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,
)


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

SCREAMING_SNAKE_CASE__		:					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     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			int=8 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     SCREAMING_SNAKE_CASE__				:				List[str]			=		width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







def     _a				(      lowercase__  :			str     ,  lowercase__  :			List[str]=5_12     ,  lowercase__  :			Dict=5_12 ):







     '''simple docstring'''


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







class       snake_case					(      UpperCamelCase_						):




  def __init__(      self     :	Dict     , a_     :	UNetaDConditionModel     , a_     :	DDPMScheduler     , a_     :	VQModel     , )->       List[Any]:




       """simple docstring"""
       super().__init__()

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




  def 	__lowercase(      self     :	Tuple     , a_     :	List[Any]     , a_     :	int     , a_     :	Tuple				)->       List[str]:




       """simple docstring"""
       # get the original timestep using init_timestep
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		min(int(num_inference_steps * strength				)     , a_				)

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

       return timesteps, num_inference_steps - t_start




  def 	__lowercase(      self     :	Dict     , a_     :	Any     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , a_     :	List[str]     , a_     :	int     , a_     :	List[Any]=None				)->       List[str]:




       """simple docstring"""
       if not isinstance(a_     , (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(a_				)}'''				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		image.to(device=a_     , dtype=a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		batch_size * num_images_per_prompt

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

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

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

            SCREAMING_SNAKE_CASE__				:				int			=		self.movq.config.scaling_factor * init_latents

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

       SCREAMING_SNAKE_CASE__				:				Dict			=		init_latents.shape
       SCREAMING_SNAKE_CASE__				:				str			=		randn_tensor(a_     , generator=a_     , device=a_     , dtype=a_				)

       # get latents
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.scheduler.add_noise(a_     , a_     , a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		init_latents

       return latents




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	List[Any]=0				)->       Dict:




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

       SCREAMING_SNAKE_CASE__				:				Any			=		torch.device(F'''cuda:{gpu_id}'''				)

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




  def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[int]=0				)->       List[str]:




       """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__				:				str			=		torch.device(F'''cuda:{gpu_id}'''				)

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

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		None
       for cpu_offloaded_model in [self.unet, self.movq]:
            SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		cpu_offload_with_hook(a_     , a_     , prev_module_hook=a_				)

       # We'll offload the last model manually.
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		hook




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




       """simple docstring"""
       if not hasattr(self.unet     , '_hf_hook'				):
            return self.device
       for module in self.unet.modules():
            if (
                hasattr(a_     , '_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(a_				)
  def __call__(      self     :	Any     , a_     :	Union[torch.FloatTensor, List[torch.FloatTensor]]     , a_     :	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     , a_     :	Union[torch.FloatTensor, List[torch.FloatTensor]]     , a_     :	int = 512     , a_     :	int = 512     , a_     :	int = 100     , a_     :	float = 4.0     , a_     :	float = 0.3     , a_     :	int = 1     , a_     :	Optional[Union[torch.Generator, List[torch.Generator]]] = None     , a_     :	Optional[str] = "pil"     , a_     :	bool = True     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		self._execution_device

       SCREAMING_SNAKE_CASE__				:				Any			=		guidance_scale > 1.0

       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.cat(a_     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		image_embeds.shape[0]
       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.cat(a_     , dim=0				)

       if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_embeds.repeat_interleave(a_     , dim=0				)
            SCREAMING_SNAKE_CASE__				:				Dict			=		negative_image_embeds.repeat_interleave(a_     , dim=0				)

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

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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.cat([prepare_image(a_     , a_     , a_				) for i in image]     , dim=0				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		image.to(dtype=image_embeds.dtype     , device=a_				)

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

            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'image_embeds': image_embeds}
            SCREAMING_SNAKE_CASE__				:				Tuple			=		self.unet(
                sample=a_     , timestep=a_     , encoder_hidden_states=a_     , added_cond_kwargs=a_     , return_dict=a_     , )[0]

            if do_classifier_free_guidance:
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		noise_pred.split(latents.shape[1]     , dim=1				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		noise_pred.chunk(2				)
                 SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		variance_pred.chunk(2				)
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		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__			,							SCREAMING_SNAKE_CASE__				:				int			=		noise_pred.split(latents.shape[1]     , dim=1				)

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.scheduler.step(
                a_     , a_     , a_     , generator=a_     , )[0]

       # post-processing
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.movq.decode(a_     , force_not_quantize=a_				)['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__				:				Dict			=		image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__				:				List[str]			=		image.clamp(0     , 1				)
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		image.cpu().permute(0     , 2     , 3     , 1				).float().numpy()

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.numpy_to_pil(a_				)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=a_				)




import math
import sys







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		''
     try:
          with open(lowercase__     ,  'rb' ) as binary_file:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		binary_file.read()
          for dat in data:
               SCREAMING_SNAKE_CASE__				:				Tuple			=		f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{'0': '0', '1': '1'}
     SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				int			=		'', ''
     SCREAMING_SNAKE_CASE__				:				Tuple			=		len(lowercase__ )

     for i in range(len(lowercase__ ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          SCREAMING_SNAKE_CASE__				:				int			=		lexicon[curr_string]
          result += last_match_id
          SCREAMING_SNAKE_CASE__				:				str			=		last_match_id + '0'

          if math.loga(lowercase__ ).is_integer():
               SCREAMING_SNAKE_CASE__				:				List[str]			=		{}
               for curr_key in list(lowercase__ ):
                    SCREAMING_SNAKE_CASE__				:				Optional[int]			=		lexicon.pop(lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		new_lex

          SCREAMING_SNAKE_CASE__				:				Any			=		last_match_id + '1'
          index += 1
          SCREAMING_SNAKE_CASE__				:				Tuple			=		''
     return result







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		8
     try:
          with open(lowercase__     ,  'wb' ) as opened_file:
               SCREAMING_SNAKE_CASE__				:				Dict			=		[
                   to_write[i : i + byte_length]
                   for i in range(0     ,  len(lowercase__ )     ,  lowercase__ )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('10000000' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(lowercase__     ,  2 ).to_bytes(1     ,  byteorder='big' ) )
     except OSError:
          print('File not accessible' )
          sys.exit()







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		data_bits[counter:]
     SCREAMING_SNAKE_CASE__				:				int			=		data_bits[counter + 1 :]
     return data_bits







def     _a				(      lowercase__  :			str     ,  lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		read_file_binary(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Dict			=		remove_prefix(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Tuple			=		decompress_data(lowercase__ )
     write_file_binary(lowercase__     ,  lowercase__ )


if __name__ == "__main__":
      compress(sys.argv[1], sys.argv[2])



