



"""simple docstring"""
import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Dict						=			s.rsplit(_lowercase,					_lowercase	)
						return new.join(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :Tuple						=			{}

						snake_case_ :Union[str, Any]						=			["""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:
																								snake_case_ :int						=			key.replace(f"""{group_key}.""",					f"""{group_key}.group."""	)

												if "res_path" in key:
																		snake_case_ :Tuple						=			key.replace("""res_path.""",					"""res_path.path."""	)

												if key.endswith(""".w"""	):
																		snake_case_ :List[str]						=			rreplace(_lowercase,					""".w""",					""".weight""",					1	)
												if key.endswith(""".b"""	):
																		snake_case_ :Dict						=			rreplace(_lowercase,					""".b""",					""".bias""",					1	)

												snake_case_ :Union[str, Any]						=			value.float()

						return upgrade
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase=None,					_lowercase=True	):







						'''simple docstring'''


						from dall_e import Encoder

						snake_case_ :Tuple						=			Encoder()
						if os.path.exists(_lowercase	):
												snake_case_ :Any						=			torch.load(_lowercase	)
						else:
												snake_case_ :str						=			torch.hub.load_state_dict_from_url(_lowercase	)

						if isinstance(_lowercase,					_lowercase	):
												snake_case_ :str						=			ckpt.state_dict()
						encoder.load_state_dict(_lowercase	)

						if config_path is not None:
												snake_case_ :Tuple						=			FlavaImageCodebookConfig.from_pretrained(_lowercase	)
						else:
												snake_case_ :int						=			FlavaImageCodebookConfig()

						snake_case_ :List[Any]						=			FlavaImageCodebook(_lowercase	).eval()
						snake_case_ :Union[str, Any]						=			encoder.state_dict()

						snake_case_ :List[Any]						=			upgrade_state_dict(_lowercase	)
						hf_model.load_state_dict(_lowercase	)
						snake_case_ :str						=			hf_model.state_dict()
						snake_case_ :Tuple						=			count_parameters(_lowercase	)
						snake_case_ :Optional[Any]						=			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__":
						__a      =				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")
						__a      =				parser.parse_args()

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





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a      =				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				["Mask2FormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      =				[
						    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "Mask2FormerForUniversalSegmentation",
						    "Mask2FormerModel",
						    "Mask2FormerPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_maskaformer import MaskaFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskaformer import (
												    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskaFormerForUniversalSegmentation,
												    MaskaFormerModel,
												    MaskaFormerPreTrainedModel,
												)


else:
						import sys

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