
"""simple docstring"""




import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint

def lowercase_						(     _snake_case		):
							# 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 lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     {}

							for key, value in state_dict.items():
														if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
																					continue

														SCREAMING_SNAKE_CASE__		: Tuple     =     key.replace("""heads.cmd.mim_head.cls.predictions"""     ,"""mmm_image_head"""		)
														SCREAMING_SNAKE_CASE__		: int     =     key.replace("""heads.cmd.mlm_head.cls.predictions"""     ,"""mmm_text_head"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""heads.cmd.itm_head.cls"""     ,"""itm_head"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""heads.cmd.itm_head.pooler"""     ,"""itm_head.pooler"""		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     key.replace("""heads.cmd.clip_head.logit_scale"""     ,"""flava.logit_scale"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""heads.fairseq_mlm.cls.predictions"""     ,"""mlm_head"""		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     key.replace("""heads.imagenet.mim_head.cls.predictions"""     ,"""mim_head"""		)
														SCREAMING_SNAKE_CASE__		: Dict     =     key.replace("""mm_text_projection"""     ,"""flava.text_to_mm_projection"""		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     key.replace("""mm_image_projection"""     ,"""flava.image_to_mm_projection"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""image_encoder.module"""     ,"""flava.image_model"""		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     key.replace("""text_encoder.module"""     ,"""flava.text_model"""		)
														SCREAMING_SNAKE_CASE__		: Any     =     key.replace("""mm_encoder.module.encoder.cls_token"""     ,"""flava.multimodal_model.cls_token"""		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     key.replace("""mm_encoder.module"""     ,"""flava.multimodal_model"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     key.replace("""text_projection"""     ,"""flava.text_projection"""		)
														SCREAMING_SNAKE_CASE__		: Dict     =     key.replace("""image_projection"""     ,"""flava.image_projection"""		)

														SCREAMING_SNAKE_CASE__		: Dict     =     value.float()

							for key, value in codebook_state_dict.items():
														SCREAMING_SNAKE_CASE__		: List[str]     =     value

							return upgrade

@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case=None		):
							if config_path is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     FlavaConfig.from_pretrained(_snake_case		)
							else:
														SCREAMING_SNAKE_CASE__		: List[str]     =     FlavaConfig()

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     FlavaForPreTraining(_snake_case		).eval()

							SCREAMING_SNAKE_CASE__		: Tuple     =     convert_dalle_checkpoint(_snake_case     ,_snake_case     ,save_checkpoint=_snake_case		)

							if os.path.exists(_snake_case		):
														SCREAMING_SNAKE_CASE__		: int     =     torch.load(_snake_case     ,map_location="""cpu"""		)
							else:
														SCREAMING_SNAKE_CASE__		: Tuple     =     torch.hub.load_state_dict_from_url(_snake_case     ,map_location="""cpu"""		)

							SCREAMING_SNAKE_CASE__		: Dict     =     upgrade_state_dict(_snake_case     ,_snake_case		)
							hf_model.load_state_dict(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Dict     =     hf_model.state_dict()
							SCREAMING_SNAKE_CASE__		: Any     =     count_parameters(_snake_case		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     count_parameters(_snake_case		) + count_parameters(_snake_case		)

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

							hf_model.save_pretrained(_snake_case		)


if __name__ == "__main__":
					UpperCAmelCase__   :					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('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					UpperCAmelCase__   :					int												=				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_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


UpperCAmelCase__   :					Tuple												=				{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					str												=				['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__   :					Any												=				[
					    '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

					UpperCAmelCase__   :					Union[str, Any]												=				_LazyModule(__name__, globals()['__file__'], _import_structure)




