






from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				str  					=1
     __snake_case		:				Union[str, Any]  					=2
     __snake_case		:				Tuple  					=3





     def __repr__(		self					:	str)							->	Optional[int]:

      """simple docstring"""
      return f'''{self.__class__.__name__}.{self.name}'''





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		point
 return degrees(atana(y - miny	,  x - minx			)			)





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->Direction:







 """simple docstring"""

 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		starting
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		via
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		target
 SCREAMING_SNAKE_CASE				    =		degrees(atana(ya - ya	,  xa - xa			)			)
 via_angle %= 3_6_0
 SCREAMING_SNAKE_CASE				    =		degrees(atana(ya - ya	,  xa - xa			)			)
 target_angle %= 3_6_0
 # t-
 #  \ \
 #   \ v
 #    \|
 #     s
 # via_angle is always lower than target_angle, if direction is left.
 # If they are same, it means they are on a same line of convex hull.
 if target_angle > via_angle:
  return Direction.left
 elif target_angle == via_angle:
  return Direction.straight
 else:
  return Direction.right





def 						A_					(						lowercase_			)				->list[tuple[int, int]]:







 """simple docstring"""

 if len(lowercase_			) <= 2:
  # There is no convex hull
  raise ValueError('graham_scan: argument must contain more than 3 points.'			)
 if len(lowercase_			) == 3:
  return points
 # find the lowest and the most left point
 SCREAMING_SNAKE_CASE				    =		0
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		maxsize, maxsize
 for i, point in enumerate(lowercase_			):
  SCREAMING_SNAKE_CASE				    =		point[0]
  SCREAMING_SNAKE_CASE				    =		point[1]
  if y < miny:
   SCREAMING_SNAKE_CASE				    =		y
   SCREAMING_SNAKE_CASE				    =		x
   SCREAMING_SNAKE_CASE				    =		i
  if y == miny and x < minx:
   SCREAMING_SNAKE_CASE				    =		x
   SCREAMING_SNAKE_CASE				    =		i

    # remove the lowest and the most left point from points for preparing for sort
 points.pop(lowercase_			)

 SCREAMING_SNAKE_CASE				    =		sorted(lowercase_	,  key=lambda lowercase_			: angle_comparer(lowercase_	,  lowercase_	,  lowercase_			)			)
 # This insert actually costs complexity,
 # and you should instead add (minx, miny) into stack later.
 # I'm using insert just for easy understanding.
 sorted_points.insert(0	,  (minx, miny)			)

 SCREAMING_SNAKE_CASE				    =		deque()
 stack.append(sorted_points[0]			)
 stack.append(sorted_points[1]			)
 stack.append(sorted_points[2]			)
 # In any ways, the first 3 points line are towards left.
 # Because we sort them the angle from minx, miny.
 SCREAMING_SNAKE_CASE				    =		Direction.left

 for i in range(3	,  len(lowercase_			)			):
  while True:
   SCREAMING_SNAKE_CASE				    =		stack[-2]
   SCREAMING_SNAKE_CASE				    =		stack[-1]
   SCREAMING_SNAKE_CASE				    =		sorted_points[i]
   SCREAMING_SNAKE_CASE				    =		check_direction(lowercase_	,  lowercase_	,  lowercase_			)

   if next_direction == Direction.left:
    SCREAMING_SNAKE_CASE				    =		Direction.left
    break
   if next_direction == Direction.straight:
    if current_direction == Direction.left:
     # We keep current_direction as left.
     # Because if the straight line keeps as straight,
     # we want to know if this straight line is towards left.
     break
    elif current_direction == Direction.right:
     # If the straight line is towards right,
     # every previous points on those straigh line is not convex hull.
     stack.pop()
   if next_direction == Direction.right:
    stack.pop()
  stack.append(sorted_points[i]			)
 return list(lowercase_			)






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				=UNetaDModel(**a_		)
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.0.W"].data
		A				=checkpoint["all_modules.1.weight"].data
		A				=checkpoint["all_modules.1.bias"].data

		A				=checkpoint["all_modules.2.weight"].data
		A				=checkpoint["all_modules.2.bias"].data

		A				=checkpoint["all_modules.3.weight"].data
		A				=checkpoint["all_modules.3.bias"].data

		A				=checkpoint[list(checkpoint.keys()		)[-4]].data
		A				=checkpoint[list(checkpoint.keys()		)[-3]].data
		A				=checkpoint[list(checkpoint.keys()		)[-2]].data
		A				=checkpoint[list(checkpoint.keys()		)[-1]].data

		A				=4

		def set_attention_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

				A				=old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
				A				=old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
				A				=old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

		def set_resnet_weights(a_  ,				a_  ,				a_		):
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
				A				=old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

				if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
						A				=old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

		for i, block in enumerate(new_model_architecture.downsample_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
						if has_attentions:
								set_attention_weights(block.attentions[j]  ,				a_  ,				a_		)
								module_index += 1

				if hasattr(a_  ,				"downsamplers"		) and block.downsamplers is not None:
						set_resnet_weights(block.resnet_down  ,				a_  ,				a_		)
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
						module_index += 1

		set_resnet_weights(new_model_architecture.mid_block.resnets[0]  ,				a_  ,				a_		)
		module_index += 1
		set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,				a_  ,				a_		)
		module_index += 1
		set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,				a_  ,				a_		)
		module_index += 1

		for i, block in enumerate(new_model_architecture.up_blocks		):
				A				=hasattr(a_  ,				"attentions"		)
				for j in range(len(block.resnets		)		):
						set_resnet_weights(block.resnets[j]  ,				a_  ,				a_		)
						module_index += 1
				if has_attentions:
						set_attention_weights(
						    block.attentions[0]  ,				a_  ,				a_		)  # why can there only be a single attention layer for up?
						module_index += 1

				if hasattr(a_  ,				"resnet_up"		) and block.resnet_up is not None:
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
						A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
						module_index += 1
						set_resnet_weights(block.resnet_up  ,				a_  ,				a_		)
						module_index += 1

		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data
		module_index += 1
		A				=checkpoint[f'''all_modules.{module_index}.weight'''].data
		A				=checkpoint[f'''all_modules.{module_index}.bias'''].data

		return new_model_architecture.state_dict()


if __name__ == "__main__":
	__a							  =						argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
	    type=str,
	    required=False,
	    help="""Path to the checkpoint to convert.""",
	)

	parser.add_argument(
	    """--config_file""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
	    type=str,
	    required=False,
	    help="""The config json file corresponding to the architecture.""",
	)

	parser.add_argument(
	    """--dump_path""",
	    default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
	    type=str,
	    required=False,
	    help="""Path to the output model.""",
	)

	__a							  =						parser.parse_args()

	__a							  =						torch.load(args.checkpoint_path, map_location="""cpu""")

	with open(args.config_file) as f:
		__a							  =						json.loads(f.read())

	__a							  =						convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	__a							  =						UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__a							  =						ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

		__a							  =						ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)




