

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


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

def 	lowerCamelCase__	(   self							,     _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    image.to(device=_a)
	SCREAMING_SNAKE_CASE				: int			       =    image  # take image as image_embeddings
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    image_embeddings.unsqueeze(1)

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

	if do_classifier_free_guidance:
		SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.zeros_like(_a)

		# 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
		SCREAMING_SNAKE_CASE				: int			       =    torch.cat([uncond_embeddings, image_embeddings])

	return image_embeddings






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			def __init__(     self							:	int , a							:	PriorTransformer , a							:	CLIPTokenizer , a							:	CLIPTextModelWithProjection , a							:	UnCLIPScheduler , a							:	Optional[dict] = None , )							->    List[Any]:

				"""simple docstring"""

				super().__init__()

				SCREAMING_SNAKE_CASE				: int			       =    {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

				SCREAMING_SNAKE_CASE				: str			       =    decoder_pipe_kwargs.get("torch_dtype" , a		) or prior.dtype

				SCREAMING_SNAKE_CASE				: List[str]			       =    StableDiffusionImageVariationPipeline.from_pretrained(
				    "lambdalabs/sd-image-variations-diffusers" , **a		)

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

				self.register_modules(
				    prior=a , tokenizer=a , text_encoder=a , prior_scheduler=a , )
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	Any , a							:	str , a							:	Optional[int] , a							:	Optional[Any] , a							:	Optional[Union[CLIPTextModelOutput, Tuple]] = None , a							:	Optional[torch.Tensor] = None , )							->    str:

				"""simple docstring"""

				if text_model_output is None:
					SCREAMING_SNAKE_CASE				: List[str]			       =    len(a		) if isinstance(a , a		) else 1
					# get prompt text embeddings
					SCREAMING_SNAKE_CASE				: Any			       =    self.tokenizer(
					    a , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
					SCREAMING_SNAKE_CASE				: Dict			       =    text_inputs.input_ids
					SCREAMING_SNAKE_CASE				: List[Any]			       =    text_inputs.attention_mask.bool().to(a		)

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						SCREAMING_SNAKE_CASE				: 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}"		)
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    text_input_ids[:, : self.tokenizer.model_max_length]

					SCREAMING_SNAKE_CASE				: Dict			       =    self.text_encoder(text_input_ids.to(a		)		)

					SCREAMING_SNAKE_CASE				: Dict			       =    text_encoder_output.text_embeds
					SCREAMING_SNAKE_CASE				: Tuple			       =    text_encoder_output.last_hidden_state

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    text_embeddings.repeat_interleave(a , dim=0		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    text_encoder_hidden_states.repeat_interleave(a , dim=0		)
				SCREAMING_SNAKE_CASE				: List[Any]			       =    text_mask.repeat_interleave(a , dim=0		)

				if do_classifier_free_guidance:
					SCREAMING_SNAKE_CASE				: Any			       =    [""] * batch_size

					SCREAMING_SNAKE_CASE				: List[str]			       =    self.tokenizer(
					    a , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=a , return_tensors="pt" , )
					SCREAMING_SNAKE_CASE				: str			       =    uncond_input.attention_mask.bool().to(a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    self.text_encoder(uncond_input.input_ids.to(a		)		)

					SCREAMING_SNAKE_CASE				: str			       =    uncond_embeddings_text_encoder_output.text_embeds
					SCREAMING_SNAKE_CASE				: List[str]			       =    uncond_embeddings_text_encoder_output.last_hidden_state

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

					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    uncond_embeddings.shape[1]
					SCREAMING_SNAKE_CASE				: List[Any]			       =    uncond_embeddings.repeat(1 , a		)
					SCREAMING_SNAKE_CASE				: Dict			       =    uncond_embeddings.view(batch_size * num_images_per_prompt , a		)

					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    uncond_text_encoder_hidden_states.shape[1]
					SCREAMING_SNAKE_CASE				: str			       =    uncond_text_encoder_hidden_states.repeat(1 , a , 1		)
					SCREAMING_SNAKE_CASE				: Tuple			       =    uncond_text_encoder_hidden_states.view(
					    batch_size * num_images_per_prompt , a , -1		)
					SCREAMING_SNAKE_CASE				: List[Any]			       =    uncond_text_mask.repeat_interleave(a , 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
					SCREAMING_SNAKE_CASE				: Any			       =    torch.cat([uncond_embeddings, text_embeddings]		)
					SCREAMING_SNAKE_CASE				: List[Any]			       =    torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)

					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.cat([uncond_text_mask, text_mask]		)

				return text_embeddings, text_encoder_hidden_states, text_mask
			@property
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Optional[int]:

				"""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(a , "_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							:	Any , a							:	Union[str, Any] , a							:	Tuple , a							:	List[Any] , a							:	Union[str, Any] , a							:	Optional[int] , a							:	Optional[Any]		)							->    List[str]:

				"""simple docstring"""

				if latents is None:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    randn_tensor(a , generator=a , device=a , dtype=a		)
				else:
					if latents.shape != shape:
						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"		)
					SCREAMING_SNAKE_CASE				: int			       =    latents.to(a		)

				SCREAMING_SNAKE_CASE				: List[str]			       =    latents * scheduler.init_noise_sigma
				return latents
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	Optional[Union[str, torch.device]] = None		)							->    Optional[Any]:

				"""simple docstring"""

				self.decoder_pipe.to(a		)
				super().to(a		)






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

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: str			       =    self._execution_device

				SCREAMING_SNAKE_CASE				: List[str]			       =    batch_size * num_images_per_prompt

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Tuple			       =    self._encode_prompt(
				    a , a , a , a , a , a		)

				# prior

				self.prior_scheduler.set_timesteps(a , device=a		)
				SCREAMING_SNAKE_CASE				: str			       =    self.prior_scheduler.timesteps

				SCREAMING_SNAKE_CASE				: List[Any]			       =    self.prior.config.embedding_dim

				SCREAMING_SNAKE_CASE				: str			       =    self.prepare_latents(
				    (batch_size, embedding_dim) , text_embeddings.dtype , a , a , a , self.prior_scheduler , )

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

					SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.prior(
					    a , timestep=a , proj_embedding=a , encoder_hidden_states=a , attention_mask=a , ).predicted_image_embedding

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

					if i + 1 == prior_timesteps_tensor.shape[0]:
						SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    None
					else:
						SCREAMING_SNAKE_CASE				: Any			       =    prior_timesteps_tensor[i + 1]

					SCREAMING_SNAKE_CASE				: str			       =    self.prior_scheduler.step(
					    a , timestep=a , sample=a , generator=a , prev_timestep=a , ).prev_sample

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.prior.post_process_latents(a		)

				SCREAMING_SNAKE_CASE				: List[Any]			       =    prior_latents

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.decoder_pipe(
				    image=a , height=a , width=a , num_inference_steps=a , guidance_scale=a , generator=a , output_type=a , return_dict=a , num_images_per_prompt=a , eta=a , )
				return output


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

				super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets