




"""simple docstring"""


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
         MODEL_MAPPING,
         LevitForImageClassification,
         LevitForImageClassificationWithTeacher,
         LevitModel,
     )
     from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor






class       lowercase(  __a				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.config_class(**self.inputs_dict	)
   self.parent.assertTrue(hasattr(a_,   """hidden_sizes"""	)	)
   self.parent.assertTrue(hasattr(a_,   """num_attention_heads"""	)	)







class       lowercase:







 '''simple docstring'''





 def __init__(       self:	int,   a_:	Optional[Any],   a_:	Union[str, Any]=13,   a_:	Dict=64,   a_:	Union[str, Any]=3,   a_:	Union[str, Any]=3,   a_:	Optional[int]=2,   a_:	List[str]=1,   a_:	Optional[Any]=16,   a_:	str=[128, 256, 384],   a_:	Optional[int]=[4, 6, 8],   a_:	Optional[Any]=[2, 3, 4],   a_:	Optional[Any]=[16, 16, 16],   a_:	Tuple=0,   a_:	Dict=[2, 2, 2],   a_:	Dict=[2, 2, 2],   a_:	Optional[int]=0.02,   a_:	Optional[int]=True,   a_:	List[Any]=True,   a_:	Optional[int]=2,   ):




   '''simple docstring'''



   _snake_case							:	int			=						parent
   _snake_case							:	Tuple			=						batch_size
   _snake_case							:	int			=						image_size
   _snake_case							:	Dict			=						num_channels
   _snake_case							:	Union[str, Any]			=						kernel_size
   _snake_case							:	Optional[Any]			=						stride
   _snake_case							:	int			=						padding
   _snake_case							:	Optional[int]			=						hidden_sizes
   _snake_case							:	Dict			=						num_attention_heads
   _snake_case							:	List[Any]			=						depths
   _snake_case							:	List[str]			=						key_dim
   _snake_case							:	Dict			=						drop_path_rate
   _snake_case							:	Tuple			=						patch_size
   _snake_case							:	Optional[int]			=						attention_ratio
   _snake_case							:	List[Any]			=						mlp_ratio
   _snake_case							:	Dict			=						initializer_range
   _snake_case							:	List[Any]			=						[
       ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
       ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
   ]
   _snake_case							:	List[Any]			=						is_training
   _snake_case							:	str			=						use_labels
   _snake_case							:	List[str]			=						num_labels
   _snake_case							:	List[str]			=						initializer_range


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   _snake_case							:	Union[str, Any]			=						None
   if self.use_labels:
     _snake_case							:	Tuple			=						ids_tensor([self.batch_size],   self.num_labels	)

   _snake_case							:	str			=						self.get_config()
   return config, pixel_values, labels


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   return LevitConfig(
       image_size=self.image_size,   num_channels=self.num_channels,   kernel_size=self.kernel_size,   stride=self.stride,   padding=self.padding,   patch_size=self.patch_size,   hidden_sizes=self.hidden_sizes,   num_attention_heads=self.num_attention_heads,   depths=self.depths,   key_dim=self.key_dim,   drop_path_rate=self.drop_path_rate,   mlp_ratio=self.mlp_ratio,   attention_ratio=self.attention_ratio,   initializer_range=self.initializer_range,   down_ops=self.down_ops,   )


 def 				UpperCamelCase_     (       self:	str,   a_:	str,   a_:	Union[str, Any],   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Any			=						LevitModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[Any]			=						model(a_	)
   _snake_case							:	Optional[int]			=						(self.image_size, self.image_size)
   _snake_case	,      _snake_case							:	Optional[int]			=						image_size[0], image_size[1]
   for _ in range(4	):
     _snake_case							:	List[Any]			=						floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
     _snake_case							:	str			=						floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
   self.parent.assertEqual(
       result.last_hidden_state.shape,   (self.batch_size, ceil(height / 4	) * ceil(width / 4	), self.hidden_sizes[-1]),   )


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	List[Any],   a_:	Optional[Any],   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.num_labels
   _snake_case							:	Any			=						LevitForImageClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Union[str, Any]			=						model(a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.prepare_config_and_inputs()
   _snake_case	,      _snake_case	,      _snake_case							:	Tuple			=						config_and_inputs
   _snake_case							:	List[str]			=						{"""pixel_values""": pixel_values}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {
         "feature-extraction": LevitModel,
         "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						LevitModelTester(self	)
   _snake_case							:	List[str]			=						ConfigTester(self,   config_class=a_,   has_text_modality=a_,   hidden_size=37	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   return


