





from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


_UpperCamelCase    =	getLogger(__name__)


_UpperCamelCase    =	4
_UpperCamelCase    =	7

_UpperCamelCase    =	{
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}







def  lowerCAmelCase__(		lowercase    :      int			)	->      Optional[Any]:
  if isinstance(lowercase				,       torch.Tensor			):
    return image
  elif isinstance(lowercase				,       PIL.Image.Image			):
    __snake_case				:			List[Any]            =    [image]

  if isinstance(image[0]				,       PIL.Image.Image			):
    __snake_case      ,				__snake_case				:			List[Any]            =    image[0].size
    __snake_case      ,				__snake_case				:			Any            =    (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

    __snake_case				:			Tuple            =    [np.array(i.resize((w, h)			)			)[None, :] for i in image]
    __snake_case				:			Tuple            =    np.concatenate(lowercase				,       axis=0			)
    __snake_case				:			Optional[int]            =    np.array(lowercase			).astype(np.floataa			) / 2_5_5.0
    __snake_case				:			Optional[Any]            =    image.transpose(0				,       3				,       1				,       2			)
    __snake_case				:			Tuple            =    2.0 * image - 1.0
    __snake_case				:			List[Any]            =    torch.from_numpy(lowercase			)
  elif isinstance(image[0]				,       torch.Tensor			):
    __snake_case				:			List[Any]            =    torch.cat(lowercase				,       dim=0			)

  return image




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = 350			,  )    ->				List[str]:






        '''simple docstring'''






        super().__init__(
            vae=UpperCAmelCase			,  text_encoder=UpperCAmelCase			,  tokenizer=UpperCAmelCase			,  unet=UpperCAmelCase			,  low_res_scheduler=UpperCAmelCase			,  scheduler=UpperCAmelCase			,  safety_checker=UpperCAmelCase			,  feature_extractor=UpperCAmelCase			,  watermarker=UpperCAmelCase			,  max_noise_level=UpperCAmelCase			,  )







      def __call__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = 75			,  UpperCAmelCase = 9.0			,  UpperCAmelCase = 20			,  UpperCAmelCase = None			,  UpperCAmelCase = 1			,  UpperCAmelCase = 0.0			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = "pil"			,  UpperCAmelCase = True			,  UpperCAmelCase = None			,  UpperCAmelCase = 1			,  )    ->				Tuple:






        '''simple docstring'''






        self.check_inputs(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)

        # 2. Define call parameters
        if prompt is not None and isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          __snake_case				:			Dict            =    1
        elif prompt is not None and isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          __snake_case				:			Union[str, Any]            =    len(UpperCAmelCase				)
        else:
          __snake_case				:			Tuple            =    prompt_embeds.shape[0]

        __snake_case				:			int            =    self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        __snake_case				:			List[Any]            =    guidance_scale > 1.0

        # 3. Encode input prompt
        __snake_case				:			Any            =    self._encode_prompt(
            UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  prompt_embeds=UpperCAmelCase			,  negative_prompt_embeds=UpperCAmelCase			,  )

        __snake_case				:			List[str]            =    ORT_TO_PT_TYPE[str(text_embeddings.dtype				)]

        # 4. Preprocess image
        __snake_case				:			int            =    preprocess(UpperCAmelCase				)
        __snake_case				:			Optional[int]            =    image.cpu()

        # 5. set timesteps
        self.scheduler.set_timesteps(UpperCAmelCase			,  device=UpperCAmelCase				)
        __snake_case				:			Any            =    self.scheduler.timesteps

        # 5. Add noise to image
        __snake_case				:			Optional[int]            =    torch.tensor([noise_level]			,  dtype=torch.long			,  device=UpperCAmelCase				)
        __snake_case				:			Optional[int]            =    torch.randn(image.shape			,  generator=UpperCAmelCase			,  device=UpperCAmelCase			,  dtype=UpperCAmelCase				)
        __snake_case				:			List[Any]            =    self.low_res_scheduler.add_noise(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)

