'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def     __lowerCAmelCase						(       a_       ,      a_=1		)      ->	Dict:





     '''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     __lowerCAmelCase						(       a_       ,      a_=0		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       []
     for old_item in old_list:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       old_item.replace('in_layers.0'       ,      'norm1'		)
          SCREAMING_SNAKE_CASE					:			int    	=       new_item.replace('in_layers.2'       ,      'conv1'		)

          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       new_item.replace('out_layers.0'       ,      'norm2'		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       new_item.replace('out_layers.3'       ,      'conv2'		)

          SCREAMING_SNAKE_CASE					:			int    	=       new_item.replace('emb_layers.1'       ,      'time_emb_proj'		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       new_item.replace('skip_connection'       ,      'conv_shortcut'		)

          SCREAMING_SNAKE_CASE					:			str    	=       shave_segments(a_       ,      n_shave_prefix_segments=a_		)

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

     return mapping


def     __lowerCAmelCase						(       a_       ,      a_=0		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []
     for old_item in old_list:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       old_item

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

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

          SCREAMING_SNAKE_CASE					:			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		)      ->	Optional[int]:





     '''simple docstring'''





     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():
               SCREAMING_SNAKE_CASE					:			Dict    	=       old_checkpoint[path]
               SCREAMING_SNAKE_CASE					:			Tuple    	=       old_tensor.shape[0] // 3

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

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

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

               SCREAMING_SNAKE_CASE					:			Any    	=       query.reshape(a_		)
               SCREAMING_SNAKE_CASE					:			Tuple    	=       key.reshape(a_		)
               SCREAMING_SNAKE_CASE					:			Tuple    	=       value.reshape(a_		)

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

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    SCREAMING_SNAKE_CASE					:			Optional[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:
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       old_checkpoint[path['old']][:, :, 0]
          else:
               SCREAMING_SNAKE_CASE					:			int    	=       old_checkpoint[path['old']]


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       {}

     SCREAMING_SNAKE_CASE					:			Dict    	=       checkpoint['time_embed.0.weight']
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['time_embed.0.bias']
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       checkpoint['time_embed.2.weight']
     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['time_embed.2.bias']

     SCREAMING_SNAKE_CASE					:			int    	=       checkpoint['input_blocks.0.0.weight']
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       checkpoint['input_blocks.0.0.bias']

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

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

          SCREAMING_SNAKE_CASE					:			str    	=       [key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
          SCREAMING_SNAKE_CASE					:			Union[str, 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:
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       checkpoint[
                   f"""input_blocks.{i}.0.op.weight"""
               ]
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       checkpoint[
                   f"""input_blocks.{i}.0.op.bias"""
               ]
               continue

          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_resnet_paths(a_		)
          SCREAMING_SNAKE_CASE					:			Any    	=       {'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
          SCREAMING_SNAKE_CASE					:			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_		):
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_attention_paths(a_		)
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
                   'old': f"""input_blocks.{i}.1""",
                   'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
               }
               SCREAMING_SNAKE_CASE					:			Optional[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_       ,      )

     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       middle_blocks[0]
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       middle_blocks[1]
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       middle_blocks[2]

     SCREAMING_SNAKE_CASE					:			Any    	=       renew_resnet_paths(a_		)
     assign_to_checkpoint(a_       ,      a_       ,      a_       ,      config=a_		)

     SCREAMING_SNAKE_CASE					:			int    	=       renew_resnet_paths(a_		)
     assign_to_checkpoint(a_       ,      a_       ,      a_       ,      config=a_		)

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

          for layer in output_block_layers:
               SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       layer.split('.'		)[0], shave_segments(a_       ,      1		)
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(a_		)
               else:
                    SCREAMING_SNAKE_CASE					:			Dict    	=       [layer_name]

          if len(a_		) > 1:
               SCREAMING_SNAKE_CASE					:			int    	=       [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
               SCREAMING_SNAKE_CASE					:			int    	=       [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

               SCREAMING_SNAKE_CASE					:			List[Any]    	=       renew_resnet_paths(a_		)
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       renew_resnet_paths(a_		)

