





"""simple docstring"""
from typing import Any

import numpy as np






class    _UpperCAmelCase  :


					def __init__(   self     :Union[str, Any]	,			__UpperCamelCase     :np.ndarray	,			__UpperCamelCase     :int						):





												if np.any(tableau[:, -1]	,			where=tableau[:, -1] < 0						):
																			raise ValueError("RHS must be > 0"						)

												A						=    tableau
												A,      A						=    tableau.shape

												# Number of decision variables x1, x2, x3...
												A						=    n_vars

												# Number of artificial variables to be minimised
												A						=    len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

												# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
												A						=    (self.n_art_vars > 0) + 1

												# Number of slack variables added to make inequalities into equalities
												A						=    self.n_rows - self.n_stages

												# Objectives for each stage
												A						=    ["max"]

												# In two stage simplex, first minimise then maximise
												if self.n_art_vars:
																			self.objectives.append("min"						)

												A						=    [""]

												# Index of current pivot row and column
												A						=    None
												A						=    None

												# Does objective row only contain (non)-negative values?
												A						=    False


					@staticmethod
					def       lowerCamelCase			(   *__UpperCamelCase     :int						):





												if len(snake_case__						) != 3:
																			raise ValueError("Must provide n_vars, n_slack, and n_art_vars"						)

												if not all(x >= 0 and isinstance(snake_case__	,			snake_case__						) for x in args						):
																			raise ValueError("All arguments must be non-negative integers"						)

												# decision | slack | artificial
												A						=    ["x", "s", "a"]
												A						=    []
												for i in range(3						):
																			for j in range(args[i]						):
																										titles.append(string_starts[i] + str(j + 1						)						)
												titles.append("RHS"						)
												return titles


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :np.ndarray						):
												A						=    self.objectives[-1]

												# Find entries of highest magnitude in objective rows
												A						=    (objective == "min") - (objective == "max")
												A						=    np.argmax(sign * tableau[0, : self.n_vars]						)

												# Choice is only valid if below 0 for maximise, and above for minimise
												if sign * self.tableau[0, col_idx] <= 0:
																			A						=    True
																			return 0, 0

												# Pivot row is chosen as having the lowest quotient when elements of
												# the pivot column divide the right-hand side

												# Slice excluding the objective rows
												A						=    slice(self.n_stages	,			self.n_rows						)

												# RHS
												A						=    tableau[s, -1]

												# Elements of pivot column within slice
												A						=    tableau[s, col_idx]

												# Array filled with nans
												A						=    np.full(self.n_rows - self.n_stages	,			np.nan						)

												# If element in pivot column is greater than zeron_stages, return
												# quotient or nan otherwise
												A						=    np.divide(snake_case__	,			snake_case__	,			out=snake_case__	,			where=divisor > 0						)

												# Arg of minimum quotient excluding the nan values. n_stages is added
												# to compensate for earlier exclusion of objective columns
												A						=    np.nanargmin(snake_case__						) + self.n_stages
												return row_idx, col_idx


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :np.ndarray	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int						):
												A						=    tableau[row_idx].copy()

												A						=    piv_row[col_idx]

												# Entry becomes 1
												piv_row *= 1 / piv_val

												# Variable in pivot column becomes basic, ie the only non-zero entry
												for idx, coeff in enumerate(tableau[:, col_idx]						):
																			tableau[idx] += -coeff * piv_row
												A						=    piv_row
												return tableau


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :np.ndarray						):
												self.objectives.pop()

												if not self.objectives:
																			return tableau

												# Slice containing ids for artificial columns
												A						=    slice(-self.n_art_vars - 1	,			-1						)

												# Delete the artificial variable columns
												A						=    np.delete(snake_case__	,			snake_case__	,			axis=1						)

												# Delete the objective row of the first stage
												A						=    np.delete(snake_case__	,			0	,			axis=0						)

												A						=    1
												self.n_rows -= 1
												A						=    0
												A						=    False
												return tableau


					def       lowerCamelCase			(   self     :Dict						):





												for _ in range(1_00						):
																			# Completion of each stage removes an objective. If both stages
																			# are complete, then no objectives are left
																			if not self.objectives:
																										A						=    self.generate_col_titles(
																										    self.n_vars	,			self.n_slack	,			self.n_art_vars						)

																										# Find the values of each variable at optimal solution
																										return self.interpret_tableau(self.tableau	,			self.col_titles						)

																			A,      A						=    self.find_pivot(self.tableau						)

																			# If there are no more negative values in objective row
																			if self.stop_iter:
																										# Delete artificial variable columns and rows. Update attributes
																										A						=    self.change_stage(self.tableau						)
																			else:
																										A						=    self.pivot(self.tableau	,			snake_case__	,			snake_case__						)
												return {}


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :np.ndarray	,			__UpperCamelCase     :list[str]						):
												A						=    {"P": abs(tableau[0, -1]						)}

												for i in range(self.n_vars						):
																			# Gives ids of nonzero entries in the ith column
																			A						=    np.nonzero(tableau[:, i]						)
																			A						=    len(nonzero[0]						)

																			# First entry in the nonzero ids
																			A						=    nonzero[0][0]
																			A						=    tableau[nonzero_rowidx, i]

																			# If there is only one nonzero value in column, which is one
																			if n_nonzero == nonzero_val == 1:
																										A						=    tableau[nonzero_rowidx, -1]
																										A						=    rhs_val

        # Check for basic variables
												for title in col_titles:
																			# Don't add RHS or slack variables to output dict
																			if title[0] not in "R-s-a":
																										output_dict.setdefault(snake_case__	,			0						)
												return output_dict


if __name__ == "__main__":
		import doctest

		doctest.testmod()




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)




