


import argparse

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


logging.set_verbosity_info()







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_			:							Dict      =       CanineConfig()
	lowerCamelCase_			:							Any      =       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_			:							Tuple      =       CanineTokenizer()
	print(F"""Save tokenizer files to {pytorch_dump_path}""")
	tokenizer.save_pretrained(lowerCAmelCase_)


if __name__ == "__main__":
							__magic_name__       					=      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.''',
							)
							__magic_name__       					=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)





from typing import TYPE_CHECKING

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


__magic_name__       					=      {'''configuration_levit''': ['''LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LevitConfig''', '''LevitOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      ['''LevitFeatureExtractor''']
							__magic_name__       					=      ['''LevitImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       					=      [
							    '''LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LevitForImageClassification''',
							    '''LevitForImageClassificationWithTeacher''',
							    '''LevitModel''',
							    '''LevitPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_levit import LevitFeatureExtractor
														from .image_processing_levit import LevitImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_levit import (
														    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LevitForImageClassification,
														    LevitForImageClassificationWithTeacher,
														    LevitModel,
														    LevitPreTrainedModel,
														)
else:
							import sys

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