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

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

               if len(a_		):
                    SCREAMING_SNAKE_CASE					:			Any    	=       renew_attention_paths(a_		)
                    SCREAMING_SNAKE_CASE					:			Dict    	=       {
                        'old': f"""output_blocks.{i}.1""",
                        'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                    }
                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
                        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:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       renew_resnet_paths(a_       ,      n_shave_prefix_segments=1		)
               for path in resnet_0_paths:
                    SCREAMING_SNAKE_CASE					:			str    	=       '.'.join(['output_blocks', str(a_		), path['old']]		)
                    SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       '.'.join(['up_blocks', str(a_		), 'resnets', str(a_		), path['new']]		)

                    SCREAMING_SNAKE_CASE					:			Optional[int]    	=       checkpoint[old_path]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase  :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.""")

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

 _lowerCAmelCase  :str  				=    torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase  :List[Any]  				=    json.loads(f.read())

 _lowerCAmelCase  :Optional[int]  				=    convert_ldm_checkpoint(checkpoint, config)

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

 _lowerCAmelCase  :List[str]  				=    UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase  :Tuple  				=    DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  _lowerCAmelCase  :Union[str, Any]  				=    VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

  _lowerCAmelCase  :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'''





import argparse
import struct
import unittest






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> None:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       data

         # Initialize hash values
         SCREAMING_SNAKE_CASE					:			str    	=       [
             0X6A_09_E6_67,
             0XBB_67_AE_85,
             0X3C_6E_F3_72,
             0XA5_4F_F5_3A,
             0X51_0E_52_7F,
             0X9B_05_68_8C,
             0X1F_83_D9_AB,
             0X5B_E0_CD_19,
         ]

         # Initialize round constants
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       [
             0X42_8A_2F_98,
             0X71_37_44_91,
             0XB5_C0_FB_CF,
             0XE9_B5_DB_A5,
             0X39_56_C2_5B,
             0X59_F1_11_F1,
             0X92_3F_82_A4,
             0XAB_1C_5E_D5,
             0XD8_07_AA_98,
             0X12_83_5B_01,
             0X24_31_85_BE,
             0X55_0C_7D_C3,
             0X72_BE_5D_74,
             0X80_DE_B1_FE,
             0X9B_DC_06_A7,
             0XC1_9B_F1_74,
             0XE4_9B_69_C1,
             0XEF_BE_47_86,
             0X0F_C1_9D_C6,
             0X24_0C_A1_CC,
             0X2D_E9_2C_6F,
             0X4A_74_84_AA,
             0X5C_B0_A9_DC,
             0X76_F9_88_DA,
             0X98_3E_51_52,
             0XA8_31_C6_6D,
             0XB0_03_27_C8,
             0XBF_59_7F_C7,
             0XC6_E0_0B_F3,
             0XD5_A7_91_47,
             0X06_CA_63_51,
             0X14_29_29_67,
             0X27_B7_0A_85,
             0X2E_1B_21_38,
             0X4D_2C_6D_FC,
             0X53_38_0D_13,
             0X65_0A_73_54,
             0X76_6A_0A_BB,
             0X81_C2_C9_2E,
             0X92_72_2C_85,
             0XA2_BF_E8_A1,
             0XA8_1A_66_4B,
             0XC2_4B_8B_70,
             0XC7_6C_51_A3,
             0XD1_92_E8_19,
             0XD6_99_06_24,
             0XF4_0E_35_85,
             0X10_6A_A0_70,
             0X19_A4_C1_16,
             0X1E_37_6C_08,
             0X27_48_77_4C,
             0X34_B0_BC_B5,
             0X39_1C_0C_B3,
             0X4E_D8_AA_4A,
             0X5B_9C_CA_4F,
             0X68_2E_6F_F3,
             0X74_8F_82_EE,
             0X78_A5_63_6F,
             0X84_C8_78_14,
             0X8C_C7_02_08,
             0X90_BE_FF_FA,
             0XA4_50_6C_EB,
             0XBE_F9_A3_F7,
             0XC6_71_78_F2,
         ]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.preprocessing(self.data )
         self.final_hash()






