


import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


snake_case				:    Optional[Any]       				=     {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 10_00,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case				:    List[str]       				=     {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 10_00,
    '''block_out_channels''': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case				:    Optional[Any]       				=     {
    '''sample_size''': 2_56,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

snake_case				:    List[str]       				=     {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

snake_case				:    int       				=     {
    '''num_train_timesteps''': 2_01,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}

snake_case				:    Any       				=     {
    '''num_train_timesteps''': 1_51,
    '''sigma_min''': 0.0_02,
    '''sigma_max''': 80.0,
}
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      if isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('''boolean value expected'''						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		Union[str, Any]=False						):




      """simple docstring"""





      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      a						:str 							=       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      a						:str 							=       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      a						:List[str] 							=       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      a						:str 							=       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      a						:str 							=       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      a						:Optional[int] 							=       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      a						:Optional[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      a						:Optional[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      a						:List[Any] 							=       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            a						:Optional[int] 							=       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            a						:str 							=       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint
def 				__lowerCamelCase						( UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Dict=None						):




      """simple docstring"""





      a							,				a							,				a						:List[Any] 							=       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 ,  dim=0						)
      a							,				a							,				a						:Any 							=       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 ,  dim=0						)

      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.norm.weight''']
      a						:Union[str, Any] 							=       checkpoint[F'''{old_prefix}.norm.bias''']

      a						:int 							=       weight_q.squeeze(-1						).squeeze(-1						)
      a						:Any 							=       bias_q.squeeze(-1						).squeeze(-1						)
      a						:Union[str, Any] 							=       weight_k.squeeze(-1						).squeeze(-1						)
      a						:str 							=       bias_k.squeeze(-1						).squeeze(-1						)
      a						:List[str] 							=       weight_v.squeeze(-1						).squeeze(-1						)
      a						:List[str] 							=       bias_v.squeeze(-1						).squeeze(-1						)

      a						:Dict 							=       (
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1						).squeeze(-1						)
      )
      a						:int 							=       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1						).squeeze(-1						)

      return new_checkpoint
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:Any 							=       torch.load(UpperCAmelCase_ ,  map_location='''cpu'''						)
      a						:Optional[int] 							=       {}

      a						:Optional[int] 							=       checkpoint['''time_embed.0.weight''']
      a						:Optional[int] 							=       checkpoint['''time_embed.0.bias''']
      a						:Any 							=       checkpoint['''time_embed.2.weight''']
      a						:List[Any] 							=       checkpoint['''time_embed.2.bias''']

      if unet_config["num_class_embeds"] is not None:
            a						:Optional[Any] 							=       checkpoint['''label_emb.weight''']

      a						:Optional[int] 							=       checkpoint['''input_blocks.0.0.weight''']
      a						:List[Any] 							=       checkpoint['''input_blocks.0.0.bias''']

      a						:List[str] 							=       unet_config['''down_block_types''']
      a						:Optional[int] 							=       unet_config['''layers_per_block''']
      a						:int 							=       unet_config['''attention_head_dim''']
      a						:Optional[int] 							=       unet_config['''block_out_channels''']
      a						:Union[str, Any] 							=       1
      a						:Optional[Any] 							=       channels_list[0]

      for i, layer_type in enumerate(UpperCAmelCase_						):
            a						:str 							=       channels_list[i]
            a						:int 							=       current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(UpperCAmelCase_						):
                        a						:Dict 							=       F'''down_blocks.{i}.resnets.{j}'''
                        a						:Optional[int] 							=       F'''input_blocks.{current_layer}.0'''
                        a						:Dict 							=       True if j == 0 and downsample_block_has_skip else False
                        a						:Dict 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  has_skip=UpperCAmelCase_						)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(UpperCAmelCase_						):
                        a						:Any 							=       F'''down_blocks.{i}.resnets.{j}'''
                        a						:Dict 							=       F'''input_blocks.{current_layer}.0'''
                        a						:Optional[Any] 							=       True if j == 0 and downsample_block_has_skip else False
                        a						:Union[str, Any] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  has_skip=UpperCAmelCase_						)
                        a						:Tuple 							=       F'''down_blocks.{i}.attentions.{j}'''
                        a						:Union[str, Any] 							=       F'''input_blocks.{current_layer}.1'''
                        a						:Optional[int] 							=       convert_attention(
                            UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                        current_layer += 1

