





"""simple docstring"""



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,
)


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

a_			=      """
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""







def 		__lowercase					(       snake_case_  :  Tuple    ,snake_case_  :  Tuple    ,snake_case_  :  Tuple=8						)       ->List[Any]:



    '''simple docstring'''







    __A   :						Optional[int]									=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    __A   :						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







def 		__lowercase					(       snake_case_  :  List[str]    ,snake_case_  :  Tuple=512    ,snake_case_  :  Dict=512						)       ->str:



    '''simple docstring'''







    __A   :						Dict									=					pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1						)
    __A   :						Any									=					np.array(pil_image.convert('''RGB'''						)						)
    __A   :						str									=					arr.astype(np.floataa						) / 127.5 - 1
    __A   :						List[str]									=					np.transpose(snake_case_    ,[2, 0, 1]						)
    __A   :						Optional[int]									=					torch.from_numpy(snake_case_						).unsqueeze(0						)
    return image






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__()

     self.register_modules(
         unet=__lowerCamelCase ,						scheduler=__lowerCamelCase ,						movq=__lowerCamelCase ,						)
     __A   :						Optional[Any]									=					2 ** (len(self.movq.config.block_out_channels  ) - 1)

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					min(int(num_inference_steps * strength  ) ,						__lowerCamelCase  )

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

     return timesteps, num_inference_steps - t_start

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     if not isinstance(__lowerCamelCase ,						(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(__lowerCamelCase  )}"""  )

     __A   :						Optional[Any]									=					image.to(device=__lowerCamelCase ,						dtype=__lowerCamelCase  )

     __A   :						Any									=					batch_size * num_images_per_prompt

     if image.shape[1] == 4:
         __A   :						str									=					image

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

         elif isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						List[str]									=					[
                 self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(__lowerCamelCase  )
             ]
             __A   :						Optional[int]									=					torch.cat(__lowerCamelCase ,						dim=0  )
         else:
             __A   :						Optional[Any]									=					self.movq.encode(__lowerCamelCase  ).latent_dist.sample(__lowerCamelCase  )

         __A   :						List[str]									=					self.movq.config.scaling_factor * init_latents

     __A   :						int									=					torch.cat([init_latents] ,						dim=0  )

     __A   :						Optional[Any]									=					init_latents.shape
     __A   :						List[str]									=					randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=__lowerCamelCase ,						dtype=__lowerCamelCase  )

     # get latents
     __A   :						str									=					self.scheduler.add_noise(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						Any									=					init_latents

     return latents

 def  UpperCamelCase__(   self ,						__lowerCamelCase=0  ):



     '''simple docstring'''




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

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

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

 def  UpperCamelCase__(   self ,						__lowerCamelCase=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.'''  )

     __A   :						Optional[Any]									=					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)

     __A   :						Dict									=					None
     for cpu_offloaded_model in [self.unet, self.movq]:
         __A			,	__A   :						str									=					cpu_offload_with_hook(__lowerCamelCase ,						__lowerCamelCase ,						prev_module_hook=__lowerCamelCase  )

     # We'll offload the last model manually.
     __A   :						List[Any]									=					hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  UpperCamelCase__(   self  ):



     '''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 = 512 ,						__lowerCamelCase = 512 ,						__lowerCamelCase = 100 ,						__lowerCamelCase = 4.0 ,						__lowerCamelCase = 0.3 ,						__lowerCamelCase = 1 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						):



     '''simple docstring'''




     __A   :						List[str]									=					self._execution_device

     __A   :						Union[str, Any]									=					guidance_scale > 1.0

     if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						List[Any]									=					torch.cat(__lowerCamelCase ,						dim=0  )
     __A   :						List[str]									=					image_embeds.shape[0]
     if isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
         __A   :						List[str]									=					torch.cat(__lowerCamelCase ,						dim=0  )

     if do_classifier_free_guidance:
         __A   :						int									=					image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )
         __A   :						str									=					negative_image_embeds.repeat_interleave(__lowerCamelCase ,						dim=0  )

     __A   :						List[Any]									=					torch.cat([negative_image_embeds, image_embeds] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=__lowerCamelCase  )

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

     __A   :						List[Any]									=					torch.cat([prepare_image(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ) for i in image] ,						dim=0  )
     __A   :						str									=					image.to(dtype=image_embeds.dtype ,						device=__lowerCamelCase  )

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

         __A   :						Optional[int]									=					{'''image_embeds''': image_embeds}
         __A   :						Optional[int]									=					self.unet(
             sample=__lowerCamelCase ,						timestep=__lowerCamelCase ,						encoder_hidden_states=__lowerCamelCase ,						added_cond_kwargs=__lowerCamelCase ,						return_dict=__lowerCamelCase ,						)[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         __A   :						Tuple									=					self.scheduler.step(
             __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						generator=__lowerCamelCase ,						)[0]

     # post-processing
     __A   :						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"]:
         __A   :						Optional[Any]									=					image * 0.5 + 0.5
         __A   :						Dict									=					image.clamp(0 ,						1  )
         __A   :						List[str]									=					image.cpu().permute(0 ,						2 ,						3 ,						1  ).float().numpy()

     if output_type == "pil":
         __A   :						List[Any]									=					self.numpy_to_pil(__lowerCamelCase  )

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=__lowerCamelCase  )











"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_			=      {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
a_			=      """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
a_			=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def 		__lowercase					(       snake_case_  :  str						)       ->dict[str, int]:



    '''simple docstring'''







    __A   :						Any									=					{letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def 		__lowercase					(       snake_case_  :  tuple						)       ->str:



    '''simple docstring'''







    return x[0]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					get_letter_count(snake_case_						)
    __A   :						dict[int, list[str]]									=					{
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(snake_case_						)

    __A   :						dict[int, str]									=					{}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find    ,reverse=snake_case_						)
        __A   :						Optional[int]									=					''''''.join(freq_to_letter[freq]						)

    __A   :						str									=					list(freq_to_letter_str.items()						)
    freq_pairs.sort(key=snake_case_    ,reverse=snake_case_						)

    __A   :						list[str]									=					[freq_pair[1] for freq_pair in freq_pairs]

    return "".join(snake_case_						)







def 		__lowercase					(       snake_case_  :  str						)       ->int:



    '''simple docstring'''







    __A   :						Any									=					get_frequency_order(snake_case_						)
    __A   :						str									=					0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()




