


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		(		_lowercase					,  _lowercase					,  _lowercase					,  _lowercase					,  _lowercase )					-> Optional[Any]:


     """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_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__":
    __snake_case   							=						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   							=						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 sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

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

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__snake_case   							=						get_tests_dir('''fixtures''')



class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			mock.Mock()
            __UpperCamelCase =			500
            __UpperCamelCase =			{}
            __UpperCamelCase =			HTTPError
            __UpperCamelCase =			{}

            # Download this model to make sure it's in the cache.
            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request',return_value=A_					) as mock_head:
                 __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'					)
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'					)



@is_staging_test
class 	__lowerCamelCase			(unittest.TestCase						):







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			TOKEN
            HfFolder.save_token(A_					)







       @classmethod
       def 		snake_case_	(			cls: Tuple					):






            '''simple docstring'''




            try:
                 delete_repo(token=cls._token,repo_id='test-feature-extractor'					)
            except HTTPError:
                 pass

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

            try:
                 delete_repo(token=cls._token,repo_id='test-dynamic-feature-extractor'					)
            except HTTPError:
                 pass







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

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

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     A_,repo_id='test-feature-extractor',push_to_hub=A_,use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(F'''{USER}/test-feature-extractor'''					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained(A_					)
            feature_extractor.push_to_hub('valid_org/test-feature-extractor',use_auth_token=self._token					)

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)

            # Reset repo
            delete_repo(token=self._token,repo_id='valid_org/test-feature-extractor'					)

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

            __UpperCamelCase =			WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'					)
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(A_,getattr(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            CustomFeatureExtractor.register_for_auto_class()
            __UpperCamelCase =			CustomFeatureExtractor.from_pretrained(A_					)

            feature_extractor.push_to_hub('test-dynamic-feature-extractor',use_auth_token=self._token					)

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map,{'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'},)

            __UpperCamelCase =			AutoFeatureExtractor.from_pretrained(
                F'''{USER}/test-dynamic-feature-extractor''',trust_remote_code=A_					)
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__,'CustomFeatureExtractor'					)


