

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def   __UpperCAmelCase					(      __a :     int						,__a :     Dict=1      )     ->	str:

					"""simple docstring"""

					if n_shave_prefix_segments >= 0:
										return ".".join(path.split('''.'''      )[n_shave_prefix_segments:]      )
					else:
										return ".".join(path.split('''.'''      )[:n_shave_prefix_segments]      )



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     List[str]=0      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Dict   =						[]
					for old_item in old_list:
										_a      :       Optional[Any]   =						old_item.replace('''in_layers.0'''						,'''norm1'''      )
										_a      :       int   =						new_item.replace('''in_layers.2'''						,'''conv1'''      )

										_a      :       List[Any]   =						new_item.replace('''out_layers.0'''						,'''norm2'''      )
										_a      :       int   =						new_item.replace('''out_layers.3'''						,'''conv2'''      )

										_a      :       Optional[Any]   =						new_item.replace('''emb_layers.1'''						,'''time_emb_proj'''      )
										_a      :       int   =						new_item.replace('''skip_connection'''						,'''conv_shortcut'''      )

										_a      :       Optional[int]   =						shave_segments(__a						,n_shave_prefix_segments=__a      )

										mapping.append({'''old''': old_item, '''new''': new_item}      )

					return mapping



def   __UpperCAmelCase					(      __a :     str						,__a :     Tuple=0      )     ->	Dict:

					"""simple docstring"""

					_a      :       int   =						[]
					for old_item in old_list:
										_a      :       Union[str, Any]   =						old_item

										_a      :       str   =						new_item.replace('''norm.weight'''						,'''group_norm.weight'''      )
										_a      :       str   =						new_item.replace('''norm.bias'''						,'''group_norm.bias'''      )

										_a      :       Optional[int]   =						new_item.replace('''proj_out.weight'''						,'''proj_attn.weight'''      )
										_a      :       List[str]   =						new_item.replace('''proj_out.bias'''						,'''proj_attn.bias'''      )

										_a      :       Union[str, Any]   =						shave_segments(__a						,n_shave_prefix_segments=__a      )

										mapping.append({'''old''': old_item, '''new''': new_item}      )

					return mapping



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Dict						,__a :     Dict						,__a :     List[str]=None						,__a :     Any=None						,__a :     List[str]=None      )     ->	Any:

					"""simple docstring"""

					assert isinstance(__a						,__a      ), "Paths should be a list of dicts containing 'old' and 'new' keys."

					# Splits the attention layers into three variables.
					if attention_paths_to_split is not None:
										for path, path_map in attention_paths_to_split.items():
															_a      :       Any   =						old_checkpoint[path]
															_a      :       Any   =						old_tensor.shape[0] // 3

															_a      :       str   =						(-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

															_a      :       Tuple   =						old_tensor.shape[0] // config['''num_head_channels'''] // 3

															_a      :       List[Any]   =						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]      )
															_a ,   _a ,   _a      :       Optional[Any]   =						old_tensor.split(channels // num_heads						,dim=1      )

															_a      :       List[str]   =						query.reshape(__a      )
															_a      :       List[str]   =						key.reshape(__a      )
															_a      :       Dict   =						value.reshape(__a      )

					for path in paths:
										_a      :       Optional[int]   =						path['''new''']

										# These have already been assigned
										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
															continue

										# Global renaming happens here
										_a      :       Optional[int]   =						new_path.replace('''middle_block.0'''						,'''mid_block.resnets.0'''      )
										_a      :       List[Any]   =						new_path.replace('''middle_block.1'''						,'''mid_block.attentions.0'''      )
										_a      :       Tuple   =						new_path.replace('''middle_block.2'''						,'''mid_block.resnets.1'''      )

										if additional_replacements is not None:
															for replacement in additional_replacements:
																				_a      :       Dict   =						new_path.replace(replacement['''old''']						,replacement['''new''']      )

        # proj_attn.weight has to be converted from conv 1D to linear
										if "proj_attn.weight" in new_path:
															_a      :       Any   =						old_checkpoint[path['''old''']][:, :, 0]
										else:
															_a      :       str   =						old_checkpoint[path['''old''']]



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Dict      )     ->	Tuple:

					"""simple docstring"""

					_a      :       List[Any]   =						{}

					_a      :       Tuple   =						checkpoint['''time_embed.0.weight''']
					_a      :       str   =						checkpoint['''time_embed.0.bias''']
					_a      :       Dict   =						checkpoint['''time_embed.2.weight''']
					_a      :       int   =						checkpoint['''time_embed.2.bias''']

