


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      Adafactor,
      AdamW,
      get_constant_schedule,
      get_constant_schedule_with_warmup,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_inverse_sqrt_schedule,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=10   ):
   __a					=  []
   for _ in range(lowerCAmelCase__   ):
      lrs.append(scheduler.get_lr()[0]   )
      scheduler.step()
   return lrs




def  a   (lowerCAmelCase__		,				lowerCAmelCase__=10   ):
   __a					=  []
   for step in range(lowerCAmelCase__   ):
      lrs.append(scheduler.get_lr()[0]   )
      scheduler.step()
      if step == num_steps // 2:
         with tempfile.TemporaryDirectory() as tmpdirname:
            __a					=  os.path.join(lowerCAmelCase__		,				"""schedule.bin"""   )
            torch.save(scheduler.state_dict()		,				lowerCAmelCase__   )

            __a					=  torch.load(lowerCAmelCase__   )
            scheduler.load_state_dict(lowerCAmelCase__   )
   return lrs


@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A    ):
        self.assertEqual(len(__A    )  ,       len(__A    )    )
        for a, b in zip(__A  ,       __A    ):
           self.assertAlmostEqual(__A  ,       __A  ,       delta=__A    )


     def 	snake_case_  (	self    ):
        __a					=  torch.tensor([0.1, -0.2, -0.1]  ,       requires_grad=__A    )
        __a					=  torch.tensor([0.4, 0.2, -0.5]    )
        __a					=  nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __a					=  AdamW(params=[w]  ,       lr=2E-1  ,       weight_decay=0.0    )
        for _ in range(100    ):
           __a					=  criterion(__A  ,       __A    )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()  ,       [0.4, 0.2, -0.5]  ,       tol=1E-2    )


     def 	snake_case_  (	self    ):
        __a					=  torch.tensor([0.1, -0.2, -0.1]  ,       requires_grad=__A    )
        __a					=  torch.tensor([0.4, 0.2, -0.5]    )
        __a					=  nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        __a					=  Adafactor(
            params=[w]  ,       lr=1E-2  ,       eps=(1E-30, 1E-3)  ,       clip_threshold=1.0  ,       decay_rate=-0.8  ,       betaa=__A  ,       weight_decay=0.0  ,       relative_step=__A  ,       scale_parameter=__A  ,       warmup_init=__A  ,       )
        for _ in range(1000    ):
           __a					=  criterion(__A  ,       __A    )
           loss.backward()
           optimizer.step()
           w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
           w.grad.zero_()
        self.assertListAlmostEqual(w.tolist()  ,       [0.4, 0.2, -0.5]  ,       tol=1E-2    )







@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      nn.Linear(50 ,					50							) if is_torch_available() else None
     _lowerCamelCase   =      AdamW(m.parameters() ,					lr=10.0							) if is_torch_available() else None
     _lowerCamelCase   =      10


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A=None    ):
        self.assertEqual(len(__A    )  ,       len(__A    )    )
        for a, b in zip(__A  ,       __A    ):
           self.assertAlmostEqual(__A  ,       __A  ,       delta=__A  ,       msg=__A    )


     def 	snake_case_  (	self    ):
        __a					=  {"""num_warmup_steps""": 2, """num_training_steps""": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        __a					=  {
            get_constant_schedule: ({}, [10.0] * self.num_steps),
            get_constant_schedule_with_warmup: (
                {"""num_warmup_steps""": 4},
                [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
            ),
            get_linear_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
            ),
            get_cosine_schedule_with_warmup: (
                {**common_kwargs},
                [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
            ),
            get_cosine_with_hard_restarts_schedule_with_warmup: (
                {**common_kwargs, """num_cycles""": 2},
                [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
            ),
            get_polynomial_decay_schedule_with_warmup: (
                {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
                [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
            ),
            get_inverse_sqrt_schedule: (
                {"""num_warmup_steps""": 2},
                [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
            ),
        }

        for scheduler_func, data in scheds.items():
           __a	,    __a					=  data

           __a					=  scheduler_func(self.optimizer  ,       **__A    )
           self.assertEqual(len([scheduler.get_lr()[0]]    )  ,       1    )
           __a					=  unwrap_schedule(__A  ,       self.num_steps    )
           self.assertListAlmostEqual(
               __A  ,       __A  ,       tol=1E-2  ,       msg=f'''failed for {scheduler_func} in normal scheduler'''  ,       )

           __a					=  scheduler_func(self.optimizer  ,       **__A    )
           if scheduler_func.__name__ != "get_constant_schedule":
              LambdaScheduleWrapper.wrap_scheduler(__A    )  # wrap to test picklability of the schedule
           __a					=  unwrap_and_save_reload_schedule(__A  ,       self.num_steps    )
           self.assertListEqual(__A  ,       __A  ,       msg=f'''failed for {scheduler_func} in save and reload'''    )







class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        __a					=  fn


     def __call__(	self  ,       *__A  ,       **__A    ):
        return self.fn(*__A  ,       **__A    )


     @classmethod
     def 	snake_case_  (	self  ,       __A    ):
        __a					=  list(map(self  ,       scheduler.lr_lambdas    )    )








"""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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _UpperCAmelCase     :      Optional[int]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WTQ":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      int														=       True
          # hparam_utils.py hparams
          _UpperCAmelCase     :      Optional[int]														=       0.664_694
          _UpperCAmelCase     :      Any														=       0.207_951
          _UpperCAmelCase     :      Any														=       0.121_194
          _UpperCAmelCase     :      List[Any]														=       True
          _UpperCAmelCase     :      Dict														=       True
          _UpperCAmelCase     :      Tuple														=       False
          _UpperCAmelCase     :      List[Any]														=       0.0_352_513

          _UpperCAmelCase     :      Union[str, Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _UpperCAmelCase     :      List[str]														=       4
          _UpperCAmelCase     :      Optional[int]														=       False
          # hparam_utils.py hparams
          _UpperCAmelCase     :      List[Any]														=       36.4_519
          _UpperCAmelCase     :      Union[str, Any]														=       0.903_421
          _UpperCAmelCase     :      List[Any]														=       222.088
          _UpperCAmelCase     :      Optional[Any]														=       True
          _UpperCAmelCase     :      str														=       True
          _UpperCAmelCase     :      Union[str, Any]														=       True
          _UpperCAmelCase     :      str														=       0.763_141

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_SNAKE_CASE__							)
     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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

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

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

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


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				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							:   Tuple				=				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,
 )
