


import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


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




def      a_				(   self :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]		,      SCREAMING_SNAKE_CASE__ :				int					):
       '''simple docstring'''





       _lowerCamelCase	:		int          =image.to(device=SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		str          =image  # take image as image_embeddings
       _lowerCamelCase	:		Optional[Any]          =image_embeddings.unsqueeze(1					)

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =image_embeddings.shape
       _lowerCamelCase	:		List[str]          =image_embeddings.repeat(1		,      SCREAMING_SNAKE_CASE__		,      1					)
       _lowerCamelCase	:		Optional[int]          =image_embeddings.view(bs_embed * num_images_per_prompt		,      SCREAMING_SNAKE_CASE__		,      -1					)

       if do_classifier_free_guidance:
              _lowerCamelCase	:		Optional[int]          =torch.zeros_like(SCREAMING_SNAKE_CASE__					)

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              _lowerCamelCase	:		Union[str, Any]          =torch.cat([uncond_embeddings, image_embeddings]					)

       return image_embeddings




class A  (       UpperCamelCase_					):






      def __init__( self						:					Dict      ,       lowercase_						:					PriorTransformer      ,       lowercase_						:					CLIPTokenizer      ,       lowercase_						:					CLIPTextModelWithProjection      ,       lowercase_						:					UnCLIPScheduler      ,       lowercase_						:					Optional[dict] = None      ,       ) ->						Dict:
             """simple docstring"""




             super().__init__()

             _lowerCamelCase	:		Optional[Any]          ={'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

             _lowerCamelCase	:		int          =decoder_pipe_kwargs.get('torch_dtype'      ,       lowercase_			) or prior.dtype

             _lowerCamelCase	:		str          =StableDiffusionImageVariationPipeline.from_pretrained(
                 'lambdalabs/sd-image-variations-diffusers'      ,       **lowercase_			)

             # replace `_encode_image` method
             _lowerCamelCase	:		Union[str, Any]          =types.MethodType(_encode_image      ,       self.decoder_pipe			)

             self.register_modules(
                 prior=lowercase_      ,       tokenizer=lowercase_      ,       text_encoder=lowercase_      ,       prior_scheduler=lowercase_      ,       )






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,       lowercase_						:					Optional[torch.Tensor] = None      ,       ) ->						Optional[int]:
             """simple docstring"""




             if text_model_output is None:
                    _lowerCamelCase	:		Dict          =len(lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else 1
                    # get prompt text embeddings
                    _lowerCamelCase	:		Tuple          =self.tokenizer(
                        lowercase_      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       return_tensors='pt'      ,       )
                    _lowerCamelCase	:		Optional[Any]          =text_inputs.input_ids
                    _lowerCamelCase	:		Any          =text_inputs.attention_mask.bool().to(lowercase_			)

                    if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                           _lowerCamelCase	:		List[str]          =self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
                           logger.warning(
                               'The following part of your input was truncated because CLIP can only handle sequences up to'
                               F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)
                           _lowerCamelCase	:		Any          =text_input_ids[:, : self.tokenizer.model_max_length]

                    _lowerCamelCase	:		int          =self.text_encoder(text_input_ids.to(lowercase_			)			)

                    _lowerCamelCase	:		List[Any]          =text_encoder_output.text_embeds
                    _lowerCamelCase	:		Union[str, Any]          =text_encoder_output.last_hidden_state

             else:
                    _lowerCamelCase	:		List[str]          =text_model_output[0].shape[0]
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =text_model_output[0], text_model_output[1]
                    _lowerCamelCase	:		int          =text_attention_mask

             _lowerCamelCase	:		List[Any]          =text_embeddings.repeat_interleave(lowercase_      ,       dim=0			)
             _lowerCamelCase	:		Optional[int]          =text_encoder_hidden_states.repeat_interleave(lowercase_      ,       dim=0			)
             _lowerCamelCase	:		Union[str, Any]          =text_mask.repeat_interleave(lowercase_      ,       dim=0			)

             if do_classifier_free_guidance:
                    _lowerCamelCase	:		Any          =[''] * batch_size