        __snake_case				:			Any            =    2 if do_classifier_free_guidance else 1
        __snake_case				:			Dict            =    np.concatenate([image] * batch_multiplier * num_images_per_prompt				)
        __snake_case				:			str            =    np.concatenate([noise_level] * image.shape[0]				)

        # 6. Prepare latent variables
        __snake_case      ,				__snake_case				:			Union[str, Any]            =    image.shape[2:]
        __snake_case				:			List[str]            =    self.prepare_latents(
            batch_size * num_images_per_prompt			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  )

        # 7. Check that sizes of image and latents match
        __snake_case				:			Tuple            =    image.shape[1]
        if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
          raise ValueError(
              "Incorrect configuration settings! The config of `pipeline.unet` expects"
              F""" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"""
              F""" `num_channels_image`: {num_channels_image} """
              F""" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"""
              " `pipeline.unet` or your `image` input."				)

        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        __snake_case				:			Optional[int]            =    self.prepare_extra_step_kwargs(UpperCAmelCase			,  UpperCAmelCase				)

        __snake_case				:			Tuple            =    next(
            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")			,  "tensor(float)"				)
        __snake_case				:			Optional[Any]            =    ORT_TO_NP_TYPE[timestep_dtype]

        # 9. Denoising loop
        __snake_case				:			Tuple            =    len(UpperCAmelCase				) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=UpperCAmelCase				) as progress_bar:
          for i, t in enumerate(UpperCAmelCase				):
            # expand the latents if we are doing classifier free guidance
            __snake_case				:			int            =    np.concatenate([latents] * 2				) if do_classifier_free_guidance else latents

            # concat latents, mask, masked_image_latents in the channel dimension
            __snake_case				:			List[str]            =    self.scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase				)
            __snake_case				:			Tuple            =    np.concatenate([latent_model_input, image]			,  axis=1				)

            # timestep to tensor
            __snake_case				:			Optional[int]            =    np.array([t]			,  dtype=UpperCAmelCase				)

            # predict the noise residual
            __snake_case				:			List[str]            =    self.unet(
                sample=UpperCAmelCase			,  timestep=UpperCAmelCase			,  encoder_hidden_states=UpperCAmelCase			,  class_labels=noise_level.astype(np.intaa				)			,  )[0]

            # perform guidance
            if do_classifier_free_guidance:
              __snake_case      ,				__snake_case				:			List[Any]            =    np.split(UpperCAmelCase			,  2				)
            __snake_case				:			Optional[Any]            =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # compute the previous noisy sample x_t -> x_t-1
            __snake_case				:			Optional[int]            =    self.scheduler.step(
                torch.from_numpy(UpperCAmelCase				)			,  UpperCAmelCase			,  UpperCAmelCase			,  **UpperCAmelCase				).prev_sample

            # call the callback, if provided
            if i == len(UpperCAmelCase				) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
              progress_bar.update()
              if callback is not None and i % callback_steps == 0:
                callback(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)

        # 10. Post-processing
        __snake_case				:			Tuple            =    self.decode_latents(latents.float()				)

        # 11. Convert to PIL
        if output_type == "pil":
          __snake_case				:			Any            =    self.numpy_to_pil(UpperCAmelCase				)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Dict:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    1 / 0.08_333 * latents
        __snake_case				:			List[str]            =    self.vae(latent_sample=UpperCAmelCase				)[0]
        __snake_case				:			Union[str, Any]            =    np.clip(image / 2 + 0.5			,  0			,  1				)
        __snake_case				:			Optional[int]            =    image.transpose((0, 2, 3, 1)				)
        return image







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  )    ->				Tuple:






        '''simple docstring'''






        if prompt is not None and isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          __snake_case				:			Optional[int]            =    1
        elif prompt is not None and isinstance(UpperCAmelCase			,  UpperCAmelCase				):
          __snake_case				:			Tuple            =    len(UpperCAmelCase				)
        else:
          __snake_case				:			Dict            =    prompt_embeds.shape[0]

        if prompt_embeds is None:
          __snake_case				:			Optional[int]            =    self.tokenizer(
              UpperCAmelCase			,  padding="max_length"			,  max_length=self.tokenizer.model_max_length			,  truncation=UpperCAmelCase			,  return_tensors="pt"			,  )
          __snake_case				:			Tuple            =    text_inputs.input_ids
          __snake_case				:			str            =    self.tokenizer(UpperCAmelCase			,  padding="longest"			,  return_tensors="pt"				).input_ids

