
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def   lowerCamelCase__							(					a		,		a=1      )      ->   Optional[int]:
   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      )      ->   int:
   _A:  Tuple					=       []
   for old_item in old_list:
      _A:  Optional[Any]					=       old_item.replace('''in_layers.0'''		,		'''norm1'''      )
      _A:  Tuple					=       new_item.replace('''in_layers.2'''		,		'''conv1'''      )

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

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

      _A:  List[Any]					=       shave_segments(a		,		n_shave_prefix_segments=a      )

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

   return mapping






def   lowerCamelCase__							(					a		,		a=0      )      ->   List[str]:
   _A:  int					=       []
   for old_item in old_list:
      _A:  Optional[Any]					=       old_item

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

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

      _A:  Optional[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      )      ->   Tuple:
   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:  List[str]					=       old_checkpoint[path]
         _A:  Union[str, Any]					=       old_tensor.shape[0] // 3

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

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

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

         _A:  int					=       query.reshape(a      )
         _A:  Any					=       key.reshape(a      )
         _A:  Union[str, Any]					=       value.reshape(a      )

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

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






def   lowerCamelCase__							(					a		,		a      )      ->   Union[str, Any]:
   _A:  List[Any]					=       {}

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

   _A:  str					=       checkpoint['''input_blocks.0.0.weight''']
   _A:  Any					=       checkpoint['''input_blocks.0.0.bias''']

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

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

      _A:  Tuple					=       [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
      _A:  int					=       [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:  int					=       checkpoint[
             f"""input_blocks.{i}.0.op.weight"""
         ]
         _A:  Any					=       checkpoint[
             f"""input_blocks.{i}.0.op.bias"""
         ]
         continue

      _A:  Optional[int]					=       renew_resnet_paths(a      )
      _A:  Dict					=       {'''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:  Dict					=       renew_attention_paths(a      )
         _A:  str					=       {
             '''old''': f"""input_blocks.{i}.1""",
             '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
         }
         _A:  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		,		)

   _A:  List[Any]					=       middle_blocks[0]
   _A:  Optional[int]					=       middle_blocks[1]
   _A:  Dict					=       middle_blocks[2]

   _A:  List[str]					=       renew_resnet_paths(a      )
   assign_to_checkpoint(a		,		a		,		a		,		config=a      )

   _A:  Any					=       renew_resnet_paths(a      )
   assign_to_checkpoint(a		,		a		,		a		,		config=a      )

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

      for layer in output_block_layers:
         _A			,  _A:  Optional[int]					=       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:  Tuple					=       [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
         _A:  Tuple					=       [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

         _A:  Optional[Any]					=       renew_resnet_paths(a      )
         _A:  Optional[int]					=       renew_resnet_paths(a      )

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

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

         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:  Optional[Any]					=       renew_resnet_paths(a		,		n_shave_prefix_segments=1      )
         for path in resnet_0_paths:
            _A:  Optional[int]					=       '''.'''.join(['''output_blocks''', str(a      ), path['''old''']]      )
            _A:  List[Any]					=       '''.'''.join(['''up_blocks''', str(a      ), '''resnets''', str(a      ), path['''new''']]      )

            _A:  Union[str, Any]					=       checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
   UpperCAmelCase__		:     List[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.')

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

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

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

   UpperCAmelCase__		:     str     			=      convert_ldm_checkpoint(checkpoint, config)

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

   UpperCAmelCase__		:     Dict     			=      UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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





import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__		:     Any     			=      3






def   lowerCamelCase__							(					a      )      ->   int:
   print('''Generating primitive root of p'''      )
   while True:
      _A:  Union[str, Any]					=       random.randrange(3		,		a      )
      if pow(a		,		2		,		a      ) == 1:
         continue
      if pow(a		,		a		,		a      ) == 1:
         continue
      return g






def   lowerCamelCase__							(					a      )      ->   tuple[tuple[int, int, int, int], tuple[int, int]]:
   print('''Generating prime p...'''      )
   _A:  Dict					=       rabin_miller.generate_large_prime(a      )  # select large prime number.
   _A:  Any					=       primitive_root(a      )  # one primitive root on modulo p.
   _A:  Optional[Any]					=       random.randrange(3		,		a      )  # private_key -> have to be greater than 2 for safety.
   _A:  Dict					=       cryptomath.find_mod_inverse(pow(a		,		a		,		a      )		,		a      )

   _A:  Union[str, Any]					=       (key_size, e_a, e_a, p)
   _A:  Union[str, Any]					=       (key_size, d)

   return public_key, private_key






def   lowerCamelCase__							(					a		,		a      )      ->   None:
   if os.path.exists(f"""{name}_pubkey.txt"""      ) or os.path.exists(f"""{name}_privkey.txt"""      ):
      print('''\nWARNING:'''      )
      print(
          f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
          '''Use a different name or delete these files and re-run this program.'''      )
      sys.exit()

   _A			,  _A:  Any					=       generate_key(a      )
   print(f"""\nWriting public key to file {name}_pubkey.txt..."""      )
   with open(f"""{name}_pubkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""      )

   print(f"""Writing private key to file {name}_privkey.txt..."""      )
   with open(f"""{name}_privkey.txt"""		,		'''w'''      ) as fo:
      fo.write(f"""{private_key[0]},{private_key[1]}"""      )






def   lowerCamelCase__							(					)      ->   None:
   print('''Making key files...'''      )
   make_key_files('''elgamal'''		,		20_48      )
   print('''Key files generation successful'''      )


if __name__ == "__main__":
   main()



