


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       (		__A ,						__A			)						->					Union[str, Any]:



       '''simple docstring'''





       assert isinstance(__A ,						__A			)
       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       (		__A ,						__A ,						__A			)						->					Tuple:



       '''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(__A ,						cache_dir=__A ,						keep_in_memory=__A			).read()
       _check_json_dataset(__A ,						__A			)





@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       (		__A ,						__A ,						__A			)						->					Union[str, Any]:



       '''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(__A			) for feature, dtype in features.items()}			) if features is not None else None
       )
       UpperCAmelCase__										=     JsonDatasetReader(__A ,						features=__A ,						cache_dir=__A			).read()
       _check_json_dataset(__A ,						__A			)





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



       '''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(__A			) for feature, dtype in features.items()}			) if features is not None else None
       )
       UpperCAmelCase__										=     JsonDatasetReader(__A ,						features=__A ,						cache_dir=__A			).read()
       assert isinstance(__A ,						__A			)
       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       (		__A ,						__A			)						->					Tuple:



       '''simple docstring'''





       UpperCAmelCase__										=     {"col_2": "int64", "col_3": "float64", "col_1": "string"}
       UpperCAmelCase__										=     features.copy()
       UpperCAmelCase__										=     (
           Features({feature: Value(__A			) for feature, dtype in features.items()}			) if features is not None else None
       )
       UpperCAmelCase__										=     tmp_path / "cache"
       UpperCAmelCase__										=     JsonDatasetReader(__A ,						features=__A ,						cache_dir=__A			).read()
       assert isinstance(__A ,						__A			)
       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       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





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





@pytest.mark.parametrize("path_type" ,						[str, list]			)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[str]:



       '''simple docstring'''





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





def __UpperCAmelCase       (		__A ,						__A ,						__A=("train",)			)						->					Any:



       '''simple docstring'''





       assert isinstance(__A ,						__A			)
       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       (		__A ,						__A ,						__A			)						->					Tuple:



       '''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=__A ,						keep_in_memory=__A			).read()
       _check_json_datasetdict(__A ,						__A			)





@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       (		__A ,						__A ,						__A			)						->					Union[str, Any]:



       '''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(__A			) for feature, dtype in features.items()}			) if features is not None else None
       )
       UpperCAmelCase__										=     JsonDatasetReader({"train": jsonl_path} ,						features=__A ,						cache_dir=__A			).read()
       _check_json_datasetdict(__A ,						__A			)





@pytest.mark.parametrize("split" ,						[None, NamedSplit("train"			), "train", "test"]			)
def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					int:



       '''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(__A ,						cache_dir=__A			).read()
       _check_json_datasetdict(__A ,						__A ,						splits=list(path.keys()			)			)
       assert all(dataset[split].split == split for split in path.keys()			)





def __UpperCAmelCase       (		__A			)						->					Any:



       '''simple docstring'''





       return json.load(__A			)





def __UpperCAmelCase       (		__A			)						->					List[Any]:



       '''simple docstring'''





       return [json.loads(__A			) for line in buffer]




class     lowercase__  :






  @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)] )
  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Dict		,						_lowercase      :					Optional[int]		,						_lowercase      :					int ):




         """simple docstring"""







         with io.BytesIO() as buffer:
                JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase ).write()
                buffer.seek(0 )
                UpperCAmelCase__										=     load_json_function(_lowercase )
         assert isinstance(_lowercase		,						_lowercase )
         assert isinstance(exported_content[0]		,						_lowercase )
         assert len(_lowercase ) == 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 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Optional[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					int ):




         """simple docstring"""







         with io.BytesIO() as buffer:
                JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						orient=_lowercase ).write()
                buffer.seek(0 )
                UpperCAmelCase__										=     load_json(_lowercase )
         assert isinstance(_lowercase		,						_lowercase )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(_lowercase		,						"keys" ) and not hasattr(exported_content[0]		,						"keys" )
         if len_at:
                assert len(exported_content[len_at] ) == 10
         else:
                assert len(_lowercase ) == 10






  @pytest.mark.parametrize("lines, load_json_function"		,						[(True, load_json_lines), (False, load_json)] )
  def 			_UpperCAmelCase  (					self      :					int		,						_lowercase      :					str		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         with io.BytesIO() as buffer:
                JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						num_proc=2 ).write()
                buffer.seek(0 )
                UpperCAmelCase__										=     load_json_function(_lowercase )
         assert isinstance(_lowercase		,						_lowercase )
         assert isinstance(exported_content[0]		,						_lowercase )
         assert len(_lowercase ) == 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 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[str]		,						_lowercase      :					List[Any]		,						_lowercase      :					Any ):




         """simple docstring"""







         with io.BytesIO() as buffer:
                JsonDatasetWriter(_lowercase		,						_lowercase		,						lines=_lowercase		,						orient=_lowercase		,						num_proc=2 ).write()
                buffer.seek(0 )
                UpperCAmelCase__										=     load_json(_lowercase )
         assert isinstance(_lowercase		,						_lowercase )
         if keys:
                if container is dict:
                       assert exported_content.keys() == keys
                else:
                       assert exported_content[0].keys() == keys
         else:
                assert not hasattr(_lowercase		,						"keys" ) and not hasattr(exported_content[0]		,						"keys" )
         if len_at:
                assert len(exported_content[len_at] ) == 10
         else:
                assert len(_lowercase ) == 10






  def 			_UpperCAmelCase  (					self      :					Union[str, Any]		,						_lowercase      :					int ):




         """simple docstring"""







         with pytest.raises(_lowercase ):
                with io.BytesIO() as buffer:
                       JsonDatasetWriter(_lowercase		,						_lowercase		,						num_proc=0 )






  @pytest.mark.parametrize("compression, extension"		,						[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")] )
  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Tuple		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					List[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     tmp_path_factory.mktemp("data" ) / F"""test.json.{extension}"""
         UpperCAmelCase__										=     str(shared_datadir / F"""test_file.json.{extension}""" )
         JsonDatasetWriter(_lowercase		,						_lowercase		,						compression=_lowercase ).write()

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









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





