


from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

__lowerCAmelCase		       =							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''




def    snake_case_     (  snake_case			,							snake_case			,							snake_case=8     )  ->				List[str]:
       lowercase__:   List[str]  			=   h // scale_factor**2
       if h % scale_factor**2 != 0:
              new_h += 1
       lowercase__:   Any  			=   w // scale_factor**2
       if w % scale_factor**2 != 0:
              new_w += 1
       return new_h * scale_factor, new_w * scale_factor





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Dict:




        '''simple docstring'''
        super().__init__()

        self.register_modules(
            text_encoder=lowerCAmelCase__				, tokenizer=lowerCAmelCase__				, unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, movq=lowerCAmelCase__				, )
        lowercase__:   Dict  			=   2 ** (len(self.movq.config.block_out_channels ) - 1)
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Union[str, Any]:




        '''simple docstring'''
        if latents is None:
               lowercase__:   List[str]  			=   randn_tensor(lowerCAmelCase__				, generator=lowerCAmelCase__				, device=lowerCAmelCase__				, dtype=lowerCAmelCase__ )
        else:
               if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
               lowercase__:   Any  			=   latents.to(lowerCAmelCase__ )

        lowercase__:   str  			=   latents * scheduler.init_noise_sigma
        return latents
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=None				, )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   len(lowerCAmelCase__ ) if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ) else 1
        # get prompt text embeddings
        lowercase__:   List[str]  			=   self.tokenizer(
            lowerCAmelCase__				, padding='max_length'				, truncation=lowerCAmelCase__				, max_length=77				, return_attention_mask=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__				, return_tensors='pt'				, )

        lowercase__:   Optional[int]  			=   text_inputs.input_ids
        lowercase__:   Union[str, Any]  			=   self.tokenizer(lowerCAmelCase__				, padding='longest'				, return_tensors='pt' ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   List[Any]  			=   self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
               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[str]  			=   text_input_ids.to(lowerCAmelCase__ )
        lowercase__:   Dict  			=   text_inputs.attention_mask.to(lowerCAmelCase__ )

        lowercase__			,						lowercase__:   int  			=   self.text_encoder(
            input_ids=lowerCAmelCase__				, attention_mask=lowerCAmelCase__ )

        lowercase__:   List[str]  			=   prompt_embeds.repeat_interleave(lowerCAmelCase__				, dim=0 )
        lowercase__:   int  			=   text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__				, dim=0 )
        lowercase__:   Optional[int]  			=   text_mask.repeat_interleave(lowerCAmelCase__				, dim=0 )

        if do_classifier_free_guidance:
               lowercase__:   List[str]
               if negative_prompt is None:
                      lowercase__:   Union[str, Any]  			=   [''] * batch_size
               elif type(lowerCAmelCase__ ) is not type(lowerCAmelCase__ ):
                      raise TypeError(
                          F'`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__ )} !='
                          F' {type(lowerCAmelCase__ )}.' )
               elif isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   Union[str, Any]  			=   [negative_prompt]
               elif batch_size != len(lowerCAmelCase__ ):
                      raise ValueError(
                          F'`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__ )}, but `prompt`:'
                          F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                          ' the batch size of `prompt`.' )
               else:
                      lowercase__:   int  			=   negative_prompt

               lowercase__:   int  			=   self.tokenizer(
                   lowerCAmelCase__				, padding='max_length'				, max_length=77				, truncation=lowerCAmelCase__				, return_attention_mask=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__				, return_tensors='pt'				, )
               lowercase__:   List[str]  			=   uncond_input.input_ids.to(lowerCAmelCase__ )
               lowercase__:   int  			=   uncond_input.attention_mask.to(lowerCAmelCase__ )

               lowercase__			,						lowercase__:   Optional[int]  			=   self.text_encoder(
                   input_ids=lowerCAmelCase__				, attention_mask=lowerCAmelCase__ )

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

               lowercase__:   Tuple  			=   negative_prompt_embeds.shape[1]
               lowercase__:   Union[str, Any]  			=   negative_prompt_embeds.repeat(1				, lowerCAmelCase__ )
               lowercase__:   Tuple  			=   negative_prompt_embeds.view(batch_size * num_images_per_prompt				, lowerCAmelCase__ )

               lowercase__:   Optional[int]  			=   uncond_text_encoder_hidden_states.shape[1]
               lowercase__:   Optional[Any]  			=   uncond_text_encoder_hidden_states.repeat(1				, lowerCAmelCase__				, 1 )
               lowercase__:   Union[str, Any]  			=   uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt				, lowerCAmelCase__				, -1 )
               lowercase__:   Dict  			=   uncond_text_mask.repeat_interleave(lowerCAmelCase__				, 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__:   List[Any]  			=   torch.cat([negative_prompt_embeds, prompt_embeds] )
               lowercase__:   List[str]  			=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

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

        return prompt_embeds, text_encoder_hidden_states, text_mask
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0 )					->				Optional[int]:




