





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__SCREAMING_SNAKE_CASE							:    Tuple          =False
__SCREAMING_SNAKE_CASE							:    int          =True
__SCREAMING_SNAKE_CASE							:    Tuple          =False


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE							:    Any          =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							:    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							:    Union[str, Any]          ={
					    '''time_steps''': '''time_proj''',
					    '''mid''': '''mid_block''',
					    '''downsample_blocks''': '''down_blocks''',
					    '''upsample_blocks''': '''up_blocks''',
					}

					__SCREAMING_SNAKE_CASE							:    Optional[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							:    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							:    List[str]          =UNetaDModel(**config)
					else:
										__SCREAMING_SNAKE_CASE							:    Tuple          =UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
										__SCREAMING_SNAKE_CASE							:    Union[str, Any]          =class_name(**config)

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

					__SCREAMING_SNAKE_CASE							:    Tuple          =dict(model.config)

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

										__SCREAMING_SNAKE_CASE							:    List[str]          =[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
										__SCREAMING_SNAKE_CASE							:    Dict          =[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							:    Union[str, 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							:    Optional[int]          =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[Any]          =param_value
																									__SCREAMING_SNAKE_CASE							:    int          =True
															if not has_changed:
																				__SCREAMING_SNAKE_CASE							:    Dict          =param_value

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







import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Any						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Tuple						)	->    List[Any]:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]						)	->    Any:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase					, split=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)
					assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type"""					, [str, list]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]						)	->    Any:
					if issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =text_path
					elif issubclass(__lowerCamelCase					, __lowerCamelCase						):
										__lowerCAmelCase						 =[text_path]
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_dataset(__lowerCamelCase					, __lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     str=("train",)						)	->    Dict:
					assert isinstance(__lowerCamelCase					, __lowerCamelCase						)
					for split in splits:
										__lowerCAmelCase						 =dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory"""					, [False, True]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]						)	->    Dict:
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, cache_dir=__lowerCamelCase					, keep_in_memory=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize(
    """features"""					, [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]					, )
def        __lowerCAmelCase		(	__lowerCamelCase						:     Any					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Union[str, Any]						)	->    Tuple:
					__lowerCAmelCase						 =tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =features.copy() if features else default_expected_features
					__lowerCAmelCase						 =(
					    Features({feature: Value(__lowerCamelCase						) for feature, dtype in features.items()}						) if features is not None else None
					)
					__lowerCAmelCase						 =TextDatasetReader({"""train""": text_path}					, features=__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase						)






@pytest.mark.parametrize("""split"""					, [None, NamedSplit("""train"""						), """train""", """test"""]						)
def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Any						)	->    int:
					if split:
										__lowerCAmelCase						 ={split: text_path}
					else:
										__lowerCAmelCase						 ="""train"""
										__lowerCAmelCase						 ={"""train""": text_path, """test""": text_path}
					__lowerCAmelCase						 =tmp_path / """cache"""
					__lowerCAmelCase						 ={"""text""": """string"""}
					__lowerCAmelCase						 =TextDatasetReader(__lowerCamelCase					, cache_dir=__lowerCamelCase						).read()
					_check_text_datasetdict(__lowerCamelCase					, __lowerCamelCase					, splits=list(path.keys()						)						)
					assert all(dataset[split].split == split for split in path.keys()						)




