code: 
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  def 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		str    ,  a_			:		List[str]    ,  a_			:		List[str]=None    ,  a_			:		Any=None    ,  a_			:		str=None				)     ->       int:
     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():
           _UpperCAmelCase   	=					old_checkpoint[path]
           _UpperCAmelCase   	=					old_tensor.shape[0] // 3
  
           _UpperCAmelCase   	=					(-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
           _UpperCAmelCase   	=					old_tensor.shape[0] // config["num_head_channels"] // 3
  
           _UpperCAmelCase   	=					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
           _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					old_tensor.split(channels // num_heads    ,  dim=1				)
  
           _UpperCAmelCase   	=					query.reshape(a_				)
           _UpperCAmelCase   	=					key.reshape(a_				)
           _UpperCAmelCase   	=					value.reshape(a_				)
  
     for path in paths:
        _UpperCAmelCase   	=					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:
              _UpperCAmelCase   	=					new_path.replace(replacement["old"]    ,  replacement["new"]				)
  
          # proj_attn.weight has to be converted from conv 1D to linear
        _UpperCAmelCase   	=					old_checkpoint[path["old"]]
        _UpperCAmelCase   	=					["proj_attn.weight"]
        _UpperCAmelCase   	=					["proj_out.weight", "proj_in.weight"]
        if any(k in new_path for k in names				):
           _UpperCAmelCase   	=					weight[:, :, 0]
        elif any(k in new_path for k in names_a				) and len(weight.shape				) > 2 and ".attentions." not in new_path:
           _UpperCAmelCase   	=					weight[:, :, 0]
        else:
           _UpperCAmelCase   	=					weight
  def 			__UpperCAmelCase		(		a_			:		Tuple    ,  a_			:		Any=0				)     ->       List[Any]:
     _UpperCAmelCase   	=					[]
     for old_item in old_list:
        _UpperCAmelCase   	=					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 			__UpperCAmelCase		(		a_			:		Optional[int]    ,  a_			:		Dict=1				)     ->       List[str]:
     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_			:		Union[str, Any]    ,  a_			:		int=0				)     ->       Optional[int]:
     _UpperCAmelCase   	=					[]
     for old_item in old_list:
        mapping.append({"old": old_item, "new": old_item}				)
  
     return mapping
  def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		Any=0				)     ->       Union[str, Any]:
     _UpperCAmelCase   	=					[]
     for old_item in old_list:
        _UpperCAmelCase   	=					old_item.replace("in_layers.0"    ,  "norm1"				)
        _UpperCAmelCase   	=					new_item.replace("in_layers.2"    ,  "conv1"				)
  
        _UpperCAmelCase   	=					new_item.replace("out_layers.0"    ,  "norm2"				)
        _UpperCAmelCase   	=					new_item.replace("out_layers.3"    ,  "conv2"				)
  
        _UpperCAmelCase   	=					new_item.replace("emb_layers.1"    ,  "time_emb_proj"				)
        _UpperCAmelCase   	=					new_item.replace("skip_connection"    ,  "conv_shortcut"				)
  
        _UpperCAmelCase   	=					shave_segments(a_    ,  n_shave_prefix_segments=a_				)
  
        if "temopral_conv" not in old_item:
           mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 			__UpperCAmelCase		(		a_			:		str    ,  a_			:		str    ,  a_			:		str=None    ,  a_			:		Tuple=False				)     ->       Optional[Any]:
     _UpperCAmelCase   	=					{}
     _UpperCAmelCase   	=					list(checkpoint.keys()				)
  
     _UpperCAmelCase   	=					"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_00 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"				):
              _UpperCAmelCase   	=					"model_ema." + "".join(key.split("."				)[1:]				)
              _UpperCAmelCase   	=					checkpoint.pop(a_				)
     else:
        if sum(k.startswith("model_ema"				) for k in keys				) > 1_00:
           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:
           _UpperCAmelCase   	=					checkpoint.pop(a_				)
  
     _UpperCAmelCase   	=					{}
  
     _UpperCAmelCase   	=					unet_state_dict["time_embed.0.weight"]
     _UpperCAmelCase   	=					unet_state_dict["time_embed.0.bias"]
     _UpperCAmelCase   	=					unet_state_dict["time_embed.2.weight"]
     _UpperCAmelCase   	=					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":
        _UpperCAmelCase   	=					unet_state_dict["label_emb.0.0.weight"]
        _UpperCAmelCase   	=					unet_state_dict["label_emb.0.0.bias"]
        _UpperCAmelCase   	=					unet_state_dict["label_emb.0.2.weight"]
        _UpperCAmelCase   	=					unet_state_dict["label_emb.0.2.bias"]
     else:
        raise NotImplementedError(F'''Not implemented `class_embed_type`: {config['class_embed_type']}'''				)
  
