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



def 						__snake_case (       self       :     List[str]      ,  lowerCAmelCase       :     str      ,  lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     Union[str, Any]      ,  lowerCAmelCase       :     int			):
 __UpperCAmelCase         =					image.to(device=lowerCAmelCase			)
 __UpperCAmelCase         =					image  # take image as image_embeddings
 __UpperCAmelCase         =					image_embeddings.unsqueeze(1			)

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

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

 return image_embeddings

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    Optional[Any]			,a:    PriorTransformer			,a:    CLIPTokenizer			,a:    CLIPTextModelWithProjection			,a:    UnCLIPScheduler			,a:    Optional[dict] = None			,):
    super().__init__()

    __UpperCAmelCase         =					{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

    __UpperCAmelCase         =					decoder_pipe_kwargs.get('torch_dtype'			,a	) or prior.dtype

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

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

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





   def 			snake_case	(							self:    List[Any]			,a:    Tuple			,a:    Tuple			,a:    Dict			,a:    Tuple			,a:    Optional[Union[CLIPTextModelOutput, Tuple]] = None			,a:    Optional[torch.Tensor] = None			,):
    if text_model_output is None:
     __UpperCAmelCase         =					len(a	) if isinstance(a			,a	) else 1
     # get prompt text embeddings
     __UpperCAmelCase         =					self.tokenizer(
         a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,return_tensors='pt'			,)
     __UpperCAmelCase         =					text_inputs.input_ids
     __UpperCAmelCase         =					text_inputs.attention_mask.bool().to(a	)

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

     __UpperCAmelCase         =					self.text_encoder(text_input_ids.to(a	)	)

     __UpperCAmelCase         =					text_encoder_output.text_embeds
     __UpperCAmelCase         =					text_encoder_output.last_hidden_state

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

    __UpperCAmelCase         =					text_embeddings.repeat_interleave(a			,dim=0	)
    __UpperCAmelCase         =					text_encoder_hidden_states.repeat_interleave(a			,dim=0	)
    __UpperCAmelCase         =					text_mask.repeat_interleave(a			,dim=0	)

    if do_classifier_free_guidance:
     __UpperCAmelCase         =					[''] * batch_size

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

     __UpperCAmelCase         =					uncond_embeddings_text_encoder_output.text_embeds
     __UpperCAmelCase         =					uncond_embeddings_text_encoder_output.last_hidden_state

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

     __UpperCAmelCase         =					uncond_embeddings.shape[1]
     __UpperCAmelCase         =					uncond_embeddings.repeat(1			,a	)
     __UpperCAmelCase         =					uncond_embeddings.view(batch_size * num_images_per_prompt			,a	)

     __UpperCAmelCase         =					uncond_text_encoder_hidden_states.shape[1]
     __UpperCAmelCase         =					uncond_text_encoder_hidden_states.repeat(1			,a			,1	)
     __UpperCAmelCase         =					uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt			,a			,-1	)
     __UpperCAmelCase         =					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
     __UpperCAmelCase         =					torch.cat([uncond_embeddings, text_embeddings]	)
     __UpperCAmelCase         =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

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

    return text_embeddings, text_encoder_hidden_states, text_mask





   @property
   def 			snake_case	(							self:    List[str]	):
    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 			snake_case	(							self:    List[str]			,a:    List[Any]			,a:    Optional[Any]			,a:    List[Any]			,a:    Tuple			,a:    str			,a:    Union[str, Any]	):
    if latents is None:
     __UpperCAmelCase         =					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}"""	)
     __UpperCAmelCase         =					latents.to(a	)

    __UpperCAmelCase         =					latents * scheduler.init_noise_sigma
    return latents





   def 			snake_case	(							self:    int			,a:    Optional[Union[str, torch.device]] = None	):
    self.decoder_pipe.to(a	)
    super().to(a	)





   @torch.no_grad()
   def __call__(							self:    str			,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			,):
    if prompt is not None:
     if isinstance(a			,a	):
      __UpperCAmelCase         =					1
     elif isinstance(a			,a	):
      __UpperCAmelCase         =					len(a	)
     else:
      raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a	)}"""	)
    else:
     __UpperCAmelCase         =					text_model_output[0].shape[0]

    __UpperCAmelCase         =					self._execution_device

    __UpperCAmelCase         =					batch_size * num_images_per_prompt

    __UpperCAmelCase         =					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

    __UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase         =					self._encode_prompt(
        a			,a			,a			,a			,a			,a	)

    # prior

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

    __UpperCAmelCase         =					self.prior.config.embedding_dim

    __UpperCAmelCase         =					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
     __UpperCAmelCase         =					torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents

     __UpperCAmelCase         =					self.prior(
         a			,timestep=a			,proj_embedding=a			,encoder_hidden_states=a			,attention_mask=a			,).predicted_image_embedding

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

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

     __UpperCAmelCase         =					self.prior_scheduler.step(
         a			,timestep=a			,sample=a			,generator=a			,prev_timestep=a			,).prev_sample

    __UpperCAmelCase         =					self.prior.post_process_latents(a	)

    __UpperCAmelCase         =					prior_latents

    __UpperCAmelCase         =					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'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
     from .testing_utils import (
         floats_tensor,
         load_hf_numpy,
         load_image,
         load_numpy,
         load_pt,
         nightly,
         parse_flag_from_env,
         print_tensor_test,
         require_torch_a,
         require_torch_gpu,
         skip_mps,
         slow,
         torch_all_close,
         torch_device,
     )
     from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_UpperCamelCase				:		List[Any]       			=		get_logger(__name__)



def 						__snake_case (       lowerCAmelCase       :     List[str]			):
 if version.parse(lowerCAmelCase			) < version.parse(lowerCAmelCase			):
  if "dev" in min_version:
   __UpperCAmelCase         =					(
       'This example requires a source install from HuggingFace diffusers (see '
       '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
   )
  else:
   __UpperCAmelCase         =					F"""This example requires a minimum version of {min_version},"""
  error_message += F""" but the version found is {__version__}.\n"""
  raise ImportError(lowerCAmelCase			)



