'''simple docstring'''

from typing import TYPE_CHECKING

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


lowerCAmelCase_ : str						  =      {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Any						  =      ["AlbertTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : str						  =      ["AlbertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "AlbertForMaskedLM",
           "AlbertForMultipleChoice",
           "AlbertForPreTraining",
           "AlbertForQuestionAnswering",
           "AlbertForSequenceClassification",
           "AlbertForTokenClassification",
           "AlbertModel",
           "AlbertPreTrainedModel",
           "load_tf_weights_in_albert",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFAlbertForMaskedLM",
           "TFAlbertForMultipleChoice",
           "TFAlbertForPreTraining",
           "TFAlbertForQuestionAnswering",
           "TFAlbertForSequenceClassification",
           "TFAlbertForTokenClassification",
           "TFAlbertMainLayer",
           "TFAlbertModel",
           "TFAlbertPreTrainedModel",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : List[Any]						  =      [
           "FlaxAlbertForMaskedLM",
           "FlaxAlbertForMultipleChoice",
           "FlaxAlbertForPreTraining",
           "FlaxAlbertForQuestionAnswering",
           "FlaxAlbertForSequenceClassification",
           "FlaxAlbertForTokenClassification",
           "FlaxAlbertModel",
           "FlaxAlbertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert import AlbertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_albert_fast import AlbertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_albert import (
                  ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  AlbertForMaskedLM,
                  AlbertForMultipleChoice,
                  AlbertForPreTraining,
                  AlbertForQuestionAnswering,
                  AlbertForSequenceClassification,
                  AlbertForTokenClassification,
                  AlbertModel,
                  AlbertPreTrainedModel,
                  load_tf_weights_in_albert,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_albert import (
                  TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFAlbertForMaskedLM,
                  TFAlbertForMultipleChoice,
                  TFAlbertForPreTraining,
                  TFAlbertForQuestionAnswering,
                  TFAlbertForSequenceClassification,
                  TFAlbertForTokenClassification,
                  TFAlbertMainLayer,
                  TFAlbertModel,
                  TFAlbertPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_albert import (
                  FlaxAlbertForMaskedLM,
                  FlaxAlbertForMultipleChoice,
                  FlaxAlbertForPreTraining,
                  FlaxAlbertForQuestionAnswering,
                  FlaxAlbertForSequenceClassification,
                  FlaxAlbertForTokenClassification,
                  FlaxAlbertModel,
                  FlaxAlbertPreTrainedModel,
              )
else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







'''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 __a (	__lowerCamelCase			:						List[Any]				)	->							Tuple:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							List[str]:
      '''simple docstring'''


      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 __a (	__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple=None				)	->							Optional[int]:
      '''simple docstring'''


      if config_path is not None:
            lowercase_     =      FlavaConfig.from_pretrained(__lowerCamelCase				)
      else:
            lowercase_     =      FlavaConfig()

      lowercase_     =      FlavaForPreTraining(__lowerCamelCase				).eval()

      lowercase_     =      convert_dalle_checkpoint(__lowerCamelCase ,		__lowerCamelCase ,		save_checkpoint=__lowerCamelCase				)

      if os.path.exists(__lowerCamelCase				):
            lowercase_     =      torch.load(__lowerCamelCase ,		map_location="cpu"				)
      else:
            lowercase_     =      torch.hub.load_state_dict_from_url(__lowerCamelCase ,		map_location="cpu"				)

      lowercase_     =      upgrade_state_dict(__lowerCamelCase ,		__lowerCamelCase				)
      hf_model.load_state_dict(__lowerCamelCase				)
      lowercase_     =      hf_model.state_dict()
      lowercase_     =      count_parameters(__lowerCamelCase				)
      lowercase_     =      count_parameters(__lowerCamelCase				) + count_parameters(__lowerCamelCase				)

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

      hf_model.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      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")
       lowerCAmelCase_ : List[Any]						  =      parser.parse_args()

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