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

        lowercase__:   List[str]  			=   torch.device(F'cuda:{gpu_id}' )

        lowercase__:   Optional[int]  			=   [
            self.unet,
            self.text_encoder,
            self.movq,
        ]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0 )					->				Optional[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.' )

        lowercase__:   Optional[int]  			=   torch.device(F'cuda:{gpu_id}' )

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

        lowercase__:   List[Any]  			=   None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
               lowercase__			,						lowercase__:   Optional[Any]  			=   cpu_offload_with_hook(lowerCAmelCase__				, lowerCAmelCase__				, prev_module_hook=lowerCAmelCase__ )

        if self.safety_checker is not None:
               lowercase__			,						lowercase__:   Union[str, Any]  			=   cpu_offload_with_hook(self.safety_checker				, lowerCAmelCase__				, prev_module_hook=lowerCAmelCase__ )

        # We'll offload the last model manually.
        lowercase__:   int  			=   hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        if not hasattr(self.unet				, '_hf_hook' ):
               return self.device
        for module in self.unet.modules():
               if (
                   hasattr(lowerCAmelCase__				, '_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(lowerCAmelCase__ )
 def __call__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = 512				, lowerCAmelCase__ = 512				, lowerCAmelCase__ = 100				, lowerCAmelCase__ = 4.0				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = "pil"				, lowerCAmelCase__ = True				, )					->				str:




        '''simple docstring'''

        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Tuple  			=   1
        elif isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   List[str]  			=   len(lowerCAmelCase__ )
        else:
               raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__ )}' )

        lowercase__:   int  			=   self._execution_device

        lowercase__:   str  			=   batch_size * num_images_per_prompt
        lowercase__:   Optional[Any]  			=   guidance_scale > 1.0

        lowercase__			,						lowercase__			,						lowercase__:   Optional[int]  			=   self._encode_prompt(
            lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )

        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   List[Any]  			=   torch.cat(lowerCAmelCase__				, dim=0 )
        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Optional[Any]  			=   torch.cat(lowerCAmelCase__				, dim=0 )

        if do_classifier_free_guidance:
               lowercase__:   List[str]  			=   image_embeds.repeat_interleave(lowerCAmelCase__				, dim=0 )
               lowercase__:   Tuple  			=   negative_image_embeds.repeat_interleave(lowerCAmelCase__				, dim=0 )

        lowercase__:   Dict  			=   torch.cat([negative_image_embeds, image_embeds]				, dim=0 ).to(
            dtype=prompt_embeds.dtype				, device=lowerCAmelCase__ )

        self.scheduler.set_timesteps(lowerCAmelCase__				, device=lowerCAmelCase__ )
        lowercase__:   int  			=   self.scheduler.timesteps

        lowercase__:   Union[str, Any]  			=   self.unet.config.in_channels

        lowercase__			,						lowercase__:   List[Any]  			=   get_new_h_w(lowerCAmelCase__				, lowerCAmelCase__				, self.movq_scale_factor )

        # create initial latent
        lowercase__:   Any  			=   self.prepare_latents(
            (batch_size, num_channels_latents, height, width)				, text_encoder_hidden_states.dtype				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, self.scheduler				, )

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

               lowercase__:   Dict  			=   {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
               lowercase__:   str  			=   self.unet(
                   sample=lowerCAmelCase__				, timestep=lowerCAmelCase__				, encoder_hidden_states=lowerCAmelCase__				, added_cond_kwargs=lowerCAmelCase__				, return_dict=lowerCAmelCase__				, )[0]

               if do_classifier_free_guidance:
                      lowercase__			,						lowercase__:   Optional[Any]  			=   noise_pred.split(latents.shape[1]				, dim=1 )
                      lowercase__			,						lowercase__:   List[str]  			=   noise_pred.chunk(2 )
                      lowercase__			,						lowercase__:   List[Any]  			=   variance_pred.chunk(2 )
                      lowercase__:   Dict  			=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      lowercase__:   Tuple  			=   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"]
               ):
                      lowercase__			,						lowercase__:   List[Any]  			=   noise_pred.split(latents.shape[1]				, dim=1 )

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__:   Dict  			=   self.scheduler.step(
                   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, generator=lowerCAmelCase__				, ).prev_sample
        # post-processing
        lowercase__:   Optional[int]  			=   self.movq.decode(lowerCAmelCase__				, force_not_quantize=lowerCAmelCase__ )['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"]:
               lowercase__:   Optional[int]  			=   image * 0.5 + 0.5
               lowercase__:   int  			=   image.clamp(0				, 1 )
               lowercase__:   Dict  			=   image.cpu().permute(0				, 2				, 3				, 1 ).float().numpy()

        if output_type == "pil":
               lowercase__:   List[Any]  			=   self.numpy_to_pil(lowerCAmelCase__ )

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase__ )






def    snake_case_     (  snake_case			,							snake_case     )  ->				bool:
       return numa ^ numa < 0


if __name__ == "__main__":
 import doctest

 doctest.testmod()


