


"""simple docstring"""
import argparse

import torch

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


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      Union[str, Any]     ,   _snake_case						:      Dict     ,   _snake_case						:      Union[str, Any]     )     ->Optional[Any]:
	"""simple docstring"""





	__snake_case	:						Optional[Any]						=				AlbertConfig.from_json_file(_snake_case     )
	print(f"""Building PyTorch model from configuration: {config}"""     )
	__snake_case	:						Tuple						=				AlbertForPreTraining(_snake_case     )

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Optional[Any]									=				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."""
			)
			SCREAMING_SNAKE_CASE :    Union[str, Any]									=				parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)







from typing import TYPE_CHECKING

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


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

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


