



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_:Dict					=						True
SCREAMING_SNAKE_CASE_:int					=						False


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:Optional[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_:List[Any]					=						parser.parse_args()

     SCREAMING_SNAKE_CASE_: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""",
     }

     SCREAMING_SNAKE_CASE_:Dict					=						{
         """time_steps""": """time_proj""",
         """mid""": """mid_block""",
         """downsample_blocks""": """down_blocks""",
         """upsample_blocks""": """up_blocks""",
     }

     SCREAMING_SNAKE_CASE_:str					=						"""""" 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_:str					=						reader.read()
          SCREAMING_SNAKE_CASE_:Union[str, 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_:int					=						UNetaDModel(**config)
     else:
          SCREAMING_SNAKE_CASE_:List[Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
          SCREAMING_SNAKE_CASE_:Tuple					=						class_name(**config)

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

     SCREAMING_SNAKE_CASE_:Dict					=						dict(model.config)

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

          SCREAMING_SNAKE_CASE_:Dict					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
          SCREAMING_SNAKE_CASE_:List[Any]					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

          SCREAMING_SNAKE_CASE_: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_: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_:Dict					=						param_value
                         SCREAMING_SNAKE_CASE_:Any					=						True
               if not has_changed:
                    SCREAMING_SNAKE_CASE_:Union[str, Any]					=						param_value

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







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:Optional[int]					=						{
    """configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
    """tokenization_tapas""": ["""TapasTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[int]					=						[
         """TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TapasForMaskedLM""",
         """TapasForQuestionAnswering""",
         """TapasForSequenceClassification""",
         """TapasModel""",
         """TapasPreTrainedModel""",
         """load_tf_weights_in_tapas""",
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Optional[Any]					=						[
         """TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFTapasForMaskedLM""",
         """TFTapasForQuestionAnswering""",
         """TFTapasForSequenceClassification""",
         """TFTapasModel""",
         """TFTapasPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE_:str					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


