





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_UpperCAmelCase       : str			     =       False
_UpperCAmelCase       : List[Any]			     =       True
_UpperCAmelCase       : str			     =       False


if __name__ == "__main__":
    _UpperCAmelCase       : 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.""")

    _UpperCAmelCase       : Optional[Any]			     =       parser.parse_args()

    _UpperCAmelCase       : List[str]			     =       {
        """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""",
    }

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

    _UpperCAmelCase       : 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:
        _UpperCAmelCase       : str			     =       reader.read()
        _UpperCAmelCase       : Tuple			     =       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"""):
        _UpperCAmelCase       : Optional[int]			     =       UNetaDModel(**config)
    else:
        _UpperCAmelCase       : Tuple			     =       UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
        _UpperCAmelCase       : str			     =       class_name(**config)

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

    _UpperCAmelCase       : List[Any]			     =       dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                _UpperCAmelCase       : Union[str, Any]			     =       config[key]
                del config[key]

        _UpperCAmelCase       : Union[str, Any]			     =       [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
        _UpperCAmelCase       : Any			     =       [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

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

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












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.activations import gelu_new, gelu_python, get_activation

@require_torch
class      lowerCAmelCase							(      unittest.TestCase    ):
 def   A_			(   self						:   Tuple			)    ->			int:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   Dict      				=	get_activation('gelu'			)
     self.assertTrue(torch.allclose(gelu_python(UpperCAmelCase			)    ,  torch_builtin(UpperCAmelCase			)			)			)
     self.assertFalse(torch.allclose(gelu_python(UpperCAmelCase			)    ,  gelu_new(UpperCAmelCase			)			)			)
 def   A_			(   self						:   Dict			)    ->			str:
     lowerCamelCase__					:   Optional[int]      				=	torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]			)
     lowerCamelCase__					:   str      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Tuple      				=	get_activation('gelu_10'			)

     lowerCamelCase__					:   Tuple      				=	torch_builtin(UpperCAmelCase			)
     lowerCamelCase__					:   List[str]      				=	geluaa(UpperCAmelCase			)

     lowerCamelCase__					:   Tuple      				=	torch.where(y_gelu_aa < 1_0.0    ,  1    ,  0			)

     self.assertTrue(torch.max(UpperCAmelCase			).item() == 1_0.0			)
     self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,  y_gelu_aa * clipped_mask			)			)
 def   A_			(   self						:   str			)    ->			List[str]:
     get_activation('gelu'			)
     get_activation('gelu_10'			)
     get_activation('gelu_fast'			)
     get_activation('gelu_new'			)
     get_activation('gelu_python'			)
     get_activation('gelu_pytorch_tanh'			)
     get_activation('linear'			)
     get_activation('mish'			)
     get_activation('quick_gelu'			)
     get_activation('relu'			)
     get_activation('sigmoid'			)
     get_activation('silu'			)
     get_activation('swish'			)
     get_activation('tanh'			)
     with self.assertRaises(UpperCAmelCase			):
         get_activation('bogus'			)
     with self.assertRaises(UpperCAmelCase			):
         get_activation(UpperCAmelCase			)







 def   A_			(   self						:   Any			)    ->			Optional[Any]:
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     lowerCamelCase__					:   Union[str, Any]      				=	1
     lowerCamelCase__					:   Optional[Any]      				=	get_activation('gelu'			)
     self.assertEqual(acta.a    ,  1			)
     with self.assertRaises(UpperCAmelCase			):
         lowerCamelCase__					:   str      				=	acta.a





