





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]     )      ->						Tuple:
 # 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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict			,							snake_case__  :Optional[int]     )      ->						str:
 _lowercase							= {}

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

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

  _lowercase							= value.float()

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

 return upgrade

@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any			,							snake_case__  :Dict			,							snake_case__  :Dict			,							snake_case__  :Any=None     )      ->						int:
 if config_path is not None:
  _lowercase							= FlavaConfig.from_pretrained(snake_case__     )
 else:
  _lowercase							= FlavaConfig()

 _lowercase							= FlavaForPreTraining(snake_case__     ).eval()

 _lowercase							= convert_dalle_checkpoint(snake_case__			,							snake_case__			,							save_checkpoint=snake_case__     )

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

 _lowercase							= upgrade_state_dict(snake_case__			,							snake_case__     )
 hf_model.load_state_dict(snake_case__     )
 _lowercase							= hf_model.state_dict()
 _lowercase							= count_parameters(snake_case__     )
 _lowercase							= 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__":
      snake_case 				=       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""")
      snake_case 				=       parser.parse_args()

      convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

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