
"""simple docstring"""

import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def 						UpperCAmelCase   (						a_     ):





		'''simple docstring'''


		return EnvironmentCommand()






class 			_lowercase  (							__UpperCAmelCase						):




	@staticmethod
	def      _UpperCamelCase       (					UpperCAmelCase_   )						->				Optional[Any]:
			lowerCamelCase	:     int            =       parser.add_parser('env'						,					help='Print relevant system environment info.'   )
			download_parser.set_defaults(func=UpperCAmelCase_   )




	def      _UpperCamelCase       (					self   )						->				int:
			lowerCamelCase	:     Optional[int]            =       {
			    '`datasets` version': version,
			    'Platform': platform.platform(),
			    'Python version': platform.python_version(),
			    'Huggingface_hub version': huggingface_hub.__version__,
			    'PyArrow version': pyarrow.__version__,
			    'Pandas version': pandas.__version__,
			}

			print('\nCopy-and-paste the text below in your GitHub issue.\n'   )
			print(self.format_dict(UpperCAmelCase_   )   )

			return info




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












import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							# Initialize PyTorch model
							lowerCAmelCase =		CanineConfig()
							lowerCAmelCase =		CanineModel(_snake_case				)
							model.eval()

							print(F"""Building PyTorch model from configuration: {config}"""				)

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

							# Save pytorch-model (weights and configuration)
							print(F"""Save PyTorch model to {pytorch_dump_path}"""				)
							model.save_pretrained(_snake_case				)

							# Save tokenizer files
							lowerCAmelCase =		CanineTokenizer()
							print(F"""Save tokenizer files to {pytorch_dump_path}"""				)
							tokenizer.save_pretrained(_snake_case				)


if __name__ == "__main__":
		UpperCAmelCase_    =argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the TensorFlow checkpoint. Should end with model.ckpt""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to a folder where the PyTorch model will be placed.""",
		)
		UpperCAmelCase_    =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



