





import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[int]  				=     s.rsplit(lowercase				,       lowercase				)
							return new.join(lowercase				)





def  __magic_name__  (				lowercase				)				-> Optional[Any]:

							"""simple docstring"""



							return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()				)





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



							lowercase_	:   List[str]  				=     {}

							lowercase_	:   str  				=     ["""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:
																												lowercase_	:   int  				=     key.replace(f"""{group_key}."""				,       f"""{group_key}.group."""				)

														if "res_path" in key:
																					lowercase_	:   str  				=     key.replace("""res_path."""				,       """res_path.path."""				)

														if key.endswith(""".w"""				):
																					lowercase_	:   Union[str, Any]  				=     rreplace(lowercase				,       """.w"""				,       """.weight"""				,       1				)
														if key.endswith(""".b"""				):
																					lowercase_	:   int  				=     rreplace(lowercase				,       """.b"""				,       """.bias"""				,       1				)

														lowercase_	:   Optional[int]  				=     value.float()

							return upgrade





@torch.no_grad()
def  __magic_name__  (				lowercase				,       lowercase				,       lowercase=None				,       lowercase=True				)				-> int:

							"""simple docstring"""



							from dall_e import Encoder

							lowercase_	:   Tuple  				=     Encoder()
							if os.path.exists(lowercase				):
														lowercase_	:   List[Any]  				=     torch.load(lowercase				)
							else:
														lowercase_	:   List[str]  				=     torch.hub.load_state_dict_from_url(lowercase				)

							if isinstance(lowercase				,       lowercase				):
														lowercase_	:   Dict  				=     ckpt.state_dict()
							encoder.load_state_dict(lowercase				)

							if config_path is not None:
														lowercase_	:   Optional[Any]  				=     FlavaImageCodebookConfig.from_pretrained(lowercase				)
							else:
														lowercase_	:   str  				=     FlavaImageCodebookConfig()

							lowercase_	:   List[str]  				=     FlavaImageCodebook(lowercase				).eval()
							lowercase_	:   str  				=     encoder.state_dict()

							lowercase_	:   Optional[int]  				=     upgrade_state_dict(lowercase				)
							hf_model.load_state_dict(lowercase				)
							lowercase_	:   Tuple  				=     hf_model.state_dict()
							lowercase_	:   List[str]  				=     count_parameters(lowercase				)
							lowercase_	:   int  				=     count_parameters(lowercase				)

							assert torch.allclose(lowercase				,       lowercase				,       atol=1E-3				)

							if save_checkpoint:
														hf_model.save_pretrained(lowercase				)
							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_torch_available, is_vision_available


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

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


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

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