     _UpperCAmelCase   	=					unet_state_dict["input_blocks.0.0.weight"]
     _UpperCAmelCase   	=					unet_state_dict["input_blocks.0.0.bias"]
  
     _UpperCAmelCase   	=					[v for v in unet_state_dict if v.startswith("input_blocks.0.1"				)]
     _UpperCAmelCase   	=					renew_attention_paths(a_				)
     _UpperCAmelCase   	=					{"old": "input_blocks.0.1", "new": "transformer_in"}
     assign_to_checkpoint(a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
     _UpperCAmelCase   	=					unet_state_dict["out.0.weight"]
     _UpperCAmelCase   	=					unet_state_dict["out.0.bias"]
     _UpperCAmelCase   	=					unet_state_dict["out.2.weight"]
     _UpperCAmelCase   	=					unet_state_dict["out.2.bias"]
  
     # Retrieves the keys for the input blocks only
     _UpperCAmelCase   	=					len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "input_blocks" in layer}				)
     _UpperCAmelCase   	=					{
         layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
         for layer_id in range(a_				)
     }
  
     # Retrieves the keys for the middle blocks only
     _UpperCAmelCase   	=					len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "middle_block" in layer}				)
     _UpperCAmelCase   	=					{
         layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
         for layer_id in range(a_				)
     }
  
     # Retrieves the keys for the output blocks only
     _UpperCAmelCase   	=					len({".".join(layer.split("."				)[:2]				) for layer in unet_state_dict if "output_blocks" in layer}				)
     _UpperCAmelCase   	=					{
         layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
         for layer_id in range(a_				)
     }
  
     for i in range(1    ,  a_				):
        _UpperCAmelCase   	=					(i - 1) // (config["layers_per_block"] + 1)
        _UpperCAmelCase   	=					(i - 1) % (config["layers_per_block"] + 1)
  
        _UpperCAmelCase   	=					[
            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
        ]
        _UpperCAmelCase   	=					[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
        _UpperCAmelCase   	=					[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:
           _UpperCAmelCase   	=					unet_state_dict.pop(
               F'''input_blocks.{i}.op.weight'''				)
           _UpperCAmelCase   	=					unet_state_dict.pop(
               F'''input_blocks.{i}.op.bias'''				)
  
        _UpperCAmelCase   	=					renew_resnet_paths(a_				)
        _UpperCAmelCase   	=					{"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
        assign_to_checkpoint(
            a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
        _UpperCAmelCase   	=					[key for key in resnets if "temopral_conv" in key]
        _UpperCAmelCase   	=					renew_temp_conv_paths(a_				)
        _UpperCAmelCase   	=					{
            "old": F'''input_blocks.{i}.0.temopral_conv''',
            "new": F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
        }
        assign_to_checkpoint(
            a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
        if len(a_				):
           _UpperCAmelCase   	=					renew_attention_paths(a_				)
           _UpperCAmelCase   	=					{"old": F'''input_blocks.{i}.1''', "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
           assign_to_checkpoint(
               a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
        if len(a_				):
           _UpperCAmelCase   	=					renew_attention_paths(a_				)
           _UpperCAmelCase   	=					{
               "old": F'''input_blocks.{i}.2''',
               "new": F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
           }
           assign_to_checkpoint(
               a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
     _UpperCAmelCase   	=					middle_blocks[0]
     _UpperCAmelCase   	=					[key for key in resnet_a if "temopral_conv" in key]
     _UpperCAmelCase   	=					middle_blocks[1]
     _UpperCAmelCase   	=					middle_blocks[2]
     _UpperCAmelCase   	=					middle_blocks[3]
     _UpperCAmelCase   	=					[key for key in resnet_a if "temopral_conv" in key]
  
     _UpperCAmelCase   	=					renew_resnet_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.0", "new": "mid_block.resnets.0"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  config=a_    ,  additional_replacements=[meta_path]				)
  
     _UpperCAmelCase   	=					renew_temp_conv_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  config=a_    ,  additional_replacements=[meta_path]				)
  
     _UpperCAmelCase   	=					renew_resnet_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.3", "new": "mid_block.resnets.1"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  config=a_    ,  additional_replacements=[meta_path]				)
  
     _UpperCAmelCase   	=					renew_temp_conv_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  config=a_    ,  additional_replacements=[meta_path]				)
  
