

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    lowerCAmelCase__							(		self:							List[Any]		,    SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Optional[int]		,    SCREAMING_SNAKE_CASE_:							Optional[Any]		,    SCREAMING_SNAKE_CASE_:							Any )     -> Optional[int]:




				'''simple docstring'''





				A__            =     image.to(device=SCREAMING_SNAKE_CASE_ )
				A__            =     image  # take image as image_embeddings
				A__            =     image_embeddings.unsqueeze(1 )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				A__      ,			A__      ,			A__            =     image_embeddings.shape
				A__            =     image_embeddings.repeat(1		,    SCREAMING_SNAKE_CASE_		,    1 )
				A__            =     image_embeddings.view(bs_embed * num_images_per_prompt		,    SCREAMING_SNAKE_CASE_		,    -1 )

				if do_classifier_free_guidance:
								A__            =     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
								A__            =     torch.cat([uncond_embeddings, image_embeddings] )

				return image_embeddings





class    a__  (   snake_case     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase , lowercase , lowercase , lowercase = None , )					->  List[str]:
							'''simple docstring'''






							super().__init__()

							A__            =     {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

							A__            =     decoder_pipe_kwargs.get("torch_dtype" , lowercase    ) or prior.dtype

							A__            =     StableDiffusionImageVariationPipeline.from_pretrained(
							    "lambdalabs/sd-image-variations-diffusers" , **lowercase    )

							# replace `_encode_image` method
							A__            =     types.MethodType(_encode_image , self.decoder_pipe    )

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


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase , lowercase = None , lowercase = None , )					->  Any:
							'''simple docstring'''






							if text_model_output is None:
											A__            =     len(lowercase    ) if isinstance(lowercase , lowercase    ) else 1
											# get prompt text embeddings
											A__            =     self.tokenizer(
											    lowercase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
											A__            =     text_inputs.input_ids
											A__            =     text_inputs.attention_mask.bool().to(lowercase    )

											if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															A__            =     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}'    )
															A__            =     text_input_ids[:, : self.tokenizer.model_max_length]

											A__            =     self.text_encoder(text_input_ids.to(lowercase    )    )

											A__            =     text_encoder_output.text_embeds
											A__            =     text_encoder_output.last_hidden_state

							else:
											A__            =     text_model_output[0].shape[0]
											A__      ,			A__            =     text_model_output[0], text_model_output[1]
											A__            =     text_attention_mask

							A__            =     text_embeddings.repeat_interleave(lowercase , dim=0    )
							A__            =     text_encoder_hidden_states.repeat_interleave(lowercase , dim=0    )
							A__            =     text_mask.repeat_interleave(lowercase , dim=0    )

							if do_classifier_free_guidance:
											A__            =     [""] * batch_size

											A__            =     self.tokenizer(
											    lowercase , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowercase , return_tensors="pt" , )
											A__            =     uncond_input.attention_mask.bool().to(lowercase    )
											A__            =     self.text_encoder(uncond_input.input_ids.to(lowercase    )    )

											A__            =     uncond_embeddings_text_encoder_output.text_embeds
											A__            =     uncond_embeddings_text_encoder_output.last_hidden_state

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

											A__            =     uncond_embeddings.shape[1]
											A__            =     uncond_embeddings.repeat(1 , lowercase    )
											A__            =     uncond_embeddings.view(batch_size * num_images_per_prompt , lowercase    )

											A__            =     uncond_text_encoder_hidden_states.shape[1]
											A__            =     uncond_text_encoder_hidden_states.repeat(1 , lowercase , 1    )
											A__            =     uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt , lowercase , -1    )
											A__            =     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
											A__            =     torch.cat([uncond_embeddings, text_embeddings]    )
											A__            =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]    )

											A__            =     torch.cat([uncond_text_mask, text_mask]    )

							return text_embeddings, text_encoder_hidden_states, text_mask


			@property
			def        UpperCamelCase						(      self    )					->  Any:
							'''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        UpperCamelCase						(      self , lowercase , lowercase , lowercase , lowercase , lowercase , lowercase    )					->  Union[str, Any]:
							'''simple docstring'''






							if latents is None:
											A__            =     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}'    )
											A__            =     latents.to(lowercase    )

							A__            =     latents * scheduler.init_noise_sigma
							return latents


			def        UpperCamelCase						(      self , lowercase = None    )					->  str:
							'''simple docstring'''






							self.decoder_pipe.to(lowercase    )
							super().to(lowercase    )


			@torch.no_grad()
			def __call__(      self , lowercase = None , lowercase = None , lowercase = None , lowercase = 1 , lowercase = 25 , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = 4.0 , lowercase = 8.0 , lowercase = 50 , lowercase = 1 , lowercase = 0.0 , lowercase = "pil" , lowercase = True , )					->  Optional[Any]:
							'''simple docstring'''






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

							A__            =     self._execution_device

							A__            =     batch_size * num_images_per_prompt

							A__            =     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

							A__      ,			A__      ,			A__            =     self._encode_prompt(
							    lowercase , lowercase , lowercase , lowercase , lowercase , lowercase    )

							# prior

							self.prior_scheduler.set_timesteps(lowercase , device=lowercase    )
							A__            =     self.prior_scheduler.timesteps

							A__            =     self.prior.config.embedding_dim

							A__            =     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
											A__            =     torch.cat([prior_latents] * 2    ) if do_classifier_free_guidance else prior_latents

											A__            =     self.prior(
											    lowercase , timestep=lowercase , proj_embedding=lowercase , encoder_hidden_states=lowercase , attention_mask=lowercase , ).predicted_image_embedding

											if do_classifier_free_guidance:
															A__      ,			A__            =     predicted_image_embedding.chunk(2    )
															A__            =     predicted_image_embedding_uncond + prior_guidance_scale * (
															    predicted_image_embedding_text - predicted_image_embedding_uncond
															)

											if i + 1 == prior_timesteps_tensor.shape[0]:
															A__            =     None
											else:
															A__            =     prior_timesteps_tensor[i + 1]

											A__            =     self.prior_scheduler.step(
											    lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , prev_timestep=lowercase , ).prev_sample

							A__            =     self.prior.post_process_latents(lowercase    )

							A__            =     prior_latents

							A__            =     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




def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							list		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							int )     -> int:




				'''simple docstring'''





				A__            =     int((lower_bound + upper_bound) // 2 )
				if array[r] == value:
								return r
				if lower_bound >= upper_bound:
								return -1
				if array[r] < value:
								return binary_search(SCREAMING_SNAKE_CASE_		,    r + 1		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_ )
				else:
								return binary_search(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    r - 1		,    SCREAMING_SNAKE_CASE_ )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							list )     -> list:




				'''simple docstring'''





				A__            =     0
				if matrix[index][0] == value:
								return [index, 0]
				while index < len(SCREAMING_SNAKE_CASE_ ) and matrix[index][0] < value:
								A__            =     binary_search(matrix[index]		,    0		,    len(matrix[index] ) - 1		,    SCREAMING_SNAKE_CASE_ )
								if r != -1:
												return [index, r]
								index += 1
				return [-1, -1]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

