'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def     __lowerCAmelCase						(       a_       ,      a_=1		)      ->	Dict:





     '''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						(       a_       ,      a_=0		)      ->	Tuple:





     '''simple docstring'''





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

          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       new_item.replace('out_layers.0'       ,      'norm2'		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       new_item.replace('out_layers.3'       ,      'conv2'		)

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

          SCREAMING_SNAKE_CASE					:			str    	=       shave_segments(a_       ,      n_shave_prefix_segments=a_		)

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

     return mapping


def     __lowerCAmelCase						(       a_       ,      a_=0		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []
     for old_item in old_list:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       old_item

          SCREAMING_SNAKE_CASE					:			Dict    	=       new_item.replace('norm.weight'       ,      'group_norm.weight'		)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       new_item.replace('norm.bias'       ,      'group_norm.bias'		)

          SCREAMING_SNAKE_CASE					:			int    	=       new_item.replace('proj_out.weight'       ,      'proj_attn.weight'		)
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       new_item.replace('proj_out.bias'       ,      'proj_attn.bias'		)

          SCREAMING_SNAKE_CASE					:			int    	=       shave_segments(a_       ,      n_shave_prefix_segments=a_		)

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

     return mapping


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_=None       ,      a_=None       ,      a_=None		)      ->	Optional[int]:





     '''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():
               SCREAMING_SNAKE_CASE					:			Dict    	=       old_checkpoint[path]
               SCREAMING_SNAKE_CASE					:			Tuple    	=       old_tensor.shape[0] // 3

               SCREAMING_SNAKE_CASE					:			Tuple    	=       (-1, channels) if len(old_tensor.shape		) == 3 else (-1)

               SCREAMING_SNAKE_CASE					:			List[Any]    	=       old_tensor.shape[0] // config['num_head_channels'] // 3

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

               SCREAMING_SNAKE_CASE					:			Any    	=       query.reshape(a_		)
               SCREAMING_SNAKE_CASE					:			Tuple    	=       key.reshape(a_		)
               SCREAMING_SNAKE_CASE					:			Tuple    	=       value.reshape(a_		)

     for path in paths:
          SCREAMING_SNAKE_CASE					:			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
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       new_path.replace('middle_block.0'       ,      'mid_block.resnets.0'		)
          SCREAMING_SNAKE_CASE					:			List[str]    	=       new_path.replace('middle_block.1'       ,      'mid_block.attentions.0'		)
          SCREAMING_SNAKE_CASE					:			Any    	=       new_path.replace('middle_block.2'       ,      'mid_block.resnets.1'		)

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       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:
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       old_checkpoint[path['old']][:, :, 0]
          else:
               SCREAMING_SNAKE_CASE					:			int    	=       old_checkpoint[path['old']]


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       {}

     SCREAMING_SNAKE_CASE					:			Dict    	=       checkpoint['time_embed.0.weight']
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['time_embed.0.bias']
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       checkpoint['time_embed.2.weight']
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['time_embed.2.bias']

     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['input_blocks.0.0.weight']
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       checkpoint['input_blocks.0.0.bias']

     SCREAMING_SNAKE_CASE					:			Dict    	=       checkpoint['out.0.weight']
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['out.0.bias']
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       checkpoint['out.2.weight']
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       checkpoint['out.2.bias']

     # Retrieves the keys for the input blocks only
     SCREAMING_SNAKE_CASE					:			Dict    	=       len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'input_blocks' in layer}		)
     SCREAMING_SNAKE_CASE					:			Union[str, 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
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'middle_block' in layer}		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       {
         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
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'output_blocks' in layer}		)
     SCREAMING_SNAKE_CASE					:			int    	=       {
         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_		):
          SCREAMING_SNAKE_CASE					:			str    	=       (i - 1) // (config['num_res_blocks'] + 1)
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (i - 1) % (config['num_res_blocks'] + 1)

          SCREAMING_SNAKE_CASE					:			str    	=       [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [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:
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       checkpoint[
                   f"""input_blocks.{i}.0.op.weight"""
               ]
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       checkpoint[
                   f"""input_blocks.{i}.0.op.bias"""
               ]
               continue

