



import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	Dict     ,				_SCREAMING_SNAKE_CASE   :	str				)->Union[str, Any]:
						_lowerCAmelCase 			=  s.rsplit(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
						return new.join(_SCREAMING_SNAKE_CASE				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Tuple				)->Any:
						# encoder.embeddings are double copied in original FLAVA
						return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Dict				)->Optional[Any]:
						_lowerCAmelCase 			=  {}

						_lowerCAmelCase 			=  ['''group_1''', '''group_2''', '''group_3''', '''group_4''']
						for key, value in state_dict.items():
												for group_key in group_keys:
																		if group_key in key:
																								_lowerCAmelCase 			=  key.replace(f'''{group_key}.'''     ,				f'''{group_key}.group.'''				)

												if "res_path" in key:
																		_lowerCAmelCase 			=  key.replace('''res_path.'''     ,				'''res_path.path.'''				)

												if key.endswith('''.w'''				):
																		_lowerCAmelCase 			=  rreplace(_SCREAMING_SNAKE_CASE     ,				'''.w'''     ,				'''.weight'''     ,				1				)
												if key.endswith('''.b'''				):
																		_lowerCAmelCase 			=  rreplace(_SCREAMING_SNAKE_CASE     ,				'''.b'''     ,				'''.bias'''     ,				1				)

												_lowerCAmelCase 			=  value.float()

						return upgrade
@torch.no_grad()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Union[str, Any]     ,				_SCREAMING_SNAKE_CASE   :	Any     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]=None     ,				_SCREAMING_SNAKE_CASE   :	List[str]=True				)->str:
						from dall_e import Encoder

						_lowerCAmelCase 			=  Encoder()
						if os.path.exists(_SCREAMING_SNAKE_CASE				):
												_lowerCAmelCase 			=  torch.load(_SCREAMING_SNAKE_CASE				)
						else:
												_lowerCAmelCase 			=  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE				)

						if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				):
												_lowerCAmelCase 			=  ckpt.state_dict()
						encoder.load_state_dict(_SCREAMING_SNAKE_CASE				)

						if config_path is not None:
												_lowerCAmelCase 			=  FlavaImageCodebookConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
						else:
												_lowerCAmelCase 			=  FlavaImageCodebookConfig()

						_lowerCAmelCase 			=  FlavaImageCodebook(_SCREAMING_SNAKE_CASE				).eval()
						_lowerCAmelCase 			=  encoder.state_dict()

						_lowerCAmelCase 			=  upgrade_state_dict(_SCREAMING_SNAKE_CASE				)
						hf_model.load_state_dict(_SCREAMING_SNAKE_CASE				)
						_lowerCAmelCase 			=  hf_model.state_dict()
						_lowerCAmelCase 			=  count_parameters(_SCREAMING_SNAKE_CASE				)
						_lowerCAmelCase 			=  count_parameters(_SCREAMING_SNAKE_CASE				)

						assert torch.allclose(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				atol=1e-3				)

						if save_checkpoint:
												hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)
						else:
												return hf_state_dict


if __name__ == "__main__":
				UpperCAmelCase_									=							argparse.ArgumentParser()
				parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				UpperCAmelCase_									=							parser.parse_args()

				convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)