"""simple docstring"""


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Union[str, Any]   							=							'unispeech'


    def __init__(    self		:     List[Any]      , __lowerCAmelCase		:     List[Any]=32      , __lowerCAmelCase		:     List[str]=768      , __lowerCAmelCase		:     int=12      , __lowerCAmelCase		:     int=12      , __lowerCAmelCase		:     str=3072      , __lowerCAmelCase		:     List[str]="gelu"      , __lowerCAmelCase		:     Dict=0.1      , __lowerCAmelCase		:     Dict=0.1      , __lowerCAmelCase		:     Tuple=0.1      , __lowerCAmelCase		:     Tuple=0.0      , __lowerCAmelCase		:     Optional[Any]=0.0      , __lowerCAmelCase		:     str=0.1      , __lowerCAmelCase		:     Optional[Any]=0.1      , __lowerCAmelCase		:     int=0.02      , __lowerCAmelCase		:     Any=1e-5      , __lowerCAmelCase		:     int="group"      , __lowerCAmelCase		:     int="gelu"      , __lowerCAmelCase		:     List[Any]=(512, 512, 512, 512, 512, 512, 512)      , __lowerCAmelCase		:     Dict=(5, 2, 2, 2, 2, 2, 2)      , __lowerCAmelCase		:     Tuple=(10, 3, 3, 3, 3, 2, 2)      , __lowerCAmelCase		:     Union[str, Any]=False      , __lowerCAmelCase		:     Optional[int]=128      , __lowerCAmelCase		:     int=16      , __lowerCAmelCase		:     str=False      , __lowerCAmelCase		:     Tuple=True      , __lowerCAmelCase		:     Any=0.05      , __lowerCAmelCase		:     Any=10      , __lowerCAmelCase		:     Union[str, Any]=2      , __lowerCAmelCase		:     Dict=0.0      , __lowerCAmelCase		:     Union[str, Any]=10      , __lowerCAmelCase		:     List[str]=0      , __lowerCAmelCase		:     List[Any]=320      , __lowerCAmelCase		:     Optional[int]=2      , __lowerCAmelCase		:     Any=0.1      , __lowerCAmelCase		:     Optional[Any]=100      , __lowerCAmelCase		:     List[str]=256      , __lowerCAmelCase		:     List[Any]=256      , __lowerCAmelCase		:     Tuple=0.1      , __lowerCAmelCase		:     List[str]="mean"      , __lowerCAmelCase		:     Dict=False      , __lowerCAmelCase		:     str=False      , __lowerCAmelCase		:     Optional[int]=256      , __lowerCAmelCase		:     Optional[int]=80      , __lowerCAmelCase		:     Optional[Any]=0      , __lowerCAmelCase		:     Any=1      , __lowerCAmelCase		:     str=2      , __lowerCAmelCase		:     str=0.5      , **__lowerCAmelCase		:     List[str]      , ):
      super().__init__(**__lowerCAmelCase      , pad_token_id=__lowerCAmelCase      , bos_token_id=__lowerCAmelCase      , eos_token_id=__lowerCAmelCase    )
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					feat_extract_norm
      _UpperCAmelCase         =					feat_extract_activation
      _UpperCAmelCase         =					list(__lowerCAmelCase    )
      _UpperCAmelCase         =					list(__lowerCAmelCase    )
      _UpperCAmelCase         =					list(__lowerCAmelCase    )
      _UpperCAmelCase         =					conv_bias
      _UpperCAmelCase         =					num_conv_pos_embeddings
      _UpperCAmelCase         =					num_conv_pos_embedding_groups
      _UpperCAmelCase         =					len(self.conv_dim    )
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					hidden_dropout
      _UpperCAmelCase         =					attention_dropout
      _UpperCAmelCase         =					activation_dropout
      _UpperCAmelCase         =					feat_proj_dropout
      _UpperCAmelCase         =					final_dropout
      _UpperCAmelCase         =					layerdrop
      _UpperCAmelCase         =					layer_norm_eps
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					num_ctc_classes
      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					do_stable_layer_norm
      _UpperCAmelCase         =					use_weighted_layer_sum
      _UpperCAmelCase         =					classifier_proj_size

      if (
          (len(self.conv_stride    ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
          or (len(self.conv_dim    ) != self.num_feat_extract_layers)
      ):
        raise ValueError(
            """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
            """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
            f''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
            f''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _UpperCAmelCase         =					apply_spec_augment
      _UpperCAmelCase         =					mask_time_prob
      _UpperCAmelCase         =					mask_time_length
      _UpperCAmelCase         =					mask_time_min_masks
      _UpperCAmelCase         =					mask_feature_prob
      _UpperCAmelCase         =					mask_feature_length
      _UpperCAmelCase         =					mask_feature_min_masks

      # parameters for pretraining with codevector quantized representations
      _UpperCAmelCase         =					num_codevectors_per_group
      _UpperCAmelCase         =					num_codevector_groups
      _UpperCAmelCase         =					contrastive_logits_temperature
      _UpperCAmelCase         =					feat_quantizer_dropout
      _UpperCAmelCase         =					num_negatives
      _UpperCAmelCase         =					codevector_dim
      _UpperCAmelCase         =					proj_codevector_dim
      _UpperCAmelCase         =					diversity_loss_weight

      # ctc loss
      _UpperCAmelCase         =					ctc_loss_reduction
      _UpperCAmelCase         =					ctc_zero_infinity

      # pretraining loss
      _UpperCAmelCase         =					replace_prob


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return functools.reduce(operator.mul      , self.conv_stride      , 1    )

"""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    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # 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
  _UpperCAmelCase         =					TapasConfig.from_json_file(lowercase						)
  # set absolute/relative position embeddings parameter
  _UpperCAmelCase         =					reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WTQ":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					True
    # hparam_utils.py hparams
    _UpperCAmelCase         =					0.66_46_94
    _UpperCAmelCase         =					0.20_79_51
    _UpperCAmelCase         =					0.12_11_94
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					False
    _UpperCAmelCase         =					0.0_35_25_13

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					False
    # hparam_utils.py hparams
    _UpperCAmelCase         =					36.45_19
    _UpperCAmelCase         =					0.90_34_21
    _UpperCAmelCase         =					2_22.0_88
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					0.76_31_41

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "TABFACT":
    _UpperCAmelCase         =					TapasForSequenceClassification(config=lowercase						)
  elif task == "MLM":
    _UpperCAmelCase         =					TapasForMaskedLM(config=lowercase						)
  elif task == "INTERMEDIATE_PRETRAINING":
    _UpperCAmelCase         =					TapasModel(config=lowercase						)
  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(lowercase      ,lowercase      ,lowercase						)

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

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

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


if __name__ == "__main__":
    UpperCAmelCase__     						=  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."""
    )
    UpperCAmelCase__     						=  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,
    )
