





import argparse

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


logging.set_verbosity_info()


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		# Initialize PyTorch model
		lowerCamelCase_  :    Any   	=			CanineConfig()
		lowerCamelCase_  :    Tuple   	=			CanineModel(lowerCAmelCase__	)
		model.eval()

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

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

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

		# Save tokenizer files
		lowerCamelCase_  :    Dict   	=			CanineTokenizer()
		print(F"Save tokenizer files to {pytorch_dump_path}"	)
		tokenizer.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
		_lowercase		:     Any 					=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.""",
		)
		_lowercase		:     Optional[int] 					=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

_lowercase		:     Optional[Any] 					=[(1,)]


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    Union[str, Any]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow:
				assert True
		else:
				assert False, "should have been skipped"


def 		_SCREAMING_SNAKE_CASE    (   ):
		lowerCamelCase_  :    List[str]   	=			bool(os.getenv('RUN_SLOW'       ,0	)	)
		if run_slow and torch_device == "cuda":
				assert True
		else:
				assert False, "should have been skipped"






@require_torch
class 	UpperCamelCase_     (     unittest.TestCase       ):



					@slow
					@require_torch_gpu
					def      __a    (		self    :    Tuple	):
							check_slow_torch_cuda()



					@require_torch_gpu
					@slow
					def      __a    (		self    :    Optional[int]	):
							check_slow_torch_cuda()





					@parameterized.expand(lowerCamelCase	)
					@slow
					def      __a    (		self    :    List[str]					,			lowerCamelCase    :    Optional[Any]=None	):
							check_slow()


@slow
@require_torch_gpu
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@require_torch_gpu
@slow
def 		_SCREAMING_SNAKE_CASE    (   ):
		check_slow_torch_cuda()


@slow
@pytest.mark.parametrize('param'       ,[1]	)
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()





@pytest.mark.parametrize('param'       ,[1]	)
@slow
def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
		check_slow()
