




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      str=1      )     ->       Optional[int]:





				"""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 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      str=0      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				for old_item in old_list:
								__lowerCamelCase		       =    old_item.replace('in_layers.0' ,     'norm1'      )
								__lowerCamelCase		       =    new_item.replace('in_layers.2' ,     'conv1'      )

								__lowerCamelCase		       =    new_item.replace('out_layers.0' ,     'norm2'      )
								__lowerCamelCase		       =    new_item.replace('out_layers.3' ,     'conv2'      )

								__lowerCamelCase		       =    new_item.replace('emb_layers.1' ,     'time_emb_proj'      )
								__lowerCamelCase		       =    new_item.replace('skip_connection' ,     'conv_shortcut'      )

								__lowerCamelCase		       =    shave_segments(_SCREAMING_SNAKE_CASE ,     n_shave_prefix_segments=_SCREAMING_SNAKE_CASE      )

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

				return mapping







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int=0      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				for old_item in old_list:
								__lowerCamelCase		       =    old_item

								__lowerCamelCase		       =    new_item.replace('norm.weight' ,     'group_norm.weight'      )
								__lowerCamelCase		       =    new_item.replace('norm.bias' ,     'group_norm.bias'      )

								__lowerCamelCase		       =    new_item.replace('proj_out.weight' ,     'proj_attn.weight'      )
								__lowerCamelCase		       =    new_item.replace('proj_out.bias' ,     'proj_attn.bias'      )

								__lowerCamelCase		       =    shave_segments(_SCREAMING_SNAKE_CASE ,     n_shave_prefix_segments=_SCREAMING_SNAKE_CASE      )

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

				return mapping







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Any ,     UpperCamelCase__				:      int=None ,     UpperCamelCase__				:      Tuple=None ,     UpperCamelCase__				:      int=None      )     ->       Dict:





				"""simple docstring"""


				assert isinstance(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE      ), "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():
												__lowerCamelCase		       =    old_checkpoint[path]
												__lowerCamelCase		       =    old_tensor.shape[0] // 3

												__lowerCamelCase		       =    (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

												__lowerCamelCase		       =    old_tensor.shape[0] // config["num_head_channels"] // 3

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

												__lowerCamelCase		       =    query.reshape(_SCREAMING_SNAKE_CASE      )
												__lowerCamelCase		       =    key.reshape(_SCREAMING_SNAKE_CASE      )
												__lowerCamelCase		       =    value.reshape(_SCREAMING_SNAKE_CASE      )

				for path in paths:
								__lowerCamelCase		       =    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
								__lowerCamelCase		       =    new_path.replace('middle_block.0' ,     'mid_block.resnets.0'      )
								__lowerCamelCase		       =    new_path.replace('middle_block.1' ,     'mid_block.attentions.0'      )
								__lowerCamelCase		       =    new_path.replace('middle_block.2' ,     'mid_block.resnets.1'      )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																__lowerCamelCase		       =    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:
												__lowerCamelCase		       =    old_checkpoint[path["old"]][:, :, 0]
								else:
												__lowerCamelCase		       =    old_checkpoint[path["old"]]







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      List[str]      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    {}

				__lowerCamelCase		       =    checkpoint["time_embed.0.weight"]
				__lowerCamelCase		       =    checkpoint["time_embed.0.bias"]
				__lowerCamelCase		       =    checkpoint["time_embed.2.weight"]
				__lowerCamelCase		       =    checkpoint["time_embed.2.bias"]

				__lowerCamelCase		       =    checkpoint["input_blocks.0.0.weight"]
				__lowerCamelCase		       =    checkpoint["input_blocks.0.0.bias"]

				__lowerCamelCase		       =    checkpoint["out.0.weight"]
				__lowerCamelCase		       =    checkpoint["out.0.bias"]
				__lowerCamelCase		       =    checkpoint["out.2.weight"]
				__lowerCamelCase		       =    checkpoint["out.2.bias"]

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

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

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

				for i in range(1 ,     _SCREAMING_SNAKE_CASE      ):
								__lowerCamelCase		       =    (i - 1) // (config["num_res_blocks"] + 1)
								__lowerCamelCase		       =    (i - 1) % (config["num_res_blocks"] + 1)

								__lowerCamelCase		       =    [key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								__lowerCamelCase		       =    [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:
												__lowerCamelCase		       =    checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												__lowerCamelCase		       =    checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