            if i != len(UpperCAmelCase_						) - 1:
                  a						:int 							=       F'''down_blocks.{i}.downsamplers.0'''
                  a						:List[str] 							=       F'''input_blocks.{current_layer}.0'''
                  a						:List[Any] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                  current_layer += 1

            a						:Union[str, Any] 							=       current_channels

      # hardcoded the mid-block for now
      a						:List[str] 							=       '''mid_block.resnets.0'''
      a						:Any 							=       '''middle_block.0'''
      a						:Union[str, Any] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
      a						:int 							=       '''mid_block.attentions.0'''
      a						:Any 							=       '''middle_block.1'''
      a						:Union[str, Any] 							=       convert_attention(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
      a						:int 							=       '''mid_block.resnets.1'''
      a						:Union[str, Any] 							=       '''middle_block.2'''
      a						:Dict 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

      a						:int 							=       0
      a						:Any 							=       unet_config['''up_block_types''']

      for i, layer_type in enumerate(UpperCAmelCase_						):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1						):
                        a						:Any 							=       F'''up_blocks.{i}.resnets.{j}'''
                        a						:str 							=       F'''output_blocks.{current_layer}.0'''
                        a						:Dict 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  has_skip=UpperCAmelCase_						)
                        current_layer += 1

                  if i != len(UpperCAmelCase_						) - 1:
                        a						:str 							=       F'''up_blocks.{i}.upsamplers.0'''
                        a						:Any 							=       F'''output_blocks.{current_layer-1}.1'''
                        a						:List[str] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1						):
                        a						:Tuple 							=       F'''up_blocks.{i}.resnets.{j}'''
                        a						:Tuple 							=       F'''output_blocks.{current_layer}.0'''
                        a						:List[str] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  has_skip=UpperCAmelCase_						)
                        a						:List[str] 							=       F'''up_blocks.{i}.attentions.{j}'''
                        a						:Dict 							=       F'''output_blocks.{current_layer}.1'''
                        a						:List[str] 							=       convert_attention(
                            UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                        current_layer += 1

                  if i != len(UpperCAmelCase_						) - 1:
                        a						:Optional[int] 							=       F'''up_blocks.{i}.upsamplers.0'''
                        a						:Optional[Any] 							=       F'''output_blocks.{current_layer-1}.2'''
                        a						:Optional[int] 							=       convert_resnet(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

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

      return new_checkpoint


if __name__ == "__main__":
     snake_case				:    List[Any]       				=     argparse.ArgumentParser()

     parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
     parser.add_argument(
         '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
     )
     parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

     snake_case				:    Union[str, Any]       				=     parser.parse_args()
     snake_case				:    int       				=     strabool(args.class_cond)

     snake_case				:    Optional[Any]       				=     os.path.basename(args.unet_path)
     print(F"""Checkpoint: {ckpt_name}""")

     # Get U-Net config
     if "imagenet64" in ckpt_name:
          snake_case				:    Dict       				=     IMAGENET_64_UNET_CONFIG
     elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
          snake_case				:    Union[str, Any]       				=     LSUN_256_UNET_CONFIG
     elif "test" in ckpt_name:
          snake_case				:    Any       				=     TEST_UNET_CONFIG
     else:
          raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

     if not args.class_cond:
          snake_case				:    Optional[Any]       				=     None

     snake_case				:    Optional[int]       				=     con_pt_to_diffuser(args.unet_path, unet_config)

     snake_case				:    Tuple       				=     UNetaDModel(**unet_config)
     image_unet.load_state_dict(converted_unet_ckpt)

     # Get scheduler config
     if "cd" in ckpt_name or "test" in ckpt_name:
          snake_case				:    Union[str, Any]       				=     CD_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
          snake_case				:    str       				=     CT_IMAGENET_64_SCHEDULER_CONFIG
     elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
          snake_case				:    Optional[Any]       				=     CT_LSUN_256_SCHEDULER_CONFIG
     else:
          raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

     snake_case				:    Optional[int]       				=     CMStochasticIterativeScheduler(**scheduler_config)

     snake_case				:    Any       				=     ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
     consistency_model.save_pretrained(args.dump_path)









import string

import numpy
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return b if a == 0 else greatest_common_divisor(b % a ,  UpperCAmelCase_						)







class 		_snake_case  :
 SCREAMING_SNAKE_CASE__      =       string.ascii_uppercase + string.digits
 # This cipher takes alphanumerics into account
 # i.e. a total of 36 characters

