




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


__SCREAMING_SNAKE_CASE     : List[str]      =getLogger(__name__)


__SCREAMING_SNAKE_CASE     : str      =4
__SCREAMING_SNAKE_CASE     : int      =7

__SCREAMING_SNAKE_CASE     : Any      ={
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]    )       ->      Optional[int]:


      '''simple docstring'''




      if isinstance(__a  ,					torch.Tensor    ):
            return image
      elif isinstance(__a  ,					PIL.Image.Image    ):
            lowerCamelCase__   	=					[image]

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

            lowerCamelCase__   	=					[np.array(i.resize((w, h)    )    )[None, :] for i in image]
            lowerCamelCase__   	=					np.concatenate(__a  ,					axis=0    )
            lowerCamelCase__   	=					np.array(__a    ).astype(np.floataa    ) / 255.0
            lowerCamelCase__   	=					image.transpose(0  ,					3  ,					1  ,					2    )
            lowerCamelCase__   	=					2.0 * image - 1.0
            lowerCamelCase__   	=					torch.from_numpy(__a    )
      elif isinstance(image[0]  ,					torch.Tensor    ):
            lowerCamelCase__   	=					torch.cat(__a  ,					dim=0    )

      return image






class       A      (		lowerCamelCase_		):
     """simple docstring"""

     def __init__(     self	: int						,	A__	: str						,	A__	: int						,	A__	: Any						,	A__	: List[Any]						,	A__	: Tuple						,	A__	: Optional[Any]						,	A__	: str = 3_5_0						,	)					->							Dict:
           super().__init__(
               vae=lowerCAmelCase_						,	text_encoder=lowerCAmelCase_						,	tokenizer=lowerCAmelCase_						,	unet=lowerCAmelCase_						,	low_res_scheduler=lowerCAmelCase_						,	scheduler=lowerCAmelCase_						,	safety_checker=lowerCAmelCase_						,	feature_extractor=lowerCAmelCase_						,	watermarker=lowerCAmelCase_						,	max_noise_level=lowerCAmelCase_						,	)




     def __call__(     self	: Tuple						,	A__	: Tuple						,	A__	: List[str]						,	A__	: str = 7_5						,	A__	: Dict = 9.0						,	A__	: Union[str, Any] = 2_0						,	A__	: Optional[Any] = None						,	A__	: List[str] = 1						,	A__	: Tuple = 0.0						,	A__	: Optional[Any] = None						,	A__	: Optional[Any] = None						,	A__	: List[Any] = None						,	A__	: List[str] = None						,	A__	: Union[str, Any] = "pil"						,	A__	: Tuple = True						,	A__	: Any = None						,	A__	: Optional[int] = 1						,	)					->							str:
           self.check_inputs(lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_      )

           # 2. Define call parameters
           if prompt is not None and isinstance(lowerCAmelCase_						,	lowerCAmelCase_      ):
                 lowerCamelCase__   	=					1
           elif prompt is not None and isinstance(lowerCAmelCase_						,	lowerCAmelCase_      ):
                 lowerCamelCase__   	=					len(lowerCAmelCase_      )
           else:
                 lowerCamelCase__   	=					prompt_embeds.shape[0]

           lowerCamelCase__   	=					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.
           lowerCamelCase__   	=					guidance_scale > 1.0

           # 3. Encode input prompt
           lowerCamelCase__   	=					self._encode_prompt(
               lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	prompt_embeds=lowerCAmelCase_						,	negative_prompt_embeds=lowerCAmelCase_						,	)

           lowerCamelCase__   	=					ORT_TO_PT_TYPE[str(text_embeddings.dtype      )]

           # 4. Preprocess image
           lowerCamelCase__   	=					preprocess(lowerCAmelCase_      )
           lowerCamelCase__   	=					image.cpu()

           # 5. set timesteps
           self.scheduler.set_timesteps(lowerCAmelCase_						,	device=lowerCAmelCase_      )
           lowerCamelCase__   	=					self.scheduler.timesteps

           # 5. Add noise to image
           lowerCamelCase__   	=					torch.tensor([noise_level]						,	dtype=torch.long						,	device=lowerCAmelCase_      )
           lowerCamelCase__   	=					torch.randn(image.shape						,	generator=lowerCAmelCase_						,	device=lowerCAmelCase_						,	dtype=lowerCAmelCase_      )
           lowerCamelCase__   	=					self.low_res_scheduler.add_noise(lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_      )

           lowerCamelCase__   	=					2 if do_classifier_free_guidance else 1
           lowerCamelCase__   	=					np.concatenate([image] * batch_multiplier * num_images_per_prompt      )
           lowerCamelCase__   	=					np.concatenate([noise_level] * image.shape[0]      )

