


"""simple docstring"""



import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )







class _lowerCAmelCase    (				unittest.TestCase     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=2						,     UpperCamelCase__=56						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=True						,     UpperCamelCase__=99						,     UpperCamelCase__=32						,     UpperCamelCase__=2						,     UpperCamelCase__=2						,     UpperCamelCase__=7						,     UpperCamelCase__="gelu_new"						,     UpperCamelCase__=0.1						,     UpperCamelCase__=0.1						,     UpperCamelCase__=512						,     UpperCamelCase__=16						,     UpperCamelCase__=2						,     UpperCamelCase__=0.02						,     UpperCamelCase__=4						,     UpperCamelCase__="block_sparse"						,     UpperCamelCase__=True						,     UpperCamelCase__=False						,     UpperCamelCase__=2						,     UpperCamelCase__=3						,     )     ->		Tuple:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   parent
       snake_case   :				Tuple				 =   batch_size
       snake_case   :				Tuple				 =   seq_length
       snake_case   :				str				 =   is_training
       snake_case   :				List[str]				 =   use_attention_mask
       snake_case   :				int				 =   use_token_type_ids
       snake_case   :				List[Any]				 =   use_labels
       snake_case   :				Tuple				 =   vocab_size
       snake_case   :				Tuple				 =   hidden_size
       snake_case   :				Dict				 =   num_hidden_layers
       snake_case   :				List[str]				 =   num_attention_heads
       snake_case   :				List[str]				 =   intermediate_size
       snake_case   :				Optional[int]				 =   hidden_act
       snake_case   :				int				 =   hidden_dropout_prob
       snake_case   :				Tuple				 =   attention_probs_dropout_prob
       snake_case   :				Any				 =   max_position_embeddings
       snake_case   :				int				 =   type_vocab_size
       snake_case   :				Tuple				 =   type_sequence_label_size
       snake_case   :				Union[str, Any]				 =   initializer_range
       snake_case   :				Union[str, Any]				 =   num_choices

       snake_case   :				Dict				 =   rescale_embeddings
       snake_case   :				int				 =   attention_type
       snake_case   :				List[Any]				 =   use_bias
       snake_case   :				Tuple				 =   block_size
       snake_case   :				List[Any]				 =   num_random_blocks







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size						)

       snake_case   :				str				 =   None
       if self.use_attention_mask:
           snake_case   :				int				 =   random_attention_mask([self.batch_size, self.seq_length]						)

       snake_case   :				List[Any]				 =   None
       if self.use_token_type_ids:
           snake_case   :				Tuple				 =   ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size						)

       snake_case   :				Optional[int]				 =   BigBirdConfig(
           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_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=UpperCamelCase__						,     initializer_range=self.initializer_range						,     attention_type=self.attention_type						,     block_size=self.block_size						,     num_random_blocks=self.num_random_blocks						,     use_bias=self.use_bias						,     rescale_embeddings=self.rescale_embeddings						,     )

       return config, input_ids, token_type_ids, attention_mask







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				str				 =   self.prepare_config_and_inputs()
       snake_case ,snake_case ,snake_case ,snake_case   :				List[str]				 =   config_and_inputs
       snake_case   :				Dict				 =   {
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict







@require_flax
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	Union[str, Any]							  =  (
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   __UpperCAmelCase			:	int							  =  False
   __UpperCAmelCase			:	str							  =  False







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       snake_case   :				List[str]				 =   FlaxBigBirdModelTester(self						)







   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       super().test_from_pretrained_save_pretrained()







   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       super().test_from_pretrained_with_no_automatic_init()







   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       super().test_no_automatic_init()







   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       super().test_hidden_states_output()







   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       for model_class_name in self.all_model_classes:
           snake_case   :				List[str]				 =   model_class_name.from_pretrained("google/bigbird-roberta-base"						)
           self.assertIsNotNone(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       if self.test_attn_probs:
           super().test_attention_outputs()







   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case ,snake_case   :				Union[str, Any]				 =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__						):
               snake_case   :				List[Any]				 =   self._prepare_for_class(UpperCamelCase__						,     UpperCamelCase__						)
               snake_case   :				List[str]				 =   model_class(UpperCamelCase__						)

               @jax.jit
               def model_jitted(UpperCamelCase__						,     UpperCamelCase__=None						,     **UpperCamelCase__						):
                   return model(input_ids=UpperCamelCase__						,     attention_mask=UpperCamelCase__						,     **UpperCamelCase__						)

               with self.subTest("JIT Enabled"						):
                   snake_case   :				Tuple				 =   model_jitted(**UpperCamelCase__						).to_tuple()

               with self.subTest("JIT Disabled"						):
                   with jax.disable_jit():
                       snake_case   :				List[str]				 =   model_jitted(**UpperCamelCase__						).to_tuple()

               self.assertEqual(len(UpperCamelCase__						)						,     len(UpperCamelCase__						)						)
               for jitted_output, output in zip(UpperCamelCase__						,     UpperCamelCase__						):
                   self.assertEqual(jitted_output.shape						,     output.shape						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__=1e-5						,     UpperCamelCase__="outputs"						,     UpperCamelCase__=None						)     ->		str:

       '''simple docstring'''




       if name.startswith("outputs.attentions"						):
           return
       else:
           super().check_pt_flax_outputs(UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    for split in ds.keys():
        print(F'Splitting {split} with {ds[split].num_rows} records'			)

        # to save to val.source, val.target like summary datasets
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   tgt_path.open("w+"			)

        # reader is the bottleneck so writing one record at a time doesn't slow things down
        for x in tqdm(ds[split]			):
            snake_case   :				int				 =   x["translation"]
            src_fp.write(ex[src_lang] + "\n"			)
            tgt_fp.write(ex[tgt_lang] + "\n"			)

    print(F'Saved {dataset} dataset to {save_dir}'			)


if __name__ == "__main__":
  fire.Fire(download_wmt_dataset)