          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_resnet_paths(a_		)
          SCREAMING_SNAKE_CASE					:			Any    	=       {'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
          SCREAMING_SNAKE_CASE					:			Dict    	=       {'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_		):
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_attention_paths(a_		)
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
                   'old': f"""input_blocks.{i}.1""",
                   'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
               }
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
                   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_       ,      )

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       middle_blocks[0]
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       middle_blocks[1]
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       middle_blocks[2]

     SCREAMING_SNAKE_CASE					:			Any    	=       renew_resnet_paths(a_		)
     assign_to_checkpoint(a_       ,      a_       ,      a_       ,      config=a_		)

     SCREAMING_SNAKE_CASE					:			int    	=       renew_resnet_paths(a_		)
     assign_to_checkpoint(a_       ,      a_       ,      a_       ,      config=a_		)

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       renew_attention_paths(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {
         '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_		):
          SCREAMING_SNAKE_CASE					:			Dict    	=       i // (config['num_res_blocks'] + 1)
          SCREAMING_SNAKE_CASE					:			List[str]    	=       i % (config['num_res_blocks'] + 1)
          SCREAMING_SNAKE_CASE					:			str    	=       [shave_segments(a_       ,      2		) for name in output_blocks[i]]
          SCREAMING_SNAKE_CASE					:			int    	=       {}

          for layer in output_block_layers:
               SCREAMING_SNAKE_CASE					:			Any    	=       layer.split('.'		)[0], shave_segments(a_       ,      1		)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(a_		)
               else:
                    SCREAMING_SNAKE_CASE					:			Dict    	=       [layer_name]

          if len(a_		) > 1:
               SCREAMING_SNAKE_CASE					:			int    	=       [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
               SCREAMING_SNAKE_CASE					:			int    	=       [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

               SCREAMING_SNAKE_CASE					:			List[Any]    	=       renew_resnet_paths(a_		)
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_resnet_paths(a_		)

               SCREAMING_SNAKE_CASE					:			List[Any]    	=       {'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():
                    SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       list(output_block_list.values()		).index(['conv.weight', 'conv.bias']		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       checkpoint[
                        f"""output_blocks.{i}.{index}.conv.weight"""
                    ]
                    SCREAMING_SNAKE_CASE					:			Any    	=       checkpoint[
                        f"""output_blocks.{i}.{index}.conv.bias"""
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(a_		) == 2:
                         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       []

               if len(a_		):
                    SCREAMING_SNAKE_CASE					:			Any    	=       renew_attention_paths(a_		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       {
                        'old': f"""output_blocks.{i}.1""",
                        'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                    }
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
                        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:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       renew_resnet_paths(a_       ,      n_shave_prefix_segments=1		)
               for path in resnet_0_paths:
                    SCREAMING_SNAKE_CASE					:			str    	=       '.'.join(['output_blocks', str(a_		), path['old']]		)
                    SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       '.'.join(['up_blocks', str(a_		), 'resnets', str(a_		), path['new']]		)

                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase  :str  				=    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.""")

 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 _lowerCAmelCase  :str  				=    torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase  :List[Any]  				=    json.loads(f.read())

 _lowerCAmelCase  :Optional[int]  				=    convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase  :List[str]  				=    UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase  :Tuple  				=    DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  _lowerCAmelCase  :Union[str, Any]  				=    VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

  _lowerCAmelCase  :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'''





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,							{} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,							{'a': 2} )
         self.assertDictEqual(MockClass(a=2    ,							b=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,							2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,							0.5 )
         self.assertEqual(scaler._growth_interval    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


if __name__ == "__main__":
 _lowerCAmelCase  :int  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _lowerCAmelCase  :int  				=    Accelerator(kwargs_handlers=[ddp_scaler])
 _lowerCAmelCase  :str  				=    torch.nn.Linear(100, 200)
 _lowerCAmelCase  :Optional[Any]  				=    accelerator.prepare(model)

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)



