

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''M-CLIP'''




	def __init__(     self						:     List[str]					,      __A						:     str=1024					,      __A						:     Optional[Any]=768					,      **__A						:     Any							):
		__A :			List[str]		    =			transformerDimSize
		__A :			int		    =			imageDimSize
		super().__init__(**__A							)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   List[str]     					=				MCLIPConfig




	def __init__(     self						:     Union[str, Any]					,      __A						:     Dict					,      *__A						:     Any					,      **__A						:     Union[str, Any]							):
		super().__init__(__A					,      *__A					,      **__A							)
		__A :			Any		    =			XLMRobertaModel(__A							)
		__A :			Tuple		    =			torch.nn.Linear(
		    in_features=config.transformerDimensions					,      out_features=config.numDims							)




	def        lowerCAmelCase_       (     self						:     Dict					,      __A						:     Dict					,      __A						:     int							):
		__A :			Dict		    =			self.transformer(input_ids=__A					,      attention_mask=__A							)[0]
		__A :			Optional[int]		    =			(embs * attention_mask.unsqueeze(2							)).sum(dim=1							) / attention_mask.sum(dim=1							)[:, None]
		return self.LinearTransformation(__A							), embs







from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__lowerCamelCase				=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					int=8       ):
       snake_case			:  Optional[int]     							= height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       snake_case			:  Union[str, Any]     							= width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   int       ,		snake_case__						:   UNetaDConditionModel       ,		snake_case__						:   DDPMScheduler       ,		snake_case__						:   VQModel       ,		)						->      Tuple:


          '''simple docstring'''


          super().__init__()

          self.register_modules(
              unet=snake_case__       ,		scheduler=snake_case__       ,		movq=snake_case__       ,		)
          snake_case			:  List[Any]     							= 2 ** (len(self.movq.config.block_out_channels					) - 1)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   List[Any]       ,		snake_case__						:   List[str]					)						->      Optional[Any]:


          '''simple docstring'''


          if latents is None:
                 snake_case			:  int     							= 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}"""					)
                 snake_case			:  Tuple     							= latents.to(snake_case__					)

          snake_case			:  int     							= latents * scheduler.init_noise_sigma
          return latents



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Optional[Any]=0					)						->      Tuple:


          '''simple docstring'''


          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError("Please install accelerate via `pip install accelerate`"					)

          snake_case			:  Optional[int]     							= torch.device(f"""cuda:{gpu_id}"""					)

          snake_case			:  List[str]     							= [
              self.unet,
              self.movq,
          ]
          for cpu_offloaded_model in models:
                 if cpu_offloaded_model is not None:
                        cpu_offload(snake_case__       ,		snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Union[str, Any]=0					)						->      Union[str, Any]:


          '''simple docstring'''


          if is_accelerate_available() and is_accelerate_version(">="       ,		"0.17.0.dev0"					):
                 from accelerate import cpu_offload_with_hook
          else:
                 raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."					)

          snake_case			:  Union[str, Any]     							= torch.device(f"""cuda:{gpu_id}"""					)

          if self.device.type != "cpu":
                 self.to("cpu"       ,		silence_dtype_warnings=snake_case__					)
                 torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          snake_case			:  Tuple     							= None
          for cpu_offloaded_model in [self.unet, self.movq]:
                 snake_case							,   snake_case			:  int     							= cpu_offload_with_hook(snake_case__       ,		snake_case__       ,		prev_module_hook=snake_case__					)

          # We'll offload the last model manually.
          snake_case			:  Tuple     							= hook



   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          if not hasattr(self.unet       ,		"_hf_hook"					):
                 return self.device
          for module in self.unet.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



