




'''simple docstring'''







import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      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 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Dict=0       ):
       _A					:     List[Any]     = []
       for old_item in old_list:
              _A					:     Dict     = old_item.replace('in_layers.0'  ,'norm1'       )
              _A					:     Union[str, Any]     = new_item.replace('in_layers.2'  ,'conv1'       )

              _A					:     List[Any]     = new_item.replace('out_layers.0'  ,'norm2'       )
              _A					:     Optional[int]     = new_item.replace('out_layers.3'  ,'conv2'       )

              _A					:     List[str]     = new_item.replace('emb_layers.1'  ,'time_emb_proj'       )
              _A					:     Any     = new_item.replace('skip_connection'  ,'conv_shortcut'       )

              _A					:     Dict     = shave_segments(a__  ,n_shave_prefix_segments=a__       )

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

       return mapping






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]=0       ):
       _A					:     Optional[Any]     = []
       for old_item in old_list:
              _A					:     str     = old_item

              _A					:     List[Any]     = new_item.replace('norm.weight'  ,'group_norm.weight'       )
              _A					:     List[Any]     = new_item.replace('norm.bias'  ,'group_norm.bias'       )

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

              _A					:     Optional[Any]     = shave_segments(a__  ,n_shave_prefix_segments=a__       )

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

       return mapping






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      List[Any]  ,lowerCamelCase					:      Any  ,lowerCamelCase					:      List[str]=None  ,lowerCamelCase					:      Optional[Any]=None  ,lowerCamelCase					:      Tuple=None       ):
       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():
                     _A					:     Optional[Any]     = old_checkpoint[path]
                     _A					:     Union[str, Any]     = old_tensor.shape[0] // 3

                     _A					:     Union[str, Any]     = (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

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

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

                     _A					:     int     = query.reshape(a__       )
                     _A					:     int     = key.reshape(a__       )
                     _A					:     Tuple     = value.reshape(a__       )

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

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






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]  ,lowerCamelCase					:      Any       ):
       _A					:     str     = {}

       _A					:     Any     = checkpoint['time_embed.0.weight']
       _A					:     str     = checkpoint['time_embed.0.bias']
       _A					:     Optional[int]     = checkpoint['time_embed.2.weight']
       _A					:     Any     = checkpoint['time_embed.2.bias']

       _A					:     Tuple     = checkpoint['input_blocks.0.0.weight']
       _A					:     int     = checkpoint['input_blocks.0.0.bias']

       _A					:     int     = checkpoint['out.0.weight']
       _A					:     List[Any]     = checkpoint['out.0.bias']
       _A					:     Tuple     = checkpoint['out.2.weight']
       _A					:     List[str]     = checkpoint['out.2.bias']

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

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

              _A					:     List[str]     = renew_resnet_paths(a__       )
              _A					:     str     = {'old': F'input_blocks.{i}.0', 'new': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
              _A					:     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__       ):
                     _A					:     List[Any]     = renew_attention_paths(a__       )
                     _A					:     List[str]     = {
                         'old': F'input_blocks.{i}.1',
                         'new': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
                     }
                     _A					:     List[str]     = {
                         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__  ,)

       _A					:     List[Any]     = middle_blocks[0]
       _A					:     Any     = middle_blocks[1]
       _A					:     Union[str, Any]     = middle_blocks[2]

       _A					:     Union[str, Any]     = renew_resnet_paths(a__       )
       assign_to_checkpoint(a__  ,a__  ,a__  ,config=a__       )

       _A					:     str     = renew_resnet_paths(a__       )
       assign_to_checkpoint(a__  ,a__  ,a__  ,config=a__       )

       _A					:     int     = renew_attention_paths(a__       )
       _A					:     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__       ):
              _A					:     List[str]     = i // (config['num_res_blocks'] + 1)
              _A					:     Union[str, Any]     = i % (config['num_res_blocks'] + 1)
              _A					:     Tuple     = [shave_segments(a__  ,2       ) for name in output_blocks[i]]
              _A					:     Tuple     = {}

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

              if len(a__       ) > 1:
                     _A					:     int     = [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
                     _A					:     Union[str, Any]     = [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

                     _A					:     List[Any]     = renew_resnet_paths(a__       )
                     _A					:     Dict     = renew_resnet_paths(a__       )

                     _A					:     Dict     = {'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():
                            _A					:     str     = list(output_block_list.values()       ).index(['conv.weight', 'conv.bias']       )
                            _A					:     str     = checkpoint[
                                F'output_blocks.{i}.{index}.conv.weight'
                            ]
                            _A					:     Tuple     = checkpoint[
                                F'output_blocks.{i}.{index}.conv.bias'
                            ]

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

                     if len(a__       ):
                            _A					:     Union[str, Any]     = renew_attention_paths(a__       )
                            _A					:     str     = {
                                'old': F'output_blocks.{i}.1',
                                'new': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                            }
                            _A					:     Any     = {
                                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:
                     _A					:     List[Any]     = renew_resnet_paths(a__  ,n_shave_prefix_segments=1       )
                     for path in resnet_0_paths:
                            _A					:     Union[str, Any]     = '.'.join(['output_blocks', str(a__       ), path['old']]       )
                            _A					:     List[str]     = '.'.join(['up_blocks', str(a__       ), 'resnets', str(a__       ), path['new']]       )

                            _A					:     List[str]     = checkpoint[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(
       '''--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.''')

   A				:					str    						=			parser.parse_args()

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

   with open(args.config_file) as f:
      A				:					int    						=			json.loads(f.read())

   A				:					int    						=			convert_ldm_checkpoint(checkpoint, config)

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

   A				:					str    						=			UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

      A				:					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'''







A				:					Dict    						=			'''Alexander Joslin'''

import operator as op

from .stack import Stack






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     Dict     = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

       _A					:     Stack[int]     = Stack()
       _A					:     Stack[str]     = Stack()

       for i in equation:
              if i.isdigit():
                     # RULE 1
                     operand_stack.push(int(lowerCamelCase       )       )
              elif i in operators:
                     # RULE 2
                     operator_stack.push(lowerCamelCase       )
              elif i == ")":
                     # RULE 4
                     _A					:     List[str]     = operator_stack.peek()
                     operator_stack.pop()
                     _A					:     Dict     = operand_stack.peek()
                     operand_stack.pop()
                     _A					:     Optional[int]     = operand_stack.peek()
                     operand_stack.pop()

                     _A					:     Dict     = operators[opr](lowerCamelCase  ,lowerCamelCase       )
                     operand_stack.push(lowerCamelCase       )

    # RULE 5
       return operand_stack.peek()


if __name__ == "__main__":
   A				:					Dict    						=			'''(5 + ((4 * 2) * (2 + 3)))'''
   # answer = 45
   print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")






