






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


UpperCamelCase__						:      Optional[Any]								=					logging.get_logger(__name__)  # pylint: disable=invalid-name





def   SCREAMING_SNAKE_CASE__						(   self,  snake_case_,  snake_case_,  snake_case_,  snake_case_  )   ->  Optional[Any]:


 """simple docstring"""



 a		=			image.to(device=_UpperCAmelCase  )
 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,  _UpperCAmelCase,  1  )
 a		=			image_embeddings.view(bs_embed * num_images_per_prompt,  _UpperCAmelCase,  -1  )

 if do_classifier_free_guidance:
  a		=			torch.zeros_like(_UpperCAmelCase  )

  # 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 				lowerCamelCase_     (	lowerCamelCase__     ):
       def __init__(						self		:					Union[str, Any]							,__lowerCamelCase		:					PriorTransformer							,__lowerCamelCase		:					CLIPTokenizer							,__lowerCamelCase		:					CLIPTextModelWithProjection							,__lowerCamelCase		:					UnCLIPScheduler							,__lowerCamelCase		:					Optional[dict] = None							,):





        '''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'''							,SCREAMING_SNAKE_CASE_					) or prior.dtype

        a		=			StableDiffusionImageVariationPipeline.from_pretrained(
            '''lambdalabs/sd-image-variations-diffusers'''							,**SCREAMING_SNAKE_CASE_					)

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

        self.register_modules(
            prior=SCREAMING_SNAKE_CASE_							,tokenizer=SCREAMING_SNAKE_CASE_							,text_encoder=SCREAMING_SNAKE_CASE_							,prior_scheduler=SCREAMING_SNAKE_CASE_							,)
       def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Optional[Union[CLIPTextModelOutput, Tuple]] = None							,__lowerCamelCase		:					Optional[torch.Tensor] = None							,):





        '''simple docstring'''



        if text_model_output is None:
         a		=			len(SCREAMING_SNAKE_CASE_					) if isinstance(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_					) else 1
         # get prompt text embeddings
         a		=			self.tokenizer(
             SCREAMING_SNAKE_CASE_							,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(SCREAMING_SNAKE_CASE_					)

         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(SCREAMING_SNAKE_CASE_					)					)

         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(SCREAMING_SNAKE_CASE_							,dim=0					)
        a		=			text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_							,dim=0					)
        a		=			text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_							,dim=0					)

        if do_classifier_free_guidance:
         a		=			[''''''] * batch_size

         a		=			self.tokenizer(
             SCREAMING_SNAKE_CASE_							,padding='''max_length'''							,max_length=self.tokenizer.model_max_length							,truncation=SCREAMING_SNAKE_CASE_							,return_tensors='''pt'''							,)
         a		=			uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_					)
         a		=			self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_					)					)

         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							,SCREAMING_SNAKE_CASE_					)
         a		=			uncond_embeddings.view(batch_size * num_images_per_prompt							,SCREAMING_SNAKE_CASE_					)

         a		=			uncond_text_encoder_hidden_states.shape[1]
         a		=			uncond_text_encoder_hidden_states.repeat(1							,SCREAMING_SNAKE_CASE_							,1					)
         a		=			uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt							,SCREAMING_SNAKE_CASE_							,-1					)
         a		=			uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_							,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  SCREAMING_SNAKE_CASE_ (						self		:					Optional[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(SCREAMING_SNAKE_CASE_							,'''_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  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Optional[int]					):





        '''simple docstring'''



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

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





        '''simple docstring'''



        self.decoder_pipe.to(SCREAMING_SNAKE_CASE_					)
        super().to(SCREAMING_SNAKE_CASE_					)




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





        '''simple docstring'''



        if prompt is not None:
         if isinstance(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_					):
          a		=			1
         elif isinstance(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_					):
          a		=			len(SCREAMING_SNAKE_CASE_					)
         else:
          raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_					)}"""					)
        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(
            SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_					)

        # prior

        self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_							,device=SCREAMING_SNAKE_CASE_					)
        a		=			self.prior_scheduler.timesteps

        a		=			self.prior.config.embedding_dim

        a		=			self.prepare_latents(
            (batch_size, embedding_dim)							,text_embeddings.dtype							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,self.prior_scheduler							,)

        for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_					)					):
         # 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(
             SCREAMING_SNAKE_CASE_							,timestep=SCREAMING_SNAKE_CASE_							,proj_embedding=SCREAMING_SNAKE_CASE_							,encoder_hidden_states=SCREAMING_SNAKE_CASE_							,attention_mask=SCREAMING_SNAKE_CASE_							,).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(
             SCREAMING_SNAKE_CASE_							,timestep=SCREAMING_SNAKE_CASE_							,sample=SCREAMING_SNAKE_CASE_							,generator=SCREAMING_SNAKE_CASE_							,prev_timestep=SCREAMING_SNAKE_CASE_							,).prev_sample

        a		=			self.prior.post_process_latents(SCREAMING_SNAKE_CASE_					)

        a		=			prior_latents

        a		=			self.decoder_pipe(
            image=SCREAMING_SNAKE_CASE_							,height=SCREAMING_SNAKE_CASE_							,width=SCREAMING_SNAKE_CASE_							,num_inference_steps=SCREAMING_SNAKE_CASE_							,guidance_scale=SCREAMING_SNAKE_CASE_							,generator=SCREAMING_SNAKE_CASE_							,output_type=SCREAMING_SNAKE_CASE_							,return_dict=SCREAMING_SNAKE_CASE_							,num_images_per_prompt=SCREAMING_SNAKE_CASE_							,eta=SCREAMING_SNAKE_CASE_							,)
        return output













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__						:      int								=					{
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__						:      Any								=					["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
				from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
				from .tokenization_fsmt import FSMTTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
				import sys

				UpperCamelCase__						:      List[Any]								=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





