"""simple docstring"""


import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  assert isinstance(lowercase ,lowercase		)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory""" ,[False, True]		)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,cache_dir=lowercase ,keep_in_memory=lowercase		).read()
  _check_json_dataset(lowercase ,lowercase		)






@pytest.mark.parametrize(
    """features""" ,[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase    			=   features.copy() if features else default_expected_features
  _UpperCAmelCase    			=   (
      Features({feature: Value(lowercase		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,features=lowercase ,cache_dir=lowercase		).read()
  _check_json_dataset(lowercase ,lowercase		)






@pytest.mark.parametrize(
    """features""" ,[
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
  _UpperCAmelCase    			=   features.copy() if features else default_expected_features
  _UpperCAmelCase    			=   (
      Features({feature: Value(lowercase		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,features=lowercase ,cache_dir=lowercase		).read()
  assert isinstance(lowercase ,lowercase		)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_3", "col_1", "col_2"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
  _UpperCAmelCase    			=   {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
  _UpperCAmelCase    			=   features.copy()
  _UpperCAmelCase    			=   (
      Features({feature: Value(lowercase		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,features=lowercase ,cache_dir=lowercase		).read()
  assert isinstance(lowercase ,lowercase		)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_2", "col_3", "col_1"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""split""" ,[None, NamedSplit("""train"""		), """train""", """test"""]		)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,cache_dir=lowercase ,split=lowercase		).read()
  _check_json_dataset(lowercase ,lowercase		)
  assert dataset.split == split if split else "train"






@pytest.mark.parametrize("""path_type""" ,[str, list]		)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  if issubclass(lowercase ,lowercase		):
    _UpperCAmelCase    			=   jsonl_path
  elif issubclass(lowercase ,lowercase		):
    _UpperCAmelCase    			=   [jsonl_path]
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,cache_dir=lowercase		).read()
  _check_json_dataset(lowercase ,lowercase		)






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase=("train",)		):







  """simple docstring"""
  assert isinstance(lowercase ,lowercase		)
  for split in splits:
    _UpperCAmelCase    			=   dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("""keep_in_memory""" ,[False, True]		)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _UpperCAmelCase    			=   JsonDatasetReader({"""train""": jsonl_path} ,cache_dir=lowercase ,keep_in_memory=lowercase		).read()
  _check_json_datasetdict(lowercase ,lowercase		)






@pytest.mark.parametrize(
    """features""" ,[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase    			=   features.copy() if features else default_expected_features
  _UpperCAmelCase    			=   (
      Features({feature: Value(lowercase		) for feature, dtype in features.items()}		) if features is not None else None
  )
  _UpperCAmelCase    			=   JsonDatasetReader({"""train""": jsonl_path} ,features=lowercase ,cache_dir=lowercase		).read()
  _check_json_datasetdict(lowercase ,lowercase		)






@pytest.mark.parametrize("""split""" ,[None, NamedSplit("""train"""		), """train""", """test"""]		)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  if split:
    _UpperCAmelCase    			=   {split: jsonl_path}
  else:
    _UpperCAmelCase    			=   """train"""
    _UpperCAmelCase    			=   {"""train""": jsonl_path, """test""": jsonl_path}
  _UpperCAmelCase    			=   tmp_path / """cache"""
  _UpperCAmelCase    			=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _UpperCAmelCase    			=   JsonDatasetReader(lowercase ,cache_dir=lowercase		).read()
  _check_json_datasetdict(lowercase ,lowercase ,splits=list(path.keys()		)		)
  assert all(dataset[split].split == split for split in path.keys()		)






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  return json.load(lowercase		)






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  return [json.loads(lowercase		) for line in buffer]


class 							a					:


    @pytest.mark.parametrize("""lines, load_json_function""" ,					[(True, load_json_lines), (False, load_json)]       )
    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[Any]       ):
      with io.BytesIO() as buffer:
        JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					lines=__lowerCAmelCase       ).write()
        buffer.seek(0       )
        _UpperCAmelCase    			=   load_json_function(__lowerCAmelCase       )
      assert isinstance(__lowerCAmelCase ,					__lowerCAmelCase       )
      assert isinstance(exported_content[0] ,					__lowerCAmelCase       )
      assert len(__lowerCAmelCase       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,					[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,					)
    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple       ):
      with io.BytesIO() as buffer:
        JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					lines=__lowerCAmelCase ,					orient=__lowerCAmelCase       ).write()
        buffer.seek(0       )
        _UpperCAmelCase    			=   load_json(__lowerCAmelCase       )
      assert isinstance(__lowerCAmelCase ,					__lowerCAmelCase       )
      if keys:
        if container is dict:
          assert exported_content.keys() == keys
        else:
          assert exported_content[0].keys() == keys
      else:
        assert not hasattr(__lowerCAmelCase ,					"""keys"""       ) and not hasattr(exported_content[0] ,					"""keys"""       )
      if len_at:
        assert len(exported_content[len_at]       ) == 10
      else:
        assert len(__lowerCAmelCase       ) == 10


    @pytest.mark.parametrize("""lines, load_json_function""" ,					[(True, load_json_lines), (False, load_json)]       )
    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	str       ):
      with io.BytesIO() as buffer:
        JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					lines=__lowerCAmelCase ,					num_proc=2       ).write()
        buffer.seek(0       )
        _UpperCAmelCase    			=   load_json_function(__lowerCAmelCase       )
      assert isinstance(__lowerCAmelCase ,					__lowerCAmelCase       )
      assert isinstance(exported_content[0] ,					__lowerCAmelCase       )
      assert len(__lowerCAmelCase       ) == 10


    @pytest.mark.parametrize(
        """orient, container, keys, len_at""" ,					[
            ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
            ("""split""", dict, {"""columns""", """data"""}, """data"""),
            ("""index""", dict, set("""0123456789"""       ), None),
            ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
            ("""values""", list, None, None),
            ("""table""", dict, {"""schema""", """data"""}, """data"""),
        ] ,					)
    def   lowerCAmelCase_	(       self						:	int ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Optional[Any]       ):
      with io.BytesIO() as buffer:
        JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					lines=__lowerCAmelCase ,					orient=__lowerCAmelCase ,					num_proc=2       ).write()
        buffer.seek(0       )
        _UpperCAmelCase    			=   load_json(__lowerCAmelCase       )
      assert isinstance(__lowerCAmelCase ,					__lowerCAmelCase       )
      if keys:
        if container is dict:
          assert exported_content.keys() == keys
        else:
          assert exported_content[0].keys() == keys
      else:
        assert not hasattr(__lowerCAmelCase ,					"""keys"""       ) and not hasattr(exported_content[0] ,					"""keys"""       )
      if len_at:
        assert len(exported_content[len_at]       ) == 10
      else:
        assert len(__lowerCAmelCase       ) == 10


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any]       ):
      with pytest.raises(__lowerCAmelCase       ):
        with io.BytesIO() as buffer:
          JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					num_proc=0       )


    @pytest.mark.parametrize("""compression, extension""" ,					[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]       )
    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   tmp_path_factory.mktemp("""data"""       ) / f'''test.json.{extension}'''
      _UpperCAmelCase    			=   str(shared_datadir / f'''test_file.json.{extension}'''       )
      JsonDatasetWriter(__lowerCAmelCase ,					__lowerCAmelCase ,					compression=__lowerCAmelCase       ).write()

      with fsspec.open(__lowerCAmelCase ,					"""rb""" ,					compression="""infer"""       ) as f:
        _UpperCAmelCase    			=   f.read()
      with fsspec.open(__lowerCAmelCase ,					"""rb""" ,					compression="""infer"""       ) as f:
        _UpperCAmelCase    			=   f.read()
      assert exported_content == original_content

"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


class 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      train_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      # same for validation dataset
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
