

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:     List[Any]     ,					lowerCAmelCase:     List[str]       )      ->				Union[str, Any]:
				# Initialize PyTorch model
				_UpperCAmelCase				:						List[Any]       						=			CanineConfig()
				_UpperCAmelCase				:						str       						=			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
				_UpperCAmelCase				:						int       						=			CanineTokenizer()
				print(F'Save tokenizer files to {pytorch_dump_path}'       )
				tokenizer.save_pretrained(lowerCAmelCase       )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					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.',
					)
					SCREAMING_SNAKE_CASE_						=					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_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

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


