
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__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


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






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						Any       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			{}

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

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

						lowerCAmelCase_      			=			value.float()

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

			return upgrade






@torch.no_grad()
def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Union[str, Any]					,							__lowerCAmelCase						:						Any					,							__lowerCAmelCase						:						Dict=None       ):




			"""simple docstring"""


			if config_path is not None:
						lowerCAmelCase_      			=			FlavaConfig.from_pretrained(__lowerCAmelCase       )
			else:
						lowerCAmelCase_      			=			FlavaConfig()

			lowerCAmelCase_      			=			FlavaForPreTraining(__lowerCAmelCase       ).eval()

			lowerCAmelCase_      			=			convert_dalle_checkpoint(__lowerCAmelCase					,							__lowerCAmelCase					,							save_checkpoint=__lowerCAmelCase       )

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

			lowerCAmelCase_      			=			upgrade_state_dict(__lowerCAmelCase					,							__lowerCAmelCase       )
			hf_model.load_state_dict(__lowerCAmelCase       )
			lowerCAmelCase_      			=			hf_model.state_dict()
			lowerCAmelCase_      			=			count_parameters(__lowerCAmelCase       )
			lowerCAmelCase_      			=			count_parameters(__lowerCAmelCase       ) + count_parameters(__lowerCAmelCase       )

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

			hf_model.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
			_A     =  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     =  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_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

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



