

'''simple docstring'''







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[int]:
       # Initialise PyTorch model.
       # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
       # TapasConfig to False.

       # initialize configuration from json file
       A_						       =   TapasConfig.from_json_file(UpperCAmelCase__					)
       # set absolute/relative position embeddings parameter
       A_						       =   reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              A_						       =   TapasForQuestionAnswering(config=UpperCAmelCase__					)
       elif task == "WTQ":
              # run_task_main.py hparams
              A_						       =   4
              A_						       =   True
              # hparam_utils.py hparams
              A_						       =   0.664_694
              A_						       =   0.207_951
              A_						       =   0.121_194
              A_						       =   True
              A_						       =   True
              A_						       =   False
              A_						       =   0.0_352_513

              A_						       =   TapasForQuestionAnswering(config=UpperCAmelCase__					)
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              A_						       =   4
              A_						       =   False
              # hparam_utils.py hparams
              A_						       =   36.4_519
              A_						       =   0.903_421
              A_						       =   222.088
              A_						       =   True
              A_						       =   True
              A_						       =   True
              A_						       =   0.763_141

              A_						       =   TapasForQuestionAnswering(config=UpperCAmelCase__					)
       elif task == "TABFACT":
              A_						       =   TapasForSequenceClassification(config=UpperCAmelCase__					)
       elif task == "MLM":
              A_						       =   TapasForMaskedLM(config=UpperCAmelCase__					)
       elif task == "INTERMEDIATE_PRETRAINING":
              A_						       =   TapasModel(config=UpperCAmelCase__					)
       else:
              raise ValueError(F'''Task {task} not supported.'''					)

       print(F'''Building PyTorch model from configuration: {config}'''					)
       # Load weights from tf checkpoint
       load_tf_weights_in_tapas(UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

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

       # Save tokenizer files
       print(F'''Save tokenizer files to {pytorch_dump_path}'''					)
       A_						       =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""",							model_max_length=5_12					)
       tokenizer.save_pretrained(UpperCAmelCase__					)

       print("""Used relative position embeddings:""",							model.config.reset_position_index_per_cell					)


if __name__ == "__main__":
      __lowerCamelCase									=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
      )
      parser.add_argument(
          '''--reset_position_index_per_cell''',
          default=False,
          action='''store_true''',
          help='''Whether to use relative position embeddings or not. Defaults to True.''',
      )
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--tapas_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained TAPAS model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __lowerCamelCase									=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.task,
          args.reset_position_index_per_cell,
          args.tf_checkpoint_path,
          args.tapas_config_file,
          args.pytorch_dump_path,
      )



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=     {
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     ['''WhisperTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''WhisperForConditionalGeneration''',
          '''WhisperModel''',
          '''WhisperPreTrainedModel''',
          '''WhisperForAudioClassification''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWhisperForConditionalGeneration''',
          '''TFWhisperModel''',
          '''TFWhisperPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''FlaxWhisperForConditionalGeneration''',
          '''FlaxWhisperModel''',
          '''FlaxWhisperPreTrainedModel''',
          '''FlaxWhisperForAudioClassification''',
      ]


if TYPE_CHECKING:
      from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
      from .feature_extraction_whisper import WhisperFeatureExtractor
      from .processing_whisper import WhisperProcessor
      from .tokenization_whisper import WhisperTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_whisper_fast import WhisperTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_whisper import (
                WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                WhisperForAudioClassification,
                WhisperForConditionalGeneration,
                WhisperModel,
                WhisperPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_whisper import (
                TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWhisperForConditionalGeneration,
                TFWhisperModel,
                TFWhisperPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_whisper import (
                FlaxWhisperForAudioClassification,
                FlaxWhisperForConditionalGeneration,
                FlaxWhisperModel,
                FlaxWhisperPreTrainedModel,
            )

else:
      import sys

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