




'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





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

     """simple docstring"""




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

          _snake_case							=				new_item.replace("out_layers.0" ,						"norm2"   )
          _snake_case							=				new_item.replace("out_layers.3" ,						"conv2"   )

          _snake_case							=				new_item.replace("emb_layers.1" ,						"time_emb_proj"   )
          _snake_case							=				new_item.replace("skip_connection" ,						"conv_shortcut"   )

          _snake_case							=				shave_segments(snake_case_ ,						n_shave_prefix_segments=snake_case_   )

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

     return mapping





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Dict=0   )						->			int:

     """simple docstring"""




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

          _snake_case							=				new_item.replace("norm.weight" ,						"group_norm.weight"   )
          _snake_case							=				new_item.replace("norm.bias" ,						"group_norm.bias"   )

          _snake_case							=				new_item.replace("proj_out.weight" ,						"proj_attn.weight"   )
          _snake_case							=				new_item.replace("proj_out.bias" ,						"proj_attn.bias"   )

          _snake_case							=				shave_segments(snake_case_ ,						n_shave_prefix_segments=snake_case_   )

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

     return mapping





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[Any]=None ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   Any=None   )						->			Optional[int]:

     """simple docstring"""




     assert isinstance(snake_case_ ,						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():
               _snake_case							=				old_checkpoint[path]
               _snake_case							=				old_tensor.shape[0] // 3

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

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

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

               _snake_case							=				query.reshape(snake_case_   )
               _snake_case							=				key.reshape(snake_case_   )
               _snake_case							=				value.reshape(snake_case_   )

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

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





def      __a     (       _UpperCamelCase:   List[Any] ,						_UpperCamelCase:   Dict   )						->			List[str]:

     """simple docstring"""




     _snake_case							=				{}

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

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

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

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

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

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

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

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

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

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

     _snake_case							=				middle_blocks[0]
     _snake_case							=				middle_blocks[1]
     _snake_case							=				middle_blocks[2]

     _snake_case							=				renew_resnet_paths(snake_case_   )
     assign_to_checkpoint(snake_case_ ,						snake_case_ ,						snake_case_ ,						config=snake_case_   )

     _snake_case							=				renew_resnet_paths(snake_case_   )
     assign_to_checkpoint(snake_case_ ,						snake_case_ ,						snake_case_ ,						config=snake_case_   )

     _snake_case							=				renew_attention_paths(snake_case_   )
     _snake_case							=				{
         """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(
         snake_case_ ,						snake_case_ ,						snake_case_ ,						attention_paths_to_split=snake_case_ ,						config=snake_case_   )

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

          for layer in output_block_layers:
               _snake_case							=				layer.split("."   )[0], shave_segments(snake_case_ ,						1   )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(snake_case_   )
               else:
                    _snake_case							=				[layer_name]

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

               _snake_case							=				renew_resnet_paths(snake_case_   )
               _snake_case							=				renew_resnet_paths(snake_case_   )

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

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

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

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

                    _snake_case							=				checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
   UpperCamelCase_						:      Optional[int]       =						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.''')

   UpperCamelCase_						:      str       =						parser.parse_args()

   UpperCamelCase_						:      int       =						torch.load(args.checkpoint_path)

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

   UpperCamelCase_						:      int       =						convert_ldm_checkpoint(checkpoint, config)

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

   UpperCamelCase_						:      int       =						UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

      UpperCamelCase_						:      List[str]       =						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)








'''simple docstring'''





from __future__ import annotations

from collections.abc import Callable





def      __a     (       _UpperCamelCase:   Callable[[int | float], int | float] ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int | float ,						_UpperCamelCase:   int = 100 ,						)						->			float:

     """simple docstring"""




     _snake_case							=				x_start
     _snake_case							=				fnc(_UpperCamelCase   )
     _snake_case							=				0.0
     for _ in range(_UpperCamelCase   ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          _snake_case							=				(x_end - x_start) / steps + xa
          _snake_case							=				fnc(_UpperCamelCase   )
          area += abs(fxa + fxa   ) * (xa - xa) / 2
          # Increment step
          _snake_case							=				xa
          _snake_case							=				fxa
     return area


if __name__ == "__main__":





   def      __a     (       _UpperCamelCase:   Any   )						->			Optional[int]:

        """simple docstring"""




        return x**3 + x**2

   print('''f(x) = x^3 + x^2''')
   print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
   UpperCamelCase_						:      Optional[int]       =						10
   while i <= 100000:
      print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
      i *= 10