 @unittest.skip(reason="""Levit does not use inputs_embeds"""	)
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""Levit does not support input and output embeddings"""	)
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   pass


 @unittest.skip(reason="""Levit does not output attentions"""	)
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	str			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	Dict			=						model_class(a_	)
     _snake_case							:	Optional[Any]			=						inspect.signature(model.forward	)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     _snake_case							:	List[Any]			=						[*signature.parameters.keys()]

     _snake_case							:	str			=						["""pixel_values"""]
     self.assertListEqual(arg_names[:1],   a_	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   def check_hidden_states_output(a_:	Optional[int],   a_:	Tuple,   a_:	List[Any]	):
     _snake_case							:	str			=						model_class(a_	)
     model.to(a_	)
     model.eval()

     with torch.no_grad():
       _snake_case							:	Optional[Any]			=						model(**self._prepare_for_class(a_,   a_	)	)

     _snake_case							:	Optional[Any]			=						outputs.hidden_states

     _snake_case							:	Dict			=						len(self.model_tester.depths	) + 1
     self.assertEqual(len(a_	),   a_	)

     _snake_case							:	Union[str, Any]			=						(self.model_tester.image_size, self.model_tester.image_size)
     _snake_case	,      _snake_case							:	List[str]			=						image_size[0], image_size[1]
     for _ in range(4	):
       _snake_case							:	Tuple			=						floor(
           (
               (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
               / self.model_tester.stride
           )
           + 1	)
       _snake_case							:	List[str]			=						floor(
           (
               (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
               / self.model_tester.stride
           )
           + 1	)
     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-2:]	),   [
             height * width,
             self.model_tester.hidden_sizes[0],
         ],   )

   _snake_case	,      _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _snake_case							:	int			=						True
     check_hidden_states_output(a_,   a_,   a_	)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _snake_case							:	int			=						True

     check_hidden_states_output(a_,   a_,   a_	)


 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""	)
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	List[Any],   a_:	List[Any],   a_:	Optional[int]=False	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						super()._prepare_for_class(a_,   a_,   return_labels=a_	)

   if return_labels:
     if model_class.__name__ == "LevitForImageClassificationWithTeacher":
       del inputs_dict["labels"]

   return inputs_dict


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*a_	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   if not self.model_tester.is_training:
     return

   _snake_case	,      _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()
   _snake_case							:	int			=						True

   for model_class in self.all_model_classes:
     # LevitForImageClassificationWithTeacher supports inference-only
     if (
         model_class in get_values(a_	)
         or model_class.__name__ == "LevitForImageClassificationWithTeacher"
     ):
       continue
     _snake_case							:	int			=						model_class(a_	)
     model.to(a_	)
     model.train()
     _snake_case							:	int			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	List[Any]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
     return

   _snake_case							:	Tuple			=						False
   _snake_case							:	int			=						True

   for model_class in self.all_model_classes:
     if model_class in get_values(a_	) or not model_class.supports_gradient_checkpointing:
       continue
     # LevitForImageClassificationWithTeacher supports inference-only
     if model_class.__name__ == "LevitForImageClassificationWithTeacher":
       continue
     _snake_case							:	Optional[Any]			=						model_class(a_	)
     model.gradient_checkpointing_enable()
     model.to(a_	)
     model.train()
     _snake_case							:	int			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)
     _snake_case							:	List[str]			=						model(**a_	).loss
     loss.backward()


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

   _snake_case							:	Optional[Any]			=						[
       {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
       {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
       {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
   ]

   for model_class in self.all_model_classes:
     if (
         model_class
         not in [
             *get_values(a_	),
         ]
         or model_class.__name__ == "LevitForImageClassificationWithTeacher"
     ):
       continue

     for problem_type in problem_types:
       with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"	):
         _snake_case							:	str			=						problem_type["""title"""]
         _snake_case							:	Tuple			=						problem_type["""num_labels"""]

         _snake_case							:	Optional[int]			=						model_class(a_	)
         model.to(a_	)
         model.train()

         _snake_case							:	Any			=						self._prepare_for_class(a_,   a_,   return_labels=a_	)

         if problem_type["num_labels"] > 1:
           _snake_case							:	Tuple			=						inputs["""labels"""].unsqueeze(1	).repeat(1,   problem_type["""num_labels"""]	)

         _snake_case							:	Optional[int]			=						inputs["""labels"""].to(problem_type["""dtype"""]	)

