





'''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


A__       :     Dict          =logging.get_logger(__name__)  # pylint: disable=invalid-name
def      UpperCamelCase__   (	self							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							image.to(device=lowerCAmelCase						)
      _lowerCAmelCase							  =							image  # take image as image_embeddings
      _lowerCAmelCase							  =							image_embeddings.unsqueeze(1						)

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

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

      return image_embeddings





class 				UpperCAmelCase  (    snake_case_     ):




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				PriorTransformer    ,    __snake_case    :				CLIPTokenizer    ,    __snake_case    :				CLIPTextModelWithProjection    ,    __snake_case    :				UnCLIPScheduler    ,    __snake_case    :				Optional[dict] = None    ,    )       ->   int:
            super().__init__()

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

            _lowerCAmelCase							  =							decoder_pipe_kwargs.get("""torch_dtype"""    ,    __snake_case						) or prior.dtype

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

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

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




      def        lowercase__					(    self    :				Any    ,    __snake_case    :				str    ,    __snake_case    :				List[Any]    ,    __snake_case    :				Dict    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				Optional[Union[CLIPTextModelOutput, Tuple]] = None    ,    __snake_case    :				Optional[torch.Tensor] = None    ,    )       ->   str:
            if text_model_output is None:
                  _lowerCAmelCase							  =							len(__snake_case						) if isinstance(__snake_case    ,    __snake_case						) else 1
                  # get prompt text embeddings
                  _lowerCAmelCase							  =							self.tokenizer(
                      __snake_case    ,    padding="""max_length"""    ,    max_length=self.tokenizer.model_max_length    ,    return_tensors="""pt"""    ,    )
                  _lowerCAmelCase							  =							text_inputs.input_ids
                  _lowerCAmelCase							  =							text_inputs.attention_mask.bool().to(__snake_case						)

                  if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                        _lowerCAmelCase							  =							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							  =							text_input_ids[:, : self.tokenizer.model_max_length]

                  _lowerCAmelCase							  =							self.text_encoder(text_input_ids.to(__snake_case						)						)

                  _lowerCAmelCase							  =							text_encoder_output.text_embeds
                  _lowerCAmelCase							  =							text_encoder_output.last_hidden_state

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

            _lowerCAmelCase							  =							text_embeddings.repeat_interleave(__snake_case    ,    dim=0						)
            _lowerCAmelCase							  =							text_encoder_hidden_states.repeat_interleave(__snake_case    ,    dim=0						)
            _lowerCAmelCase							  =							text_mask.repeat_interleave(__snake_case    ,    dim=0						)

            if do_classifier_free_guidance:
                  _lowerCAmelCase							  =							[""""""] * batch_size

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

                  _lowerCAmelCase							  =							uncond_embeddings_text_encoder_output.text_embeds
                  _lowerCAmelCase							  =							uncond_embeddings_text_encoder_output.last_hidden_state

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

                  _lowerCAmelCase							  =							uncond_embeddings.shape[1]
                  _lowerCAmelCase							  =							uncond_embeddings.repeat(1    ,    __snake_case						)
                  _lowerCAmelCase							  =							uncond_embeddings.view(batch_size * num_images_per_prompt    ,    __snake_case						)

                  _lowerCAmelCase							  =							uncond_text_encoder_hidden_states.shape[1]
                  _lowerCAmelCase							  =							uncond_text_encoder_hidden_states.repeat(1    ,    __snake_case    ,    1						)
                  _lowerCAmelCase							  =							uncond_text_encoder_hidden_states.view(
                      batch_size * num_images_per_prompt    ,    __snake_case    ,    -1						)
                  _lowerCAmelCase							  =							uncond_text_mask.repeat_interleave(__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
                  _lowerCAmelCase							  =							torch.cat([uncond_embeddings, text_embeddings]						)
                  _lowerCAmelCase							  =							torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

