


"""simple docstring"""






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 					A_      (      _lowerCAmelCase   :    Optional[int],   _lowerCAmelCase   :    int=1				):






     """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_      (      _lowerCAmelCase   :    str,   _lowerCAmelCase   :    Dict=0				):






     """simple docstring"""

     _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(UpperCamelCase__,   n_shave_prefix_segments=UpperCamelCase__				)

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

     return mapping
def 					A_      (      _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Tuple=0				):






     """simple docstring"""

     _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(UpperCamelCase__,   n_shave_prefix_segments=UpperCamelCase__				)

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

     return mapping
def 					A_      (      _lowerCAmelCase   :    Optional[int],   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Dict=None,   _lowerCAmelCase   :    Union[str, Any]=None,   _lowerCAmelCase   :    Optional[int]=None				):






     """simple docstring"""

     assert isinstance(UpperCamelCase__,   UpperCamelCase__				), "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(UpperCamelCase__				)
               _a					= key.reshape(UpperCamelCase__				)
               _a					= value.reshape(UpperCamelCase__				)

     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 					A_      (      _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Optional[Any]				):






     """simple docstring"""

     _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(UpperCamelCase__				)
     }

     # 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(UpperCamelCase__				)
     }

     # 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(UpperCamelCase__				)
     }

     for i in range(1,   UpperCamelCase__				):
          _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(UpperCamelCase__				)
          _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(
              UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   additional_replacements=[meta_path, resnet_op],   config=UpperCamelCase__				)

          if len(UpperCamelCase__				):
               _a					= renew_attention_paths(UpperCamelCase__				)
               _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(
                   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   additional_replacements=[meta_path],   attention_paths_to_split=UpperCamelCase__,   config=UpperCamelCase__,   )

     _a					= middle_blocks[0]
     _a					= middle_blocks[1]
     _a					= middle_blocks[2]

     _a					= renew_resnet_paths(UpperCamelCase__				)
     assign_to_checkpoint(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   config=UpperCamelCase__				)

     _a					= renew_resnet_paths(UpperCamelCase__				)
     assign_to_checkpoint(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   config=UpperCamelCase__				)

     _a					= renew_attention_paths(UpperCamelCase__				)
     _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(
         UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   attention_paths_to_split=UpperCamelCase__,   config=UpperCamelCase__				)

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

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

          if len(UpperCamelCase__				) > 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(UpperCamelCase__				)
               _a					= renew_resnet_paths(UpperCamelCase__				)

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

               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(UpperCamelCase__				) == 2:
                         _a					= []

               if len(UpperCamelCase__				):
                    _a					= renew_attention_paths(UpperCamelCase__				)
                    _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(
                        UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   additional_replacements=[meta_path],   attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions				) else None,   config=UpperCamelCase__,   )
          else:
               _a					= renew_resnet_paths(UpperCamelCase__,   n_shave_prefix_segments=1				)
               for path in resnet_0_paths:
                    _a					= """.""".join(['''output_blocks''', str(UpperCamelCase__				), path['''old''']]				)
                    _a					= """.""".join(['''up_blocks''', str(UpperCamelCase__				), '''resnets''', str(UpperCamelCase__				), path['''new''']]				)

                    _a					= checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
   __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(
       '''--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.''')

   __snake_case				=				parser.parse_args()

   __snake_case				=				torch.load(args.checkpoint_path)

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

   __snake_case				=				convert_ldm_checkpoint(checkpoint, config)

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

   __snake_case				=				UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

      __snake_case				=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)







"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''

  # Check if the input is valid
  if not len(UpperCamelCase__				) == len(UpperCamelCase__				) == 3:
    raise ValueError("""Please enter a valid equation."""				)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError("""Both a & b of two equations can't be zero."""				)

  # Extract the coefficients
  _a      ,  _a      ,  _a  :       Any             =	equationa
  _a      ,  _a      ,  _a  :       Tuple             =	equationa

  # Calculate the determinants of the matrices
  _a  :       int             =	aa * ba - aa * ba
  _a  :       str             =	ca * ba - ca * ba
  _a  :       str             =	aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError("""Infinite solutions. (Consistent system)"""				)
    else:
      raise ValueError("""No solution. (Inconsistent system)"""				)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      _a  :       Dict             =	determinant_x / determinant
      _a  :       str             =	determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)

