


"""simple docstring"""







import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM






@require_torch
class 						_A  :







    """simple docstring"""




    def __init__( self    :   Optional[Any]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Tuple=99    ,			__UpperCAmelCase    :   int=13    ,			__UpperCAmelCase    :   Dict=16    ,			__UpperCAmelCase    :   List[str]=7    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Optional[int]=True    ,			__UpperCAmelCase    :   int=True    ,			__UpperCAmelCase    :   Optional[int]=False    ,			__UpperCAmelCase    :   Any=True    ,			__UpperCAmelCase    :   Dict=2    ,			__UpperCAmelCase    :   Dict=32    ,			__UpperCAmelCase    :   int=4    ,			__UpperCAmelCase    :   List[str]=4    ,			__UpperCAmelCase    :   Optional[Any]=30    ,			__UpperCAmelCase    :   Dict=0    ,			__UpperCAmelCase    :   Optional[int]=1    ,			__UpperCAmelCase    :   List[str]=2    ,			__UpperCAmelCase    :   Tuple=None    ,			):
      a						:		Optional[int]											=      parent
      a						:		Tuple											=      batch_size
      a						:		Union[str, Any]											=      decoder_seq_length
      # For common tests
      a						:		str											=      self.decoder_seq_length
      a						:		Dict											=      is_training
      a						:		str											=      use_attention_mask
      a						:		Dict											=      use_labels

      a						:		str											=      vocab_size
      a						:		Optional[Any]											=      d_model
      a						:		Union[str, Any]											=      d_model
      a						:		Optional[int]											=      decoder_layers
      a						:		int											=      decoder_layers
      a						:		List[Any]											=      decoder_ffn_dim
      a						:		Union[str, Any]											=      decoder_attention_heads
      a						:		Dict											=      decoder_attention_heads
      a						:		List[str]											=      eos_token_id
      a						:		str											=      bos_token_id
      a						:		List[str]											=      pad_token_id
      a						:		Optional[Any]											=      decoder_start_token_id
      a						:		Tuple											=      use_cache
      a						:		int											=      max_position_embeddings

      a						:		List[Any]											=      None
      a						:		int											=      decoder_seq_length
      a						:		Union[str, Any]											=      2
      a						:		List[Any]											=      1






    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size)

      a						:		str											=      None
      if self.use_attention_mask:
        a						:		List[Any]											=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,			vocab_size=2)

      a						:		Tuple											=      None
      if self.use_labels:
        a						:		Optional[Any]											=      ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size)

      a						:		List[str]											=      TrOCRConfig(
          vocab_size=self.vocab_size    ,			d_model=self.d_model    ,			decoder_layers=self.decoder_layers    ,			decoder_ffn_dim=self.decoder_ffn_dim    ,			decoder_attention_heads=self.decoder_attention_heads    ,			eos_token_id=self.eos_token_id    ,			bos_token_id=self.bos_token_id    ,			use_cache=self.use_cache    ,			pad_token_id=self.pad_token_id    ,			decoder_start_token_id=self.decoder_start_token_id    ,			max_position_embeddings=self.max_position_embeddings    ,			)

      return (config, input_ids, attention_mask, lm_labels)






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[Any]    ,			):
      a						:		List[str]											=      True
      a						:		List[str]											=      TrOCRDecoder(config=__UpperCAmelCase).to(__UpperCAmelCase).eval()
      a						:		int											=      input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      a						:		Union[str, Any]											=      model(__UpperCAmelCase    ,			use_cache=__UpperCAmelCase)
      a						:		Union[str, Any]											=      model(__UpperCAmelCase)
      a						:		str											=      model(__UpperCAmelCase    ,			use_cache=__UpperCAmelCase)

      self.parent.assertTrue(len(__UpperCAmelCase) == len(__UpperCAmelCase))
      self.parent.assertTrue(len(__UpperCAmelCase) == len(__UpperCAmelCase) + 1)

      a						:		Optional[int]											=      outputs["past_key_values"]

      # create hypothetical next token and extent to next_input_ids
      a						:		Dict											=      ids_tensor((2, 1)    ,			config.vocab_size - 1) + 1

      # append to next input_ids and
      a						:		str											=      torch.cat([input_ids, next_tokens]    ,			dim=-1)

      a						:		Optional[int]											=      model(__UpperCAmelCase)["last_hidden_state"]
      a						:		Tuple											=      model(__UpperCAmelCase    ,			past_key_values=__UpperCAmelCase)["last_hidden_state"]

      # select random slice
      a						:		Any											=      ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
      a						:		Tuple											=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
      a						:		Optional[int]											=      output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      assert torch.allclose(__UpperCAmelCase    ,			__UpperCAmelCase    ,			atol=1e-3)






    def      __snake_case							( self    :   Tuple):
      a						:		Optional[int]											=      self.prepare_config_and_inputs()
      a							,		a							,		a							,		a						:		Union[str, Any]											=      config_and_inputs

      a						:		str											=      {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict






@require_torch
class 						_A  (  _a   ,_a   ,_a   ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
    UpperCAmelCase	:							Union[str, Any]     =	(TrOCRForCausalLM,) if is_torch_available() else ()
    UpperCAmelCase	:							List[str]     =	{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
    UpperCAmelCase	:							Dict     =	True
    UpperCAmelCase	:							Union[str, Any]     =	False






    def      __snake_case							( self    :   Tuple):
      a						:		List[Any]											=      TrOCRStandaloneDecoderModelTester(self    ,			is_training=__UpperCAmelCase)
      a						:		int											=      ConfigTester(self    ,			config_class=__UpperCAmelCase)






    def      __snake_case							( self    :   Dict):
      pass






    def      __snake_case							( self    :   Optional[Any]):
      pass






    def      __snake_case							( self    :   Tuple):
      pass






    def      __snake_case							( self    :   str):
      self.config_tester.run_common_tests()






    def      __snake_case							( self    :   List[Any]):
      a						:		Tuple											=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*__UpperCAmelCase)






    def      __snake_case							( self    :   List[Any]):
      return






    @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
    def      __snake_case							( self    :   Tuple):
      pass










"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  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(A_  ,       A_  ,       A_	)

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

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

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


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






