




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      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Any:
       # 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   :      Optional[int]			 =					TapasConfig.from_json_file(__lowerCAmelCase    )
       # set absolute/relative position embeddings parameter
       lowerCamelCase   :      Union[str, Any]			 =					reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              lowerCamelCase   :      Any			 =					TapasForQuestionAnswering(config=__lowerCAmelCase    )
       elif task == "WTQ":
              # run_task_main.py hparams
              lowerCamelCase   :      Optional[Any]			 =					4
              lowerCamelCase   :      Optional[Any]			 =					True
              # hparam_utils.py hparams
              lowerCamelCase   :      Dict			 =					0.664694
              lowerCamelCase   :      Optional[int]			 =					0.207951
              lowerCamelCase   :      Any			 =					0.121194
              lowerCamelCase   :      int			 =					True
              lowerCamelCase   :      Union[str, Any]			 =					True
              lowerCamelCase   :      Any			 =					False
              lowerCamelCase   :      List[Any]			 =					0.0352513

              lowerCamelCase   :      Union[str, Any]			 =					TapasForQuestionAnswering(config=__lowerCAmelCase    )
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              lowerCamelCase   :      List[str]			 =					4
              lowerCamelCase   :      str			 =					False
              # hparam_utils.py hparams
              lowerCamelCase   :      str			 =					36.4519
              lowerCamelCase   :      List[str]			 =					0.903421
              lowerCamelCase   :      List[Any]			 =					222.088
              lowerCamelCase   :      Optional[int]			 =					True
              lowerCamelCase   :      List[str]			 =					True
              lowerCamelCase   :      List[str]			 =					True
              lowerCamelCase   :      str			 =					0.763141

              lowerCamelCase   :      Dict			 =					TapasForQuestionAnswering(config=__lowerCAmelCase    )
       elif task == "TABFACT":
              lowerCamelCase   :      Tuple			 =					TapasForSequenceClassification(config=__lowerCAmelCase    )
       elif task == "MLM":
              lowerCamelCase   :      Tuple			 =					TapasForMaskedLM(config=__lowerCAmelCase    )
       elif task == "INTERMEDIATE_PRETRAINING":
              lowerCamelCase   :      Optional[int]			 =					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   :      Optional[Any]			 =					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__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   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__       :      int  =   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 unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
 import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 			__a  (    unittest.TestCase       ,   A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_tool("text-to-speech"						)
      self.tool.setup()







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   Any       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   int       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)