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__     						=			'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=8     ):
       lowerCAmelCase__					     = h // scale_factor**2
       if h % scale_factor**2 != 0:
              new_h += 1
       lowerCAmelCase__					     = w // scale_factor**2
       if w % scale_factor**2 != 0:
              new_w += 1
       return new_h * scale_factor, new_w * scale_factor



class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  List[str]      ,   lowercase__ :  MultilingualCLIP      ,   lowercase__ :  XLMRobertaTokenizer      ,   lowercase__ :  UNetaDConditionModel      ,   lowercase__ :  Union[DDIMScheduler, DDPMScheduler]      ,   lowercase__ :  VQModel      ,   ):







        '''simple docstring'''



        super().__init__()

        self.register_modules(
            text_encoder=lowercase__      ,   tokenizer=lowercase__      ,   unet=lowercase__      ,   scheduler=lowercase__      ,   movq=lowercase__      ,   )
        lowerCAmelCase__					     = 2 ** (len(self.movq.config.block_out_channels) - 1)




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]      ,   lowercase__ :  str      ,   lowercase__ :  List[Any]      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  str):







        '''simple docstring'''



        if latents is None:
               lowerCAmelCase__					     = randn_tensor(lowercase__      ,   generator=lowercase__      ,   device=lowercase__      ,   dtype=lowercase__)
        else:
               if latents.shape != shape:
                      raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""")
               lowerCAmelCase__					     = latents.to(lowercase__)

        lowerCAmelCase__					     = latents * scheduler.init_noise_sigma
        return latents




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple      ,   lowercase__ :  Union[str, Any]      ,   lowercase__ :  List[str]      ,   lowercase__ :  List[Any]=None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = len(lowercase__) if isinstance(lowercase__      ,   lowercase__) else 1
        # get prompt text embeddings
        lowerCAmelCase__					     = self.tokenizer(
            lowercase__      ,   padding='max_length'      ,   truncation=lowercase__      ,   max_length=77      ,   return_attention_mask=lowercase__      ,   add_special_tokens=lowercase__      ,   return_tensors='pt'      ,   )

        lowerCAmelCase__					     = text_inputs.input_ids
        lowerCAmelCase__					     = self.tokenizer(lowercase__      ,   padding='longest'      ,   return_tensors='pt').input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase__      ,   lowercase__):
               lowerCAmelCase__					     = 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}""")

        lowerCAmelCase__					     = text_input_ids.to(lowercase__)
        lowerCAmelCase__					     = text_inputs.attention_mask.to(lowercase__)

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

        lowerCAmelCase__					     = prompt_embeds.repeat_interleave(lowercase__      ,   dim=0)
        lowerCAmelCase__					     = text_encoder_hidden_states.repeat_interleave(lowercase__      ,   dim=0)
        lowerCAmelCase__					     = text_mask.repeat_interleave(lowercase__      ,   dim=0)

        if do_classifier_free_guidance:
               lowerCAmelCase__					     = 42
               if negative_prompt is None:
                      lowerCAmelCase__					     = [''] * batch_size
               elif type(lowercase__) is not type(lowercase__):
                      raise TypeError(
                          F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowercase__)} !="""
                          F""" {type(lowercase__)}.""")
               elif isinstance(lowercase__      ,   lowercase__):
                      lowerCAmelCase__					     = [negative_prompt]
               elif batch_size != len(lowercase__):
                      raise ValueError(
                          F"""`negative_prompt`: {negative_prompt} has batch size {len(lowercase__)}, but `prompt`:"""
                          F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                          ' the batch size of `prompt`.')
               else:
                      lowerCAmelCase__					     = negative_prompt

               lowerCAmelCase__					     = self.tokenizer(
                   lowercase__      ,   padding='max_length'      ,   max_length=77      ,   truncation=lowercase__      ,   return_attention_mask=lowercase__      ,   add_special_tokens=lowercase__      ,   return_tensors='pt'      ,   )
               lowerCAmelCase__					     = uncond_input.input_ids.to(lowercase__)
               lowerCAmelCase__					     = uncond_input.attention_mask.to(lowercase__)

