





import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @parameterized.expand([(None,), ('foo.json',)]					)
  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     GenerationConfig(
         do_sample=lowerCAmelCase_ ,  temperature=0.7 ,  length_penalty=1.0 ,  bad_words_ids=[[1, 2, 3], [4, 5]] ,  )
     with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(lowerCAmelCase_ ,  config_name=lowerCAmelCase_					)
        __lowerCAmelCase							     =     GenerationConfig.from_pretrained(lowerCAmelCase_ ,  config_name=lowerCAmelCase_					)

     # Checks parameters that were specified
     self.assertEqual(loaded_config.do_sample ,  lowerCAmelCase_					)
     self.assertEqual(loaded_config.temperature ,  0.7					)
     self.assertEqual(loaded_config.length_penalty ,  1.0					)
     self.assertEqual(loaded_config.bad_words_ids ,  [[1, 2, 3], [4, 5]]					)

     # Checks parameters that were not specified (defaults)
     self.assertEqual(loaded_config.top_k ,  5_0					)
     self.assertEqual(loaded_config.max_length ,  2_0					)
     self.assertEqual(loaded_config.max_time ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     __lowerCAmelCase							     =     AutoConfig.from_pretrained('gpt2'					)
     __lowerCAmelCase							     =     GenerationConfig.from_model_config(lowerCAmelCase_					)
     __lowerCAmelCase							     =     GenerationConfig()

     # The generation config has loaded a few non-default parameters from the model config
     self.assertNotEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # One of those parameters is eos_token_id -- check if it matches
     self.assertNotEqual(generation_config_from_model.eos_token_id ,  default_generation_config.eos_token_id					)
     self.assertEqual(generation_config_from_model.eos_token_id ,  model_config.eos_token_id					)






  def     lowercase		(    self			:		str					)							->						Optional[Any]:
     __lowerCAmelCase							     =     GenerationConfig()
     __lowerCAmelCase							     =     {
         'max_new_tokens': 1_0_2_4,
         'foo': 'bar',
     }
     __lowerCAmelCase							     =     copy.deepcopy(lowerCAmelCase_					)
     __lowerCAmelCase							     =     generation_config.update(**lowerCAmelCase_					)

     # update_kwargs was not modified (no side effects)
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # update_kwargs was used to update the config on valid attributes
     self.assertEqual(generation_config.max_new_tokens ,  1_0_2_4					)

     # `.update()` returns a dictionary of unused kwargs
     self.assertEqual(lowerCAmelCase_ ,  {'foo': 'bar'}					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     GenerationConfig()
     __lowerCAmelCase							     =     'bar'

     with tempfile.TemporaryDirectory('test-generation-config'					) as tmp_dir:
        generation_config.save_pretrained(lowerCAmelCase_					)

        __lowerCAmelCase							     =     GenerationConfig.from_pretrained(lowerCAmelCase_					)
     # update_kwargs was used to update the config on valid attributes
     self.assertEqual(new_config.foo ,  'bar'					)

     __lowerCAmelCase							     =     GenerationConfig.from_model_config(lowerCAmelCase_					)
     assert not hasattr(lowerCAmelCase_ ,  'foo'					)  # no new kwargs should be initialized if from config






  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     GenerationConfig()
     self.assertEqual(default_config.temperature ,  1.0					)
     self.assertEqual(default_config.do_sample ,  lowerCAmelCase_					)
     self.assertEqual(default_config.num_beams ,  1					)

     __lowerCAmelCase							     =     GenerationConfig(
         do_sample=lowerCAmelCase_ ,  temperature=0.7 ,  length_penalty=1.0 ,  bad_words_ids=[[1, 2, 3], [4, 5]] ,  )
     self.assertEqual(config.temperature ,  0.7					)
     self.assertEqual(config.do_sample ,  lowerCAmelCase_					)
     self.assertEqual(config.num_beams ,  1					)

     with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(lowerCAmelCase_					)
        __lowerCAmelCase							     =     GenerationConfig.from_pretrained(lowerCAmelCase_ ,  temperature=1.0					)

     self.assertEqual(loaded_config.temperature ,  1.0					)
     self.assertEqual(loaded_config.do_sample ,  lowerCAmelCase_					)
     self.assertEqual(loaded_config.num_beams ,  1					)  # default value





@is_staging_test
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @classmethod
  def     lowercase		(    cls			:		Optional[int]					)							->						Tuple:
     __lowerCAmelCase							     =     TOKEN
     HfFolder.save_token(lowerCAmelCase_					)






  @classmethod
  def     lowercase		(    cls			:		List[Any]					)							->						Dict:
     try:
        delete_repo(token=cls._token ,  repo_id='test-generation-config'					)
     except HTTPError:
        pass

     try:
        delete_repo(token=cls._token ,  repo_id='valid_org/test-generation-config-org'					)
     except HTTPError:
        pass






  def     lowercase		(    self			:		Union[str, Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     GenerationConfig(
         do_sample=lowerCAmelCase_ ,  temperature=0.7 ,  length_penalty=1.0 ,  )
     config.push_to_hub('test-generation-config' ,  use_auth_token=self._token					)

     __lowerCAmelCase							     =     GenerationConfig.from_pretrained(f"""{USER}/test-generation-config"""					)
     for k, v in config.to_dict().items():
        if k != "transformers_version":
           self.assertEqual(lowerCAmelCase_ ,  getattr(lowerCAmelCase_ ,  lowerCAmelCase_					)					)

        # Reset repo
     delete_repo(token=self._token ,  repo_id='test-generation-config'					)

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(
            lowerCAmelCase_ ,  repo_id='test-generation-config' ,  push_to_hub=lowerCAmelCase_ ,  use_auth_token=self._token					)

     __lowerCAmelCase							     =     GenerationConfig.from_pretrained(f"""{USER}/test-generation-config"""					)
     for k, v in config.to_dict().items():
        if k != "transformers_version":
           self.assertEqual(lowerCAmelCase_ ,  getattr(lowerCAmelCase_ ,  lowerCAmelCase_					)					)






  def     lowercase		(    self			:		str					)							->						Any:
     __lowerCAmelCase							     =     GenerationConfig(
         do_sample=lowerCAmelCase_ ,  temperature=0.7 ,  length_penalty=1.0 ,  )
     config.push_to_hub('valid_org/test-generation-config-org' ,  use_auth_token=self._token					)

     __lowerCAmelCase							     =     GenerationConfig.from_pretrained('valid_org/test-generation-config-org'					)
     for k, v in config.to_dict().items():
        if k != "transformers_version":
           self.assertEqual(lowerCAmelCase_ ,  getattr(lowerCAmelCase_ ,  lowerCAmelCase_					)					)

        # Reset repo
     delete_repo(token=self._token ,  repo_id='valid_org/test-generation-config-org'					)

     # Push to hub via save_pretrained
     with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(
            lowerCAmelCase_ ,  repo_id='valid_org/test-generation-config-org' ,  push_to_hub=lowerCAmelCase_ ,  use_auth_token=self._token					)

     __lowerCAmelCase							     =     GenerationConfig.from_pretrained('valid_org/test-generation-config-org'					)
     for k, v in config.to_dict().items():
        if k != "transformers_version":
           self.assertEqual(lowerCAmelCase_ ,  getattr(lowerCAmelCase_ ,  lowerCAmelCase_					)					)








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   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # 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
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

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

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

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

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   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.'
     )
     _snake_case  :   Optional[Any]						   =  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,
     )

