


import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter

class   A__							(						TensorFormatter[Mapping, np.ndarray, Mapping]     ):




						def __init__(				self    :					int ,			_a    :					List[str]=None ,			**_a    :					Union[str, Any]     )							->   Optional[Any]:


										"""simple docstring"""





										super().__init__(features=_a     )
										_SCREAMING_SNAKE_CASE	     =np_array_kwargs




						def 							__UpperCamelCase (				self    :					Union[str, Any] ,			_a    :					Union[str, Any]     )							->   List[str]:


										"""simple docstring"""





										if isinstance(_a ,			_a     ):
														if column and all(
														    isinstance(_a ,			np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																		return np.stack(_a     )
														else:
																		# don't use np.array(column, dtype=object)
																		# since it fails in certain cases
																		# see https://stackoverflow.com/q/51005699
																		_SCREAMING_SNAKE_CASE	     =np.empty(len(_a     ) ,			dtype=_a     )
																		_SCREAMING_SNAKE_CASE	     =column
																		return out
										return column




						def 							__UpperCamelCase (				self    :					List[str] ,			_a    :					str     )							->   Union[str, Any]:


										"""simple docstring"""





										if isinstance(_a ,			(str, bytes, type(_a     ))     ):
														return value
										elif isinstance(_a ,			(np.character, np.ndarray)     ) and np.issubdtype(value.dtype ,			np.character     ):
														return value
										elif isinstance(_a ,			np.number     ):
														return value

										_SCREAMING_SNAKE_CASE	     ={}

										if isinstance(_a ,			np.ndarray     ) and np.issubdtype(value.dtype ,			np.integer     ):
														_SCREAMING_SNAKE_CASE	     ={'''dtype''': np.intaa}
										elif isinstance(_a ,			np.ndarray     ) and np.issubdtype(value.dtype ,			np.floating     ):
														_SCREAMING_SNAKE_CASE	     ={'''dtype''': np.floataa}
										elif config.PIL_AVAILABLE and "PIL" in sys.modules:
														import PIL.Image

														if isinstance(_a ,			PIL.Image.Image     ):
																		return np.asarray(_a ,			**self.np_array_kwargs     )

										return np.asarray(_a ,			**{**default_dtype, **self.np_array_kwargs}     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					List[Any]     )							->   List[Any]:


										"""simple docstring"""





										if config.TORCH_AVAILABLE and "torch" in sys.modules:
														import torch

														if isinstance(_a ,			torch.Tensor     ):
																		return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
										if hasattr(_a ,			'''__array__'''     ) and not isinstance(_a ,			(np.ndarray, np.character, np.number)     ):
														_SCREAMING_SNAKE_CASE	     =data_struct.__array__()
										# support for nested types like struct of list of struct
										if isinstance(_a ,			np.ndarray     ):
														if data_struct.dtype == object:
																		return self._consolidate([self.recursive_tensorize(_a     ) for substruct in data_struct]     )
										if isinstance(_a ,			(list, tuple)     ):
														return self._consolidate([self.recursive_tensorize(_a     ) for substruct in data_struct]     )
										return self._tensorize(_a     )




						def 							__UpperCamelCase (				self    :					List[Any] ,			_a    :					dict     )							->   List[str]:


										"""simple docstring"""





										return map_nested(self._recursive_tensorize ,			_a ,			map_list=_a     )




						def 							__UpperCamelCase (				self    :					int ,			_a    :					pa.Table     )							->   Mapping:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.numpy_arrow_extractor().extract_row(_a     )
										_SCREAMING_SNAKE_CASE	     =self.python_features_decoder.decode_row(_a     )
										return self.recursive_tensorize(_a     )




						def 							__UpperCamelCase (				self    :					int ,			_a    :					pa.Table     )							->   np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.numpy_arrow_extractor().extract_column(_a     )
										_SCREAMING_SNAKE_CASE	     =self.python_features_decoder.decode_column(_a ,			pa_table.column_names[0]     )
										_SCREAMING_SNAKE_CASE	     =self.recursive_tensorize(_a     )
										_SCREAMING_SNAKE_CASE	     =self._consolidate(_a     )
										return column




						def 							__UpperCamelCase (				self    :					str ,			_a    :					pa.Table     )							->   Mapping:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =self.numpy_arrow_extractor().extract_batch(_a     )
										_SCREAMING_SNAKE_CASE	     =self.python_features_decoder.decode_batch(_a     )
										_SCREAMING_SNAKE_CASE	     =self.recursive_tensorize(_a     )
										for column_name in batch:
														_SCREAMING_SNAKE_CASE	     =self._consolidate(batch[column_name]     )
										return batch



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =UNetaDModel(**a__)
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.0.W'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.1.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.2.bias'''].data

				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.weight'''].data
				_SCREAMING_SNAKE_CASE	     =checkpoint['''all_modules.3.bias'''].data

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

				_SCREAMING_SNAKE_CASE	     =4

				def set_attention_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

				def set_resnet_weights(a__		,a__		,a__):
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

								_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
												_SCREAMING_SNAKE_CASE	     =old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

				for i, block in enumerate(new_model_architecture.downsample_blocks):
								_SCREAMING_SNAKE_CASE	     =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
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
												_SCREAMING_SNAKE_CASE	     =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):
								_SCREAMING_SNAKE_CASE	     =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:
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
												_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
												module_index += 1
												set_resnet_weights(block.resnet_up		,a__		,a__)
												module_index += 1

				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data
				module_index += 1
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.weight"].data
				_SCREAMING_SNAKE_CASE	     =checkpoint[f"all_modules.{module_index}.bias"].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
							snake_case_  :	Tuple							  =							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.''',
							)

							snake_case_  :	List[str]							  =							parser.parse_args()

							snake_case_  :	List[Any]							  =							torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														snake_case_  :	List[str]							  =							json.loads(f.read())

							snake_case_  :	Optional[int]							  =							convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							snake_case_  :	Union[str, Any]							  =							UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														snake_case_  :	List[Any]							  =							ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

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