


"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




	'''simple docstring'''


	__SCREAMING_SNAKE_CASE		     =       AlbertConfig.from_json_file(lowerCAmelCase_     )
	print(f"""Building PyTorch model from configuration: {config}"""     )
	__SCREAMING_SNAKE_CASE		     =       AlbertForPreTraining(lowerCAmelCase_     )

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

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


if __name__ == "__main__":
		a__       :							Tuple        =  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__       :							Dict        =  parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)




"""simple docstring"""







import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					return EnvironmentCommand()




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       ArgumentParser     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       parser.add_parser("env"     )
						download_parser.set_defaults(func=UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       List[Any]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       huggingface_hub.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						__SCREAMING_SNAKE_CASE		     =       "NA"
						if is_torch_available():
											import torch

											__SCREAMING_SNAKE_CASE		     =       torch.__version__
											__SCREAMING_SNAKE_CASE		     =       torch.cuda.is_available()

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_transformers_available():
											import transformers

											__SCREAMING_SNAKE_CASE		     =       transformers.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_accelerate_available():
											import accelerate

											__SCREAMING_SNAKE_CASE		     =       accelerate.__version__

						__SCREAMING_SNAKE_CASE		     =       "not installed"
						if is_xformers_available():
											import xformers

											__SCREAMING_SNAKE_CASE		     =       xformers.__version__

						__SCREAMING_SNAKE_CASE		     =       {
						    "`diffusers` version": version,
						    "Platform": platform.platform(),
						    "Python version": platform.python_version(),
						    "PyTorch version (GPU?)": F"""{pt_version} ({pt_cuda_available})""",
						    "Huggingface_hub version": hub_version,
						    "Transformers version": transformers_version,
						    "Accelerate version": accelerate_version,
						    "xFormers version": xformers_version,
						    "Using GPU in script?": "<fill in>",
						    "Using distributed or parallel set-up in script?": "<fill in>",
						}

						print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"     )
						print(self.format_dict(UpperCAmelCase__     )     )

						return info




	@staticmethod
	def 						UpperCAmelCase_   (				UpperCAmelCase__    :       Optional[Any]     ) ->			str:
						return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]     ) + "\n"
