


'''simple docstring'''




import argparse

import torch

from diffusers import UNetaDConditionModel





def      _a		(lowercase__ :      Union[str, Any]       ,      lowercase__ :      List[Any]       ,      lowercase__ :      str       ,      lowercase__ :      Tuple=None       ,      lowercase__ :      int=None       ,      lowercase__ :      str=None	) -> List[str]:






     """simple docstring"""
     assert isinstance(lowercase__       ,      lowercase__	), "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(lowercase__	)
               __snake_case    			=	key.reshape(lowercase__	)
               __snake_case    			=	value.reshape(lowercase__	)

     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

          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
          __snake_case    			=	old_checkpoint[path["old"]]
          __snake_case    			=	["proj_attn.weight"]
          __snake_case    			=	["proj_out.weight", "proj_in.weight"]
          if any(k in new_path for k in names	):
               __snake_case    			=	weight[:, :, 0]
          elif any(k in new_path for k in names_a	) and len(weight.shape	) > 2 and ".attentions." not in new_path:
               __snake_case    			=	weight[:, :, 0]
          else:
               __snake_case    			=	weight





def      _a		(lowercase__ :      Any       ,      lowercase__ :      List[Any]=0	) -> Any:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          __snake_case    			=	old_item

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

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

          #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

     return mapping





def      _a		(lowercase__ :      Optional[int]       ,      lowercase__ :      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      _a		(lowercase__ :      str       ,      lowercase__ :      str=0	) -> List[str]:






     """simple docstring"""
     __snake_case    			=	[]
     for old_item in old_list:
          mapping.append({'old': old_item, 'new': old_item}	)

     return mapping





def      _a		(lowercase__ :      Tuple       ,      lowercase__ :      Optional[Any]=0	) -> Dict:






     """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(lowercase__       ,      n_shave_prefix_segments=lowercase__	)

          if "temopral_conv" not in old_item:
               mapping.append({'old': old_item, 'new': new_item}	)

     return mapping





def      _a		(lowercase__ :      Dict       ,      lowercase__ :      Any       ,      lowercase__ :      Optional[Any]=None       ,      lowercase__ :      Optional[Any]=False	) -> Any:






     """simple docstring"""
     __snake_case    			=	{}
     __snake_case    			=	list(checkpoint.keys()	)

     __snake_case    			=	"model.diffusion_model."

     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith('model_ema'	) for k in keys	) > 1_0_0 and extract_ema:
          print(f'Checkpoint {path} has both EMA and non-EMA weights.'	)
          print(
              'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
              ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'	)
          for key in keys:
               if key.startswith('model.diffusion_model'	):
                    __snake_case    			=	"model_ema." + "".join(key.split('.'	)[1:]	)
                    __snake_case    			=	checkpoint.pop(lowercase__	)
     else:
          if sum(k.startswith('model_ema'	) for k in keys	) > 1_0_0:
               print(
                   'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
                   ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'	)

          for key in keys:
               __snake_case    			=	checkpoint.pop(lowercase__	)

     __snake_case    			=	{}

     __snake_case    			=	unet_state_dict["time_embed.0.weight"]
     __snake_case    			=	unet_state_dict["time_embed.0.bias"]
     __snake_case    			=	unet_state_dict["time_embed.2.weight"]
     __snake_case    			=	unet_state_dict["time_embed.2.bias"]

     if config["class_embed_type"] is None:
          # No parameters to port
          ...
     elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
          __snake_case    			=	unet_state_dict["label_emb.0.0.weight"]
          __snake_case    			=	unet_state_dict["label_emb.0.0.bias"]
          __snake_case    			=	unet_state_dict["label_emb.0.2.weight"]
          __snake_case    			=	unet_state_dict["label_emb.0.2.bias"]
     else:
          raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'	)

     __snake_case    			=	unet_state_dict["input_blocks.0.0.weight"]
     __snake_case    			=	unet_state_dict["input_blocks.0.0.bias"]

     __snake_case    			=	[v for v in unet_state_dict if v.startswith('input_blocks.0.1'	)]
     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{"old": "input_blocks.0.1", "new": "transformer_in"}
     assign_to_checkpoint(lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	unet_state_dict["out.0.weight"]
     __snake_case    			=	unet_state_dict["out.0.bias"]
     __snake_case    			=	unet_state_dict["out.2.weight"]
     __snake_case    			=	unet_state_dict["out.2.bias"]

