
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'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__lowerCAmelCase									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
__lowerCAmelCase									=   'tiny-wmt19-en-de'
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de




