


"""simple docstring"""
__SCREAMING_SNAKE_CASE				:     Tuple		=     1_000







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					float:
							return x * x







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = 4					)     ->					float:
							assert callable(
							    _SCREAMING_SNAKE_CASE					), f"""the function(object) passed should be callable your input : {function}"""
							assert isinstance(_SCREAMING_SNAKE_CASE					,			(float, int)					), f"""a should be float or integer your input : {a}"""
							assert isinstance(function(_SCREAMING_SNAKE_CASE					)					,			(float, int)					), (
							    "the function should return integer or float return type of your function, "
							    f"""{type(_SCREAMING_SNAKE_CASE					)}"""
							)
							assert isinstance(_SCREAMING_SNAKE_CASE					,			(float, int)					), f"""b should be float or integer your input : {b}"""
							assert (
							    isinstance(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					) and precision > 0
							), f"""precision should be positive integer your input : {precision}"""

							# just applying the formula of simpson for approximate integration written in
							# mentioned article in first comment of this file and above this function

							snake_case_     							=		(b - a) / N_STEPS
							snake_case_     							=		function(_SCREAMING_SNAKE_CASE					) + function(_SCREAMING_SNAKE_CASE					)

							for i in range(1					,			_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		a + h * i
														result += function(_SCREAMING_SNAKE_CASE					) * (4 if i % 2 else 2)

							result *= h / 3
							return round(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()







"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class   __A  (snake_case__    ,		snake_case__    ,		snake_case__):





							'''simple docstring'''

							@register_to_config
							def __init__(					self				:   List[str]		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   float		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   int		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   bool = False		,							)       ->Dict:



														"""simple docstring"""



														super().__init__()

														snake_case_     							=		nn.Linear(UpperCAmelCase_		,							UpperCAmelCase_		,							bias=UpperCAmelCase_							)

														snake_case_     							=		nn.Embedding(UpperCAmelCase_		,							UpperCAmelCase_							)
														snake_case_     							=		False

														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)

														snake_case_     							=		TaConfig(
														    d_model=UpperCAmelCase_		,							num_heads=UpperCAmelCase_		,							d_kv=UpperCAmelCase_		,							d_ff=UpperCAmelCase_		,							feed_forward_proj=UpperCAmelCase_		,							dropout_rate=UpperCAmelCase_		,							is_decoder=UpperCAmelCase_		,							is_encoder_decoder=UpperCAmelCase_		,							)
														snake_case_     							=		nn.ModuleList()
														for lyr_num in range(UpperCAmelCase_							):
																					snake_case_     							=		TaBlock(UpperCAmelCase_							)
																					self.encoders.append(UpperCAmelCase_							)

														snake_case_     							=		TaLayerNorm(UpperCAmelCase_							)
														snake_case_     							=		nn.Dropout(p=UpperCAmelCase_							)



							def 		lowerCAmelCase							(					self				:   Optional[Any]		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   List[str]							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		self.input_proj(UpperCAmelCase_							)

														# terminal relative positional encodings
														snake_case_     							=		encoder_inputs.shape[1]
														snake_case_     							=		torch.arange(UpperCAmelCase_		,							device=encoder_inputs.device							)

														snake_case_     							=		encoder_inputs_mask.sum(-1							)
														snake_case_     							=		torch.roll(input_positions.unsqueeze(0							)		,							tuple(seq_lens.tolist()							)		,							dims=0							)
														x += self.position_encoding(UpperCAmelCase_							)

														snake_case_     							=		self.dropout_pre(UpperCAmelCase_							)

														# inverted the attention mask
														snake_case_     							=		encoder_inputs.size()
														snake_case_     							=		self.get_extended_attention_mask(UpperCAmelCase_		,							UpperCAmelCase_							)

														for lyr in self.encoders:
																					snake_case_     							=		lyr(UpperCAmelCase_		,							UpperCAmelCase_							)[0]
														snake_case_     							=		self.layer_norm(UpperCAmelCase_							)

														return self.dropout_post(UpperCAmelCase_							), encoder_inputs_mask



