"""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__      (     _UpperCamelCase    :			Optional[Any]   )   ->			Union[str, Any]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[int]   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     {}

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

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

												snake_case        =     value.float()

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

						return upgrade





@torch.no_grad()
def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[str]=None   )   ->			Union[str, Any]:





						"""simple docstring"""






						if config_path is not None:
												snake_case        =     FlavaConfig.from_pretrained(_UpperCamelCase   )
						else:
												snake_case        =     FlavaConfig()

						snake_case        =     FlavaForPreTraining(_UpperCamelCase   ).eval()

						snake_case        =     convert_dalle_checkpoint(_UpperCamelCase	,     _UpperCamelCase	,     save_checkpoint=_UpperCamelCase   )

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

						snake_case        =     upgrade_state_dict(_UpperCamelCase	,     _UpperCamelCase   )
						hf_model.load_state_dict(_UpperCamelCase   )
						snake_case        =     hf_model.state_dict()
						snake_case        =     count_parameters(_UpperCamelCase   )
						snake_case        =     count_parameters(_UpperCamelCase   ) + count_parameters(_UpperCamelCase   )

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

						hf_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
					SCREAMING_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")
					SCREAMING_SNAKE_CASE__   =					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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

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


