




'''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__(			__UpperCamelCase:					Any    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						{}

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

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

            SCREAMING_SNAKE_CASE   :      Dict    					=						value.float()

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

      return upgrade




@torch.no_grad()
def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=None    ):

      """simple docstring"""






      if config_path is not None:
            SCREAMING_SNAKE_CASE   :      int    					=						FlavaConfig.from_pretrained(__UpperCamelCase    )
      else:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						FlavaConfig()

      SCREAMING_SNAKE_CASE   :      str    					=						FlavaForPreTraining(__UpperCamelCase    ).eval()

      SCREAMING_SNAKE_CASE   :      int    					=						convert_dalle_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,save_checkpoint=__UpperCamelCase    )

      if os.path.exists(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      int    					=						torch.load(__UpperCamelCase      ,map_location='cpu'    )
      else:
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.hub.load_state_dict_from_url(__UpperCamelCase      ,map_location='cpu'    )

      SCREAMING_SNAKE_CASE   :      int    					=						upgrade_state_dict(__UpperCamelCase      ,__UpperCamelCase    )
      hf_model.load_state_dict(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hf_model.state_dict()
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						count_parameters(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      int    					=						count_parameters(__UpperCamelCase    ) + count_parameters(__UpperCamelCase    )

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

      hf_model.save_pretrained(__UpperCamelCase    )


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("--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_    	=				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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    	=				{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				["RoFormerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RoFormerForCausalLM",
          "RoFormerForMaskedLM",
          "RoFormerForMultipleChoice",
          "RoFormerForQuestionAnswering",
          "RoFormerForSequenceClassification",
          "RoFormerForTokenClassification",
          "RoFormerLayer",
          "RoFormerModel",
          "RoFormerPreTrainedModel",
          "load_tf_weights_in_roformer",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRoFormerForCausalLM",
          "TFRoFormerForMaskedLM",
          "TFRoFormerForMultipleChoice",
          "TFRoFormerForQuestionAnswering",
          "TFRoFormerForSequenceClassification",
          "TFRoFormerForTokenClassification",
          "TFRoFormerLayer",
          "TFRoFormerModel",
          "TFRoFormerPreTrainedModel",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_    	=				[
          "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxRoFormerForMaskedLM",
          "FlaxRoFormerForMultipleChoice",
          "FlaxRoFormerForQuestionAnswering",
          "FlaxRoFormerForSequenceClassification",
          "FlaxRoFormerForTokenClassification",
          "FlaxRoFormerModel",
          "FlaxRoFormerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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


