



'''simple docstring'''

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowerCAmelCase									=   False
__lowerCAmelCase									=   True
__lowerCAmelCase									=   False


if __name__ == "__main__":
							__lowerCAmelCase									=   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.''')

							__lowerCAmelCase									=   parser.parse_args()

							__lowerCAmelCase									=   {
							    '''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''',
							}

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

							__lowerCAmelCase									=   '''''' 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:
														__lowerCAmelCase									=   reader.read()
														__lowerCAmelCase									=   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'''):
														__lowerCAmelCase									=   UNetaDModel(**config)
							else:
														__lowerCAmelCase									=   UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
														__lowerCAmelCase									=   class_name(**config)

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

							__lowerCAmelCase									=   dict(model.config)

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

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

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

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

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









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




