




'''simple docstring'''






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


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




def 		a_( self   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->List[str]:






       '''simple docstring'''




       lowercase  :		List[Any]						=      image.to(device=lowerCamelCase      )
       lowercase  :		Any						=      image  # take image as image_embeddings
       lowercase  :		Optional[Any]						=      image_embeddings.unsqueeze(1      )

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

       if do_classifier_free_guidance:
              lowercase  :		List[Any]						=      torch.zeros_like(lowerCamelCase      )

              # 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
              lowercase  :		Union[str, Any]						=      torch.cat([uncond_embeddings, image_embeddings]      )

       return image_embeddings





class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def __init__(  self   ,		A   ,		A   ,		A   ,		A   ,		A = None   ,		):






        '''simple docstring'''




        super().__init__()

        lowercase  :		Tuple						=      {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        lowercase  :		Optional[int]						=      decoder_pipe_kwargs.get('''torch_dtype'''   ,		A ) or prior.dtype

        lowercase  :		Union[str, Any]						=      StableDiffusionImageVariationPipeline.from_pretrained(
            '''lambdalabs/sd-image-variations-diffusers'''   ,		**A )

        # replace `_encode_image` method
        lowercase  :		int						=      types.MethodType(_encode_image   ,		self.decoder_pipe )

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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A   ,		A = None   ,		A = None   ,		):






        '''simple docstring'''




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

               if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                      lowercase  :		Dict						=      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}""" )
                      lowercase  :		List[Any]						=      text_input_ids[:, : self.tokenizer.model_max_length]

               lowercase  :		Union[str, Any]						=      self.text_encoder(text_input_ids.to(A ) )

               lowercase  :		Tuple						=      text_encoder_output.text_embeds
               lowercase  :		Optional[Any]						=      text_encoder_output.last_hidden_state

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

        lowercase  :		Optional[int]						=      text_embeddings.repeat_interleave(A   ,		dim=0 )
        lowercase  :		Optional[int]						=      text_encoder_hidden_states.repeat_interleave(A   ,		dim=0 )
        lowercase  :		Dict						=      text_mask.repeat_interleave(A   ,		dim=0 )

        if do_classifier_free_guidance:
               lowercase  :		List[Any]						=      [''''''] * batch_size

               lowercase  :		List[str]						=      self.tokenizer(
                   A   ,		padding='''max_length'''   ,		max_length=self.tokenizer.model_max_length   ,		truncation=A   ,		return_tensors='''pt'''   ,		)
               lowercase  :		Optional[int]						=      uncond_input.attention_mask.bool().to(A )
               lowercase  :		List[str]						=      self.text_encoder(uncond_input.input_ids.to(A ) )

               lowercase  :		List[Any]						=      uncond_embeddings_text_encoder_output.text_embeds
               lowercase  :		Dict						=      uncond_embeddings_text_encoder_output.last_hidden_state

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

               lowercase  :		int						=      uncond_embeddings.shape[1]
               lowercase  :		Optional[int]						=      uncond_embeddings.repeat(1   ,		A )
               lowercase  :		Optional[int]						=      uncond_embeddings.view(batch_size * num_images_per_prompt   ,		A )

               lowercase  :		str						=      uncond_text_encoder_hidden_states.shape[1]
               lowercase  :		Optional[Any]						=      uncond_text_encoder_hidden_states.repeat(1   ,		A   ,		1 )
               lowercase  :		List[str]						=      uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt   ,		A   ,		-1 )
               lowercase  :		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
               lowercase  :		Dict						=      torch.cat([uncond_embeddings, text_embeddings] )
               lowercase  :		List[Any]						=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

               lowercase  :		Optional[Any]						=      torch.cat([uncond_text_mask, text_mask] )

        return text_embeddings, text_encoder_hidden_states, text_mask



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''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    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        if latents is None:
               lowercase  :		Union[str, 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}""" )
               lowercase  :		List[str]						=      latents.to(A )

        lowercase  :		Optional[int]						=      latents * scheduler.init_noise_sigma
        return latents



 def    SCREAMING_SNAKE_CASE(  self   ,		A = None ):






        '''simple docstring'''




        self.decoder_pipe.to(A )
        super().to(A )





 @torch.no_grad()
 def __call__(  self   ,		A = None   ,		A = None   ,		A = None   ,		A = 1   ,		A = 25   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = 4.0   ,		A = 8.0   ,		A = 50   ,		A = 1   ,		A = 0.0   ,		A = "pil"   ,		A = True   ,		):






        '''simple docstring'''




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

        lowercase  :		int						=      self._execution_device

        lowercase  :		str						=      batch_size * num_images_per_prompt

        lowercase  :		Dict						=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        lowercase  :		Dict						=      self._encode_prompt(
            A   ,		A   ,		A   ,		A   ,		A   ,		A )

        # prior

        self.prior_scheduler.set_timesteps(A   ,		device=A )
        lowercase  :		Optional[int]						=      self.prior_scheduler.timesteps

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

        lowercase  :		Any						=      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
               lowercase  :		Tuple						=      torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

               lowercase  :		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:
                      lowercase  :		Optional[Any]						=      predicted_image_embedding.chunk(2 )
                      lowercase  :		Optional[int]						=      predicted_image_embedding_uncond + prior_guidance_scale * (
                          predicted_image_embedding_text - predicted_image_embedding_uncond
                      )

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

               lowercase  :		int						=      self.prior_scheduler.step(
                   A   ,		timestep=A   ,		sample=A   ,		generator=A   ,		prev_timestep=A   ,		).prev_sample

        lowercase  :		Union[str, Any]						=      self.prior.post_process_latents(A )

        lowercase  :		List[Any]						=      prior_latents

        lowercase  :		int						=      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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




