

"""simple docstring"""


import torch
from torch import nn

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





class    snake_case     (    __snake_case,							__snake_case  ):

				@register_to_config
				def __init__(     self							:    List[str]					,  *,
				    UpperCamelCase__							:    int = 4					,  UpperCamelCase__							:    int = 7_6_8					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Optional[Any]					,  )->       List[Any]:



						'''simple docstring'''


						super().__init__()

						__lowerCAmelCase:	List[Any]   				=      nn.Parameter(torch.zeros(UpperCamelCase__))

						# parameters for additional clip time embeddings
						__lowerCAmelCase:	Any   				=      nn.Linear(UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	Optional[int]   				=      nn.Linear(UpperCamelCase__					,  UpperCamelCase__)

						# parameters for encoder hidden states
						__lowerCAmelCase:	List[str]   				=      clip_extra_context_tokens
						__lowerCAmelCase:	Optional[Any]   				=      nn.Linear(
						    UpperCamelCase__					,  self.clip_extra_context_tokens * cross_attention_dim)
						__lowerCAmelCase:	str   				=      nn.Linear(UpperCamelCase__					,  UpperCamelCase__)
						__lowerCAmelCase:	str   				=      nn.LayerNorm(UpperCamelCase__)




				def 							lowercase_					(     self							:    List[Any]					,  *, UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Any					,  UpperCamelCase__							:    List[str])->       Optional[Any]:



						'''simple docstring'''


						if do_classifier_free_guidance:
								# Add the classifier free guidance embeddings to the image embeddings
								__lowerCAmelCase:	Dict   				=      image_embeddings.shape[0]
								__lowerCAmelCase:	Optional[Any]   				=      self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
								__lowerCAmelCase:	int   				=      classifier_free_guidance_embeddings.expand(
								    UpperCamelCase__					,  -1)
								__lowerCAmelCase:	List[str]   				=      torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,  dim=0)

						# The image embeddings batch size and the text embeddings batch size are equal
						assert image_embeddings.shape[0] == prompt_embeds.shape[0]

						__lowerCAmelCase:	List[Any]   				=      prompt_embeds.shape[0]

						# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
						# adding CLIP embeddings to the existing timestep embedding, ...
						__lowerCAmelCase:	Dict   				=      self.embedding_proj(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      time_projected_image_embeddings + time_projected_prompt_embeds

						# ... and by projecting CLIP embeddings into four
						# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
						__lowerCAmelCase:	Union[str, Any]   				=      self.clip_extra_context_tokens_proj(UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      clip_extra_context_tokens.reshape(UpperCamelCase__					,  -1					,  self.clip_extra_context_tokens)
						__lowerCAmelCase:	Dict   				=      clip_extra_context_tokens.permute(0					,  2					,  1)

						__lowerCAmelCase:	Dict   				=      self.encoder_hidden_states_proj(UpperCamelCase__)
						__lowerCAmelCase:	Dict   				=      self.text_encoder_hidden_states_norm(UpperCamelCase__)
						__lowerCAmelCase:	Tuple   				=      torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,  dim=1)

						return text_encoder_hidden_states, additive_clip_time_embeddings





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