   @torch.no_grad()
   @replace_example_docstring(snake_case__					)
   def __call__(self						:   int       ,		snake_case__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		snake_case__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		snake_case__						:   int = 5_12       ,		snake_case__						:   int = 5_12       ,		snake_case__						:   int = 1_00       ,		snake_case__						:   float = 4.0       ,		snake_case__						:   int = 1       ,		snake_case__						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		snake_case__						:   Optional[torch.FloatTensor] = None       ,		snake_case__						:   Optional[str] = "pil"       ,		snake_case__						:   bool = True       ,		)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self._execution_device

          snake_case			:  List[str]     							= guidance_scale > 1.0

          if isinstance(snake_case__       ,		snake_case__					):
                 snake_case			:  Optional[int]     							= torch.cat(snake_case__       ,		dim=0					)
          snake_case			:  Optional[int]     							= image_embeds.shape[0] * num_images_per_prompt
          if isinstance(snake_case__       ,		snake_case__					):
                 snake_case			:  List[Any]     							= torch.cat(snake_case__       ,		dim=0					)

          if do_classifier_free_guidance:
                 snake_case			:  str     							= image_embeds.repeat_interleave(snake_case__       ,		dim=0					)
                 snake_case			:  Optional[int]     							= negative_image_embeds.repeat_interleave(snake_case__       ,		dim=0					)

          snake_case			:  Optional[Any]     							= torch.cat([negative_image_embeds, image_embeds]       ,		dim=0					).to(dtype=self.unet.dtype       ,		device=snake_case__					)

          self.scheduler.set_timesteps(snake_case__       ,		device=snake_case__					)
          snake_case			:  Optional[int]     							= self.scheduler.timesteps

          snake_case			:  int     							= self.unet.config.in_channels

          snake_case							,   snake_case			:  Any     							= downscale_height_and_width(snake_case__       ,		snake_case__       ,		self.movq_scale_factor					)

          # create initial latent
          snake_case			:  List[Any]     							= self.prepare_latents(
              (batch_size, num_channels_latents, height, width)       ,		image_embeds.dtype       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		self.scheduler       ,		)

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

                 snake_case			:  Tuple     							= {"image_embeds": image_embeds}
                 snake_case			:  str     							= self.unet(
                     sample=snake_case__       ,		timestep=snake_case__       ,		encoder_hidden_states=snake_case__       ,		added_cond_kwargs=snake_case__       ,		return_dict=snake_case__       ,		)[0]

                 if do_classifier_free_guidance:
                        snake_case							,   snake_case			:  List[Any]     							= noise_pred.split(latents.shape[1]       ,		dim=1					)
                        snake_case							,   snake_case			:  Any     							= noise_pred.chunk(2					)
                        snake_case							,   snake_case			:  Union[str, Any]     							= variance_pred.chunk(2					)
                        snake_case			:  Optional[Any]     							= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                        snake_case			:  str     							= torch.cat([noise_pred, variance_pred_text]       ,		dim=1					)

                 if not (
                     hasattr(self.scheduler.config       ,		"variance_type"					)
                     and self.scheduler.config.variance_type in ["learned", "learned_range"]
                 ):
                        snake_case							,   snake_case			:  List[Any]     							= noise_pred.split(latents.shape[1]       ,		dim=1					)

                 # compute the previous noisy sample x_t -> x_t-1
                 snake_case			:  Tuple     							= self.scheduler.step(
                     snake_case__       ,		snake_case__       ,		snake_case__       ,		generator=snake_case__       ,		)[0]
          # post-processing
          snake_case			:  Optional[Any]     							= self.movq.decode(snake_case__       ,		force_not_quantize=snake_case__					)["sample"]

          if output_type not in ["pt", "np", "pil"]:
                 raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""					)

          if output_type in ["np", "pil"]:
                 snake_case			:  Union[str, Any]     							= image * 0.5 + 0.5
                 snake_case			:  int     							= image.clamp(0       ,		1					)
                 snake_case			:  Optional[int]     							= image.cpu().permute(0       ,		2       ,		3       ,		1					).float().numpy()

          if output_type == "pil":
                 snake_case			:  Any     							= self.numpy_to_pil(snake_case__					)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=snake_case__					)





