





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

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

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

      snake_case 						=   TapasForQuestionAnswering(config=__lowerCAmelCase		)
   elif task == "TABFACT":
      snake_case 						=   TapasForSequenceClassification(config=__lowerCAmelCase		)
   elif task == "MLM":
      snake_case 						=   TapasForMaskedLM(config=__lowerCAmelCase		)
   elif task == "INTERMEDIATE_PRETRAINING":
      snake_case 						=   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}'''		)
   snake_case 						=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""      ,    model_max_length=5_12		)
   tokenizer.save_pretrained(__lowerCAmelCase		)

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


if __name__ == "__main__":
 _SCREAMING_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."
 )
 _SCREAMING_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,
 )








'''simple docstring'''




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

