



import argparse
import hashlib  # hashlib is only used inside the Test class
import struct

class 				snake_case__:

     """simple docstring"""






     def __init__(							self :     Any				,       SCREAMING_SNAKE_CASE :     List[str]					):
         lowercase__					:   Union[str, Any]													=					data
         lowercase__					:   List[Any]													=					[0x6_7_4_5_2_3_0_1, 0xE_F_C_D_A_B_8_9, 0x9_8_B_A_D_C_F_E, 0x1_0_3_2_5_4_7_6, 0xC_3_D_2_E_1_F_0]







     @staticmethod
     def 			snake_case					(							SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     List[Any]					):
         return ((n << b) | (n >> (32 - b))) & 0xF_F_F_F_F_F_F_F







     def 			snake_case					(							self :     Optional[Any]					):
         lowercase__					:   Optional[int]													=					b"\x80" + b"\x00" * (63 - (len(self.data					) + 8) % 64)
         lowercase__					:   Optional[int]													=					self.data + padding + struct.pack(">Q"				,       8 * len(self.data					)					)
         return padded_data







     def 			snake_case					(							self :     int					):
         return [
             self.padded_data[i : i + 64] for i in range(0				,       len(self.padded_data					)				,       64					)
         ]







     def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
         lowercase__					:   int													=					list(struct.unpack(">16L"				,       _a					)					) + [0] * 64
         for i in range(16				,       80					):
             lowercase__					:   Dict													=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])				,       1					)
         return w







     def 			snake_case					(							self :     Optional[Any]					):
         lowercase__					:   str													=					self.padding()
         lowercase__					:   Dict													=					self.split_blocks()
         for block in self.blocks:
             lowercase__					:   Tuple													=					self.expand_block(_a					)
             lowercase__					:   int													=					self.h
             for i in range(0				,       80					):
                 if 0 <= i < 20:
                     lowercase__					:   Dict													=					(b & c) | ((~b) & d)
                     lowercase__					:   Optional[Any]													=					0x5_A_8_2_7_9_9_9
                 elif 20 <= i < 40:
                     lowercase__					:   Optional[Any]													=					b ^ c ^ d
                     lowercase__					:   Any													=					0x6_E_D_9_E_B_A_1
                 elif 40 <= i < 60:
                     lowercase__					:   Optional[int]													=					(b & c) | (b & d) | (c & d)
                     lowercase__					:   Optional[int]													=					0x8_F_1_B_B_C_D_C
                 elif 60 <= i < 80:
                     lowercase__					:   List[str]													=					b ^ c ^ d
                     lowercase__					:   int													=					0xC_A_6_2_C_1_D_6
                 lowercase__					:   List[Any]													=					(
                     self.rotate(_a				,       5					) + f + e + k + expanded_block[i] & 0xF_F_F_F_F_F_F_F,
                     a,
                     self.rotate(_a				,       30					),
                     c,
                     d,
                 )
             lowercase__					:   Dict													=					(
                 self.h[0] + a & 0xF_F_F_F_F_F_F_F,
                 self.h[1] + b & 0xF_F_F_F_F_F_F_F,
                 self.h[2] + c & 0xF_F_F_F_F_F_F_F,
                 self.h[3] + d & 0xF_F_F_F_F_F_F_F,
                 self.h[4] + e & 0xF_F_F_F_F_F_F_F,
             )
         return ("{:08x}" * 5).format(*self.h					)






def  __lowerCamelCase (       ):

    """simple docstring"""


    lowercase__					:   Optional[Any]													=					b"Test String"
    assert SHAaHash(lowerCamelCase__						).final_hash() == hashlib.shaa(lowerCamelCase__						).hexdigest()  # noqa: S324


def  __lowerCamelCase (       ):