								__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE      )
								__lowerCamelCase		       =    {"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								__lowerCamelCase		       =    {"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     additional_replacements=[meta_path, resnet_op] ,     config=_SCREAMING_SNAKE_CASE      )

								if len(_SCREAMING_SNAKE_CASE      ):
												__lowerCamelCase		       =    renew_attention_paths(_SCREAMING_SNAKE_CASE      )
												__lowerCamelCase		       =    {
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												__lowerCamelCase		       =    {
												    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(
												    _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     additional_replacements=[meta_path] ,     attention_paths_to_split=_SCREAMING_SNAKE_CASE ,     config=_SCREAMING_SNAKE_CASE ,     )

				__lowerCamelCase		       =    middle_blocks[0]
				__lowerCamelCase		       =    middle_blocks[1]
				__lowerCamelCase		       =    middle_blocks[2]

				__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     config=_SCREAMING_SNAKE_CASE      )

				__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE      )
				assign_to_checkpoint(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     config=_SCREAMING_SNAKE_CASE      )

				__lowerCamelCase		       =    renew_attention_paths(_SCREAMING_SNAKE_CASE      )
				__lowerCamelCase		       =    {
				    "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(
				    _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     attention_paths_to_split=_SCREAMING_SNAKE_CASE ,     config=_SCREAMING_SNAKE_CASE      )

				for i in range(_SCREAMING_SNAKE_CASE      ):
								__lowerCamelCase		       =    i // (config["num_res_blocks"] + 1)
								__lowerCamelCase		       =    i % (config["num_res_blocks"] + 1)
								__lowerCamelCase		       =    [shave_segments(_SCREAMING_SNAKE_CASE ,     2      ) for name in output_blocks[i]]
								__lowerCamelCase		       =    {}

								for layer in output_block_layers:
												__lowerCamelCase		       =    layer.split('.'      )[0], shave_segments(_SCREAMING_SNAKE_CASE ,     1      )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE      )
												else:
																__lowerCamelCase		       =    [layer_name]

								if len(_SCREAMING_SNAKE_CASE      ) > 1:
												__lowerCamelCase		       =    [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												__lowerCamelCase		       =    [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE      )
												__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE      )

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

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

																# Clear attentions as they have been attributed above.
																if len(_SCREAMING_SNAKE_CASE      ) == 2:
																				__lowerCamelCase		       =    []

												if len(_SCREAMING_SNAKE_CASE      ):
																__lowerCamelCase		       =    renew_attention_paths(_SCREAMING_SNAKE_CASE      )
																__lowerCamelCase		       =    {
																    "old": F"""output_blocks.{i}.1""",
																    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																__lowerCamelCase		       =    {
																    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(
																    _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     additional_replacements=[meta_path] ,     attention_paths_to_split=to_split if any('qkv' in key for key in attentions      ) else None ,     config=_SCREAMING_SNAKE_CASE ,     )
								else:
												__lowerCamelCase		       =    renew_resnet_paths(_SCREAMING_SNAKE_CASE ,     n_shave_prefix_segments=1      )
												for path in resnet_0_paths:
																__lowerCamelCase		       =    ".".join(['output_blocks', str(_SCREAMING_SNAKE_CASE      ), path['old']]      )
																__lowerCamelCase		       =    ".".join(['up_blocks', str(_SCREAMING_SNAKE_CASE      ), 'resnets', str(_SCREAMING_SNAKE_CASE      ), path['new']]      )

																__lowerCamelCase		       =    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)








def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # "extended trapezoidal rule"
       # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
       lowerCamelCase   :      str			 =					(boundary[1] - boundary[0]) / steps
       lowerCamelCase   :      List[str]			 =					boundary[0]
       lowerCamelCase   :      Union[str, Any]			 =					boundary[1]
       lowerCamelCase   :      int			 =					make_points(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[str]			 =					0.0
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       for i in x_i:
              # print(i)
              y += h * f(_SCREAMING_SNAKE_CASE    )
       y += (h / 2.0) * f(_SCREAMING_SNAKE_CASE    )
       return y

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       lowerCamelCase   :      int			 =					a + h
       while x < (b - h):
              yield x
              lowerCamelCase   :      List[str]			 =					x + h

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Optional[Any]:  # enter your function here
       lowerCamelCase   :      str			 =					(x - 0) * (x - 0)
       return y

def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					0.0  # Lower bound of integration
       lowerCamelCase   :      int			 =					1.0  # Upper bound of integration
       lowerCamelCase   :      Dict			 =					10.0  # define number of steps or resolution
       lowerCamelCase   :      int			 =					[a, b]  # define boundary of integration
       lowerCamelCase   :      str			 =					method_a(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       print(f'''y = {y}'''    )


if __name__ == "__main__":
       main()