                    _lowerCamelCase	:		Optional[Any]          =self.tokenizer(
                        lowercase_      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       truncation=lowercase_      ,       return_tensors='pt'      ,       )
                    _lowerCamelCase	:		Optional[int]          =uncond_input.attention_mask.bool().to(lowercase_			)
                    _lowerCamelCase	:		Union[str, Any]          =self.text_encoder(uncond_input.input_ids.to(lowercase_			)			)

                    _lowerCamelCase	:		str          =uncond_embeddings_text_encoder_output.text_embeds
                    _lowerCamelCase	:		Any          =uncond_embeddings_text_encoder_output.last_hidden_state

                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                    _lowerCamelCase	:		int          =uncond_embeddings.shape[1]
                    _lowerCamelCase	:		str          =uncond_embeddings.repeat(1      ,       lowercase_			)
                    _lowerCamelCase	:		str          =uncond_embeddings.view(batch_size * num_images_per_prompt      ,       lowercase_			)

                    _lowerCamelCase	:		Optional[int]          =uncond_text_encoder_hidden_states.shape[1]
                    _lowerCamelCase	:		int          =uncond_text_encoder_hidden_states.repeat(1      ,       lowercase_      ,       1			)
                    _lowerCamelCase	:		Optional[Any]          =uncond_text_encoder_hidden_states.view(
                        batch_size * num_images_per_prompt      ,       lowercase_      ,       -1			)
                    _lowerCamelCase	:		Optional[Any]          =uncond_text_mask.repeat_interleave(lowercase_      ,       dim=0			)

                    # done duplicates

                    # For classifier free guidance, we need to do two forward passes.
                    # Here we concatenate the unconditional and text embeddings into a single batch
                    # to avoid doing two forward passes
                    _lowerCamelCase	:		List[Any]          =torch.cat([uncond_embeddings, text_embeddings]			)
                    _lowerCamelCase	:		Dict          =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

                    _lowerCamelCase	:		Optional[int]          =torch.cat([uncond_text_mask, text_mask]			)

             return text_embeddings, text_encoder_hidden_states, text_mask






      @property
      def 	lowerCamelCase	( self						:					List[str]			) ->						Tuple:
             """simple docstring"""




             if self.device != torch.device('meta'			) or not hasattr(self.prior      ,       '_hf_hook'			):
                    return self.device
             for module in self.prior.modules():
                    if (
                        hasattr(lowercase_      ,       '_hf_hook'			)
                        and hasattr(module._hf_hook      ,       'execution_device'			)
                        and module._hf_hook.execution_device is not None
                    ):
                           return torch.device(module._hf_hook.execution_device			)
             return self.device






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					str      ,       lowercase_						:					str      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Optional[Any]			) ->						List[str]:
             """simple docstring"""




             if latents is None:
                    _lowerCamelCase	:		int          =randn_tensor(lowercase_      ,       generator=lowercase_      ,       device=lowercase_      ,       dtype=lowercase_			)
             else:
                    if latents.shape != shape:
                           raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
                    _lowerCamelCase	:		List[Any]          =latents.to(lowercase_			)

             _lowerCamelCase	:		Union[str, Any]          =latents * scheduler.init_noise_sigma
             return latents






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Optional[Union[str, torch.device]] = None			) ->						List[str]:
             """simple docstring"""




             self.decoder_pipe.to(lowercase_			)
             super().to(lowercase_			)