    """simple docstring"""


    lowercase__					:   Optional[int]													=					argparse.ArgumentParser(description="Process some strings or files"						)
    parser.add_argument(
        "--string"					,			dest="input_string"					,			default="Hello World!! Welcome to Cryptography"					,			help="Hash the string"					,			)
    parser.add_argument("--file"					,			dest="input_file"					,			help="Hash contents of a file"						)
    lowercase__					:   Optional[Any]													=					parser.parse_args()
    lowercase__					:   Optional[Any]													=					args.input_string
    # In any case hash input should be a bytestring
    if args.input_file:
        with open(args.input_file					,			"rb"						) as f:
            lowercase__					:   List[str]													=					f.read()
    else:
        lowercase__					:   Tuple													=					bytes(lowerCamelCase__					,			"utf-8"						)
    print(SHAaHash(lowerCamelCase__						).final_hash()						)


if __name__ == "__main__":
    main()
    import doctest

    doctest.testmod()









import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=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  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[Any]													=					old_item.replace("in_layers.0"					,			"norm1"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("in_layers.2"					,			"conv1"						)

								lowercase__					:   Optional[Any]													=					new_item.replace("out_layers.0"					,			"norm2"						)
								lowercase__					:   Union[str, Any]													=					new_item.replace("out_layers.3"					,			"conv2"						)

								lowercase__					:   Dict													=					new_item.replace("emb_layers.1"					,			"time_emb_proj"						)
								lowercase__					:   int													=					new_item.replace("skip_connection"					,			"conv_shortcut"						)

								lowercase__					:   Tuple													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

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

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=0						):

				"""simple docstring"""


				lowercase__					:   str													=					[]
				for old_item in old_list:
								lowercase__					:   Optional[int]													=					old_item

								lowercase__					:   Dict													=					new_item.replace("norm.weight"					,			"group_norm.weight"						)
								lowercase__					:   Optional[int]													=					new_item.replace("norm.bias"					,			"group_norm.bias"						)

								lowercase__					:   Tuple													=					new_item.replace("proj_out.weight"					,			"proj_attn.weight"						)
								lowercase__					:   List[Any]													=					new_item.replace("proj_out.bias"					,			"proj_attn.bias"						)

								lowercase__					:   Optional[Any]													=					shave_segments(lowerCamelCase__					,			n_shave_prefix_segments=lowerCamelCase__						)

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

				return mapping


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				assert isinstance(lowerCamelCase__					,			lowerCamelCase__						), "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():
												lowercase__					:   List[str]													=					old_checkpoint[path]
												lowercase__					:   str													=					old_tensor.shape[0] // 3

												lowercase__					:   List[str]													=					(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

												lowercase__					:   Union[str, Any]													=					old_tensor.shape[0] // config["num_head_channels"] // 3

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

												lowercase__					:   Dict													=					query.reshape(lowerCamelCase__						)
												lowercase__					:   Dict													=					key.reshape(lowerCamelCase__						)
												lowercase__					:   int													=					value.reshape(lowerCamelCase__						)

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

								if additional_replacements is not None:
												for replacement in additional_replacements:
																lowercase__					:   Tuple													=					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:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]][:, :, 0]
								else:
												lowercase__					:   List[Any]													=					old_checkpoint[path["old"]]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					{}

				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.0.weight"]
				lowercase__					:   Tuple													=					checkpoint["time_embed.0.bias"]
				lowercase__					:   Dict													=					checkpoint["time_embed.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["time_embed.2.bias"]

				lowercase__					:   Optional[int]													=					checkpoint["input_blocks.0.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["input_blocks.0.0.bias"]

				lowercase__					:   Tuple													=					checkpoint["out.0.weight"]
				lowercase__					:   List[Any]													=					checkpoint["out.0.bias"]
				lowercase__					:   Tuple													=					checkpoint["out.2.weight"]
				lowercase__					:   Optional[Any]													=					checkpoint["out.2.bias"]

				# Retrieves the keys for the input blocks only
				lowercase__					:   Dict													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "input_blocks" in layer}						)
				lowercase__					:   str													=					{
				    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the middle blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "middle_block" in layer}						)
				lowercase__					:   Union[str, Any]													=					{
				    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				# Retrieves the keys for the output blocks only
				lowercase__					:   Tuple													=					len({".".join(layer.split("."						)[:2]						) for layer in checkpoint if "output_blocks" in layer}						)
				lowercase__					:   Tuple													=					{
				    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
				    for layer_id in range(lowerCamelCase__						)
				}

				for i in range(1					,			lowerCamelCase__						):
								lowercase__					:   Tuple													=					(i - 1) // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					(i - 1) % (config["num_res_blocks"] + 1)

