


from typing import List, Optional, Union

import torch

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      :    Optional[int]		=					logging.get_logger(__name__)  # pylint: disable=invalid-name

A      :    Dict		=					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"

def       lowercase_		(		_A			:    Union[str, Any]    ,					_A			:    Union[str, Any]    ,					_A			:    Any=8						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      lowerCamelCase__		:				Optional[int]    =  width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Dict					,      __lowerCamelCase							:			UNetaDConditionModel					,      __lowerCamelCase							:			DDPMScheduler					,      __lowerCamelCase							:			VQModel					,      ):


             '''simple docstring'''



             super().__init__()

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





       def 						lowerCAmelCase     (   self							:			Dict					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if latents is None:
                   lowerCamelCase__		:				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}"       )
                   lowerCamelCase__		:				Optional[int]    =  latents.to(__lowerCamelCase       )

             lowerCamelCase__		:				Optional[int]    =  latents * scheduler.init_noise_sigma
             return latents





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			int=0       ):


             '''simple docstring'''



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

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

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





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Any=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__		:				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)

             lowerCamelCase__		:				List[Any]    =  None
             for cpu_offloaded_model in [self.unet, self.movq]:
                   lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  cpu_offload_with_hook(__lowerCamelCase					,      __lowerCamelCase					,      prev_module_hook=__lowerCamelCase       )

             # We'll offload the last model manually.
             lowerCamelCase__		:				int    =  hook





       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''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							:			List[str]					,      __lowerCamelCase							:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,      __lowerCamelCase							:			Union[torch.FloatTensor, List[torch.FloatTensor]]					,      __lowerCamelCase							:			int = 512					,      __lowerCamelCase							:			int = 512					,      __lowerCamelCase							:			int = 100					,      __lowerCamelCase							:			float = 4.0					,      __lowerCamelCase							:			int = 1					,      __lowerCamelCase							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      __lowerCamelCase							:			Optional[torch.FloatTensor] = None					,      __lowerCamelCase							:			Optional[str] = "pil"					,      __lowerCamelCase							:			bool = True					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self._execution_device

             lowerCamelCase__		:				Optional[Any]    =  guidance_scale > 1.0

             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				Union[str, Any]    =  torch.cat(__lowerCamelCase					,      dim=0       )
             lowerCamelCase__		:				Any    =  image_embeds.shape[0] * num_images_per_prompt
             if isinstance(__lowerCamelCase					,      __lowerCamelCase       ):
                   lowerCamelCase__		:				Tuple    =  torch.cat(__lowerCamelCase					,      dim=0       )

             if do_classifier_free_guidance:
                   lowerCamelCase__		:				Any    =  image_embeds.repeat_interleave(__lowerCamelCase					,      dim=0       )
                   lowerCamelCase__		:				Dict    =  negative_image_embeds.repeat_interleave(__lowerCamelCase					,      dim=0       )

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

             self.scheduler.set_timesteps(__lowerCamelCase					,      device=__lowerCamelCase       )
             lowerCamelCase__		:				Any    =  self.scheduler.timesteps

             lowerCamelCase__		:				Any    =  self.unet.config.in_channels

             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  downscale_height_and_width(__lowerCamelCase					,      __lowerCamelCase					,      self.movq_scale_factor       )

             # create initial latent
             lowerCamelCase__		:				Optional[Any]    =  self.prepare_latents(
                 (batch_size, num_channels_latents, height, width)					,      image_embeds.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
                   lowerCamelCase__		:				List[Any]    =  torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

                   lowerCamelCase__		:				List[str]    =  {"image_embeds": image_embeds}
                   lowerCamelCase__		:				Any    =  self.unet(
                       sample=__lowerCamelCase					,      timestep=__lowerCamelCase					,      encoder_hidden_states=__lowerCamelCase					,      added_cond_kwargs=__lowerCamelCase					,      return_dict=__lowerCamelCase					,      )[0]

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

                   # compute the previous noisy sample x_t -> x_t-1
                   lowerCamelCase__		:				int    =  self.scheduler.step(
                       __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      generator=__lowerCamelCase					,      )[0]
             # post-processing
             lowerCamelCase__		:				Union[str, Any]    =  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"]:
                   lowerCamelCase__		:				List[str]    =  image * 0.5 + 0.5
                   lowerCamelCase__		:				Optional[int]    =  image.clamp(0					,      1       )
                   lowerCamelCase__		:				Any    =  image.cpu().permute(0					,      2					,      3					,      1       ).float().numpy()

             if output_type == "pil":
                   lowerCamelCase__		:				int    =  self.numpy_to_pil(__lowerCamelCase       )

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=__lowerCamelCase       )






import math
import os
import sys

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				str    =  ""
      try:
            with open(_A    ,					"rb"						) as binary_file:
                  lowerCamelCase__		:				Union[str, Any]    =  binary_file.read()
            for dat in data:
                  lowerCamelCase__		:				Optional[Any]    =  F"{dat:08b}"
                  result += curr_byte
            return result
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    dict[str, str]    ,					_A			:    str    ,					_A			:    int    ,					_A			:    str						):

      """simple docstring"""




      lexicon.pop(_A						)
      lowerCamelCase__		:				List[Any]    =  last_match_id

      if math.loga(_A						).is_integer():
            for curr_key in lexicon:
                  lowerCamelCase__		:				Any    =  "0" + lexicon[curr_key]

      lowerCamelCase__		:				Tuple    =  bin(_A						)[2:]

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Tuple    =  {"0": "0", "1": "1"}
      lowerCamelCase__    ,   lowerCamelCase__		:				int    =  "", ""
      lowerCamelCase__		:				int    =  len(_A						)

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

            lowerCamelCase__		:				Optional[Any]    =  lexicon[curr_string]
            result += last_match_id
            add_key_to_lexicon(_A    ,					_A    ,					_A    ,					_A						)
            index += 1
            lowerCamelCase__		:				Dict    =  ""

      while curr_string != "" and curr_string not in lexicon:
            curr_string += "0"

      if curr_string != "":
            lowerCamelCase__		:				int    =  lexicon[curr_string]
            result += last_match_id

      return result

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  os.path.getsize(_A						)
      lowerCamelCase__		:				Dict    =  bin(_A						)[2:]
      lowerCamelCase__		:				List[Any]    =  len(_A						)

      return "0" * (length_length - 1) + file_length_binary + compressed

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  8
      try:
            with open(_A    ,					"wb"						) as opened_file:
                  lowerCamelCase__		:				Any    =  [
                      to_write[i : i + byte_length]
                      for i in range(0    ,					len(_A						)    ,					_A						)
                  ]

                  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:
                        opened_file.write(int(_A    ,					2						).to_bytes(1    ,					byteorder="big"						)						)
      except OSError:
            print("File not accessible"						)
            sys.exit()

def       lowercase_		(		_A			:    str    ,					_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				Dict    =  read_file_binary(_A						)
      lowerCamelCase__		:				List[Any]    =  compress_data(_A						)
      lowerCamelCase__		:				str    =  add_file_length(_A    ,					_A						)
      write_file_binary(_A    ,					_A						)


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


