
import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[int]:


  '''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_ )		->						Any:


  '''simple docstring'''






  lowercase__	: int     		=   tmp_path / """cache"""
  lowercase__	: Dict     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase__	: int     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_			,			keep_in_memory=lowercase_ ).read()
  _check_parquet_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_ )		->						str:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: int     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: int     		=   features.copy() if features else default_expected_features
  lowercase__	: str     		=   (
      Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
  )
  lowercase__	: str     		=   ParquetDatasetReader(lowercase_			,			features=lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )

@pytest.mark.parametrize("""split"""			,			[None, NamedSplit("""train""" ), """train""", """test"""] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Tuple     		=   tmp_path / """cache"""
  lowercase__	: Any     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[Any]     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_			,			split=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )
  assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""			,			[str, list] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  if issubclass(lowercase_			,			lowercase_ ):
    lowercase__	: Any     		=   parquet_path
  elif issubclass(lowercase_			,			lowercase_ ):
    lowercase__	: str     		=   [parquet_path]
  lowercase__	: Any     		=   tmp_path / """cache"""
  lowercase__	: List[Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[str]     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_dataset(lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=("train",) )		->						int:


  '''simple docstring'''






  assert isinstance(lowercase_			,			lowercase_ )
  for split in splits:
    lowercase__	: List[Any]     		=   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_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: Any     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowercase__	: Tuple     		=   ParquetDatasetReader(
        {"""train""": parquet_path}			,			cache_dir=lowercase_			,			keep_in_memory=lowercase_ ).read()
  _check_parquet_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_ )		->						List[str]:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   tmp_path / """cache"""
  lowercase__	: Union[str, Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: List[str]     		=   features.copy() if features else default_expected_features
  lowercase__	: Tuple     		=   (
      Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
  )
  lowercase__	: Union[str, Any]     		=   ParquetDatasetReader({"""train""": parquet_path}			,			features=lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_datasetdict(lowercase_			,			lowercase_ )

@pytest.mark.parametrize("""split"""			,			[None, NamedSplit("""train""" ), """train""", """test"""] )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  if split:
    lowercase__	: Union[str, Any]     		=   {split: parquet_path}
  else:
    lowercase__	: Optional[int]     		=   """train"""
    lowercase__	: List[Any]     		=   {"""train""": parquet_path, """test""": parquet_path}
  lowercase__	: Dict     		=   tmp_path / """cache"""
  lowercase__	: Union[str, Any]     		=   {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  lowercase__	: int     		=   ParquetDatasetReader(lowercase_			,			cache_dir=lowercase_ ).read()
  _check_parquet_datasetdict(lowercase_			,			lowercase_			,			splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Dict     		=   ParquetDatasetWriter(lowercase_			,			tmp_path / """foo.parquet""" )
  assert writer.write() > 0
  lowercase__	: Any     		=   pq.ParquetFile(tmp_path / """foo.parquet""" )
  lowercase__	: Optional[Any]     		=   pf.read()
  assert dataset.data.table == output_table

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Dict:


  '''simple docstring'''






  lowercase__	: List[str]     		=   str(shared_datadir / """test_image_rgb.jpg""" )
  lowercase__	: Any     		=   {"""image""": [image_path]}
  lowercase__	: str     		=   Features({"""image""": Image()} )
  lowercase__	: Dict     		=   Dataset.from_dict(lowercase_			,			features=lowercase_ )
  lowercase__	: Any     		=   ParquetDatasetWriter(lowercase_			,			tmp_path / """foo.parquet""" )
  assert writer.write() > 0

  lowercase__	: Tuple     		=   Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
  assert dataset.features == reloaded_dataset.features

  lowercase__	: Tuple     		=   ParquetDatasetReader(str(tmp_path / """foo.parquet""" )			,			streaming=lowercase_ ).read()
  assert dataset.features == reloaded_iterable_dataset.features



@pytest.mark.parametrize(
    """feature, expected"""			,			[
        (Features({"""foo""": Value("""int32""" )} ), None),
        (Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]			,			)
def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						str:


  '''simple docstring'''






  assert get_writer_batch_size(lowercase_ ) == expected











'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_UpperCamelCase											=		'docs/source/en/_toctree.yml'

def 			a_	(				_lowerCAmelCase			) -> Any:
  __lowerCamelCase			:			Optional[int]       			=					defaultdict(_lowerCAmelCase			)
  __lowerCamelCase			:			Optional[Any]       			=					[]
  __lowerCamelCase			:			Union[str, Any]       			=					[]
  for doc in doc_list:
    if "local" in doc:
      counts[doc["local"]] += 1

    if doc["title"].lower() == "overview":
      overview_doc.append({'local': doc['local'], 'title': doc['title']}			)
    else:
      new_doc_list.append(_lowerCAmelCase			)

