



"""simple docstring"""
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      False
__SCREAMING_SNAKE_CASE  :						Any    							=      True
__SCREAMING_SNAKE_CASE  :						str    							=      False


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[str]    							=      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  :						str    							=      parser.parse_args()

			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      {
			    '''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  :						List[Any]    							=      {
			    '''time_steps''': '''time_proj''',
			    '''mid''': '''mid_block''',
			    '''downsample_blocks''': '''down_blocks''',
			    '''upsample_blocks''': '''up_blocks''',
			}

			__SCREAMING_SNAKE_CASE  :						Any    							=      '''''' 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  :						Any    							=      reader.read()
						__SCREAMING_SNAKE_CASE  :						List[Any]    							=      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  :						str    							=      UNetaDModel(**config)
			else:
						__SCREAMING_SNAKE_CASE  :						int    							=      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  :						Union[str, Any]    							=      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  :						List[Any]    							=      [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
						__SCREAMING_SNAKE_CASE  :						int    							=      [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

						__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      {}
						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  :						Tuple    							=      False
									for key, new_key in key_parameters_to_change.items():
												if not has_changed and param_key.split('''.''')[0] == key:
															__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      param_value
															__SCREAMING_SNAKE_CASE  :						List[Any]    							=      True
									if not has_changed:
												__SCREAMING_SNAKE_CASE  :						int    							=      param_value

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





"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available()):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
			from .pipeline_kandinsky import KandinskyPipeline
			from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
			from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
			from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
			from .text_encoder import MultilingualCLIP
