
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase      =logging.getLogger()




@unittest.skip('''Temporarily disable the doc tests.''' )
@require_torch
@require_tf
@slow
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = None						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]				=				[file for file in os.listdir(lowerCAmelCase_  ) if os.path.isfile(os.path.join(lowerCAmelCase_						,						lowerCAmelCase_  )  )]

           if identifier is not None:
                 UpperCamelCase__    :   List[Any]				=				[file for file in files if identifier in file]

           if n_identifier is not None:
                 if isinstance(lowerCAmelCase_						,						lowerCAmelCase_  ):
                       for n_ in n_identifier:
                             UpperCamelCase__    :   str				=				[file for file in files if n_ not in file]
                 else:
                       UpperCamelCase__    :   int				=				[file for file in files if n_identifier not in file]

           UpperCamelCase__    :   int				=				ignore_files or []
           ignore_files.append('''__init__.py'''  )
           UpperCamelCase__    :   Union[str, Any]				=				[file for file in files if file not in ignore_files]

           for file in files:
                 # Open all files
                 print('''Testing'''						,						lowerCAmelCase_  )

                 if only_modules:
                       UpperCamelCase__    :   Optional[int]				=				file.split('''.'''  )[0]
                       try:
                             UpperCamelCase__    :   List[str]				=				getattr(lowerCAmelCase_						,						lowerCAmelCase_  )
                             UpperCamelCase__    :   Dict				=				doctest.DocTestSuite(lowerCAmelCase_  )
                             UpperCamelCase__    :   List[Any]				=				unittest.TextTestRunner().run(lowerCAmelCase_  )
                             self.assertIs(len(result.failures  )						,						0  )
                       except AttributeError:
                             logger.info(F'''{module_identifier} is not a module.'''  )
                 else:
                       UpperCamelCase__    :   Dict				=				doctest.testfile(str('''..''' / directory / file  )						,						optionflags=doctest.ELLIPSIS  )
                       self.assertIs(result.failed						,						0  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Tuple				=				Path('''src/transformers'''  )
           UpperCamelCase__    :   List[str]				=				'''modeling'''
           UpperCamelCase__    :   Any				=				[
               '''modeling_ctrl.py''',
               '''modeling_tf_ctrl.py''',
           ]
           self.analyze_directory(lowerCAmelCase_						,						identifier=lowerCAmelCase_						,						ignore_files=lowerCAmelCase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]				=				Path('''src/transformers'''  )
           UpperCamelCase__    :   List[Any]				=				'''tokenization'''
           self.analyze_directory(lowerCAmelCase_						,						identifier=lowerCAmelCase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				Path('''src/transformers'''  )
           UpperCamelCase__    :   Dict				=				'''configuration'''
           self.analyze_directory(lowerCAmelCase_						,						identifier=lowerCAmelCase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Dict				=				Path('''src/transformers'''  )
           UpperCamelCase__    :   List[Any]				=				['''configuration''', '''modeling''', '''tokenization''']
           self.analyze_directory(lowerCAmelCase_						,						n_identifier=lowerCAmelCase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]				=				Path('''docs/source'''  )
           UpperCamelCase__    :   Dict				=				['''favicon.ico''']
           self.analyze_directory(lowerCAmelCase_						,						ignore_files=lowerCAmelCase_						,						only_modules=lowerCAmelCase_  )




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))