    @staticmethod
    def       _UpperCamelCase    (       lowercase__ )							-> bytes:
         SCREAMING_SNAKE_CASE					:			str    	=       B'\x80' + (B'\x00' * (63 - (len(lowercase__ ) + 8) % 64))
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       struct.pack('>Q'    ,							(len(lowercase__ ) * 8) )
         return data + padding + big_endian_integer






    def       _UpperCamelCase    (       self )							-> None:
         # Convert into blocks of 64 bytes
         SCREAMING_SNAKE_CASE					:			Dict    	=       [
             self.preprocessed_data[x : x + 64]
             for x in range(0    ,							len(self.preprocessed_data )    ,							64 )
         ]

         for block in self.blocks:
              # Convert the given block into a list of 4 byte integers
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       list(struct.unpack('>16L'    ,							lowercase__ ) )
              # add 48 0-ed integers
              words += [0] * 48

              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.hashes

              for index in range(0    ,							64 ):
                   if index > 15:
                        # modify the zero-ed indexes at the end of the array
                        SCREAMING_SNAKE_CASE					:			Dict    	=       (
                            self.ror(words[index - 15]    ,							7 )
                            ^ self.ror(words[index - 15]    ,							18 )
                            ^ (words[index - 15] >> 3)
                        )
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
                            self.ror(words[index - 2]    ,							17 )
                            ^ self.ror(words[index - 2]    ,							19 )
                            ^ (words[index - 2] >> 10)
                        )

                        SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                            words[index - 16] + sa + words[index - 7] + sa
                        ) % 0X1_00_00_00_00

                   # Compression
                   SCREAMING_SNAKE_CASE					:			Any    	=       self.ror(lowercase__    ,							6 ) ^ self.ror(lowercase__    ,							11 ) ^ self.ror(lowercase__    ,							25 )
                   SCREAMING_SNAKE_CASE					:			Any    	=       (e & f) ^ ((~e & 0XFF_FF_FF_FF) & g)
                   SCREAMING_SNAKE_CASE					:			str    	=       (
                       h + sa + ch + self.round_constants[index] + words[index]
                   ) % 0X1_00_00_00_00
                   SCREAMING_SNAKE_CASE					:			Any    	=       self.ror(lowercase__    ,							2 ) ^ self.ror(lowercase__    ,							13 ) ^ self.ror(lowercase__    ,							22 )
                   SCREAMING_SNAKE_CASE					:			str    	=       (a & b) ^ (a & c) ^ (b & c)
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       (sa + maj) % 0X1_00_00_00_00

                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       (
                       g,
                       f,
                       e,
                       ((d + tempa) % 0X1_00_00_00_00),
                       c,
                       b,
                       a,
                       ((tempa + tempa) % 0X1_00_00_00_00),
                   )

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [a, b, c, d, e, f, g, h]

              # Modify final values
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [
                  ((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
                  for index, element in enumerate(self.hashes )
              ]

         SCREAMING_SNAKE_CASE					:			Tuple    	=       ''.join([hex(lowercase__ )[2:].zfill(8 ) for value in self.hashes] )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> int:
         return 0XFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> None:
         import hashlib

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bytes('Test String'    ,							'utf-8' )
         self.assertEqual(SHAaaa(lowercase__ ).hash    ,							hashlib.shaaaa(lowercase__ ).hexdigest() )






def     __lowerCAmelCase						(       )      ->	None:





     '''simple docstring'''





     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE					:			List[str]    	=       argparse.ArgumentParser()
     parser.add_argument(
         '-s'       ,      '--string'       ,      dest='input_string'       ,      default='Hello World!! Welcome to Cryptography'       ,      help='Hash the string'       ,      )
     parser.add_argument(
         '-f'       ,      '--file'       ,      dest='input_file'       ,      help='Hash contents of a file'		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       parser.parse_args()

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       args.input_string

     # hash input should be a bytestring
     if args.input_file:
          with open(args.input_file       ,      'rb'		) as f:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       f.read()
     else:
          SCREAMING_SNAKE_CASE					:			Any    	=       bytes(a_       ,      'utf-8'		)

     print(SHAaaa(a_		).hash		)


if __name__ == "__main__":
 main()