					_a      :       Dict   =						checkpoint['''input_blocks.0.0.weight''']
					_a      :       List[Any]   =						checkpoint['''input_blocks.0.0.bias''']

					_a      :       Tuple   =						checkpoint['''out.0.weight''']
					_a      :       str   =						checkpoint['''out.0.bias''']
					_a      :       str   =						checkpoint['''out.2.weight''']
					_a      :       int   =						checkpoint['''out.2.bias''']

					# Retrieves the keys for the input blocks only
					_a      :       Optional[int]   =						len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in checkpoint if '''input_blocks''' in layer}      )
					_a      :       List[Any]   =						{
					    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
					    for layer_id in range(__a      )
					}

					# Retrieves the keys for the middle blocks only
					_a      :       int   =						len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in checkpoint if '''middle_block''' in layer}      )
					_a      :       str   =						{
					    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
					    for layer_id in range(__a      )
					}

					# Retrieves the keys for the output blocks only
					_a      :       Tuple   =						len({'''.'''.join(layer.split('''.'''      )[:2]      ) for layer in checkpoint if '''output_blocks''' in layer}      )
					_a      :       Optional[Any]   =						{
					    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
					    for layer_id in range(__a      )
					}

					for i in range(1						,__a      ):
										_a      :       Any   =						(i - 1) // (config['''num_res_blocks'''] + 1)
										_a      :       List[Any]   =						(i - 1) % (config['''num_res_blocks'''] + 1)

										_a      :       str   =						[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
										_a      :       List[str]   =						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

										if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
															_a      :       Optional[Any]   =						checkpoint[
															    F"""input_blocks.{i}.0.op.weight"""
															]
															_a      :       Any   =						checkpoint[
															    F"""input_blocks.{i}.0.op.bias"""
															]
															continue

										_a      :       List[Any]   =						renew_resnet_paths(__a      )
										_a      :       Any   =						{'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
										_a      :       Optional[int]   =						{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
										assign_to_checkpoint(
										    __a						,__a						,__a						,additional_replacements=[meta_path, resnet_op]						,config=__a      )

										if len(__a      ):
															_a      :       Union[str, Any]   =						renew_attention_paths(__a      )
															_a      :       List[Any]   =						{
															    '''old''': F"""input_blocks.{i}.1""",
															    '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
															}
															_a      :       Union[str, Any]   =						{
															    F"""input_blocks.{i}.1.qkv.bias""": {
															        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
															        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
															        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
															    },
															    F"""input_blocks.{i}.1.qkv.weight""": {
															        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
															        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
															        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
															    },
															}
															assign_to_checkpoint(
															    __a						,__a						,__a						,additional_replacements=[meta_path]						,attention_paths_to_split=__a						,config=__a						,)

					_a      :       int   =						middle_blocks[0]
					_a      :       str   =						middle_blocks[1]
					_a      :       int   =						middle_blocks[2]

					_a      :       int   =						renew_resnet_paths(__a      )
					assign_to_checkpoint(__a						,__a						,__a						,config=__a      )

					_a      :       Dict   =						renew_resnet_paths(__a      )
					assign_to_checkpoint(__a						,__a						,__a						,config=__a      )

					_a      :       str   =						renew_attention_paths(__a      )
					_a      :       Dict   =						{
					    '''middle_block.1.qkv.bias''': {
					        '''key''': '''mid_block.attentions.0.key.bias''',
					        '''query''': '''mid_block.attentions.0.query.bias''',
					        '''value''': '''mid_block.attentions.0.value.bias''',
					    },
					    '''middle_block.1.qkv.weight''': {
					        '''key''': '''mid_block.attentions.0.key.weight''',
					        '''query''': '''mid_block.attentions.0.query.weight''',
					        '''value''': '''mid_block.attentions.0.value.weight''',
					    },
					}
					assign_to_checkpoint(
					    __a						,__a						,__a						,attention_paths_to_split=__a						,config=__a      )

					for i in range(__a      ):
										_a      :       str   =						i // (config['''num_res_blocks'''] + 1)
										_a      :       Union[str, Any]   =						i % (config['''num_res_blocks'''] + 1)
										_a      :       List[Any]   =						[shave_segments(__a						,2      ) for name in output_blocks[i]]
										_a      :       str   =						{}

										for layer in output_block_layers:
															_a ,   _a      :       Optional[Any]   =						layer.split('''.'''      )[0], shave_segments(__a						,1      )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(__a      )
															else:
																				_a      :       int   =						[layer_name]

										if len(__a      ) > 1:
															_a      :       Dict   =						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
															_a      :       List[Any]   =						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

