



import argparse

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


logging.set_verbosity_info()

def       A_   (						__a  :					List[str]			,		__a  :					Optional[Any]		):

				"""simple docstring"""
				# Initialize PyTorch model
				a__					   =		CanineConfig()
				a__					   =		CanineModel(__a		)
				model.eval()

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

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

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

				# Save tokenizer files
				a__					   =		CanineTokenizer()
				print(F'''Save tokenizer files to {pytorch_dump_path}'''		)
				tokenizer.save_pretrained(__a		)


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)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE     : Tuple      ={
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Dict      =["""LlamaTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : int      =["""LlamaTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE     : Optional[Any]      =[
          """OpenLlamaForCausalLM""",
          """OpenLlamaModel""",
          """OpenLlamaPreTrainedModel""",
          """OpenLlamaForSequenceClassification""",
      ]


if TYPE_CHECKING:
      from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from transformers import LlamaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )


else:
      import sys

      __SCREAMING_SNAKE_CASE     : Union[str, Any]      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)