



'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def   _A       (   _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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0					):



							"""simple docstring"""



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

														__lowercase       	=new_item.replace('out_layers.0'  ,   'norm2'					)
														__lowercase       	=new_item.replace('out_layers.3'  ,   'conv2'					)

														__lowercase       	=new_item.replace('emb_layers.1'  ,   'time_emb_proj'					)
														__lowercase       	=new_item.replace('skip_connection'  ,   'conv_shortcut'					)

														__lowercase       	=shave_segments(_lowerCAmelCase  ,   n_shave_prefix_segments=_lowerCAmelCase					)

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

							return mapping



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0					):



							"""simple docstring"""



							__lowercase       	=[]
							for old_item in old_list:
														__lowercase       	=old_item

														__lowercase       	=new_item.replace('norm.weight'  ,   'group_norm.weight'					)
														__lowercase       	=new_item.replace('norm.bias'  ,   'group_norm.bias'					)

														__lowercase       	=new_item.replace('proj_out.weight'  ,   'proj_attn.weight'					)
														__lowercase       	=new_item.replace('proj_out.bias'  ,   'proj_attn.bias'					)

														__lowercase       	=shave_segments(_lowerCAmelCase  ,   n_shave_prefix_segments=_lowerCAmelCase					)

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

							return mapping



def   _A       (   _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       	=old_checkpoint[path]
																					__lowercase       	=old_tensor.shape[0] // 3

																					__lowercase       	=(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

																					__lowercase       	=old_tensor.shape[0] // config['num_head_channels'] // 3

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

																					__lowercase       	=query.reshape(_lowerCAmelCase					)
																					__lowercase       	=key.reshape(_lowerCAmelCase					)
																					__lowercase       	=value.reshape(_lowerCAmelCase					)

							for path in paths:
														__lowercase       	=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       	=new_path.replace('middle_block.0'  ,   'mid_block.resnets.0'					)
														__lowercase       	=new_path.replace('middle_block.1'  ,   'mid_block.attentions.0'					)
														__lowercase       	=new_path.replace('middle_block.2'  ,   'mid_block.resnets.1'					)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												__lowercase       	=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       	=old_checkpoint[path['old']][:, :, 0]
														else:
																					__lowercase       	=old_checkpoint[path['old']]



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	={}

							__lowercase       	=checkpoint['time_embed.0.weight']
							__lowercase       	=checkpoint['time_embed.0.bias']
							__lowercase       	=checkpoint['time_embed.2.weight']
							__lowercase       	=checkpoint['time_embed.2.bias']

							__lowercase       	=checkpoint['input_blocks.0.0.weight']
							__lowercase       	=checkpoint['input_blocks.0.0.bias']

							__lowercase       	=checkpoint['out.0.weight']
							__lowercase       	=checkpoint['out.0.bias']
							__lowercase       	=checkpoint['out.2.weight']
							__lowercase       	=checkpoint['out.2.bias']

							# Retrieves the keys for the input blocks only
							__lowercase       	=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'input_blocks' in layer}					)
							__lowercase       	={
							    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       	=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'middle_block' in layer}					)
							__lowercase       	={
							    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       	=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'output_blocks' in layer}					)
							__lowercase       	={
							    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       	=(i - 1) // (config['num_res_blocks'] + 1)
														__lowercase       	=(i - 1) % (config['num_res_blocks'] + 1)

														__lowercase       	=[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
														__lowercase       	=[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       	=checkpoint[
																					    f"""input_blocks.{i}.0.op.weight"""
																					]
																					__lowercase       	=checkpoint[
																					    f"""input_blocks.{i}.0.op.bias"""
																					]
																					continue

														__lowercase       	=renew_resnet_paths(_lowerCAmelCase					)
														__lowercase       	={'old': f"""input_blocks.{i}.0""", 'new': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														__lowercase       	={'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       	=renew_attention_paths(_lowerCAmelCase					)
																					__lowercase       	={
																					    'old': f"""input_blocks.{i}.1""",
																					    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																					}
																					__lowercase       	={
																					    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       	=middle_blocks[0]
							__lowercase       	=middle_blocks[1]
							__lowercase       	=middle_blocks[2]

							__lowercase       	=renew_resnet_paths(_lowerCAmelCase					)
							assign_to_checkpoint(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   config=_lowerCAmelCase					)

							__lowercase       	=renew_resnet_paths(_lowerCAmelCase					)
							assign_to_checkpoint(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   config=_lowerCAmelCase					)

							__lowercase       	=renew_attention_paths(_lowerCAmelCase					)
							__lowercase       	={
							    '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       	=i // (config['num_res_blocks'] + 1)
														__lowercase       	=i % (config['num_res_blocks'] + 1)
														__lowercase       	=[shave_segments(_lowerCAmelCase  ,   2					) for name in output_blocks[i]]
														__lowercase       	={}

