

'''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__		(    SCREAMING_SNAKE_CASE   : str    ):
  # 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Optional[int]    ):
  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__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Any=None    ):
  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					:			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')
  _a					:			str							=    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_tf_available, is_torch_available


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

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