         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
         # they have the same size." which is a symptom something in wrong for the regression problem.
         # See https://github.com/huggingface/transformers/issues/11780
         with warnings.catch_warnings(record=a_	) as warning_list:
           _snake_case							:	Any			=						model(**a_	).loss
         for w in warning_list:
           if "Using a target size that is different to the input size" in str(w.message	):
             raise ValueError(
                 f"Something is going wrong in the regression problem: intercepted {w.message}"	)

         loss.backward()


 @slow
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	List[str]			=						LevitModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)







def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Union[str, Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
  return image






@require_torch
@require_vision
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 @cached_property
 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)


 @slow
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Any			=						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
       a_	)

   _snake_case							:	Any			=						self.default_image_processor
   _snake_case							:	Any			=						prepare_img()
   _snake_case							:	List[str]			=						image_processor(images=a_,   return_tensors="""pt"""	).to(a_	)

   # forward pass
   with torch.no_grad():
     _snake_case							:	Optional[int]			=						model(**a_	)

   # verify the logits
   _snake_case							:	str			=						torch.Size((1, 1_000)	)
   self.assertEqual(outputs.logits.shape,   a_	)

   _snake_case							:	List[Any]			=						torch.tensor([1.0_448, -0.3_745, -1.8_317]	).to(a_	)

   self.assertTrue(torch.allclose(outputs.logits[0, :3],   a_,   atol=1E-4	)	)








"""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       UpperCAmelCase__ (snake_case__							:      Optional[Any]	,     snake_case__							:      Dict					):
  """simple docstring"""

  assert isinstance(snake_case__	,     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       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      Dict					):
  """simple docstring"""

  _snake_case							:	str			=						tmp_path / """cache"""
  _snake_case							:	int			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	str			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     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       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      int	,     snake_case__							:      List[Any]					):
  """simple docstring"""

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


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

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


@pytest.mark.parametrize("""path_type"""	,     [str, list]					)
def       UpperCAmelCase__ (snake_case__							:      Dict	,     snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

  if issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	Optional[Any]			=						parquet_path
  elif issubclass(snake_case__	,     snake_case__					):
    _snake_case							:	int			=						[parquet_path]
  _snake_case							:	Union[str, Any]			=						tmp_path / """cache"""
  _snake_case							:	Tuple			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	List[str]			=						ParquetDatasetReader(snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_dataset(snake_case__	,     snake_case__					)


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]	,     snake_case__							:      str=("train",)					):
  """simple docstring"""

  assert isinstance(snake_case__	,     snake_case__					)
  for split in splits:
    _snake_case							:	int			=						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__ (snake_case__							:      str	,     snake_case__							:      str	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Tuple			=						tmp_path / """cache"""
  _snake_case							:	Optional[int]			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    _snake_case							:	Tuple			=						ParquetDatasetReader(
        {"""train""": parquet_path}	,     cache_dir=snake_case__	,     keep_in_memory=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     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       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Tuple	,     snake_case__							:      List[Any]					):
  """simple docstring"""

  _snake_case							:	Optional[int]			=						tmp_path / """cache"""
  _snake_case							:	Dict			=						{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
  _snake_case							:	Optional[Any]			=						features.copy() if features else default_expected_features
  _snake_case							:	Dict			=						(
      Features({feature: Value(snake_case__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  _snake_case							:	Optional[int]			=						ParquetDatasetReader({"""train""": parquet_path}	,     features=snake_case__	,     cache_dir=snake_case__					).read()
  _check_parquet_datasetdict(snake_case__	,     snake_case__					)


@pytest.mark.parametrize("""split"""	,     [None, NamedSplit("""train"""					), """train""", """test"""]					)
def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      Optional[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0
  _snake_case							:	str			=						pq.ParquetFile(tmp_path / """foo.parquet"""					)
  _snake_case							:	int			=						pf.read()
  assert dataset.data.table == output_table


def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						str(shared_datadir / """test_image_rgb.jpg"""					)
  _snake_case							:	Tuple			=						{"""image""": [image_path]}
  _snake_case							:	Optional[int]			=						Features({"""image""": Image()}					)
  _snake_case							:	int			=						Dataset.from_dict(snake_case__	,     features=snake_case__					)
  _snake_case							:	Optional[Any]			=						ParquetDatasetWriter(snake_case__	,     tmp_path / """foo.parquet"""					)
  assert writer.write() > 0

  _snake_case							:	List[str]			=						Dataset.from_parquet(str(tmp_path / """foo.parquet"""					)					)
  assert dataset.features == reloaded_dataset.features

  _snake_case							:	Optional[Any]			=						ParquetDatasetReader(str(tmp_path / """foo.parquet"""					)	,     streaming=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       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      str					):
  """simple docstring"""

  assert get_writer_batch_size(snake_case__					) == expected