     _UpperCAmelCase   	=					renew_attention_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.1", "new": "mid_block.attentions.0"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
     _UpperCAmelCase   	=					renew_attention_paths(a_				)
     _UpperCAmelCase   	=					{"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
     assign_to_checkpoint(
         a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
     for i in range(a_				):
        _UpperCAmelCase   	=					i // (config["layers_per_block"] + 1)
        _UpperCAmelCase   	=					i % (config["layers_per_block"] + 1)
        _UpperCAmelCase   	=					[shave_segments(a_    ,  2				) for name in output_blocks[i]]
        _UpperCAmelCase   	=					{}
  
        for layer in output_block_layers:
           _UpperCAmelCase	,							_UpperCAmelCase   	=					layer.split("."				)[0], shave_segments(a_    ,  1				)
           if layer_id in output_block_list:
              output_block_list[layer_id].append(a_				)
           else:
              _UpperCAmelCase   	=					[layer_name]
  
        if len(a_				) > 1:
           _UpperCAmelCase   	=					[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
           _UpperCAmelCase   	=					[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
           _UpperCAmelCase   	=					[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]
  
           _UpperCAmelCase   	=					renew_resnet_paths(a_				)
           _UpperCAmelCase   	=					renew_resnet_paths(a_				)
  
           _UpperCAmelCase   	=					{"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_				)
  
           _UpperCAmelCase   	=					[key for key in resnets if "temopral_conv" in key]
           _UpperCAmelCase   	=					renew_temp_conv_paths(a_				)
           _UpperCAmelCase   	=					{
               "old": F'''output_blocks.{i}.0.temopral_conv''',
               "new": F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
           }
           assign_to_checkpoint(
               a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
           _UpperCAmelCase   	=					{k: sorted(a_				) for k, v in output_block_list.items()}
           if ["conv.bias", "conv.weight"] in output_block_list.values():
              _UpperCAmelCase   	=					list(output_block_list.values()				).index(["conv.bias", "conv.weight"]				)
              _UpperCAmelCase   	=					unet_state_dict[
                  F'''output_blocks.{i}.{index}.conv.weight'''
              ]
              _UpperCAmelCase   	=					unet_state_dict[
                  F'''output_blocks.{i}.{index}.conv.bias'''
              ]
  
              # Clear attentions as they have been attributed above.
              if len(a_				) == 2:
                 _UpperCAmelCase   	=					[]
  
           if len(a_				):
              _UpperCAmelCase   	=					renew_attention_paths(a_				)
              _UpperCAmelCase   	=					{
                  "old": F'''output_blocks.{i}.1''',
                  "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
  
           if len(a_				):
              _UpperCAmelCase   	=					renew_attention_paths(a_				)
              _UpperCAmelCase   	=					{
                  "old": F'''output_blocks.{i}.2''',
                  "new": F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
              }
              assign_to_checkpoint(
                  a_    ,  a_    ,  a_    ,  additional_replacements=[meta_path]    ,  config=a_				)
        else:
           _UpperCAmelCase   	=					renew_resnet_paths(a_    ,  n_shave_prefix_segments=1				)
           for path in resnet_0_paths:
              _UpperCAmelCase   	=					".".join(["output_blocks", str(a_				), path["old"]]				)
              _UpperCAmelCase   	=					".".join(["up_blocks", str(a_				), "resnets", str(a_				), path["new"]]				)
              _UpperCAmelCase   	=					unet_state_dict[old_path]
  
           _UpperCAmelCase   	=					[l for l in output_block_layers if "temopral_conv" in l]
           for path in temopral_conv_paths:
              _UpperCAmelCase   	=					path.split("temopral_conv."				)[-1]
              _UpperCAmelCase   	=					".".join(["output_blocks", str(a_				), str(a_				), "temopral_conv", pruned_path]				)
              _UpperCAmelCase   	=					".".join(["up_blocks", str(a_				), "temp_convs", str(a_				), pruned_path]				)
              _UpperCAmelCase   	=					unet_state_dict[old_path]
  
     return new_checkpoint
  
  
  if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     				=   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.')
    SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
    SCREAMING_SNAKE_CASE_     				=   torch.load(args.checkpoint_path, map_location='cpu')
    SCREAMING_SNAKE_CASE_     				=   UNetaDConditionModel()
  
    SCREAMING_SNAKE_CASE_     				=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
    SCREAMING_SNAKE_CASE_     				=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
    SCREAMING_SNAKE_CASE_     				=   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 --
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import warnings
  
  from .generation import FlaxGenerationMixin
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
         # warning at import time
         warnings.warn(
             'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
             'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'				,	lowerCAmelCase_				,	)
  
  