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

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

               lowerCAmelCase__					     = negative_prompt_embeds.shape[1]
               lowerCAmelCase__					     = negative_prompt_embeds.repeat(1      ,   lowercase__)
               lowerCAmelCase__					     = negative_prompt_embeds.view(batch_size * num_images_per_prompt      ,   lowercase__)

               lowerCAmelCase__					     = uncond_text_encoder_hidden_states.shape[1]
               lowerCAmelCase__					     = uncond_text_encoder_hidden_states.repeat(1      ,   lowercase__      ,   1)
               lowerCAmelCase__					     = uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt      ,   lowercase__      ,   -1)
               lowerCAmelCase__					     = uncond_text_mask.repeat_interleave(lowercase__      ,   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([negative_prompt_embeds, prompt_embeds])
               lowerCAmelCase__					     = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])

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

        return prompt_embeds, text_encoder_hidden_states, text_mask




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  List[str]=0):







        '''simple docstring'''



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

        lowerCAmelCase__					     = torch.device(F"""cuda:{gpu_id}""")

        lowerCAmelCase__					     = [
            self.unet,
            self.text_encoder,
            self.movq,
        ]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  int=0):







        '''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.')

        lowerCAmelCase__					     = torch.device(F"""cuda:{gpu_id}""")

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

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

        if self.safety_checker is not None:
               lowerCAmelCase__     , lowerCAmelCase__					     = cpu_offload_with_hook(self.safety_checker      ,   lowercase__      ,   prev_module_hook=lowercase__)

        # We'll offload the last model manually.
        lowerCAmelCase__					     = hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        if not hasattr(self.unet      ,   '_hf_hook'):
               return self.device
        for module in self.unet.modules():
               if (
                   hasattr(lowercase__      ,   '_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(lowercase__)
 def __call__(      self :  str      ,   lowercase__ :  Union[str, List[str]]      ,   lowercase__ :  Union[torch.FloatTensor, List[torch.FloatTensor]]      ,   lowercase__ :  Union[torch.FloatTensor, List[torch.FloatTensor]]      ,   lowercase__ :  Optional[Union[str, List[str]]] = None      ,   lowercase__ :  int = 512      ,   lowercase__ :  int = 512      ,   lowercase__ :  int = 100      ,   lowercase__ :  float = 4.0      ,   lowercase__ :  int = 1      ,   lowercase__ :  Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,   lowercase__ :  Optional[torch.FloatTensor] = None      ,   lowercase__ :  Optional[str] = "pil"      ,   lowercase__ :  bool = True      ,   ):







        '''simple docstring'''



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

        lowerCAmelCase__					     = self._execution_device

        lowerCAmelCase__					     = batch_size * num_images_per_prompt
        lowerCAmelCase__					     = guidance_scale > 1.0

        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self._encode_prompt(
            lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__      ,   lowercase__)

        if isinstance(lowercase__      ,   lowercase__):
               lowerCAmelCase__					     = torch.cat(lowercase__      ,   dim=0)
        if isinstance(lowercase__      ,   lowercase__):
               lowerCAmelCase__					     = torch.cat(lowercase__      ,   dim=0)

        if do_classifier_free_guidance:
               lowerCAmelCase__					     = image_embeds.repeat_interleave(lowercase__      ,   dim=0)
               lowerCAmelCase__					     = negative_image_embeds.repeat_interleave(lowercase__      ,   dim=0)

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

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

        lowerCAmelCase__					     = self.unet.config.in_channels

        lowerCAmelCase__     , lowerCAmelCase__					     = get_new_h_w(lowercase__      ,   lowercase__      ,   self.movq_scale_factor)

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

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

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

               if do_classifier_free_guidance:
                      lowerCAmelCase__     , lowerCAmelCase__					     = noise_pred.split(latents.shape[1]      ,   dim=1)
                      lowerCAmelCase__     , lowerCAmelCase__					     = noise_pred.chunk(2)
                      lowerCAmelCase__     , lowerCAmelCase__					     = variance_pred.chunk(2)
                      lowerCAmelCase__					     = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      lowerCAmelCase__					     = 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"]
               ):
                      lowerCAmelCase__     , lowerCAmelCase__					     = noise_pred.split(latents.shape[1]      ,   dim=1)

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

        if output_type == "pil":
               lowerCAmelCase__					     = self.numpy_to_pil(lowercase__)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowercase__)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XCLIPModel',
        'XCLIPPreTrainedModel',
        'XCLIPTextModel',
        'XCLIPVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_x_clip import (
        XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XCLIPConfig,
        XCLIPTextConfig,
        XCLIPVisionConfig,
    )
    from .processing_x_clip import XCLIPProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_x_clip import (
            XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            XCLIPModel,
            XCLIPPreTrainedModel,
            XCLIPTextModel,
            XCLIPVisionModel,
        )

else:
    import sys

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