
'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A			      =      False
A			      =      True
A			      =      False


if __name__ == "__main__":
					A			      =      argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    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.')

					A			      =      parser.parse_args()

					A			      =      {
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					A			      =      {
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					A			      =      '' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										A			      =      reader.read()
										A			      =      json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										A			      =      UNetaDModel(**config)
					else:
										A			      =      UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										A			      =      class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					A			      =      dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				A			      =      config[key]
																				del config[key]

										A			      =      [k.replace('UNetRes', '') for k in config['down_block_types']]
										A			      =      [k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										A			      =      torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										A			      =      {}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															A			      =      False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									A			      =      param_value
																									A			      =      True
															if not has_changed:
																				A			      =      param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))


'''simple docstring'''






import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
A			      =      {
    'E': 12.70,
    'T': 9.06,
    'A': 8.17,
    'O': 7.51,
    'I': 6.97,
    'N': 6.75,
    'S': 6.33,
    'H': 6.09,
    'R': 5.99,
    'D': 4.25,
    'L': 4.03,
    'C': 2.78,
    'U': 2.76,
    'M': 2.41,
    'W': 2.36,
    'F': 2.23,
    'G': 2.02,
    'Y': 1.97,
    'P': 1.93,
    'B': 1.29,
    'V': 0.98,
    'K': 0.77,
    'J': 0.15,
    'X': 0.15,
    'Q': 0.10,
    'Z': 0.07,
}
A			      =      'ETAOINSHRDLCUMWFGYPBVKJXQZ'
A			      =      'ABCDEFGHIJKLMNOPQRSTUVWXYZ'





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= {letter: 0 for letter in string.ascii_uppercase}
				for letter in message.upper():
								if letter in LETTERS:
												letter_count[letter] += 1

				return letter_count





def    UpperCAmelCase							(  UpperCAmelCase__							:							tuple):
				return x[0]





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_letter_count(UpperCAmelCase__)
				lowerCamelCase					:			dict[int, list[str]]									= {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCAmelCase__)

				lowerCamelCase					:			dict[int, str]									= {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find       ,							reverse=UpperCAmelCase__)
								lowerCamelCase					:			int									= ''.join(freq_to_letter[freq])

				lowerCamelCase					:			Any									= list(freq_to_letter_str.items())
				freq_pairs.sort(key=UpperCAmelCase__       ,							reverse=UpperCAmelCase__)

				lowerCamelCase					:			list[str]									= [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Optional[Any]									= get_frequency_order(UpperCAmelCase__)
				lowerCamelCase					:			Optional[Any]									= 0
				for common_letter in ETAOIN[:6]:
								if common_letter in freq_order[:6]:
												match_score += 1

				for uncommon_letter in ETAOIN[-6:]:
								if uncommon_letter in freq_order[-6:]:
												match_score += 1

				return match_score


if __name__ == "__main__":
					import doctest

					doctest.testmod()
