




"""simple docstring"""






from __future__ import annotations

import math
from collections.abc import Callable







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = 100							,    ):
				_lowerCamelCase     :			Any    =    x_start
				_lowerCamelCase     :			Optional[int]    =    fnc(lowercase__		)
				_lowerCamelCase     :			str    =    0.0

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

								# Increment step
								_lowerCamelCase     :			List[Any]    =    xa
								_lowerCamelCase     :			Dict    =    fxa

				return length


if __name__ == "__main__":







			def      _snake_case			(				lowercase__		):
							return math.sin(10 * x		)

			print("""f(x) = sin(10 * x)""")
			print("""The length of the curve from x = -10 to x = 10 is:""")
			lowercase__     						=						10
			while i <= 10_0000:
						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


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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

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






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

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



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






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states