





"""simple docstring"""
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
		import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME






class    _UpperCAmelCase  (      A_					):


					def __init__(   self     :List[Any]						):
												self.register_to_config()
												super().__init__()


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :int						):

												if dicta == dicta:
																			return True
												else:
																			A,      A						=    self._remove_meta_keys(snake_case_						)
																			A,      A						=    self._remove_meta_keys(snake_case_						)
																			if configa == configa:
																										print(f"Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."						)
																										return True
												return False


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :Dict						):
												A						=    []
												A						=    config_dict.copy()
												for key in config_dict.keys():
																			if key.startswith("_"						):
																										temp_dict.pop(snake_case_						)
																										meta_keys.append(snake_case_						)
												return (temp_dict, meta_keys)


					@torch.no_grad()
					def       lowerCamelCase			(   self     :Union[str, Any]	,			__UpperCamelCase     :List[Union[str, os.PathLike]]	,			**__UpperCamelCase     :Dict						):
												A						=    kwargs.pop("cache_dir"	,			snake_case_						)
												A						=    kwargs.pop("resume_download"	,			snake_case_						)
												A						=    kwargs.pop("force_download"	,			snake_case_						)
												A						=    kwargs.pop("proxies"	,			snake_case_						)
												A						=    kwargs.pop("local_files_only"	,			snake_case_						)
												A						=    kwargs.pop("use_auth_token"	,			snake_case_						)
												A						=    kwargs.pop("revision"	,			snake_case_						)
												A						=    kwargs.pop("torch_dtype"	,			snake_case_						)
												A						=    kwargs.pop("device_map"	,			snake_case_						)

												A						=    kwargs.pop("alpha"	,			0.5						)
												A						=    kwargs.pop("interp"	,			snake_case_						)

												print("Received list"	,			snake_case_						)
												print(f"Combining with alpha={alpha}, interpolation mode={interp}"						)

												A						=    len(snake_case_						)
												# Ignore result from model_index_json comparision of the two checkpoints
												A						=    kwargs.pop("force"	,			snake_case_						)

												# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
												if checkpoint_count > 3 or checkpoint_count < 2:
																			raise ValueError(
																			    "Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being"
																			    " passed."						)

												print("Received the right number of checkpoints"						)
												# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
												# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

												# Validate that the checkpoints can be merged
												# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
												A						=    []
												for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																			A						=    DiffusionPipeline.load_config(
																			    snake_case_	,			cache_dir=snake_case_	,			resume_download=snake_case_	,			force_download=snake_case_	,			proxies=snake_case_	,			local_files_only=snake_case_	,			use_auth_token=snake_case_	,			revision=snake_case_	,			)
																			config_dicts.append(snake_case_						)

												A						=    True
												for idx in range(1	,			len(snake_case_						)						):
																			comparison_result &= self._compare_model_configs(config_dicts[idx - 1]	,			config_dicts[idx]						)
																			if not force and comparison_result is False:
																										raise ValueError("Incompatible checkpoints. Please check model_index.json for the models."						)
																										print(config_dicts[0]	,			config_dicts[1]						)
												print("Compatible model_index.json files found"						)
												# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
												A						=    []
												for pretrained_model_name_or_path, config_dict in zip(snake_case_	,			snake_case_						):
																			A						=    [k for k in config_dict.keys() if not k.startswith("_"						)]
																			A						=    [os.path.join(snake_case_	,			"*"						) for k in folder_names]
																			allow_patterns += [
																			    WEIGHTS_NAME,
																			    SCHEDULER_CONFIG_NAME,
																			    CONFIG_NAME,
																			    ONNX_WEIGHTS_NAME,
																			    DiffusionPipeline.config_name,
																			]
																			A						=    config_dict.get("_class_name"						)
																			A						=    {"diffusers": __version__, "pipeline_class": requested_pipeline_class}

																			A						=    (
																			    pretrained_model_name_or_path
																			    if os.path.isdir(snake_case_						)
																			    else snapshot_download(
																			        snake_case_	,			cache_dir=snake_case_	,			resume_download=snake_case_	,			proxies=snake_case_	,			local_files_only=snake_case_	,			use_auth_token=snake_case_	,			revision=snake_case_	,			allow_patterns=snake_case_	,			user_agent=snake_case_	,			)
																			)
																			print("Cached Folder"	,			snake_case_						)
																			cached_folders.append(snake_case_						)

												# Step 3:-
												# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
												A						=    DiffusionPipeline.from_pretrained(
												    cached_folders[0]	,			torch_dtype=snake_case_	,			device_map=snake_case_						)
												final_pipe.to(self.device						)

												A						=    None
												if len(snake_case_						) > 2:
																			A						=    os.path.join(cached_folders[2]						)

												if interp == "sigmoid":
																			A						=    CheckpointMergerPipeline.sigmoid
												elif interp == "inv_sigmoid":
																			A						=    CheckpointMergerPipeline.inv_sigmoid
												elif interp == "add_diff":
																			A						=    CheckpointMergerPipeline.add_difference
												else:
																			A						=    CheckpointMergerPipeline.weighted_sum

