

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      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A   ,       __A			):
  # 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(__A			)
  # 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=__A			)
  elif task == "WTQ":
    # run_task_main.py hparams
    UpperCAmelCase					      =  4
    UpperCAmelCase					      =  True
    # hparam_utils.py hparams
    UpperCAmelCase					      =  0.664694
    UpperCAmelCase					      =  0.207951
    UpperCAmelCase					      =  0.121194
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  False
    UpperCAmelCase					      =  0.0352513

    UpperCAmelCase					      =  TapasForQuestionAnswering(config=__A			)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    UpperCAmelCase					      =  4
    UpperCAmelCase					      =  False
    # hparam_utils.py hparams
    UpperCAmelCase					      =  36.4519
    UpperCAmelCase					      =  0.903421
    UpperCAmelCase					      =  222.088
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  True
    UpperCAmelCase					      =  0.763141

    UpperCAmelCase					      =  TapasForQuestionAnswering(config=__A			)
  elif task == "TABFACT":
    UpperCAmelCase					      =  TapasForSequenceClassification(config=__A			)
  elif task == "MLM":
    UpperCAmelCase					      =  TapasForMaskedLM(config=__A			)
  elif task == "INTERMEDIATE_PRETRAINING":
    UpperCAmelCase					      =  TapasModel(config=__A			)
  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(__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
  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(__A			)

  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,
      )






import numpy

# List of input, output pairs
lowerCAmelCase__       = (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCAmelCase__       = (((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCAmelCase__       = [2, 4, 1, 5]
lowerCAmelCase__       = len(train_data)
lowerCAmelCase__       = 0.0_0_9
def      _lowerCAmelCase(	__A   ,       __A="train"			):
  return calculate_hypothesis_value(__A   ,       __A			) - output(
      __A   ,       __A			)
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  0
  for i in range(len(__A			) - 1			):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def      _lowerCAmelCase(	__A   ,       __A			):
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]			)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]			)
  return None
def      _lowerCAmelCase(	__A   ,       __A=m			):
  UpperCAmelCase					      =  0
  for i in range(__A			):
    if index == -1:
      summation_value += _error(__A			)
    else:
      summation_value += _error(__A			) * train_data[i][0][index]
  return summation_value
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  summation_of_cost_derivative(__A   ,       __A			) / m
  return cost_derivative_value
def      _lowerCAmelCase(	):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  UpperCAmelCase					      =  0.000002
  UpperCAmelCase					      =  0
  UpperCAmelCase					      =  0
  while True:
    j += 1
    UpperCAmelCase					      =  [0, 0, 0, 0]
    for i in range(0   ,       len(__A			)			):
      UpperCAmelCase					      =  get_cost_derivative(i - 1			)
      UpperCAmelCase					      =  (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        __A   ,       __A   ,       atol=__A   ,       rtol=__A   ,       ):
      break
    UpperCAmelCase					      =  temp_parameter_vector
  print(("Number of iterations:", j)			)
def      _lowerCAmelCase(	):
  for i in range(len(__A			)			):
    print(("Actual output value:", output(__A   ,       "test"			))			)
    print(("Hypothesis output:", calculate_hypothesis_value(__A   ,       "test"			))			)


if __name__ == "__main__":
      run_gradient_descent()
      print("\nTesting gradient descent for a linear hypothesis function.\n")
      test_gradient_descent()