      @torch.no_grad()
      def __call__( self						:					Any      ,       lowercase_						:					Optional[Union[str, List[str]]] = None      ,       lowercase_						:					Optional[int] = None      ,       lowercase_						:					Optional[int] = None      ,       lowercase_						:					int = 1      ,       lowercase_						:					int = 25      ,       lowercase_						:					Optional[torch.Generator] = None      ,       lowercase_						:					Optional[torch.FloatTensor] = None      ,       lowercase_						:					Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,       lowercase_						:					Optional[torch.Tensor] = None      ,       lowercase_						:					float = 4.0      ,       lowercase_						:					float = 8.0      ,       lowercase_						:					int = 50      ,       lowercase_						:					Optional[int] = 1      ,       lowercase_						:					float = 0.0      ,       lowercase_						:					Optional[str] = "pil"      ,       lowercase_						:					bool = True      ,       ) ->						Dict:
             """simple docstring"""




             if prompt is not None:
                    if isinstance(lowercase_      ,       lowercase_			):
                           _lowerCamelCase	:		List[Any]          =1
                    elif isinstance(lowercase_      ,       lowercase_			):
                           _lowerCamelCase	:		Optional[Any]          =len(lowercase_			)
                    else:
                           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowercase_			)}'''			)
             else:
                    _lowerCamelCase	:		Optional[Any]          =text_model_output[0].shape[0]

             _lowerCamelCase	:		Dict          =self._execution_device

             _lowerCamelCase	:		str          =batch_size * num_images_per_prompt

             _lowerCamelCase	:		Union[str, Any]          =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

             _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		Tuple          =self._encode_prompt(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

             # prior

             self.prior_scheduler.set_timesteps(lowercase_      ,       device=lowercase_			)
             _lowerCamelCase	:		List[str]          =self.prior_scheduler.timesteps

             _lowerCamelCase	:		Any          =self.prior.config.embedding_dim

             _lowerCamelCase	:		int          =self.prepare_latents(
                 (batch_size, embedding_dim)      ,       text_embeddings.dtype      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       self.prior_scheduler      ,       )

             for i, t in enumerate(self.progress_bar(lowercase_			)			):
                    # expand the latents if we are doing classifier free guidance
                    _lowerCamelCase	:		List[Any]          =torch.cat([prior_latents] * 2			) if do_classifier_free_guidance else prior_latents

                    _lowerCamelCase	:		Dict          =self.prior(
                        lowercase_      ,       timestep=lowercase_      ,       proj_embedding=lowercase_      ,       encoder_hidden_states=lowercase_      ,       attention_mask=lowercase_      ,       ).predicted_image_embedding

                    if do_classifier_free_guidance:
                           _lowerCamelCase     ,					_lowerCamelCase	:		Tuple          =predicted_image_embedding.chunk(2			)
                           _lowerCamelCase	:		List[Any]          =predicted_image_embedding_uncond + prior_guidance_scale * (
                               predicted_image_embedding_text - predicted_image_embedding_uncond
                           )

                    if i + 1 == prior_timesteps_tensor.shape[0]:
                           _lowerCamelCase	:		List[str]          =None
                    else:
                           _lowerCamelCase	:		Any          =prior_timesteps_tensor[i + 1]

                    _lowerCamelCase	:		Optional[int]          =self.prior_scheduler.step(
                        lowercase_      ,       timestep=lowercase_      ,       sample=lowercase_      ,       generator=lowercase_      ,       prev_timestep=lowercase_      ,       ).prev_sample

             _lowerCamelCase	:		Optional[int]          =self.prior.post_process_latents(lowercase_			)

             _lowerCamelCase	:		Union[str, Any]          =prior_latents

             _lowerCamelCase	:		Optional[Any]          =self.decoder_pipe(
                 image=lowercase_      ,       height=lowercase_      ,       width=lowercase_      ,       num_inference_steps=lowercase_      ,       guidance_scale=lowercase_      ,       generator=lowercase_      ,       output_type=lowercase_      ,       return_dict=lowercase_      ,       num_images_per_prompt=lowercase_      ,       eta=lowercase_      ,       )
             return output






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


if __name__ == "__main__":
 # read original image
 lowerCamelCase						 =	imread('../image_data/lena.jpg')
 # turn image in gray scale value
 lowerCamelCase						 =	cvtColor(img, COLOR_BGR2GRAY)

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