 # take x and return x % len(key_string)
 SCREAMING_SNAKE_CASE__      =       numpy.vectorize(lambda _snake_case							: x % 36							)

 SCREAMING_SNAKE_CASE__      =       numpy.vectorize(_snake_case							)




 def __init__(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.modulus(_lowerCamelCase    )  # mod36 calc's on the encrypt key
       self.check_determinant()  # validate the determinant of the encryption key
       a						:int 							=       encrypt_key.shape[0]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string.index(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string[round(_lowerCamelCase    )]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       round(numpy.linalg.det(self.encrypt_key    )    )

       if det < 0:
             a						:Any 							=       det % len(self.key_string    )

       a						:Dict 							=       len(self.key_string    )
       if greatest_common_divisor(_lowerCamelCase					,  len(self.key_string    )    ) != 1:
             a						:int 							=       (
                 F'''determinant modular {req_l} of encryption key({det}) '''
                 F'''is not co prime w.r.t {req_l}.\nTry another key.'''
             )
             raise ValueError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       [char for char in text.upper() if char in self.key_string]

       a						:List[str] 							=       chars[-1]
       while len(_lowerCamelCase    ) % self.break_key != 0:
             chars.append(_lowerCamelCase    )

       return "".join(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Dict 							=       self.process_text(text.upper()    )
       a						:List[str] 							=       ''''''

       for i in range(0					,  len(_lowerCamelCase    ) - self.break_key + 1					,  self.break_key    ):
             a						:int 							=       text[i : i + self.break_key]
             a						:Optional[int] 							=       [self.replace_letters(_lowerCamelCase    ) for char in batch]
             a						:Union[str, Any] 							=       numpy.array([vec]    ).T
             a						:str 							=       self.modulus(self.encrypt_key.dot(_lowerCamelCase    )    ).T.tolist()[
                 0
             ]
             a						:List[Any] 							=       ''''''.join(
                 self.replace_digits(_lowerCamelCase    ) for num in batch_encrypted    )
             encrypted += encrypted_batch

       return encrypted




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       round(numpy.linalg.det(self.encrypt_key    )    )

       if det < 0:
             a						:int 							=       det % len(self.key_string    )
       a						:Tuple 							=       None
       for i in range(len(self.key_string    )    ):
             if (det * i) % len(self.key_string    ) == 1:
                   a						:Tuple 							=       i
                   break

       a						:List[str] 							=       (
           det_inv
           * numpy.linalg.det(self.encrypt_key    )
           * numpy.linalg.inv(self.encrypt_key    )
       )

       return self.to_int(self.modulus(_lowerCamelCase    )    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.make_decrypt_key()
       a						:str 							=       self.process_text(text.upper()    )
       a						:List[Any] 							=       ''''''

       for i in range(0					,  len(_lowerCamelCase    ) - self.break_key + 1					,  self.break_key    ):
             a						:Optional[Any] 							=       text[i : i + self.break_key]
             a						:List[Any] 							=       [self.replace_letters(_lowerCamelCase    ) for char in batch]
             a						:str 							=       numpy.array([vec]    ).T
             a						:Dict 							=       self.modulus(decrypt_key.dot(_lowerCamelCase    )    ).T.tolist()[0]
             a						:List[Any] 							=       ''''''.join(
                 self.replace_digits(_lowerCamelCase    ) for num in batch_decrypted    )
             decrypted += decrypted_batch

       return decrypted







def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Tuple 							=       int(input('''Enter the order of the encryption key: '''						)						)
      a						:Dict 							=       []

      print('''Enter each row of the encryption key with space separated integers'''						)
      for _ in range(UpperCAmelCase_						):
            a						:List[str] 							=       [int(UpperCAmelCase_						) for x in input().split()]
            hill_matrix.append(UpperCAmelCase_						)

      a						:Any 							=       HillCipher(numpy.array(UpperCAmelCase_						)						)

      print('''Would you like to encrypt or decrypt some text? (1 or 2)'''						)
      a						:Any 							=       input('''\n1. Encrypt\n2. Decrypt\n'''						)
      if option == "1":
            a						:str 							=       input('''What text would you like to encrypt?: '''						)
            print('''Your encrypted text is:'''						)
            print(hc.encrypt(UpperCAmelCase_						)						)
      elif option == "2":
            a						:Dict 							=       input('''What text would you like to decrypt?: '''						)
            print('''Your decrypted text is:'''						)
            print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     main()





