


from typing import TYPE_CHECKING

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


snake_case__     							= {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= ['DetrFeatureExtractor']
			snake_case__     							= ['DetrImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DetrForObjectDetection',
			    'DetrForSegmentation',
			    'DetrModel',
			    'DetrPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_detr import DetrFeatureExtractor
						from .image_processing_detr import DetrImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_detr import (
						    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DetrForObjectDetection,
						    DetrForSegmentation,
						    DetrModel,
						    DetrPreTrainedModel,
						)

else:
			import sys

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






import argparse

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


logging.set_verbosity_info()
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :List[Any]      ,						lowercase__  :str      ,						lowercase__  :List[str]      ,						lowercase__  :List[Any]				)       ->  List[Any]:
						# Initialise PyTorch model
						__magic_name__				:							Any				=      BertConfig.from_pretrained(
						    'bert-large-cased'      ,						vocab_size=lowercase__      ,						max_position_embeddings=5_1_2      ,						is_decoder=lowercase__      ,						add_cross_attention=lowercase__      ,						)
						__magic_name__				:							Optional[int]				=      bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__				:							Optional[int]				=      BertGenerationConfig(**lowercase__				)
						if is_encoder:
												__magic_name__				:							Dict				=      BertGenerationEncoder(lowercase__				)
						else:
												__magic_name__				:							str				=      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__":
			snake_case__     							= 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=5_0358, type=int, help='Vocab size of model')
			snake_case__     							= 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,
			)


