
'''simple docstring'''



import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard

     from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline

@require_flax
class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			str:
          with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
             _SCREAMING_SNAKE_CASE      =				FlaxDiffusionPipeline.from_pretrained(
                 """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,							safety_checker=A__       ,							cache_dir=A__      )

             _SCREAMING_SNAKE_CASE      =				[t[-1] for t in os.walk(os.path.join(A__       ,							os.listdir(A__      )[0]       ,							"""snapshots"""      )      )]
             _SCREAMING_SNAKE_CASE      =				[item for sublist in all_root_files for item in sublist]

             # None of the downloaded files should be a PyTorch file even if we have some here:
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
             assert not any(f.endswith(""".bin"""      ) for f in files      )






@slow
@require_flax
class    _a				(unittest.TestCase):





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """hf-internal-testing/tiny-stable-diffusion-pipe"""       ,							safety_checker=A__      )

          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.random.PRNGKey(0      )
          _SCREAMING_SNAKE_CASE      =				4

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )

          # shard inputs and rng
          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				jax.random.split(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )

          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							A__       ,							jit=A__      ).images

          assert images.shape == (num_samples, 1, 64, 64, 3)
          if jax.device_count() == 8:
             assert np.abs(np.abs(images[0, 0, :2, :2, -2:]       ,							dtype=np.floataa      ).sum() - 4.151_4745      ) < 1E-3
             assert np.abs(np.abs(A__       ,							dtype=np.floataa      ).sum() - 4_9947.875      ) < 5E-1

          _SCREAMING_SNAKE_CASE      =				pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]      )      )      )
          assert len(A__      ) == num_samples






       def        UpperCamelCase				(							self      )    ->			Optional[int]:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""flax"""       ,							safety_checker=A__      )

          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.random.PRNGKey(0      )
          _SCREAMING_SNAKE_CASE      =				50

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )

          # shard inputs and rng
          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				jax.random.split(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )

          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							A__       ,							jit=A__      ).images

          assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
          if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,							dtype=np.floataa      ).sum() - 0.0565_2401)      ) < 1E-3
             assert np.abs((np.abs(A__       ,							dtype=np.floataa      ).sum() - 238_3808.2)      ) < 5E-1






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""bf16"""       ,							dtype=jnp.bfloataa       ,							safety_checker=A__      )

          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.random.PRNGKey(0      )
          _SCREAMING_SNAKE_CASE      =				50

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )

          # shard inputs and rng
          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				jax.random.split(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )

          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							A__       ,							jit=A__      ).images

          assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
          if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,							dtype=np.floataa      ).sum() - 0.0400_3906)      ) < 1E-3
             assert np.abs((np.abs(A__       ,							dtype=np.floataa      ).sum() - 237_3516.75)      ) < 5E-1






       def        UpperCamelCase				(							self      )    ->			int:
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""bf16"""       ,							dtype=jnp.bfloataa      )

          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.random.PRNGKey(0      )
          _SCREAMING_SNAKE_CASE      =				50

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )

          # shard inputs and rng
          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				jax.random.split(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )

          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							A__       ,							jit=A__      ).images

          assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
          if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,							dtype=np.floataa      ).sum() - 0.0400_3906)      ) < 1E-3
             assert np.abs((np.abs(A__       ,							dtype=np.floataa      ).sum() - 237_3516.75)      ) < 5E-1






       def        UpperCamelCase				(							self      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				FlaxDDIMScheduler(
              beta_start=0.0_0085       ,							beta_end=0.012       ,							beta_schedule="""scaled_linear"""       ,							set_alpha_to_one=A__       ,							steps_offset=1       ,							)

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""bf16"""       ,							dtype=jnp.bfloataa       ,							scheduler=A__       ,							safety_checker=A__       ,							)
          _SCREAMING_SNAKE_CASE      =				scheduler.create_state()

          _SCREAMING_SNAKE_CASE      =				scheduler_state

          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.random.PRNGKey(0      )
          _SCREAMING_SNAKE_CASE      =				50

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )

          # shard inputs and rng
          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				jax.random.split(A__       ,							A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )

          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							A__       ,							jit=A__      ).images

          assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
          if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]       ,							dtype=np.floataa      ).sum() - 0.0_4504_3945)      ) < 1E-3
             assert np.abs((np.abs(A__       ,							dtype=np.floataa      ).sum() - 234_7693.5)      ) < 5E-1






       def        UpperCamelCase				(							self      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				(
              """A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
              """ field, close up, split lighting, cinematic"""
          )

