


"""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__ (				__lowercase   )	->	int:
				# 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      a__ (				__lowercase     ,			__lowercase   )	->	int:
				_A					  =			{}

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

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

								_A					  =			value.float()

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

				return upgrade






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase=None   )	->	Any:
				if config_path is not None:
								_A					  =			FlavaConfig.from_pretrained(__lowercase   )
				else:
								_A					  =			FlavaConfig()

				_A					  =			FlavaForPreTraining(__lowercase   ).eval()

				_A					  =			convert_dalle_checkpoint(__lowercase     ,			__lowercase     ,			save_checkpoint=__lowercase   )

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

				_A					  =			upgrade_state_dict(__lowercase     ,			__lowercase   )
				hf_model.load_state_dict(__lowercase   )
				_A					  =			hf_model.state_dict()
				_A					  =			count_parameters(__lowercase   )
				_A					  =			count_parameters(__lowercase   ) + count_parameters(__lowercase   )

				assert torch.allclose(__lowercase     ,			__lowercase     ,			atol=1E-3   )

				hf_model.save_pretrained(__lowercase   )


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)



"""simple docstring"""


from typing import TYPE_CHECKING

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


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

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