





"""simple docstring"""






from __future__ import annotations

import math
from collections.abc import Callable

def     __A      (			a_						:Callable[[int | float], int | float]   ,       a_						:int | float   ,       a_						:int | float   ,       a_						:int = 1_00   ,       )		->		float:
				__a :						Optional[Any]			       =  x_start
				__a :						List[Any]			       =  fnc(a_)
				__a :						Any			       =  0.0

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

								# Increment step
								__a :						List[Any]			       =  xa
								__a :						Tuple			       =  fxa

				return length


if __name__ == "__main__":

					def     __A      (			a_						:Optional[Any])		->		Dict:
									return math.sin(10 * x)

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






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states