





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         LayoutLMvaForQuestionAnswering,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaModel,
     )
     from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Dict       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Tuple=2       , UpperCAmelCase_				:   Dict=3       , UpperCAmelCase_				:   Dict=4       , UpperCAmelCase_				:   List[str]=2       , UpperCAmelCase_				:   Optional[Any]=7       , UpperCAmelCase_				:   int=True       , UpperCAmelCase_				:   Optional[Any]=True       , UpperCAmelCase_				:   List[Any]=True       , UpperCAmelCase_				:   Any=True       , UpperCAmelCase_				:   Any=99       , UpperCAmelCase_				:   Optional[int]=36       , UpperCAmelCase_				:   Dict=3       , UpperCAmelCase_				:   int=4       , UpperCAmelCase_				:   List[str]=37       , UpperCAmelCase_				:   Optional[Any]="gelu"       , UpperCAmelCase_				:   Tuple=0.1       , UpperCAmelCase_				:   Union[str, Any]=0.1       , UpperCAmelCase_				:   List[str]=512       , UpperCAmelCase_				:   int=16       , UpperCAmelCase_				:   int=2       , UpperCAmelCase_				:   Optional[int]=0.02       , UpperCAmelCase_				:   int=6       , UpperCAmelCase_				:   Dict=6       , UpperCAmelCase_				:   Any=3       , UpperCAmelCase_				:   int=4       , UpperCAmelCase_				:   Optional[Any]=None       , UpperCAmelCase_				:   Optional[Any]=1_000       , )       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =parent
   lowerCamelCase__:      Union[str, Any]			  =batch_size
   lowerCamelCase__:      Dict			  =num_channels
   lowerCamelCase__:      int			  =image_size
   lowerCamelCase__:      List[Any]			  =patch_size
   lowerCamelCase__:      Union[str, Any]			  =text_seq_length
   lowerCamelCase__:      str			  =is_training
   lowerCamelCase__:      Dict			  =use_input_mask
   lowerCamelCase__:      Optional[Any]			  =use_token_type_ids
   lowerCamelCase__:      List[str]			  =use_labels
   lowerCamelCase__:      int			  =vocab_size
   lowerCamelCase__:      Optional[Any]			  =hidden_size
   lowerCamelCase__:      Tuple			  =num_hidden_layers
   lowerCamelCase__:      Optional[Any]			  =num_attention_heads
   lowerCamelCase__:      Optional[int]			  =intermediate_size
   lowerCamelCase__:      Union[str, Any]			  =hidden_act
   lowerCamelCase__:      Union[str, Any]			  =hidden_dropout_prob
   lowerCamelCase__:      Dict			  =attention_probs_dropout_prob
   lowerCamelCase__:      Any			  =max_position_embeddings
   lowerCamelCase__:      Tuple			  =type_vocab_size
   lowerCamelCase__:      str			  =type_sequence_label_size
   lowerCamelCase__:      Optional[Any]			  =initializer_range
   lowerCamelCase__:      Optional[int]			  =coordinate_size
   lowerCamelCase__:      Any			  =shape_size
   lowerCamelCase__:      Optional[Any]			  =num_labels
   lowerCamelCase__:      Optional[int]			  =num_choices
   lowerCamelCase__:      int			  =scope
   lowerCamelCase__:      str			  =range_bbox

   # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
   lowerCamelCase__:      str			  =text_seq_length
   lowerCamelCase__:      List[Any]			  =(image_size // patch_size) ** 2 + 1
   lowerCamelCase__:      List[Any]			  =self.text_seq_length + self.image_seq_length

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =ids_tensor([self.batch_size, self.text_seq_length]       , self.vocab_size)

   lowerCamelCase__:      Union[str, Any]			  =ids_tensor([self.batch_size, self.text_seq_length, 4]       , self.range_bbox)
   # Ensure that bbox is legal
   for i in range(bbox.shape[0]):
     for j in range(bbox.shape[1]):
       if bbox[i, j, 3] < bbox[i, j, 1]:
         lowerCamelCase__:      Dict			  =bbox[i, j, 3]
         lowerCamelCase__:      Union[str, Any]			  =bbox[i, j, 1]
         lowerCamelCase__:      str			  =t
       if bbox[i, j, 2] < bbox[i, j, 0]:
         lowerCamelCase__:      Tuple			  =bbox[i, j, 2]
         lowerCamelCase__:      Any			  =bbox[i, j, 0]
         lowerCamelCase__:      Optional[Any]			  =t

   lowerCamelCase__:      str			  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__:      Union[str, Any]			  =None
   if self.use_input_mask:
     lowerCamelCase__:      Optional[int]			  =random_attention_mask([self.batch_size, self.text_seq_length])