								lowercase__					:   List[Any]													=					[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
								lowercase__					:   Dict													=					[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:
												lowercase__					:   int													=					checkpoint[
												    F"""input_blocks.{i}.0.op.weight"""
												]
												lowercase__					:   List[str]													=					checkpoint[
												    F"""input_blocks.{i}.0.op.bias"""
												]
												continue

								lowercase__					:   Union[str, Any]													=					renew_resnet_paths(lowerCamelCase__						)
								lowercase__					:   Optional[int]													=					{"old": F"""input_blocks.{i}.0""", "new": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
								lowercase__					:   Optional[int]													=					{"old": "resnets.2.op", "new": "downsamplers.0.op"}
								assign_to_checkpoint(
								    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path, resnet_op]					,			config=lowerCamelCase__						)

								if len(lowerCamelCase__						):
												lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
												lowercase__					:   str													=					{
												    "old": F"""input_blocks.{i}.1""",
												    "new": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												lowercase__					:   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(
												    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__					,			)

				lowercase__					:   int													=					middle_blocks[0]
				lowercase__					:   Dict													=					middle_blocks[1]
				lowercase__					:   Dict													=					middle_blocks[2]

				lowercase__					:   Any													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   List[Any]													=					renew_resnet_paths(lowerCamelCase__						)
				assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			config=lowerCamelCase__						)

				lowercase__					:   Optional[int]													=					renew_attention_paths(lowerCamelCase__						)
				lowercase__					:   Optional[int]													=					{
				    "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(
				    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			attention_paths_to_split=lowerCamelCase__					,			config=lowerCamelCase__						)

				for i in range(lowerCamelCase__						):
								lowercase__					:   List[Any]													=					i // (config["num_res_blocks"] + 1)
								lowercase__					:   Optional[int]													=					i % (config["num_res_blocks"] + 1)
								lowercase__					:   List[Any]													=					[shave_segments(lowerCamelCase__					,			2						) for name in output_blocks[i]]
								lowercase__					:   Optional[Any]													=					{}

								for layer in output_block_layers:
												lowercase__							,							lowercase__					:   str													=					layer.split("."						)[0], shave_segments(lowerCamelCase__					,			1						)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase__						)
												else:
																lowercase__					:   Tuple													=					[layer_name]

								if len(lowerCamelCase__						) > 1:
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												lowercase__					:   Dict													=					[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					renew_resnet_paths(lowerCamelCase__						)

												lowercase__					:   Tuple													=					{"old": F"""output_blocks.{i}.0""", "new": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
												assign_to_checkpoint(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			config=lowerCamelCase__						)

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																lowercase__					:   List[str]													=					list(output_block_list.values()						).index(["conv.weight", "conv.bias"]						)
																lowercase__					:   Tuple													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																lowercase__					:   Optional[Any]													=					checkpoint[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

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

												if len(lowerCamelCase__						):
																lowercase__					:   Tuple													=					renew_attention_paths(lowerCamelCase__						)
																lowercase__					:   str													=					{
																    "old": F"""output_blocks.{i}.1""",
																    "new": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																}
																lowercase__					:   Union[str, 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(
																    lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			additional_replacements=[meta_path]					,			attention_paths_to_split=to_split if any("qkv" in key for key in attentions						) else None					,			config=lowerCamelCase__					,			)
								else:
												lowercase__					:   int													=					renew_resnet_paths(lowerCamelCase__					,			n_shave_prefix_segments=1						)
												for path in resnet_0_paths:
																lowercase__					:   List[Any]													=					".".join(["output_blocks", str(lowerCamelCase__						), path["old"]]						)
																lowercase__					:   Any													=					".".join(["up_blocks", str(lowerCamelCase__						), "resnets", str(lowerCamelCase__						), path["new"]]						)

																lowercase__					:   List[Any]													=					checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
				lowerCAmelCase__					  =  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__					  =  parser.parse_args()

				lowerCAmelCase__					  =  torch.load(args.checkpoint_path)

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

				lowerCAmelCase__					  =  convert_ldm_checkpoint(checkpoint, config)

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

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

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

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