												# Find each module's state dict.
												for attr in final_pipe.config.keys():
																			if not attr.startswith("_"						):
																										A						=    os.path.join(cached_folders[1]	,			snake_case_						)
																										if os.path.exists(snake_case_						):
																																	A						=    [
																																	    *glob.glob(os.path.join(snake_case_	,			"*.safetensors"						)						),
																																	    *glob.glob(os.path.join(snake_case_	,			"*.bin"						)						),
																																	]
																																	A						=    files[0] if len(snake_case_						) > 0 else None
																										if len(snake_case_						) < 3:
																																	A						=    None
																										else:
																																	A						=    os.path.join(cached_folders[2]	,			snake_case_						)
																																	if os.path.exists(snake_case_						):
																																								A						=    [
																																								    *glob.glob(os.path.join(snake_case_	,			"*.safetensors"						)						),
																																								    *glob.glob(os.path.join(snake_case_	,			"*.bin"						)						),
																																								]
																																								A						=    files[0] if len(snake_case_						) > 0 else None
																																								       # For an attr if both checkpoint_path_1 and 2 are None, ignore.
																																								       # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
																										if checkpoint_path_a is None and checkpoint_path_a is None:
																																	print(f"Skipping {attr}: not present in 2nd or 3d model"						)
																																	continue
																										try:
																																	A						=    getattr(snake_case_	,			snake_case_						)
																																	if isinstance(snake_case_	,			snake_case_						):  # ignore requires_safety_checker boolean
																																								continue
																																	A						=    getattr(snake_case_	,			"state_dict"						)
																																	A						=    theta_a()

																																	A						=    getattr(snake_case_	,			"load_state_dict"						)
																																	A						=    (
																																	    safetensors.torch.load_file(snake_case_						)
																																	    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"						))
																																	    else torch.load(snake_case_	,			map_location="cpu"						)
																																	)
																																	A						=    None
																																	if checkpoint_path_a:
																																								A						=    (
																																								    safetensors.torch.load_file(snake_case_						)
																																								    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"						))
																																								    else torch.load(snake_case_	,			map_location="cpu"						)
																																								)

																																	if not theta_a.keys() == theta_a.keys():
																																								print(f"Skipping {attr}: key mismatch"						)
																																								continue
																																	if theta_a and not theta_a.keys() == theta_a.keys():
																																								print(f"Skipping {attr}:y mismatch"						)
																										except Exception as e:
																																	print(f"Skipping {attr} do to an unexpected error: {str(snake_case_						)}"						)
																																	continue
																										print(f"MERGING {attr}"						)

																										for key in theta_a.keys():
																																	if theta_a:
																																								A						=    theta_func(theta_a[key]	,			theta_a[key]	,			theta_a[key]	,			snake_case_						)
																																	else:
																																								A						=    theta_func(theta_a[key]	,			theta_a[key]	,			snake_case_	,			snake_case_						)

																										del theta_a
																										del theta_a
																										update_theta_a(snake_case_						)

																										del theta_a
												return final_pipe


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :int						):
												return ((1 - alpha) * thetaa) + (alpha * thetaa)


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[str]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Optional[Any]						):
												A						=    alpha * alpha * (3 - (2 * alpha))
												return thetaa + ((thetaa - thetaa) * alpha)


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :str	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :Tuple						):
												import math

												A						=    0.5 - math.sin(math.asin(1.0 - 2.0 * alpha						) / 3.0						)
												return thetaa + ((thetaa - thetaa) * alpha)


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Any						):
												return thetaa + (thetaa - thetaa) * (1.0 - alpha)








"""simple docstring"""



from __future__ import annotations

import sys




class       UpperCAmelCase_    :




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




     '''simple docstring'''

     A__			=							letter
     A__			=							freq
     A__			=							{}




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




     '''simple docstring'''

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






class       UpperCAmelCase_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			Letter | TreeNode      ,					snake_case_		:			Letter | TreeNode ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							freq
     A__			=							left
     A__			=							right







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						list[Letter]:
 A__			=							{}
 with open(lowercase_		) as f:
  while True:
   A__			=							f.read(1		)
   if not c:
    break
   A__			=							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     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						Letter | TreeNode:
 A__			=							letters  # type: ignore
 while len(lowercase_		) > 1:
  A__			=							response.pop(0		)
  A__			=							response.pop(0		)
  A__			=							left.freq + right.freq
  A__			=							TreeNode(lowercase_     ,					lowercase_     ,					lowercase_		)
  response.append(lowercase_		)
  response.sort(key=lambda lowercase_		: x.freq		)
 return response[0]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						list[Letter]:
 if isinstance(lowercase_     ,					lowercase_		):
  A__			=							bitstring
  return [root]
 A__			=							root  # type: ignore
 A__			=							[]
 letters += traverse_tree(treenode.left     ,					bitstring + "0"		)
 letters += traverse_tree(treenode.right     ,					bitstring + "1"		)
 return letters







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						None:
 A__			=							parse_file(lowercase_		)
 A__			=							build_tree(lowercase_		)
 A__			=							{
     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:
   A__			=							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])





