from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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

__lowerCamelCase			: str							   =		'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Optional[int]=8	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  SCREAMING_SNAKE_CASE__										=      width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Any=5_12 ,     __UpperCamelCase    :       Optional[int]=5_12	)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      pil_image.resize((w, h) ,     resample=Image.BICUBIC ,     reducing_gap=1	)
  SCREAMING_SNAKE_CASE__										=      np.array(pil_image.convert("""RGB"""	)	)
  SCREAMING_SNAKE_CASE__										=      arr.astype(np.floataa	) / 1_27.5 - 1
  SCREAMING_SNAKE_CASE__										=      np.transpose(snake_case__ ,     [2, 0, 1]	)
  SCREAMING_SNAKE_CASE__										=      torch.from_numpy(snake_case__	).unsqueeze(0	)
  return image







class __snake_case				(						__snake_case		):




   def __init__(    self			:				Any       ,	_lowercase			:				Tuple       ,	_lowercase			:				Dict       ,	_lowercase			:				int       ,	):



     """simple docstring"""






     super().__init__()

     self.register_modules(
         unet=_lowercase       ,	scheduler=_lowercase       ,	movq=_lowercase       ,	)
     SCREAMING_SNAKE_CASE__										=      2 ** (len(self.movq.config.block_out_channels      ) - 1)




   def 	__a						(    self			:				str       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      min(int(num_inference_steps * strength      )       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      max(num_inference_steps - init_timestep       ,	0      )
     SCREAMING_SNAKE_CASE__										=      self.scheduler.timesteps[t_start:]

     return timesteps, num_inference_steps - t_start




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				List[Any]       ,	_lowercase			:				str       ,	_lowercase			:				List[str]       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Any=None      ):



     """simple docstring"""






     if not isinstance(_lowercase       ,	(torch.Tensor, PIL.Image.Image, list)      ):
       raise ValueError(
           f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase      )}"""      )

     SCREAMING_SNAKE_CASE__										=      image.to(device=_lowercase       ,	dtype=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      batch_size * num_images_per_prompt

     if image.shape[1] == 4:
       SCREAMING_SNAKE_CASE__										=      image

     else:
       if isinstance(_lowercase       ,	_lowercase      ) and len(_lowercase      ) != batch_size:
         raise ValueError(
             f"""You have passed a list of generators of length {len(_lowercase      )}, but requested an effective batch"""
             f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""      )

       elif isinstance(_lowercase       ,	_lowercase      ):
         SCREAMING_SNAKE_CASE__										=      [
             self.movq.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(_lowercase      )
         ]
         SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase       ,	dim=0      )
       else:
         SCREAMING_SNAKE_CASE__										=      self.movq.encode(_lowercase      ).latent_dist.sample(_lowercase      )

       SCREAMING_SNAKE_CASE__										=      self.movq.config.scaling_factor * init_latents

     SCREAMING_SNAKE_CASE__										=      torch.cat([init_latents]       ,	dim=0      )

     SCREAMING_SNAKE_CASE__										=      init_latents.shape
     SCREAMING_SNAKE_CASE__										=      randn_tensor(_lowercase       ,	generator=_lowercase       ,	device=_lowercase       ,	dtype=_lowercase      )

     # get latents
     SCREAMING_SNAKE_CASE__										=      self.scheduler.add_noise(_lowercase       ,	_lowercase       ,	_lowercase      )

     SCREAMING_SNAKE_CASE__										=      init_latents

     return latents




   def 	__a						(    self			:				Any       ,	_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`"""      )

     SCREAMING_SNAKE_CASE__										=      torch.device(f"""cuda:{gpu_id}"""      )

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




   def 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Optional[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."""      )

     SCREAMING_SNAKE_CASE__										=      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)

     SCREAMING_SNAKE_CASE__										=      None
     for cpu_offloaded_model in [self.unet, self.movq]:
       SCREAMING_SNAKE_CASE__										=      cpu_offload_with_hook(_lowercase       ,	_lowercase       ,	prev_module_hook=_lowercase      )

