
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()


def  A			(   lowercase       ,			lowercase       ,			lowercase       ,			lowercase       ,			lowercase					)  ->    Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase            =	BertConfig.from_pretrained(
		    'bert-large-cased'       ,			vocab_size=lowercase       ,			max_position_embeddings=512       ,			is_decoder=lowercase       ,			add_cross_attention=lowercase       ,			)
		UpperCamelCase            =	bert_config.to_dict()
		del bert_config_dict["type_vocab_size"]
		UpperCamelCase            =	BertGenerationConfig(**lowercase					)
		if is_encoder:
				UpperCamelCase            =	BertGenerationEncoder(lowercase					)
		else:
				UpperCamelCase            =	BertGenerationDecoder(lowercase					)
		print(f'''Building PyTorch model from configuration: {config}'''					)

		# Load weights from tf checkpoint
		load_tf_weights_in_bert_generation(
		    lowercase       ,			lowercase       ,			model_class='bert'       ,			is_encoder_named_decoder=lowercase       ,			is_encoder=lowercase       ,			)

		# Save pytorch-model
		print(f'''Save PyTorch model and config to {pytorch_dump_path}'''					)
		model.save_pretrained(lowercase					)


if __name__ == "__main__":
					_UpperCAmelCase   :					Optional[Any]    =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
					)
					parser.add_argument(
					    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
					)
					parser.add_argument(
					    "--is_encoder_named_decoder",
					    action="store_true",
					    help="If decoder has to be renamed to encoder in PyTorch model.",
					)
					parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
					parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
					_UpperCAmelCase   :					List[Any]    =			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_hub_path,
					    args.pytorch_dump_path,
					    args.is_encoder_named_decoder,
					    args.vocab_size,
					    is_encoder=args.is_encoder,
					)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase   :					int    =			{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[Any]    =			["LevitFeatureExtractor"]
					_UpperCAmelCase   :					Optional[int]    =			["LevitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[int]    =			[
					    "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LevitForImageClassification",
					    "LevitForImageClassificationWithTeacher",
					    "LevitModel",
					    "LevitPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_levit import LevitFeatureExtractor
										from .image_processing_levit import LevitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_levit import (
										    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LevitForImageClassification,
										    LevitForImageClassificationWithTeacher,
										    LevitModel,
										    LevitPreTrainedModel,
										)
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
