase__	       =     initializer_range
														lowerCAmelCase__	       =     layer_norm_eps

														lowerCAmelCase__	       =     dropout
														lowerCAmelCase__	       =     mem_len
														lowerCAmelCase__	       =     reuse_len
														lowerCAmelCase__	       =     bi_data
														lowerCAmelCase__	       =     clamp_len
														lowerCAmelCase__	       =     same_length

														lowerCAmelCase__	       =     summary_type
														lowerCAmelCase__	       =     summary_use_proj
														lowerCAmelCase__	       =     summary_activation
														lowerCAmelCase__	       =     summary_last_dropout
														lowerCAmelCase__	       =     start_n_top
														lowerCAmelCase__	       =     end_n_top

														lowerCAmelCase__	       =     bos_token_id
														lowerCAmelCase__	       =     pad_token_id
														lowerCAmelCase__	       =     eos_token_id

														if "use_cache" in kwargs:
																					warnings.warn(
																					    '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
																					    ''' instead.''' ,   lowerCamelCase_ ,   )
																					lowerCAmelCase__	       =     kwargs['''use_cache''']

														lowerCAmelCase__	       =     use_mems_eval
														lowerCAmelCase__	       =     use_mems_train
														super().__init__(pad_token_id=lowerCamelCase_ ,   bos_token_id=lowerCamelCase_ ,   eos_token_id=lowerCamelCase_ ,   **lowerCamelCase_       )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[Any]:
														logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""       )
														return -1







							@max_position_embeddings.setter
							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Optional[Any]:
														# Message copied from Transformer-XL documentation
														raise NotImplementedError(
														    F"""The model {self.model_type} is one of the few models that has no sequence length limit."""       )



'''simple docstring'''






from __future__ import annotations

import math
from collections.abc import Callable


def  _snake_case       (					A			,   A			,   A			,   A = 100			,   )						->   float:
							lowerCAmelCase__	       =     x_start
							lowerCAmelCase__	       =     fnc(A	)
							lowerCAmelCase__	       =     0.0

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

														# Increment step
														lowerCAmelCase__	       =     xa
														lowerCAmelCase__	       =     fxa

							return length


if __name__ == "__main__":


		def  _snake_case       (					A	)						->   List[Any]:
									return math.sin(10 * x	)

		print('''f(x) = sin(10 * x)''')
		print('''The length of the curve from x = -10 to x = 10 is:''')
		__UpperCAmelCase       =							10
		while i <= 100_000:
				print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
				i *= 10