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     ):
    '''simple docstring'''







    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  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		{}

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

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

        __UpperCamelCase					:Optional[Any]        =		value.float()

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

    return upgrade



@torch.no_grad()
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=None     ):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase					:Optional[int]        =		FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:List[Any]        =		FlavaConfig()

    __UpperCamelCase					:Optional[Any]        =		FlavaForPreTraining(SCREAMING_SNAKE_CASE     ).eval()

    __UpperCamelCase					:Optional[int]        =		convert_dalle_checkpoint(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      save_checkpoint=SCREAMING_SNAKE_CASE     )

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

    __UpperCamelCase					:Union[str, Any]        =		upgrade_state_dict(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    hf_model.load_state_dict(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:List[Any]        =		hf_model.state_dict()
    __UpperCamelCase					:List[str]        =		count_parameters(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:List[str]        =		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__":
    __lowercase										= 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''')
    __lowercase										= 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_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowercase										= {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMv3ForQuestionAnswering''',
        '''LayoutLMv3ForSequenceClassification''',
        '''LayoutLMv3ForTokenClassification''',
        '''LayoutLMv3Model''',
        '''LayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMv3ForQuestionAnswering''',
        '''TFLayoutLMv3ForSequenceClassification''',
        '''TFLayoutLMv3ForTokenClassification''',
        '''TFLayoutLMv3Model''',
        '''TFLayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3FeatureExtractor''']
    __lowercase										= ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
    from .configuration_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMvaConfig,
        LayoutLMvaOnnxConfig,
    )
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlmva import (
            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
            TFLayoutLMvaModel,
            TFLayoutLMvaPreTrainedModel,
        )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
        from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
    import sys

    __lowercase										= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