          _SCREAMING_SNAKE_CASE      =				jax.device_count()
          _SCREAMING_SNAKE_CASE      =				num_samples * [prompt]
          _SCREAMING_SNAKE_CASE      =				jax.random.split(jax.random.PRNGKey(0      )       ,							A__      )

          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""bf16"""       ,							dtype=jnp.bfloataa       ,							safety_checker=A__       ,							)

          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )
          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							jit=A__      ).images
          assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
          _SCREAMING_SNAKE_CASE      =				images[2, 0, 2_56, 10:17, 1]

          # With memory efficient attention
          _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				FlaxStableDiffusionPipeline.from_pretrained(
              """CompVis/stable-diffusion-v1-4"""       ,							revision="""bf16"""       ,							dtype=jnp.bfloataa       ,							safety_checker=A__       ,							use_memory_efficient_attention=A__       ,							)

          _SCREAMING_SNAKE_CASE      =				replicate(A__      )
          _SCREAMING_SNAKE_CASE      =				pipeline.prepare_inputs(A__      )
          _SCREAMING_SNAKE_CASE      =				shard(A__      )
          _SCREAMING_SNAKE_CASE      =				pipeline(A__       ,							A__       ,							A__       ,							jit=A__      ).images
          assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
          _SCREAMING_SNAKE_CASE      =				images[2, 0, 2_56, 10:17, 1]

          # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
          # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
          assert abs(slice_eff - slice      ).max() < 1E-2








'''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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      split=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type"""    ,      [str, list]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   if issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				parquet_path
   elif issubclass(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						):
      _SCREAMING_SNAKE_CASE      =				[parquet_path]
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_dataset(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=("train",)						)     ->     List[str]:




   """simple docstring"""
   assert isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   for split in splits:
      _SCREAMING_SNAKE_CASE      =				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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(
          {"""train""": parquet_path}    ,      cache_dir=SCREAMING_SNAKE_CASE_    ,      keep_in_memory=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				features.copy() if features else default_expected_features
   _SCREAMING_SNAKE_CASE      =				(
       Features({feature: Value(SCREAMING_SNAKE_CASE_						) for feature, dtype in features.items()}						) if features is not None else None
   )
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader({"""train""": parquet_path}    ,      features=SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
@pytest.mark.parametrize("""split"""    ,      [None, NamedSplit("""train"""						), """train""", """test"""]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   if split:
      _SCREAMING_SNAKE_CASE      =				{split: parquet_path}
   else:
      _SCREAMING_SNAKE_CASE      =				"""train"""
      _SCREAMING_SNAKE_CASE      =				{"""train""": parquet_path, """test""": parquet_path}
   _SCREAMING_SNAKE_CASE      =				tmp_path / """cache"""
   _SCREAMING_SNAKE_CASE      =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(SCREAMING_SNAKE_CASE_    ,      cache_dir=SCREAMING_SNAKE_CASE_						).read()
   _check_parquet_datasetdict(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      splits=list(path.keys()						)						)
   assert all(dataset[split].split == split for split in path.keys()						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0
   _SCREAMING_SNAKE_CASE      =				pq.ParquetFile(tmp_path / """foo.parquet"""						)
   _SCREAMING_SNAKE_CASE      =				pf.read()
   assert dataset.data.table == output_table
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				str(shared_datadir / """test_image_rgb.jpg"""						)
   _SCREAMING_SNAKE_CASE      =				{"""image""": [image_path]}
   _SCREAMING_SNAKE_CASE      =				Features({"""image""": Image()}						)
   _SCREAMING_SNAKE_CASE      =				Dataset.from_dict(SCREAMING_SNAKE_CASE_    ,      features=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				ParquetDatasetWriter(SCREAMING_SNAKE_CASE_    ,      tmp_path / """foo.parquet"""						)
   assert writer.write() > 0

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

   _SCREAMING_SNAKE_CASE      =				ParquetDatasetReader(str(tmp_path / """foo.parquet"""						)    ,      streaming=SCREAMING_SNAKE_CASE_						).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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   assert get_writer_batch_size(SCREAMING_SNAKE_CASE_						) == expected






