'''simple docstring'''






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]=1				):
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('.'				)[n_shave_prefix_segments:]				)
     else:
          return ".".join(path.split('.'				)[:n_shave_prefix_segments]				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			List[Any]=0				):
     _A    		=  []
     for old_item in old_list:
          _A    		=  old_item.replace('in_layers.0'   ,		'norm1'				)
          _A    		=  new_item.replace('in_layers.2'   ,		'conv1'				)

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

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

          _A    		=  shave_segments(_lowerCAmelCase   ,		n_shave_prefix_segments=_lowerCAmelCase				)

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

     return mapping





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]=0				):
     _A    		=  []
     for old_item in old_list:
          _A    		=  old_item

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

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

          _A    		=  shave_segments(_lowerCAmelCase   ,		n_shave_prefix_segments=_lowerCAmelCase				)

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

     return mapping





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			str   ,		__snake_case							:			List[Any]   ,		__snake_case							:			Dict=None   ,		__snake_case							:			Union[str, Any]=None   ,		__snake_case							:			List[str]=None				):
     assert isinstance(_lowerCAmelCase   ,		_lowerCAmelCase				), "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():
               _A    		=  old_checkpoint[path]
               _A    		=  old_tensor.shape[0] // 3

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

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

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

               _A    		=  query.reshape(_lowerCAmelCase				)
               _A    		=  key.reshape(_lowerCAmelCase				)
               _A    		=  value.reshape(_lowerCAmelCase				)

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

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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]   ,		__snake_case							:			List[Any]				):
     _A    		=  {}

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

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

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

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

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

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

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

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

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

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

     _A    		=  middle_blocks[0]
     _A    		=  middle_blocks[1]
     _A    		=  middle_blocks[2]

     _A    		=  renew_resnet_paths(_lowerCAmelCase				)
     assign_to_checkpoint(_lowerCAmelCase   ,		_lowerCAmelCase   ,		_lowerCAmelCase   ,		config=_lowerCAmelCase				)

     _A    		=  renew_resnet_paths(_lowerCAmelCase				)
     assign_to_checkpoint(_lowerCAmelCase   ,		_lowerCAmelCase   ,		_lowerCAmelCase   ,		config=_lowerCAmelCase				)

     _A    		=  renew_attention_paths(_lowerCAmelCase				)
     _A    		=  {
         "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(
         _lowerCAmelCase   ,		_lowerCAmelCase   ,		_lowerCAmelCase   ,		attention_paths_to_split=_lowerCAmelCase   ,		config=_lowerCAmelCase				)

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

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

          if len(_lowerCAmelCase				) > 1:
               _A    		=  [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
               _A    		=  [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

               _A    		=  renew_resnet_paths(_lowerCAmelCase				)
               _A    		=  renew_resnet_paths(_lowerCAmelCase				)

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

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

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

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

                    _A    		=  checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
   _UpperCAmelCase    :     List[Any]         =       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    :     Optional[Any]         =       parser.parse_args()

   _UpperCAmelCase    :     Optional[Any]         =       torch.load(args.checkpoint_path)

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

   _UpperCAmelCase    :     Tuple         =       convert_ldm_checkpoint(checkpoint, config)

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

   _UpperCAmelCase    :     Optional[Any]         =       UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

      _UpperCAmelCase    :     Optional[Any]         =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)










def   __snake_case    (       _lowerCAmelCase			:			list					,					_lowerCAmelCase			:			list					,					_lowerCAmelCase			:			int						)			->    int:
   if len(_lowerCAmelCase						) != len(_lowerCAmelCase						):
      raise ValueError("The length of profit and weight must be same."						)
   if max_weight <= 0:
      raise ValueError("max_weight must greater than zero."						)
   if any(p < 0 for p in profit						):
      raise ValueError("Profit can not be negative."						)
   if any(w < 0 for w in weight						):
      raise ValueError("Weight can not be negative."						)

   # List created to store profit gained for the 1kg in case of each weight
   # respectively.  Calculate and append profit/weight for each element.
   A_       :					Dict        =      [p / w for p, w in zip(_lowerCAmelCase					,					_lowerCAmelCase						)]

   # Creating a copy of the list and sorting profit/weight in ascending order
   A_       :					Optional[Any]        =      sorted(_lowerCAmelCase						)

   # declaring useful variables
   A_       :					List[Any]        =      len(_lowerCAmelCase						)
   A_       :					List[Any]        =      0
   A_       :					Any        =      0
   A_       :					List[Any]        =      0

   # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
   while limit <= max_weight and i < length:
      # flag value for encountered greatest element in sorted_profit_by_weight
      A_       :					List[Any]        =      sorted_profit_by_weight[length - i - 1]
      A_       :					List[Any]        =      profit_by_weight.index(_lowerCAmelCase						)
      A_       :					List[Any]        =      -1

      # check if the weight encountered is less than the total weight
      # encountered before.
      if max_weight - limit >= weight[index]:
         limit += weight[index]
         # Adding profit gained for the given weight 1 ===
         # weight[index]/weight[index]
         gain += 1 * profit[index]
      else:
         # Since the weight encountered is greater than limit, therefore take the
         # required number of remaining kgs and calculate profit for it.
         # weight remaining / weight[index]
         gain += (max_weight - limit) / weight[index] * profit[index]
         break
      i += 1
   return gain


if __name__ == "__main__":
 print(
     '''Input profits, weights, and then max_weight (all positive ints) separated by '''
     '''spaces.'''
 )

 _lowerCAmelCase				:   int						       =						[int(x) for x in input('''Input profits separated by spaces: ''').split()]
 _lowerCAmelCase				:   Dict						       =						[int(x) for x in input('''Input weights separated by spaces: ''').split()]
 _lowerCAmelCase				:   Union[str, Any]						       =						int(input('''Max weight allowed: '''))

 # Function Call
 calc_profit(profit, weight, max_weight)
