



'''simple docstring'''





import functools
import operator

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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class __A			(  lowerCAmelCase_						):



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       """sew-d"""



 def __init__(self ,   A=32 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A=2 ,   A=512 ,   A=256 ,   A=True ,   A=True ,   A=("p2c", "c2p") ,   A="layer_norm" ,   A="gelu_python" ,   A=0.1 ,   A=0.1 ,   A=0.1 ,   A=0.0 ,   A=0.1 ,   A=0.02 ,   A=1E-7 ,   A=1E-5 ,   A="group" ,   A="gelu" ,   A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,   A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,   A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,   A=False ,   A=128 ,   A=16 ,   A=True ,   A=0.05 ,   A=10 ,   A=2 ,   A=0.0 ,   A=10 ,   A=0 ,   A="mean" ,   A=False ,   A=False ,   A=256 ,   A=0 ,   A=1 ,   A=2 ,   **A ,   )    ->     int:


      """simple docstring"""



      super().__init__(**snake_case_ ,   pad_token_id=snake_case_ ,   bos_token_id=snake_case_ ,   eos_token_id=snake_case_       )
      _a      =  hidden_size
      _a      =  feat_extract_norm
      _a      =  feat_extract_activation
      _a      =  list(snake_case_       )
      _a      =  list(snake_case_       )
      _a      =  list(snake_case_       )
      _a      =  conv_bias
      _a      =  num_conv_pos_embeddings
      _a      =  num_conv_pos_embedding_groups
      _a      =  len(self.conv_dim       )
      _a      =  num_hidden_layers
      _a      =  intermediate_size
      _a      =  squeeze_factor
      _a      =  max_position_embeddings
      _a      =  position_buckets
      _a      =  share_att_key
      _a      =  relative_attention
      _a      =  norm_rel_ebd
      _a      =  list(snake_case_       )
      _a      =  hidden_act
      _a      =  num_attention_heads
      _a      =  hidden_dropout
      _a      =  attention_dropout
      _a      =  activation_dropout
      _a      =  feat_proj_dropout
      _a      =  final_dropout
      _a      =  layer_norm_eps
      _a      =  feature_layer_norm_eps
      _a      =  initializer_range
      _a      =  vocab_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)`,'''
               f'''but is `len(config.conv_dim) = {len(self.conv_dim       )}`, `len(config.conv_stride)'''
               f'''= {len(self.conv_stride       )}`, `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

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

      # ctc loss
      _a      =  ctc_loss_reduction
      _a      =  ctc_zero_infinity

      # sequence classification
      _a      =  use_weighted_layer_sum
      _a      =  classifier_proj_size



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      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							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _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_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

          _UpperCAmelCase     							=   TapasForQuestionAnswering(config=__lowercase       )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     							=   4
          _UpperCAmelCase     							=   False
          # hparam_utils.py hparams
          _UpperCAmelCase     							=   36.4519
          _UpperCAmelCase     							=   0.90_3421
          _UpperCAmelCase     							=   222.088
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   0.76_3141

          _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=512       )
     tokenizer.save_pretrained(__lowercase       )

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :List[str]       =				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.'''
  )
  __SCREAMING_SNAKE_CASE     :List[str]       =				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,
  )