   lowerCamelCase__:      Any			  =None
   if self.use_token_type_ids:
     lowerCamelCase__:      Any			  =ids_tensor([self.batch_size, self.text_seq_length]       , self.type_vocab_size)

   lowerCamelCase__:      str			  =None
   lowerCamelCase__:      List[Any]			  =None
   if self.use_labels:
     lowerCamelCase__:      Tuple			  =ids_tensor([self.batch_size]       , self.type_sequence_label_size)
     lowerCamelCase__:      Tuple			  =ids_tensor([self.batch_size, self.text_seq_length]       , self.num_labels)

   lowerCamelCase__:      str			  =LayoutLMvaConfig(
       vocab_size=self.vocab_size       , hidden_size=self.hidden_size       , num_hidden_layers=self.num_hidden_layers       , num_attention_heads=self.num_attention_heads       , intermediate_size=self.intermediate_size       , hidden_act=self.hidden_act       , hidden_dropout_prob=self.hidden_dropout_prob       , attention_probs_dropout_prob=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , type_vocab_size=self.type_vocab_size       , initializer_range=self.initializer_range       , coordinate_size=self.coordinate_size       , shape_size=self.shape_size       , input_size=self.image_size       , patch_size=self.patch_size       , )

   return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =LayoutLMvaModel(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()

   # text + image
   lowerCamelCase__:      Optional[int]			  =model(UpperCAmelCase_       , pixel_values=UpperCAmelCase_)
   lowerCamelCase__:      Tuple			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_)
   lowerCamelCase__:      Any			  =model(UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_)
   lowerCamelCase__:      int			  =model(UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_)

   self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))

   # text only
   lowerCamelCase__:      str			  =model(UpperCAmelCase_)
   self.parent.assertEqual(
       result.last_hidden_state.shape       , (self.batch_size, self.text_seq_length, self.hidden_size))

   # image only
   lowerCamelCase__:      int			  =model(pixel_values=UpperCAmelCase_)
   self.parent.assertEqual(
       result.last_hidden_state.shape       , (self.batch_size, self.image_seq_length, self.hidden_size))

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Union[str, Any]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[Any]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[Any])       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.num_labels
   lowerCamelCase__:      Optional[Any]			  =LayoutLMvaForSequenceClassification(UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      int			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , labels=UpperCAmelCase_       , )
   self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Optional[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.num_labels
   lowerCamelCase__:      List[str]			  =LayoutLMvaForTokenClassification(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      Union[str, Any]			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , labels=UpperCAmelCase_       , )
   self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.text_seq_length, self.num_labels))

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   Tuple       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   List[str])       ->Tuple:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =LayoutLMvaForQuestionAnswering(config=UpperCAmelCase_)
   model.to(UpperCAmelCase_)
   model.eval()
   lowerCamelCase__:      Union[str, Any]			  =model(
       UpperCAmelCase_       , bbox=UpperCAmelCase_       , pixel_values=UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , token_type_ids=UpperCAmelCase_       , start_positions=UpperCAmelCase_       , end_positions=UpperCAmelCase_       , )
   self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length))
   self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length))







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					(
       lowerCamelCase__
   )  ,					
   ):      List[Any]			  =config_and_inputs
   lowerCamelCase__:      List[str]			  ={
       "input_ids": input_ids,
       "bbox": bbox,
       "pixel_values": pixel_values,
       "token_type_ids": token_type_ids,
       "attention_mask": input_mask,
   }
   return config, inputs_dict



@require_torch
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							unittest.TestCase    ):







 '''simple docstring'''
 lowercase_		=			False
 lowercase_		=			False
 lowercase_		=			False

 lowercase_		=			(
     (
         LayoutLMvaModel,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 lowercase_		=			(
     {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
     if is_torch_available()
     else {}
 )

 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Dict       , UpperCAmelCase_				:   Optional[int]       , UpperCAmelCase_				:   str       , UpperCAmelCase_				:   Any)       ->Optional[int]:



   '''simple docstring'''



   return True

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =LayoutLMvaModelTester(self)
   lowerCamelCase__:      Union[str, Any]			  =ConfigTester(self       , config_class=UpperCAmelCase_       , hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str]       , UpperCAmelCase_				:   List[Any]       , UpperCAmelCase_				:   Any       , UpperCAmelCase_				:   List[Any]=False)       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =copy.deepcopy(UpperCAmelCase_)
   if model_class in get_values(UpperCAmelCase_):
     lowerCamelCase__:      Union[str, Any]			  ={
         k: v.unsqueeze(1).expand(-1       , self.model_tester.num_choices       , -1).contiguous()
         if isinstance(UpperCAmelCase_       , torch.Tensor) and v.ndim > 1
         else v
         for k, v in inputs_dict.items()
     }
   if return_labels:
     if model_class in get_values(UpperCAmelCase_):
       lowerCamelCase__:      Optional[int]			  =torch.ones(self.model_tester.batch_size       , dtype=torch.long       , device=UpperCAmelCase_)
     elif model_class in get_values(UpperCAmelCase_):
       lowerCamelCase__:      int			  =torch.zeros(
           self.model_tester.batch_size       , dtype=torch.long       , device=UpperCAmelCase_)
       lowerCamelCase__:      int			  =torch.zeros(
           self.model_tester.batch_size       , dtype=torch.long       , device=UpperCAmelCase_)
     elif model_class in [
         *get_values(UpperCAmelCase_),
     ]:
       lowerCamelCase__:      Union[str, Any]			  =torch.zeros(
           self.model_tester.batch_size       , dtype=torch.long       , device=UpperCAmelCase_)
     elif model_class in [
         *get_values(UpperCAmelCase_),
     ]:
       lowerCamelCase__:      int			  =torch.zeros(
           (self.model_tester.batch_size, self.model_tester.text_seq_length)       , dtype=torch.long       , device=UpperCAmelCase_       , )

   return inputs_dict

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->int:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[int]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
     lowerCamelCase__:      Union[str, Any]			  =type
     self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Union[str, Any]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Union[str, Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      List[str]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      List[Any]			  =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Union[str, Any]:



   '''simple docstring'''



   for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     lowerCamelCase__:      int			  =LayoutLMvaModel.from_pretrained(UpperCAmelCase_)
     self.assertIsNotNone(UpperCAmelCase_)





def      lowerCAmelCase_ (       )     ->		Dict:







  """simple docstring"""





  lowerCamelCase__:      str			  =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_torch
class    _SCREAMING_SNAKE_CASE						(	unittest.TestCase    ):







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->str:



   '''simple docstring'''



   return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase_) if is_vision_available() else None







 @slow
 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base").to(UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =self.default_image_processor
   lowerCamelCase__:      List[Any]			  =prepare_img()
   lowerCamelCase__:      Union[str, Any]			  =image_processor(images=UpperCAmelCase_       , return_tensors="pt").pixel_values.to(UpperCAmelCase_)

   lowerCamelCase__:      Any			  =torch.tensor([[1, 2]])
   lowerCamelCase__:      str			  =torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)

   # forward pass
   lowerCamelCase__:      Tuple			  =model(
       input_ids=input_ids.to(UpperCAmelCase_)       , bbox=bbox.to(UpperCAmelCase_)       , pixel_values=pixel_values.to(UpperCAmelCase_)       , )

   # verify the logits
   lowerCamelCase__:      str			  =torch.Size((1, 199, 768))
   self.assertEqual(outputs.last_hidden_state.shape       , UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =torch.tensor(
       [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]).to(UpperCAmelCase_)

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








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_ (       __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      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():
    lowerCamelCase__:      int			  =ParquetDatasetReader(__a	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_dataset(__a	,    __a		)





@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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





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





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







  """simple docstring"""





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





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







  """simple docstring"""





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





def      lowerCAmelCase_ (       __a	,    __a	,    __a=("train",)		)     ->		Union[str, Any]:







  """simple docstring"""





  assert isinstance(__a	,    __a		)
  for split in splits:
    lowerCamelCase__:      Optional[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      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:      Any			  =tmp_path / "cache"
  lowerCamelCase__:      str			  ={"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    lowerCamelCase__:      List[str]			  =ParquetDatasetReader(
        {"train": parquet_path}	,    cache_dir=__a	,    keep_in_memory=__a		).read()
  _check_parquet_datasetdict(__a	,    __a		)





@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_ (       __a	,    __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





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





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







  """simple docstring"""





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





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =ParquetDatasetWriter(__a	,    tmp_path / "foo.parquet"		)
  assert writer.write() > 0
  lowerCamelCase__:      Tuple			  =pq.ParquetFile(tmp_path / "foo.parquet"		)
  lowerCamelCase__:      Optional[int]			  =pf.read()
  assert dataset.data.table == output_table





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





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

  lowerCamelCase__:      Optional[Any]			  =Dataset.from_parquet(str(tmp_path / "foo.parquet"		)		)
  assert dataset.features == reloaded_dataset.features

  lowerCamelCase__:      List[str]			  =ParquetDatasetReader(str(tmp_path / "foo.parquet"		)	,    streaming=__a		).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_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  assert get_writer_batch_size(__a		) == expected

