





import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Dict=1_3 ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=True ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		Any=True ,  lowerCAmelCase_			:		Union[str, Any]=9_9 ,  lowerCAmelCase_			:		Optional[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=5 ,  lowerCAmelCase_			:		str=4 ,  lowerCAmelCase_			:		Optional[Any]=4 ,  lowerCAmelCase_			:		Tuple="gelu" ,  lowerCAmelCase_			:		Dict=0.0 ,  lowerCAmelCase_			:		Optional[Any]=0.1 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Optional[Any]=5_1_2 ,  lowerCAmelCase_			:		Optional[Any]=1_6 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		Tuple=0.02 ,  lowerCAmelCase_			:		List[str]=3 ,  lowerCAmelCase_			:		Dict=4 ,  lowerCAmelCase_			:		List[Any]=None ,  )							->						List[str]:
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     is_training
     __lowerCAmelCase							     =     use_input_mask
     __lowerCAmelCase							     =     use_token_type_ids
     __lowerCAmelCase							     =     use_labels
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_multiple_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout
     __lowerCAmelCase							     =     attention_dropout
     __lowerCAmelCase							     =     weight_tying
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     type_vocab_size
     __lowerCAmelCase							     =     type_sequence_label_size
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     num_labels
     __lowerCAmelCase							     =     num_choices
     __lowerCAmelCase							     =     scope






  def     lowercase		(    self			:		Tuple					)							->						str:
     __lowerCAmelCase							     =     ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size					)

     __lowerCAmelCase							     =     None
     if self.use_input_mask:
        __lowerCAmelCase							     =     random_attention_mask([self.batch_size, self.seq_length]					)

     __lowerCAmelCase							     =     None
     if self.use_labels:
        __lowerCAmelCase							     =     ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels					)

     __lowerCAmelCase							     =     self.get_config()

     return config, input_ids, input_mask, token_labels






  def     lowercase		(    self			:		Optional[int]					)							->						str:
     return GPTNeoXJapaneseConfig(
         vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_multiple_size=self.intermediate_multiple_size ,  hidden_act=self.hidden_act ,  hidden_dropout=self.hidden_dropout ,  attention_dropout=self.attention_dropout ,  weight_tying=self.weight_tying ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowerCAmelCase_ ,  initializer_range=self.initializer_range ,  )






  def     lowercase		(    self			:		int					)							->						Optional[Any]:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.prepare_config_and_inputs()

     __lowerCAmelCase							     =     True

     return config, input_ids, input_mask, token_labels






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[int]					)							->						int:
     __lowerCAmelCase							     =     GPTNeoXJapaneseModel(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_					)
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     True
     __lowerCAmelCase							     =     GPTNeoXJapaneseModel(lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_					)
     self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		List[Any]					)							->						Dict:
     __lowerCAmelCase							     =     GPTNeoXJapaneseForCausalLM(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  labels=lowerCAmelCase_					)
     self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)					)






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     True
     __lowerCAmelCase							     =     GPTNeoXJapaneseForCausalLM(config=lowerCAmelCase_					)
     model.to(lowerCAmelCase_					)
     model.eval()

     # first forward pass
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  use_cache=lowerCAmelCase_					)
     __lowerCAmelCase							     =     outputs.past_key_values

     # create hypothetical multiple next token and extent to next_input_ids
     __lowerCAmelCase							     =     ids_tensor((self.batch_size, 3) ,  config.vocab_size					)
     __lowerCAmelCase							     =     ids_tensor((self.batch_size, 3) ,  vocab_size=2					)

     # append to next input_ids and
     __lowerCAmelCase							     =     torch.cat([input_ids, next_tokens] ,  dim=-1					)
     __lowerCAmelCase							     =     torch.cat([input_mask, next_mask] ,  dim=-1					)

     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  output_hidden_states=lowerCAmelCase_					)
     __lowerCAmelCase							     =     output_from_no_past['hidden_states'][0]
     __lowerCAmelCase							     =     model(
         lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_ ,  past_key_values=lowerCAmelCase_ ,  output_hidden_states=lowerCAmelCase_ ,  )['hidden_states'][0]

     # select random slice
     __lowerCAmelCase							     =     ids_tensor((1,) ,  output_from_past.shape[-1]					).item()
     __lowerCAmelCase							     =     output_from_no_past[:, -3:, random_slice_idx].detach()
     __lowerCAmelCase							     =     output_from_past[:, :, random_slice_idx].detach()

     self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

     # test that outputs are equal for slice
     self.parent.assertTrue(torch.allclose(lowerCAmelCase_ ,  lowerCAmelCase_ ,  atol=1e-3					)					)






  def     lowercase		(    self			:		List[str]					)							->						Optional[Any]:
     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     config_and_inputs
     __lowerCAmelCase							     =     {'input_ids': input_ids, 'attention_mask': input_mask}
     return config, inputs_dict





@require_torch
class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
  a_      =			(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
  a_      =			(
      {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
      if is_torch_available()
      else {}
  )
  a_      =			False
  a_      =			False
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		Optional[Any]					)							->						int:
     __lowerCAmelCase							     =     GPTNeoXJapaneseModelTester(self					)
     __lowerCAmelCase							     =     ConfigTester(self ,  config_class=lowerCAmelCase_ ,  hidden_size=3_7					)






  def     lowercase		(    self			:		List[str]					)							->						str:
     self.config_tester.run_common_tests()






  def     lowercase		(    self			:		Optional[Any]					)							->						Tuple:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int]					)							->						Tuple:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[Any]:
     # This regression test was failing with PyTorch < 1.3
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs_for_decoder()

     __lowerCAmelCase							     =     None

     self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase_					)






  @slow
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     __lowerCAmelCase							     =     'abeja/gpt-neox-japanese-2.7b'

     __lowerCAmelCase							     =     ['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

     __lowerCAmelCase							     =     [
         'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
         '100年後に必要とされる会社は、「人」が中心の会社です。',
         'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
         '国境の長いトンネルを抜けると、そこは雪国だった。',
         '美味しい日本食といえば、やっぱりお寿司ですよね。',
     ]

     __lowerCAmelCase							     =     GPTNeoXJapaneseTokenizer.from_pretrained(lowerCAmelCase_					)
     __lowerCAmelCase							     =     GPTNeoXJapaneseForCausalLM.from_pretrained(lowerCAmelCase_					)

     __lowerCAmelCase							     =     []
     for prompt in prompts:
        __lowerCAmelCase							     =     tokenizer(lowerCAmelCase_ ,  return_tensors='pt'					).input_ids
        __lowerCAmelCase							     =     model.generate(lowerCAmelCase_ ,  max_length=5_0					)
        __lowerCAmelCase							     =     tokenizer.batch_decode(lowerCAmelCase_ ,  skip_special_tokens=lowerCAmelCase_					)
        predicted_outputs += generated_string
     self.assertListEqual(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,
     )