															_a      :       Tuple   =						renew_resnet_paths(__a      )
															_a      :       Tuple   =						renew_resnet_paths(__a      )

															_a      :       str   =						{'''old''': F"""output_blocks.{i}.0""", '''new''': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
															assign_to_checkpoint(__a						,__a						,__a						,additional_replacements=[meta_path]						,config=__a      )

															if ["conv.weight", "conv.bias"] in output_block_list.values():
																				_a      :       List[Any]   =						list(output_block_list.values()      ).index(['''conv.weight''', '''conv.bias''']      )
																				_a      :       List[str]   =						checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.weight"""
																				]
																				_a      :       Union[str, Any]   =						checkpoint[
																				    F"""output_blocks.{i}.{index}.conv.bias"""
																				]

																				# Clear attentions as they have been attributed above.
																				if len(__a      ) == 2:
																									_a      :       Tuple   =						[]

															if len(__a      ):
																				_a      :       Optional[int]   =						renew_attention_paths(__a      )
																				_a      :       str   =						{
																				    '''old''': F"""output_blocks.{i}.1""",
																				    '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																				}
																				_a      :       Tuple   =						{
																				    F"""output_blocks.{i}.1.qkv.bias""": {
																				        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																				        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																				        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																				    },
																				    F"""output_blocks.{i}.1.qkv.weight""": {
																				        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																				        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																				        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																				    },
																				}
																				assign_to_checkpoint(
																				    __a						,__a						,__a						,additional_replacements=[meta_path]						,attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions      ) else None						,config=__a						,)
										else:
															_a      :       str   =						renew_resnet_paths(__a						,n_shave_prefix_segments=1      )
															for path in resnet_0_paths:
																				_a      :       Any   =						'''.'''.join(['''output_blocks''', str(__a      ), path['''old''']]      )
																				_a      :       Tuple   =						'''.'''.join(['''up_blocks''', str(__a      ), '''resnets''', str(__a      ), path['''new''']]      )

																				_a      :       Optional[int]   =						checkpoint[old_path]

					return new_checkpoint


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

			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
			)

			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the architecture.''',
			)

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			a__												=						parser.parse_args()

			a__												=						torch.load(args.checkpoint_path)

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

			a__												=						convert_ldm_checkpoint(checkpoint, config)

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

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

			try:
						a__												=						DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
						a__												=						VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

						a__												=						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
						pipe.save_pretrained(args.dump_path)
			except:  # noqa: E722
						model.save_pretrained(args.dump_path)







'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														lowerCAmelCase__			  =      length
														lowerCAmelCase__			  =      rng.normal(size=(length,)  ).astype(np.floataa  )
														lowerCAmelCase__			  =      a * self.x + b + rng.normal(scale=0.1   ,       size=(length,)  ).astype(np.floataa  )







							def __len__(						self  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a[0] + self.b[0]




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														if self.first_batch:
																					print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""  )
																					lowerCAmelCase__			  =      False
														return x * self.a + self.b


def    _snake_case  (	A					,       A = 16			)		->       Any:
							from datasets import load_dataset
							from transformers import AutoTokenizer

							lowerCAmelCase__			  =      AutoTokenizer.from_pretrained('''bert-base-cased'''			)
							lowerCAmelCase__			  =      {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
							lowerCAmelCase__			  =      load_dataset('''csv'''					,       data_files=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

							lowerCAmelCase__			  =      {v: i for i, v in enumerate(A			)}

							def tokenize_function(A			):
														# max_length=None => use the model max length (it's actually the default)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       padding='''max_length'''			)
														if "label" in examples:
																					lowerCAmelCase__			  =      [label_to_id[l] for l in examples['''label''']]
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							lowerCAmelCase__			  =      datasets.map(
							    A					,       batched=A					,       remove_columns=['''sentence1''', '''sentence2''', '''label''']					,       )

							def collate_fn(A			):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														if accelerator.distributed_type == DistributedType.TPU:
																					return tokenizer.pad(A					,       padding='''max_length'''					,       max_length=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

							# Instantiate dataloaders.
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''train''']					,       shuffle=A					,       collate_fn=A					,       batch_size=2			)
							lowerCAmelCase__			  =      DataLoader(tokenized_datasets['''validation''']					,       shuffle=A					,       collate_fn=A					,       batch_size=1			)

							return train_dataloader, eval_dataloader