     # Retrieves the keys for the input blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'input_blocks' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'input_blocks.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     # Retrieves the keys for the middle blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'middle_block' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'middle_block.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     # Retrieves the keys for the output blocks only
     __snake_case    			=	len({'.'.join(layer.split('.'	)[:2]	) for layer in unet_state_dict if 'output_blocks' in layer}	)
     __snake_case    			=	{
         layer_id: [key for key in unet_state_dict if f'output_blocks.{layer_id}' in key]
         for layer_id in range(lowercase__	)
     }

     for i in range(1       ,      lowercase__	):
          __snake_case    			=	(i - 1) // (config["layers_per_block"] + 1)
          __snake_case    			=	(i - 1) % (config["layers_per_block"] + 1)

          __snake_case    			=	[
              key for key in input_blocks[i] if f'input_blocks.{i}.0' in key and f'input_blocks.{i}.0.op' not in key
          ]
          __snake_case    			=	[key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
          __snake_case    			=	[key for key in input_blocks[i] if f'input_blocks.{i}.2' in key]

          if f'input_blocks.{i}.op.weight' in unet_state_dict:
               __snake_case    			=	unet_state_dict.pop(
                   f'input_blocks.{i}.op.weight'	)
               __snake_case    			=	unet_state_dict.pop(
                   f'input_blocks.{i}.op.bias'	)

          __snake_case    			=	renew_resnet_paths(lowercase__	)
          __snake_case    			=	{"old": f'input_blocks.{i}.0', "new": f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
          assign_to_checkpoint(
              lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          __snake_case    			=	[key for key in resnets if "temopral_conv" in key]
          __snake_case    			=	renew_temp_conv_paths(lowercase__	)
          __snake_case    			=	{
              "old": f'input_blocks.{i}.0.temopral_conv',
              "new": f'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
          }
          assign_to_checkpoint(
              lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          if len(lowercase__	):
               __snake_case    			=	renew_attention_paths(lowercase__	)
               __snake_case    			=	{"old": f'input_blocks.{i}.1', "new": f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

          if len(lowercase__	):
               __snake_case    			=	renew_attention_paths(lowercase__	)
               __snake_case    			=	{
                   "old": f'input_blocks.{i}.2',
                   "new": f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
               }
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	middle_blocks[0]
     __snake_case    			=	[key for key in resnet_a if "temopral_conv" in key]
     __snake_case    			=	middle_blocks[1]
     __snake_case    			=	middle_blocks[2]
     __snake_case    			=	middle_blocks[3]
     __snake_case    			=	[key for key in resnet_a if "temopral_conv" in key]

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.0", "new": "mid_block.resnets.0"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_temp_conv_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_resnet_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.3", "new": "mid_block.resnets.1"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_temp_conv_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      config=lowercase__       ,      additional_replacements=[meta_path]	)

     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     __snake_case    			=	renew_attention_paths(lowercase__	)
     __snake_case    			=	{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
     assign_to_checkpoint(
         lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

     for i in range(lowercase__	):
          __snake_case    			=	i // (config["layers_per_block"] + 1)
          __snake_case    			=	i % (config["layers_per_block"] + 1)
          __snake_case    			=	[shave_segments(lowercase__       ,      2	) for name in output_blocks[i]]
          __snake_case    			=	{}

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

          if len(lowercase__	) > 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    			=	[key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

               __snake_case    			=	renew_resnet_paths(lowercase__	)
               __snake_case    			=	renew_resnet_paths(lowercase__	)

               __snake_case    			=	{"old": f'output_blocks.{i}.0', "new": f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               __snake_case    			=	[key for key in resnets if "temopral_conv" in key]
               __snake_case    			=	renew_temp_conv_paths(lowercase__	)
               __snake_case    			=	{
                   "old": f'output_blocks.{i}.0.temopral_conv',
                   "new": f'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
               }
               assign_to_checkpoint(
                   lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               __snake_case    			=	{k: sorted(lowercase__	) for k, v in output_block_list.items()}
               if ["conv.bias", "conv.weight"] in output_block_list.values():
                    __snake_case    			=	list(output_block_list.values()	).index(['conv.bias', 'conv.weight']	)
                    __snake_case    			=	unet_state_dict[
                        f'output_blocks.{i}.{index}.conv.weight'
                    ]
                    __snake_case    			=	unet_state_dict[
                        f'output_blocks.{i}.{index}.conv.bias'
                    ]