           # 6. Prepare latent variables
           lowerCamelCase__	,		lowerCamelCase__   	=					image.shape[2:]
           lowerCamelCase__   	=					self.prepare_latents(
               batch_size * num_images_per_prompt						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_						,	)

           # 7. Check that sizes of image and latents match
           lowerCamelCase__   	=					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
           lowerCamelCase__   	=					self.prepare_extra_step_kwargs(lowerCAmelCase_						,	lowerCAmelCase_      )

           lowerCamelCase__   	=					next(
               (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')						,	'tensor(float)'      )
           lowerCamelCase__   	=					ORT_TO_NP_TYPE[timestep_dtype]

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

                       # concat latents, mask, masked_image_latents in the channel dimension
                       lowerCamelCase__   	=					self.scheduler.scale_model_input(lowerCAmelCase_						,	lowerCAmelCase_      )
                       lowerCamelCase__   	=					np.concatenate([latent_model_input, image]						,	axis=1      )

                       # timestep to tensor
                       lowerCamelCase__   	=					np.array([t]						,	dtype=lowerCAmelCase_      )

                       # predict the noise residual
                       lowerCamelCase__   	=					self.unet(
                           sample=lowerCAmelCase_						,	timestep=lowerCAmelCase_						,	encoder_hidden_states=lowerCAmelCase_						,	class_labels=noise_level.astype(np.intaa      )						,	)[0]

                       # perform guidance
                       if do_classifier_free_guidance:
                             lowerCamelCase__	,		lowerCamelCase__   	=					np.split(lowerCAmelCase_						,	2      )
                       lowerCamelCase__   	=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       # compute the previous noisy sample x_t -> x_t-1
                       lowerCamelCase__   	=					self.scheduler.step(
                           torch.from_numpy(lowerCAmelCase_      )						,	lowerCAmelCase_						,	lowerCAmelCase_						,	**lowerCAmelCase_      ).prev_sample

                       # call the callback, if provided
                       if i == len(lowerCAmelCase_      ) - 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(lowerCAmelCase_						,	lowerCAmelCase_						,	lowerCAmelCase_      )

        # 10. Post-processing
           lowerCamelCase__   	=					self.decode_latents(latents.float()      )

           # 11. Convert to PIL
           if output_type == "pil":
                 lowerCamelCase__   	=					self.numpy_to_pil(lowerCAmelCase_      )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=lowerCAmelCase_      )




     def 			__snake_case (     self	: Optional[Any]						,	A__	: Any      )					->							Union[str, Any]:
           lowerCamelCase__   	=					1 / 0.08333 * latents
           lowerCamelCase__   	=					self.vae(latent_sample=lowerCAmelCase_      )[0]
           lowerCamelCase__   	=					np.clip(image / 2 + 0.5						,	0						,	1      )
           lowerCamelCase__   	=					image.transpose((0, 2, 3, 1)      )
           return image




     def 			__snake_case (     self	: Optional[Any]						,	A__	: Any						,	A__	: int						,	A__	: Tuple						,	A__	: Any						,	A__	: Dict						,	A__	: Any = None						,	A__	: Dict = None						,	)					->							Optional[int]:
           if prompt is not None and isinstance(lowerCAmelCase_						,	lowerCAmelCase_      ):
                 lowerCamelCase__   	=					1
           elif prompt is not None and isinstance(lowerCAmelCase_						,	lowerCAmelCase_      ):
                 lowerCamelCase__   	=					len(lowerCAmelCase_      )
           else:
                 lowerCamelCase__   	=					prompt_embeds.shape[0]

           if prompt_embeds is None:
                 lowerCamelCase__   	=					self.tokenizer(
                     lowerCAmelCase_						,	padding='max_length'						,	max_length=self.tokenizer.model_max_length						,	truncation=lowerCAmelCase_						,	return_tensors='pt'						,	)
                 lowerCamelCase__   	=					text_inputs.input_ids
                 lowerCamelCase__   	=					self.tokenizer(lowerCAmelCase_						,	padding='longest'						,	return_tensors='pt'      ).input_ids

                 if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                     lowerCAmelCase_						,	lowerCAmelCase_      ):
                       lowerCamelCase__   	=					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
                 lowerCamelCase__   	=					self.text_encoder(
                     input_ids=text_input_ids.int().to(lowerCAmelCase_      )						,	)
                 lowerCamelCase__   	=					prompt_embeds[0]

           lowerCamelCase__	,		lowerCamelCase__	,		lowerCamelCase__   	=					prompt_embeds.shape
           # duplicate text embeddings for each generation per prompt, using mps friendly method
           lowerCamelCase__   	=					prompt_embeds.repeat(1						,	lowerCAmelCase_      )
           lowerCamelCase__   	=					prompt_embeds.reshape(bs_embed * num_images_per_prompt						,	lowerCAmelCase_						,	-1      )

