

from typing import TYPE_CHECKING

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


A_       : List[Any]         =			{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Dict         =			['ConditionalDetrFeatureExtractor']
	A_       : Union[str, Any]         =			['ConditionalDetrImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Tuple         =			[
	    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConditionalDetrForObjectDetection',
	    'ConditionalDetrForSegmentation',
	    'ConditionalDetrModel',
	    'ConditionalDetrPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_conditional_detr import (
	    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    ConditionalDetrConfig,
	    ConditionalDetrOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
		from .image_processing_conditional_detr import ConditionalDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_conditional_detr import (
		    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConditionalDetrForObjectDetection,
		    ConditionalDetrForSegmentation,
		    ConditionalDetrModel,
		    ConditionalDetrPreTrainedModel,
		)

else:
	import sys

	A_       : List[Any]         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Optional[Any]					)     ->      Tuple:
	# Initialise PyTorch model
	A__							:    str        =		AlbertConfig.from_json_file(lowercase_					)
	print(f"""Building PyTorch model from configuration: {config}"""					)
	A__							:    List[Any]        =		AlbertForPreTraining(lowercase_					)

	# Load weights from tf checkpoint
	load_tf_weights_in_albert(lowercase_    ,      lowercase_    ,      lowercase_					)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
	torch.save(model.state_dict()    ,      lowercase_					)


if __name__ == "__main__":
	A_       : int         =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	A_       : int         =			parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)



