
"""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 				lowerCamelCase__		(      __snake_case     )							->		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 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  {}

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

										_UpperCamelCase										=  key.replace('''heads.cmd.mim_head.cls.predictions''',					'''mmm_image_head'''     )
										_UpperCamelCase										=  key.replace('''heads.cmd.mlm_head.cls.predictions''',					'''mmm_text_head'''     )
										_UpperCamelCase										=  key.replace('''heads.cmd.itm_head.cls''',					'''itm_head'''     )
										_UpperCamelCase										=  key.replace('''heads.cmd.itm_head.pooler''',					'''itm_head.pooler'''     )
										_UpperCamelCase										=  key.replace('''heads.cmd.clip_head.logit_scale''',					'''flava.logit_scale'''     )
										_UpperCamelCase										=  key.replace('''heads.fairseq_mlm.cls.predictions''',					'''mlm_head'''     )
										_UpperCamelCase										=  key.replace('''heads.imagenet.mim_head.cls.predictions''',					'''mim_head'''     )
										_UpperCamelCase										=  key.replace('''mm_text_projection''',					'''flava.text_to_mm_projection'''     )
										_UpperCamelCase										=  key.replace('''mm_image_projection''',					'''flava.image_to_mm_projection'''     )
										_UpperCamelCase										=  key.replace('''image_encoder.module''',					'''flava.image_model'''     )
										_UpperCamelCase										=  key.replace('''text_encoder.module''',					'''flava.text_model'''     )
										_UpperCamelCase										=  key.replace('''mm_encoder.module.encoder.cls_token''',					'''flava.multimodal_model.cls_token'''     )
										_UpperCamelCase										=  key.replace('''mm_encoder.module''',					'''flava.multimodal_model'''     )
										_UpperCamelCase										=  key.replace('''text_projection''',					'''flava.text_projection'''     )
										_UpperCamelCase										=  key.replace('''image_projection''',					'''flava.image_projection'''     )

										_UpperCamelCase										=  value.float()

					for key, value in codebook_state_dict.items():
										_UpperCamelCase										=  value

					return upgrade



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case=None     )							->		int:


					"""simple docstring"""





					if config_path is not None:
										_UpperCamelCase										=  FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
					else:
										_UpperCamelCase										=  FlavaConfig()

					_UpperCamelCase										=  FlavaForPreTraining(SCREAMING_SNAKE_CASE_     ).eval()

					_UpperCamelCase										=  convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					save_checkpoint=SCREAMING_SNAKE_CASE_     )

					if os.path.exists(SCREAMING_SNAKE_CASE_     ):
										_UpperCamelCase										=  torch.load(SCREAMING_SNAKE_CASE_,					map_location='''cpu'''     )
					else:
										_UpperCamelCase										=  torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_,					map_location='''cpu'''     )

					_UpperCamelCase										=  upgrade_state_dict(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_     )
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_     )
					_UpperCamelCase										=  hf_model.state_dict()
					_UpperCamelCase										=  count_parameters(SCREAMING_SNAKE_CASE_     )
					_UpperCamelCase										=  count_parameters(SCREAMING_SNAKE_CASE_     ) + count_parameters(SCREAMING_SNAKE_CASE_     )

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

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_     )


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("""--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""")
				_a														=		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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_a														=		{
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TrOCRForCausalLM""",
				    """TrOCRPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
				from .processing_trocr import TrOCRProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
				import sys

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