           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance and negative_prompt_embeds is None:
                 lowerCamelCase__   	=					4_2
                 if negative_prompt is None:
                       lowerCamelCase__   	=					[''] * batch_size
                 elif type(lowerCAmelCase_      ) is not type(lowerCAmelCase_      ):
                       raise TypeError(
                           F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_      )} !="""
                           F""" {type(lowerCAmelCase_      )}."""      )
                 elif isinstance(lowerCAmelCase_						,	lowerCAmelCase_      ):
                       lowerCamelCase__   	=					[negative_prompt]
                 elif batch_size != len(lowerCAmelCase_      ):
                       raise ValueError(
                           F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_      )}, but `prompt`:"""
                           F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                           ' the batch size of `prompt`.'      )
                 else:
                       lowerCamelCase__   	=					negative_prompt

                 lowerCamelCase__   	=					text_input_ids.shape[-1]
                 lowerCamelCase__   	=					self.tokenizer(
                     lowerCAmelCase_						,	padding='max_length'						,	max_length=lowerCAmelCase_						,	truncation=lowerCAmelCase_						,	return_tensors='pt'						,	)

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

                 lowerCamelCase__   	=					self.text_encoder(
                     input_ids=uncond_input.input_ids.int().to(lowerCAmelCase_      )						,	)
                 lowerCamelCase__   	=					uncond_embeddings[0]

           if do_classifier_free_guidance:
                 lowerCamelCase__   	=					uncond_embeddings.shape[1]
                 # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                 lowerCamelCase__   	=					uncond_embeddings.repeat(1						,	lowerCAmelCase_      )
                 lowerCamelCase__   	=					uncond_embeddings.reshape(batch_size * num_images_per_prompt						,	lowerCAmelCase_						,	-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
                 lowerCamelCase__   	=					np.concatenate([uncond_embeddings, prompt_embeds]      )

           return prompt_embeds
from __future__ import annotations

import sys
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =letter
		_lowercase   =freq
		_lowercase   ={}


	def __repr__(   self       ):
		return F'''{self.letter}:{self.freq}'''

class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =freq
		_lowercase   =left
		_lowercase   =right

def 	__lowerCamelCase							(				__a    : str		)      ->       list[Letter]:
	_lowercase   ={}
	with open(__a		) as f:
		while True:
			_lowercase   =f.read(1		)
			if not c:
				break
			_lowercase   =chars[c] + 1 if c in chars else 1
	return sorted((Letter(__a      ,							__a		) for c, f in chars.items())      ,							key=lambda __a		: x.freq		)

def 	__lowerCamelCase							(				__a    : list[Letter]		)      ->       Letter | TreeNode:
	_lowercase   =letters  # type: ignore
	while len(__a		) > 1:
		_lowercase   =response.pop(0		)
		_lowercase   =response.pop(0		)
		_lowercase   =left.freq + right.freq
		_lowercase   =TreeNode(__a      ,							__a      ,							__a		)
		response.append(__a		)
		response.sort(key=lambda __a		: x.freq		)
	return response[0]

def 	__lowerCamelCase							(				__a    : Letter | TreeNode      ,							__a    : str		)      ->       list[Letter]:
	if isinstance(__a      ,							__a		):
		_lowercase   =bitstring
		return [root]
	_lowercase   =root  # type: ignore
	_lowercase   =[]
	letters += traverse_tree(treenode.left      ,							bitstring + "0"		)
	letters += traverse_tree(treenode.right      ,							bitstring + "1"		)
	return letters

def 	__lowerCamelCase							(				__a    : str		)      ->       None:
	_lowercase   =parse_file(__a		)
	_lowercase   =build_tree(__a		)
	_lowercase   ={
	    k: v for letter in traverse_tree(__a      ,							""		) for k, v in letter.bitstring.items()
	}
	print(f'''Huffman Coding  of {file_path}: '''		)
	with open(__a		) as f:
		while True:
			_lowercase   =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])