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

          # no positional arguments to text_encoder
          __snake_case				:			List[Any]            =    self.text_encoder(
              input_ids=text_input_ids.int().to(UpperCAmelCase				)			,  )
          __snake_case				:			List[Any]            =    prompt_embeds[0]

        __snake_case      ,				__snake_case      ,				__snake_case				:			Dict            =    prompt_embeds.shape
        # duplicate text embeddings for each generation per prompt, using mps friendly method
        __snake_case				:			Dict            =    prompt_embeds.repeat(1			,  UpperCAmelCase				)
        __snake_case				:			List[Any]            =    prompt_embeds.reshape(bs_embed * num_images_per_prompt			,  UpperCAmelCase			,  -1				)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance and negative_prompt_embeds is None:
          __snake_case				:			List[str]
          if negative_prompt is None:
            __snake_case				:			Union[str, Any]            =    [""] * batch_size
          elif type(UpperCAmelCase				) is not type(UpperCAmelCase				):
            raise TypeError(
                F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase				)} !="""
                F""" {type(UpperCAmelCase				)}."""				)
          elif isinstance(UpperCAmelCase			,  UpperCAmelCase				):
            __snake_case				:			List[Any]            =    [negative_prompt]
          elif batch_size != len(UpperCAmelCase				):
            raise ValueError(
                F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase				)}, but `prompt`:"""
                F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                " the batch size of `prompt`."				)
          else:
            __snake_case				:			Union[str, Any]            =    negative_prompt

          __snake_case				:			Union[str, Any]            =    text_input_ids.shape[-1]
          __snake_case				:			int            =    self.tokenizer(
              UpperCAmelCase			,  padding="max_length"			,  max_length=UpperCAmelCase			,  truncation=UpperCAmelCase			,  return_tensors="pt"			,  )

          # if hasattr(uncond_input, "attention_mask"):
          #     attention_mask = uncond_input.attention_mask.to(device)
          # else:
          #     attention_mask = None

          __snake_case				:			Optional[int]            =    self.text_encoder(
              input_ids=uncond_input.input_ids.int().to(UpperCAmelCase				)			,  )
          __snake_case				:			str            =    uncond_embeddings[0]

        if do_classifier_free_guidance:
          __snake_case				:			str            =    uncond_embeddings.shape[1]
          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          __snake_case				:			Optional[int]            =    uncond_embeddings.repeat(1			,  UpperCAmelCase				)
          __snake_case				:			Any            =    uncond_embeddings.reshape(batch_size * num_images_per_prompt			,  UpperCAmelCase			,  -1				)

          # 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
          __snake_case				:			List[Any]            =    np.concatenate([uncond_embeddings, prompt_embeds]				)

        return prompt_embeds








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






        __snake_case				:			str            =    letter
        __snake_case				:			int            =    freq
        __snake_case				:			dict[str, str]            =    {}







      def __repr__(	self				)    ->				str:






        '''simple docstring'''






        return F"""{self.letter}:{self.freq}"""




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			int            =    freq
        __snake_case				:			Letter | TreeNode            =    left
        __snake_case				:			Letter | TreeNode            =    right







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    chars[c] + 1 if c in chars else 1
  return sorted((Letter(lowercase				,       lowercase			) for c, f in chars.items())				,       key=lambda lowercase			: x.freq			)







def  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      k: v for letter in traverse_tree(lowercase				,       ""			) for k, v in letter.bitstring.items()
  }
  print(f"""Huffman Coding  of {file_path}: """			)
  with open(lowercase			) as f:
    while True:
      __snake_case				:			int            =    f.read(1			)
      if not c:
        break
      print(letters[c]				,       end=" "			)
  print()


if __name__ == "__main__":
     # pass the file path to the huffman function
     huffman(sys.argv[1])

