
"""simple docstring"""






from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
				from .features import FeatureType




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :List[str]
			UpperCAmelCase_  :Optional[str]				 =						None
			# Automatically constructed
			UpperCAmelCase_  :ClassVar[str]				 =						"dict"
			UpperCAmelCase_  :ClassVar[Any]				 =						None
			UpperCAmelCase_  :str				 =						field(default="Translation"	, init=A__	, repr=A__							)






			def __call__(					self       )			->     Any:
						return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )






			def  __lowerCAmelCase (					self       )			->     Union["FeatureType", Dict[str, "FeatureType"]]:
						from .features import Value

						return {k: Value("""string"""       ) for k in sorted(self.languages       )}




@dataclass
class 			_SCREAMING_SNAKE_CASE					:
			UpperCAmelCase_  :Optional[List]				 =						None
			UpperCAmelCase_  :Optional[int]				 =						None
			UpperCAmelCase_  :Optional[str]				 =						None
			# Automatically constructed
			UpperCAmelCase_  :ClassVar[str]				 =						"dict"
			UpperCAmelCase_  :ClassVar[Any]				 =						None
			UpperCAmelCase_  :str				 =						field(default="TranslationVariableLanguages"	, init=A__	, repr=A__							)






			def  __lowerCAmelCase (					self       )			->     Dict:
						lowerCAmelCase_    :List[str]			     =  sorted(set(self.languages       )       ) if self.languages else None
						lowerCAmelCase_    :str			     =  len(self.languages       ) if self.languages else None






			def __call__(					self       )			->     int:
						return pa.struct({"""language""": pa.list_(pa.string()       ), """translation""": pa.list_(pa.string()       )}       )






			def  __lowerCAmelCase (					self       ,   __A       )			->     Any:
						lowerCAmelCase_    :str			     =  set(self.languages       )
						if self.languages and set(__A       ) - lang_set:
									raise ValueError(
									    f"""Some languages in example ({", ".join(sorted(set(__A       ) - lang_set       )       )}) are not in valid set ({", ".join(__A       )})."""       )

						# Convert dictionary into tuples, splitting out cases where there are
						# multiple translations for a single language.
						lowerCAmelCase_    :List[Any]			     =  []
						for lang, text in translation_dict.items():
									if isinstance(__A       ,   __A       ):
												translation_tuples.append((lang, text)       )
									else:
												translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
						lowerCAmelCase_     ,       lowerCAmelCase_    :List[Any]			     =  zip(*sorted(__A       )       )

						return {"language": languages, "translation": translations}






			def  __lowerCAmelCase (					self       )			->     Union["FeatureType", Dict[str, "FeatureType"]]:
						from .features import Sequence, Value

						return {
						    "language": Sequence(Value("""string"""       )       ),
						    "translation": Sequence(Value("""string"""       )       ),
						}








import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				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.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    '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',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' 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:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				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'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

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

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

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

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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