  __lowerCamelCase			:			Dict       			=					new_doc_list
  __lowerCamelCase			:			Optional[Any]       			=					[key for key, value in counts.items() if value > 1]

  __lowerCamelCase			:			int       			=					[]
  for duplicate_key in duplicates:
    __lowerCamelCase			:			int       			=					list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}			)
    if len(_lowerCAmelCase			) > 1:
      raise ValueError(
          F'{duplicate_key} is present several times in the documentation table of content at '
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'			)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}			)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]			)
  __lowerCamelCase			:			Dict       			=					sorted(_lowerCAmelCase			,key=lambda _lowerCAmelCase			: s["title"].lower()			)

  # "overview" gets special treatment and is always first
  if len(_lowerCAmelCase			) > 1:
    raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'			)

  overview_doc.extend(_lowerCAmelCase			)

  # Sort
  return overview_doc

def 			a_	(				_lowerCAmelCase=False			) -> Optional[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			str       			=					yaml.safe_load(f.read()			)

  # Get to the API doc
  __lowerCamelCase			:			Union[str, Any]       			=					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __lowerCamelCase			:			List[str]       			=					content[api_idx]['sections']

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[scheduler_idx]["title"] != "Schedulers":
    scheduler_idx += 1

  __lowerCamelCase			:			List[Any]       			=					api_doc[scheduler_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)

  __lowerCamelCase			:			str       			=					False
  if new_scheduler_doc != scheduler_doc:
    __lowerCamelCase			:			int       			=					True
    if overwrite:
      __lowerCamelCase			:			Any       			=					new_scheduler_doc

  if diff:
    if overwrite:
      __lowerCamelCase			:			Tuple       			=					api_doc
      with open(_lowerCAmelCase			,'w'			,encoding='utf-8'			) as f:
        f.write(yaml.dump(_lowerCAmelCase			,allow_unicode=_lowerCAmelCase			)			)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'			)

def 			a_	(				_lowerCAmelCase=False			) -> List[Any]:
  with open(_lowerCAmelCase			,encoding='utf-8'			) as f:
    __lowerCamelCase			:			List[str]       			=					yaml.safe_load(f.read()			)

  # Get to the API doc
  __lowerCamelCase			:			List[Any]       			=					0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  __lowerCamelCase			:			Optional[Any]       			=					content[api_idx]['sections']

  # Then to the model doc
  __lowerCamelCase			:			List[Any]       			=					0
  while api_doc[pipeline_idx]["title"] != "Pipelines":
    pipeline_idx += 1

  __lowerCamelCase			:			int       			=					False
  __lowerCamelCase			:			str       			=					api_doc[pipeline_idx]['sections']
  __lowerCamelCase			:			Optional[Any]       			=					[]

  # sort sub pipeline docs
  for pipeline_doc in pipeline_docs:
    if "section" in pipeline_doc:
      __lowerCamelCase			:			str       			=					pipeline_doc['section']
      __lowerCamelCase			:			Optional[Any]       			=					clean_doc_toc(_lowerCAmelCase			)
      if overwrite:
        __lowerCamelCase			:			Union[str, Any]       			=					new_sub_pipeline_doc
    new_pipeline_docs.append(_lowerCAmelCase			)

  # sort overall pipeline doc
  __lowerCamelCase			:			int       			=					clean_doc_toc(_lowerCAmelCase			)

  if new_pipeline_docs != pipeline_docs:
    __lowerCamelCase			:			Tuple       			=					True
    if overwrite:
      __lowerCamelCase			:			int       			=					new_pipeline_docs

  if diff:
    if overwrite:
      __lowerCamelCase			:			Tuple       			=					api_doc
      with open(_lowerCAmelCase			,'w'			,encoding='utf-8'			) as f:
        f.write(yaml.dump(_lowerCAmelCase			,allow_unicode=_lowerCAmelCase			)			)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'			)


if __name__ == "__main__":
   _UpperCamelCase											=		argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase											=		parser.parse_args()

   check_scheduler_doc(args.fix_and_overwrite)
   check_pipeline_doc(args.fix_and_overwrite)






