


'''simple docstring'''

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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[str]:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					)
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Optional[Any]:
      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       						=      ParquetDatasetReader(__UpperCamelCase	,		cache_dir=__UpperCamelCase	,		keep_in_memory=__UpperCamelCase					).read()
      _check_parquet_dataset(__UpperCamelCase	,		__UpperCamelCase					)







@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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Dict:
      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(__UpperCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
      )
      UpperCamelCase       						=      ParquetDatasetReader(__UpperCamelCase	,		features=__UpperCamelCase	,		cache_dir=__UpperCamelCase					).read()
      _check_parquet_dataset(__UpperCamelCase	,		__UpperCamelCase					)







@pytest.mark.parametrize("""split"""	,		[None, NamedSplit("""train"""					), """train""", """test"""]					)
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      UpperCamelCase       						=      tmp_path / """cache"""
      UpperCamelCase       						=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCamelCase       						=      ParquetDatasetReader(__UpperCamelCase	,		cache_dir=__UpperCamelCase	,		split=__UpperCamelCase					).read()
      _check_parquet_dataset(__UpperCamelCase	,		__UpperCamelCase					)
      assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type"""	,		[str, list]					)
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				str:






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







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=("train",)					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					)
      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      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       						=      ParquetDatasetReader(
                {"""train""": parquet_path}	,		cache_dir=__UpperCamelCase	,		keep_in_memory=__UpperCamelCase					).read()
      _check_parquet_datasetdict(__UpperCamelCase	,		__UpperCamelCase					)







@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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      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(__UpperCamelCase					) for feature, dtype in features.items()}					) if features is not None else None
      )
      UpperCamelCase       						=      ParquetDatasetReader({"""train""": parquet_path}	,		features=__UpperCamelCase	,		cache_dir=__UpperCamelCase					).read()
      _check_parquet_datasetdict(__UpperCamelCase	,		__UpperCamelCase					)







@pytest.mark.parametrize("""split"""	,		[None, NamedSplit("""train"""					), """train""", """test"""]					)
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Optional[Any]:






      if split:
            UpperCamelCase       						=      {split: parquet_path}
      else:
            UpperCamelCase       						=      """train"""
            UpperCamelCase       						=      {"""train""": parquet_path, """test""": parquet_path}
      UpperCamelCase       						=      tmp_path / """cache"""
      UpperCamelCase       						=      {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
      UpperCamelCase       						=      ParquetDatasetReader(__UpperCamelCase	,		cache_dir=__UpperCamelCase					).read()
      _check_parquet_datasetdict(__UpperCamelCase	,		__UpperCamelCase	,		splits=list(path.keys()					)					)
      assert all(dataset[split].split == split for split in path.keys()					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      UpperCamelCase       						=      ParquetDatasetWriter(__UpperCamelCase	,		tmp_path / """foo.parquet"""					)
      assert writer.write() > 0
      UpperCamelCase       						=      pq.ParquetFile(tmp_path / """foo.parquet"""					)
      UpperCamelCase       						=      pf.read()
      assert dataset.data.table == output_table







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

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

      UpperCamelCase       						=      ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)	,		streaming=__UpperCamelCase					).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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      assert get_writer_batch_size(__UpperCamelCase					) == 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

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     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,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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

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

# Save
SCREAMING_SNAKE_CASE__											=     '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