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

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

																					__lowercase       	=renew_resnet_paths(_lowerCAmelCase					)
																					__lowercase       	=renew_resnet_paths(_lowerCAmelCase					)

																					__lowercase       	={'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(output_block_list.values()					).index(['conv.weight', 'conv.bias']					)
																												__lowercase       	=checkpoint[
																												    f"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												__lowercase       	=checkpoint[
																												    f"""output_blocks.{i}.{index}.conv.bias"""
																												]

																												# Clear attentions as they have been attributed above.
																												if len(_lowerCAmelCase					) == 2:
																																			__lowercase       	=[]

																					if len(_lowerCAmelCase					):
																												__lowercase       	=renew_attention_paths(_lowerCAmelCase					)
																												__lowercase       	={
																												    'old': f"""output_blocks.{i}.1""",
																												    'new': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												__lowercase       	={
																												    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       	=renew_resnet_paths(_lowerCAmelCase  ,   n_shave_prefix_segments=1					)
																					for path in resnet_0_paths:
																												__lowercase       	='.'.join(['output_blocks', str(_lowerCAmelCase					), path['old']]					)
																												__lowercase       	='.'.join(['up_blocks', str(_lowerCAmelCase					), 'resnets', str(_lowerCAmelCase					), path['new']]					)

																												__lowercase       	=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)








'''simple docstring'''



from __future__ import annotations



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = None  ,   _lowerCAmelCase = False  ,   ):



							"""simple docstring"""



							__lowercase       	=cipher_alphabet or [chr(_lowerCAmelCase					) for i in range(97  ,   123					)]

							# If the argument is None or the user provided an empty dictionary
							if not frequencies_dict:
														# Frequencies of letters in the english language (how much they show up)
														__lowercase       	={
														    'a': 0.0_84_97,
														    'b': 0.0_14_92,
														    'c': 0.0_22_02,
														    'd': 0.0_42_53,
														    'e': 0.1_11_62,
														    'f': 0.0_22_28,
														    'g': 0.0_20_15,
														    'h': 0.0_60_94,
														    'i': 0.0_75_46,
														    'j': 0.0_01_53,
														    'k': 0.0_12_92,
														    'l': 0.0_40_25,
														    'm': 0.0_24_06,
														    'n': 0.0_67_49,
														    'o': 0.0_75_07,
														    'p': 0.0_19_29,
														    'q': 0.0_00_95,
														    'r': 0.0_75_87,
														    's': 0.0_63_27,
														    't': 0.0_93_56,
														    'u': 0.0_27_58,
														    'v': 0.0_09_78,
														    'w': 0.0_25_60,
														    'x': 0.0_01_50,
														    'y': 0.0_19_94,
														    'z': 0.0_00_77,
														}
							else:
														# Custom frequencies dictionary
														__lowercase       	=frequencies_dict

							if not case_sensitive:
														__lowercase       	=ciphertext.lower()

							# Chi squared statistic values
							__lowercase       	={}

							# cycle through all of the shifts
							for shift in range(len(_lowerCAmelCase					)					):
														__lowercase       	=''

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												__lowercase       	=(alphabet_letters.index(letter.lower()					) - shift) % len(
																												    _lowerCAmelCase					)
																												decrypted_with_shift += (
																												    alphabet_letters[new_key].upper()
																												    if case_sensitive and letter.isupper()
																												    else alphabet_letters[new_key]
																												)
																					except ValueError:
																												# Append the character if it isn't in the alphabet
																												decrypted_with_shift += letter

														__lowercase       	=0.0

														# Loop through each letter in the decoded message with the shift
														for letter in decrypted_with_shift:
																					if case_sensitive:
																												__lowercase       	=letter.lower()
																												if letter in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase       	=decrypted_with_shift.lower().count(_lowerCAmelCase					)

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase       	=frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value
																					else:
																												if letter.lower() in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			__lowercase       	=decrypted_with_shift.count(_lowerCAmelCase					)

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			__lowercase       	=frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			__lowercase       	=((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
														__lowercase       	=(
														    chi_squared_statistic,
														    decrypted_with_shift,
														)

							# Get the most likely cipher by finding the cipher with the smallest chi squared
							# statistic
							def chi_squared_statistic_values_sorting_key(_lowerCAmelCase					) -> tuple[float, str]:
														return chi_squared_statistic_values[key]

							__lowercase       	=min(
							    _lowerCAmelCase  ,   key=_lowerCAmelCase  ,   )

							# Get all the data from the most likely cipher (key, decoded message)
							(
							    (
							    __lowercase
							)	,				(
							    __lowercase
							)	,				
							)       	=chi_squared_statistic_values[most_likely_cipher]

							# Return the data on the most likely shift
							return (
							    most_likely_cipher,
							    most_likely_cipher_chi_squared_value,
							    decoded_most_likely_cipher,
							)