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

            return text_embeddings, text_encoder_hidden_states, text_mask




      @property
      def        lowercase__					(    self    :				str						)       ->   Dict:
            if self.device != torch.device("""meta"""						) or not hasattr(self.prior    ,    """_hf_hook"""						):
                  return self.device
            for module in self.prior.modules():
                  if (
                      hasattr(__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        lowercase__					(    self    :				Any    ,    __snake_case    :				int    ,    __snake_case    :				Tuple    ,    __snake_case    :				Any    ,    __snake_case    :				Optional[int]    ,    __snake_case    :				List[Any]    ,    __snake_case    :				Dict						)       ->   Union[str, Any]:
            if latents is None:
                  _lowerCAmelCase							  =							randn_tensor(__snake_case    ,    generator=__snake_case    ,    device=__snake_case    ,    dtype=__snake_case						)
            else:
                  if latents.shape != shape:
                        raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
                  _lowerCAmelCase							  =							latents.to(__snake_case						)

            _lowerCAmelCase							  =							latents * scheduler.init_noise_sigma
            return latents




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				Optional[Union[str, torch.device]] = None						)       ->   str:
            self.decoder_pipe.to(__snake_case						)
            super().to(__snake_case						)




      @torch.no_grad()
      def __call__(    self    :				int    ,    __snake_case    :				Optional[Union[str, List[str]]] = None    ,    __snake_case    :				Optional[int] = None    ,    __snake_case    :				Optional[int] = None    ,    __snake_case    :				int = 1    ,    __snake_case    :				int = 25    ,    __snake_case    :				Optional[torch.Generator] = None    ,    __snake_case    :				Optional[torch.FloatTensor] = None    ,    __snake_case    :				Optional[Union[CLIPTextModelOutput, Tuple]] = None    ,    __snake_case    :				Optional[torch.Tensor] = None    ,    __snake_case    :				float = 4.0    ,    __snake_case    :				float = 8.0    ,    __snake_case    :				int = 50    ,    __snake_case    :				Optional[int] = 1    ,    __snake_case    :				float = 0.0    ,    __snake_case    :				Optional[str] = "pil"    ,    __snake_case    :				bool = True    ,    )       ->   Optional[Any]:
            if prompt is not None:
                  if isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							1
                  elif isinstance(__snake_case    ,    __snake_case						):
                        _lowerCAmelCase							  =							len(__snake_case						)
                  else:
                        raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__snake_case						)}"						)
            else:
                  _lowerCAmelCase							  =							text_model_output[0].shape[0]

            _lowerCAmelCase							  =							self._execution_device

            _lowerCAmelCase							  =							batch_size * num_images_per_prompt

            _lowerCAmelCase							  =							prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

            _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							self._encode_prompt(
                __snake_case    ,    __snake_case    ,    __snake_case    ,    __snake_case    ,    __snake_case    ,    __snake_case						)

            # prior

            self.prior_scheduler.set_timesteps(__snake_case    ,    device=__snake_case						)
            _lowerCAmelCase							  =							self.prior_scheduler.timesteps

            _lowerCAmelCase							  =							self.prior.config.embedding_dim

            _lowerCAmelCase							  =							self.prepare_latents(
                (batch_size, embedding_dim)    ,    text_embeddings.dtype    ,    __snake_case    ,    __snake_case    ,    __snake_case    ,    self.prior_scheduler    ,    )

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

                  _lowerCAmelCase							  =							self.prior(
                      __snake_case    ,    timestep=__snake_case    ,    proj_embedding=__snake_case    ,    encoder_hidden_states=__snake_case    ,    attention_mask=__snake_case    ,    ).predicted_image_embedding

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

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

                  _lowerCAmelCase							  =							self.prior_scheduler.step(
                      __snake_case    ,    timestep=__snake_case    ,    sample=__snake_case    ,    generator=__snake_case    ,    prev_timestep=__snake_case    ,    ).prev_sample

            _lowerCAmelCase							  =							self.prior.post_process_latents(__snake_case						)

            _lowerCAmelCase							  =							prior_latents

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













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






