'''simple docstring'''

from typing import TYPE_CHECKING

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


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''

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_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __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__":
 _A      :				int 				=    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.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






