
"""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


UpperCamelCase_						   =   logging.get_logger(__name__)  # pylint: disable=invalid-name
def    UpperCamelCase						(					self ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->int:

   """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 							snake_case       (     SCREAMING_SNAKE_CASE_		):







   def __init__( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None       ,							)   ->Optional[int]:
      super().__init__()

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

      a_						   =     decoder_pipe_kwargs.get("torch_dtype"       ,							__UpperCAmelCase) or prior.dtype

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

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

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







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							)   ->str:
      if text_model_output is None:
         a_						   =     len(__UpperCAmelCase) if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase) else 1
         # get prompt text embeddings
         a_						   =     self.tokenizer(
             __UpperCAmelCase       ,							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(__UpperCAmelCase)

         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(__UpperCAmelCase))

         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(__UpperCAmelCase       ,							dim=0)
      a_						   =     text_encoder_hidden_states.repeat_interleave(__UpperCAmelCase       ,							dim=0)
      a_						   =     text_mask.repeat_interleave(__UpperCAmelCase       ,							dim=0)

      if do_classifier_free_guidance:
         a_						   =     [""] * batch_size

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

         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       ,							__UpperCAmelCase)
         a_						   =     uncond_embeddings.view(batch_size * num_images_per_prompt       ,							__UpperCAmelCase)

         a_						   =     uncond_text_encoder_hidden_states.shape[1]
         a_						   =     uncond_text_encoder_hidden_states.repeat(1       ,							__UpperCAmelCase       ,							1)
         a_						   =     uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt       ,							__UpperCAmelCase       ,							-1)
         a_						   =     uncond_text_mask.repeat_interleave(__UpperCAmelCase       ,							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)   ->Tuple:
      if self.device != torch.device("meta") or not hasattr(self.prior       ,							"_hf_hook"):
         return self.device
      for module in self.prior.modules():
         if (
             hasattr(__UpperCAmelCase       ,							"_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       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)   ->Tuple:
      if latents is None:
         a_						   =     randn_tensor(__UpperCAmelCase       ,							generator=__UpperCAmelCase       ,							device=__UpperCAmelCase       ,							dtype=__UpperCAmelCase)
      else:
         if latents.shape != shape:
            raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''')
         a_						   =     latents.to(__UpperCAmelCase)

      a_						   =     latents * scheduler.init_noise_sigma
      return latents







   def    UpperCAmelCase__	( self       ,							__UpperCAmelCase = None)   ->Tuple:
      self.decoder_pipe.to(__UpperCAmelCase)
      super().to(__UpperCAmelCase)







   @torch.no_grad()
   def __call__( self       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = 1       ,							__UpperCAmelCase = 25       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = None       ,							__UpperCAmelCase = 4.0       ,							__UpperCAmelCase = 8.0       ,							__UpperCAmelCase = 50       ,							__UpperCAmelCase = 1       ,							__UpperCAmelCase = 0.0       ,							__UpperCAmelCase = "pil"       ,							__UpperCAmelCase = True       ,							)   ->str:
      if prompt is not None:
         if isinstance(__UpperCAmelCase       ,							__UpperCAmelCase):
            a_						   =     1
         elif isinstance(__UpperCAmelCase       ,							__UpperCAmelCase):
            a_						   =     len(__UpperCAmelCase)
         else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase)}''')
      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(
          __UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase)

      # prior

      self.prior_scheduler.set_timesteps(__UpperCAmelCase       ,							device=__UpperCAmelCase)
      a_						   =     self.prior_scheduler.timesteps

      a_						   =     self.prior.config.embedding_dim

      a_						   =     self.prepare_latents(
          (batch_size, embedding_dim)       ,							text_embeddings.dtype       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							self.prior_scheduler       ,							)

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

      a_						   =     self.prior.post_process_latents(__UpperCAmelCase)

      a_						   =     prior_latents

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

"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase_						   =   {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileViTV2ForImageClassification',
          'MobileViTV2ForSemanticSegmentation',
          'MobileViTV2Model',
          'MobileViTV2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mobilevitva import (
          MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileViTVaConfig,
          MobileViTVaOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevitva import (
                MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTVaForImageClassification,
                MobileViTVaForSemanticSegmentation,
                MobileViTVaModel,
                MobileViTVaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)