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




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__a						(    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			:				Any       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[int] = 5_12       ,	_lowercase			:				Union[str, Any] = 5_12       ,	_lowercase			:				Union[str, Any] = 1_00       ,	_lowercase			:				Optional[Any] = 4.0       ,	_lowercase			:				Optional[int] = 0.3       ,	_lowercase			:				List[str] = 1       ,	_lowercase			:				Any = None       ,	_lowercase			:				Union[str, Any] = "pil"       ,	_lowercase			:				Optional[Any] = True       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self._execution_device

     SCREAMING_SNAKE_CASE__										=      guidance_scale > 1.0

     if isinstance(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase       ,	dim=0      )
     SCREAMING_SNAKE_CASE__										=      image_embeds.shape[0]
     if isinstance(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      torch.cat(_lowercase       ,	dim=0      )

     if do_classifier_free_guidance:
       SCREAMING_SNAKE_CASE__										=      image_embeds.repeat_interleave(_lowercase       ,	dim=0      )
       SCREAMING_SNAKE_CASE__										=      negative_image_embeds.repeat_interleave(_lowercase       ,	dim=0      )

     SCREAMING_SNAKE_CASE__										=      torch.cat([negative_image_embeds, image_embeds]       ,	dim=0      ).to(dtype=self.unet.dtype       ,	device=_lowercase      )

     if not isinstance(_lowercase       ,	_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      [image]
     if not all(isinstance(_lowercase       ,	(PIL.Image.Image, torch.Tensor)      ) for i in image      ):
       raise ValueError(
           f"""Input is in incorrect format: {[type(_lowercase      ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""      )

     SCREAMING_SNAKE_CASE__										=      torch.cat([prepare_image(_lowercase       ,	_lowercase       ,	_lowercase      ) for i in image]       ,	dim=0      )
     SCREAMING_SNAKE_CASE__										=      image.to(dtype=image_embeds.dtype       ,	device=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      self.movq.encode(_lowercase      )['''latents''']
     SCREAMING_SNAKE_CASE__										=      latents.repeat_interleave(_lowercase       ,	dim=0      )
     self.scheduler.set_timesteps(_lowercase       ,	device=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.get_timesteps(_lowercase       ,	_lowercase       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      timesteps[:1].repeat(batch_size * num_images_per_prompt      )
     SCREAMING_SNAKE_CASE__										=      downscale_height_and_width(_lowercase       ,	_lowercase       ,	self.movq_scale_factor      )
     SCREAMING_SNAKE_CASE__										=      self.prepare_latents(
         _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase       ,	image_embeds.dtype       ,	_lowercase       ,	_lowercase      )
     for i, t in enumerate(self.progress_bar(_lowercase      )      ):
       # expand the latents if we are doing classifier free guidance
       SCREAMING_SNAKE_CASE__										=      torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

       SCREAMING_SNAKE_CASE__										=      {'''image_embeds''': image_embeds}
       SCREAMING_SNAKE_CASE__										=      self.unet(
           sample=_lowercase       ,	timestep=_lowercase       ,	encoder_hidden_states=_lowercase       ,	added_cond_kwargs=_lowercase       ,	return_dict=_lowercase       ,	)[0]

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

       # compute the previous noisy sample x_t -> x_t-1
       SCREAMING_SNAKE_CASE__										=      self.scheduler.step(
           _lowercase       ,	_lowercase       ,	_lowercase       ,	generator=_lowercase       ,	)[0]

     # post-processing
     SCREAMING_SNAKE_CASE__										=      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"]:
       SCREAMING_SNAKE_CASE__										=      image * 0.5 + 0.5
       SCREAMING_SNAKE_CASE__										=      image.clamp(0       ,	1      )
       SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      ).float().numpy()

     if output_type == "pil":
       SCREAMING_SNAKE_CASE__										=      self.numpy_to_pil(_lowercase      )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_lowercase      )


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         super().__init__(
             SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			do_lower_case=SCREAMING_SNAKE_CASE						,			remove_space=SCREAMING_SNAKE_CASE						,			keep_accents=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
             return
         A       :   Any					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)




