import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig




def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Any	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict				      =    s.rsplit(lowerCAmelCase   , lowerCAmelCase	)
		return new.join(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







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




def       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {}

		SCREAMING_SNAKE_CASE_  :		int				      =    ["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:
								SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    key.replace(f'{group_key}.'   , f'{group_key}.group.'	)

				if "res_path" in key:
						SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    key.replace("res_path."   , "res_path.path."	)

				if key.endswith(".w"	):
						SCREAMING_SNAKE_CASE_  :		int				      =    rreplace(lowerCAmelCase   , ".w"   , ".weight"   , 1	)
				if key.endswith(".b"	):
						SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    rreplace(lowerCAmelCase   , ".b"   , ".bias"   , 1	)

				SCREAMING_SNAKE_CASE_  :		Dict				      =    value.float()

		return upgrade




@torch.no_grad()
def       _snake_case	(   lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Any=None   , lowerCAmelCase							:   Optional[int]=True	):
		"""simple docstring"""







		from dall_e import Encoder

		SCREAMING_SNAKE_CASE_  :		Dict				      =    Encoder()
		if os.path.exists(lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.load(lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.hub.load_state_dict_from_url(lowerCAmelCase	)

		if isinstance(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    ckpt.state_dict()
		encoder.load_state_dict(lowerCAmelCase	)

		if config_path is not None:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    FlavaImageCodebookConfig.from_pretrained(lowerCAmelCase	)
		else:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    FlavaImageCodebookConfig()

		SCREAMING_SNAKE_CASE_  :		str				      =    FlavaImageCodebook(lowerCAmelCase	).eval()
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    encoder.state_dict()

		SCREAMING_SNAKE_CASE_  :		str				      =    upgrade_state_dict(lowerCAmelCase	)
		hf_model.load_state_dict(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    hf_model.state_dict()
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    count_parameters(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    count_parameters(lowerCAmelCase	)

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

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


if __name__ == "__main__":
		__lowerCamelCase					:	int       =  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''')
		__lowerCamelCase					:	List[Any]       =  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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase					:	Dict       =  {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  [
		    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMAEForPreTraining''',
		    '''ViTMAELayer''',
		    '''ViTMAEModel''',
		    '''ViTMAEPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Optional[Any]       =  [
		    '''TFViTMAEForPreTraining''',
		    '''TFViTMAEModel''',
		    '''TFViTMAEPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_mae import (
				    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMAEForPreTraining,
				    ViTMAELayer,
				    ViTMAEModel,
				    ViTMAEPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