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

               if len(lowercase__	):
                    __snake_case    			=	renew_attention_paths(lowercase__	)
                    __snake_case    			=	{
                        "old": f'output_blocks.{i}.1',
                        "new": f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                    }
                    assign_to_checkpoint(
                        lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)

               if len(lowercase__	):
                    __snake_case    			=	renew_attention_paths(lowercase__	)
                    __snake_case    			=	{
                        "old": f'output_blocks.{i}.2',
                        "new": f'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                    }
                    assign_to_checkpoint(
                        lowercase__       ,      lowercase__       ,      lowercase__       ,      additional_replacements=[meta_path]       ,      config=lowercase__	)
          else:
               __snake_case    			=	renew_resnet_paths(lowercase__       ,      n_shave_prefix_segments=1	)
               for path in resnet_0_paths:
                    __snake_case    			=	".".join(['output_blocks', str(lowercase__	), path['old']]	)
                    __snake_case    			=	".".join(['up_blocks', str(lowercase__	), 'resnets', str(lowercase__	), path['new']]	)
                    __snake_case    			=	unet_state_dict[old_path]

               __snake_case    			=	[l for l in output_block_layers if "temopral_conv" in l]
               for path in temopral_conv_paths:
                    __snake_case    			=	path.split('temopral_conv.'	)[-1]
                    __snake_case    			=	".".join(['output_blocks', str(lowercase__	), str(lowercase__	), 'temopral_conv', pruned_path]	)
                    __snake_case    			=	".".join(['up_blocks', str(lowercase__	), 'temp_convs', str(lowercase__	), pruned_path]	)
                    __snake_case    			=	unet_state_dict[old_path]

     return new_checkpoint


if __name__ == "__main__":
       _a      :      str       				=      argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
       _a      :      List[str]       				=      parser.parse_args()

       _a      :      Union[str, Any]       				=      torch.load(args.checkpoint_path, map_location="cpu")
       _a      :      int       				=      UNetaDConditionModel()

       _a      :      Tuple       				=      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

       _a      :      Any       				=      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
       _a      :      List[str]       				=      set(converted_ckpt.keys()) - set(unet.state_dict().keys())

       assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

       # load state_dict
       unet.load_state_dict(converted_ckpt)

       unet.save_pretrained(args.dump_path)

       # -- finish converting the unet --








'''simple docstring'''




_a      :      List[Any]       				=      [0.33, 0.44, 0.55, 0.44, 0.33]
_a      :      Union[str, Any]       				=      3
_a      :      Optional[Any]       				=      5

# Buffer Space (with Parameters Space)
_a      :      list[float]       				=      []
_a      :      list[float]       				=      []

# Machine Time
_a      :      str       				=      0





def      _a		(lowercase__ :      Optional[int]	) -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(lowercase__	):
          # Evolution Parameter
          __snake_case    			=	float(seed / value	)

          # Control Theory: Orbit Change
          __snake_case    			=	(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          __snake_case    			=	(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	r  # Saving to Parameters Space

     # Logistic Map
     assert max(lowercase__	) < 1
     assert max(lowercase__	) < 4

     # Machine Time
     machine_time += 1





def      _a		() -> Tuple:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(lowercase__ :      Tuple       ,      lowercase__ :      int	):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     __snake_case    			=	machine_time % m

     # Evolution (Time Length)
     for _ in range(0       ,      lowercase__	):
          # Variables (Position + Parameters)
          __snake_case    			=	params_space[key]
          __snake_case    			=	buffer_space[key]

          # Modification (Transition Function) - Flow
          __snake_case    			=	round(float(r * value * (1 - value)	)       ,      1_0	)
          __snake_case    			=	(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     __snake_case    			=	int(buffer_space[(key + 2) % m] * (1_0**1_0)	)
     __snake_case    			=	int(buffer_space[(key - 2) % m] * (1_0**1_0)	)

     # Machine Time
     machine_time += 1

     return xorshift(lowercase__       ,      lowercase__	) % 0xF_F_F_F_F_F_F_F





def      _a		() -> List[str]:






     """simple docstring"""
     global buffer_space, params_space, machine_time, K, m, t

     __snake_case    			=	K
     __snake_case    			=	[0] * m
     __snake_case    			=	0


if __name__ == "__main__":
       # Initialization
       reset()

       # Pushing Data (Input)
       import random

       _a      :      Tuple       				=      random.sample(range(0xFFFF_FFFF), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       _a      :      List[Any]       				=      ""

       # Pulling Data (Output)
       while inp in ("e", "E"):
              print(f'''{format(pull(), "#04x")}''')
              print(buffer_space)
              print(params_space)
              _a      :      List[str]       				=      input("(e)exit? ").strip()




