


"""simple docstring"""





import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import NatBackbone, NatForImageClassification, NatModel
 from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 		snake_case_:






     def __init__(						self	:       str       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       str=1_3       ,					UpperCamelCase_	:       Dict=6_4       ,					UpperCamelCase_	:       Any=4       ,					UpperCamelCase_	:       List[Any]=3       ,					UpperCamelCase_	:       int=1_6       ,					UpperCamelCase_	:       Optional[int]=[1, 2, 1]       ,					UpperCamelCase_	:       str=[2, 4, 8]       ,					UpperCamelCase_	:       int=3       ,					UpperCamelCase_	:       Any=2.0       ,					UpperCamelCase_	:       Union[str, Any]=True       ,					UpperCamelCase_	:       Union[str, Any]=0.0       ,					UpperCamelCase_	:       Any=0.0       ,					UpperCamelCase_	:       Optional[Any]=0.1       ,					UpperCamelCase_	:       int="gelu"       ,					UpperCamelCase_	:       int=True       ,					UpperCamelCase_	:       Union[str, Any]=0.02       ,					UpperCamelCase_	:       List[Any]=1E-5       ,					UpperCamelCase_	:       str=True       ,					UpperCamelCase_	:       List[Any]=None       ,					UpperCamelCase_	:       List[Any]=True       ,					UpperCamelCase_	:       Tuple=1_0       ,					UpperCamelCase_	:       Any=["stage1", "stage2"]       ,					UpperCamelCase_	:       Union[str, Any]=[1, 2]       ,					):
           lowerCAmelCase       :						int							= parent
           lowerCAmelCase       :						Optional[int]							= batch_size
           lowerCAmelCase       :						Any							= image_size
           lowerCAmelCase       :						str							= patch_size
           lowerCAmelCase       :						Dict							= num_channels
           lowerCAmelCase       :						Optional[Any]							= embed_dim
           lowerCAmelCase       :						Any							= depths
           lowerCAmelCase       :						int							= num_heads
           lowerCAmelCase       :						Any							= kernel_size
           lowerCAmelCase       :						Union[str, Any]							= mlp_ratio
           lowerCAmelCase       :						Tuple							= qkv_bias
           lowerCAmelCase       :						Dict							= hidden_dropout_prob
           lowerCAmelCase       :						int							= attention_probs_dropout_prob
           lowerCAmelCase       :						Optional[int]							= drop_path_rate
           lowerCAmelCase       :						int							= hidden_act
           lowerCAmelCase       :						int							= patch_norm
           lowerCAmelCase       :						Dict							= layer_norm_eps
           lowerCAmelCase       :						List[str]							= initializer_range
           lowerCAmelCase       :						int							= is_training
           lowerCAmelCase       :						Optional[Any]							= scope
           lowerCAmelCase       :						Any							= use_labels
           lowerCAmelCase       :						str							= num_labels
           lowerCAmelCase       :						int							= out_features
           lowerCAmelCase       :						Dict							= out_indices






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						List[Any]							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

           lowerCAmelCase       :						Union[str, Any]							= self.get_config()

           return config, pixel_values, labels






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           return NatConfig(
               num_labels=self.num_labels       ,					image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					embed_dim=self.embed_dim       ,					depths=self.depths       ,					num_heads=self.num_heads       ,					kernel_size=self.kernel_size       ,					mlp_ratio=self.mlp_ratio       ,					qkv_bias=self.qkv_bias       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					drop_path_rate=self.drop_path_rate       ,					hidden_act=self.hidden_act       ,					patch_norm=self.patch_norm       ,					layer_norm_eps=self.layer_norm_eps       ,					initializer_range=self.initializer_range       ,					out_features=self.out_features       ,					out_indices=self.out_indices       ,					)






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Union[str, Any]       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       int					):
           lowerCAmelCase       :						Optional[Any]							= NatModel(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Any							= model(__snake_case					)

           lowerCAmelCase       :						List[Any]							= (config.image_size // config.patch_size) // (2 ** (len(config.depths					) - 1))
           lowerCAmelCase       :						Dict							= int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

           self.parent.assertEqual(
               result.last_hidden_state.shape       ,					(self.batch_size, expected_height, expected_width, expected_dim)					)






     def   lowerCamelCase__  (						self	:       Any       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						str							= NatForImageClassification(__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Any							= model(__snake_case       ,					labels=__snake_case					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)					)

           # test greyscale images
           lowerCAmelCase       :						Tuple							= 1
           lowerCAmelCase       :						Union[str, Any]							= NatForImageClassification(__snake_case					)
           model.to(__snake_case					)
           model.eval()

           lowerCAmelCase       :						int							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
           lowerCAmelCase       :						str							= model(__snake_case					)
           self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)					)






     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Optional[int]       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						Any							= NatBackbone(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Dict							= model(__snake_case					)

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps					)       ,					len(config.out_features					)					)
           self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,					[self.batch_size, model.channels[0], 1_6, 1_6]					)

           # verify channels
           self.parent.assertEqual(len(model.channels					)       ,					len(config.out_features					)					)

           # verify backbone works with out_features=None
           lowerCAmelCase       :						Any							= None
           lowerCAmelCase       :						str							= NatBackbone(config=__snake_case					)
           model.to(__snake_case					)
           model.eval()
           lowerCAmelCase       :						Optional[int]							= model(__snake_case					)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps					)       ,					1					)
           self.parent.assertListEqual(list(result.feature_maps[0].shape					)       ,					[self.batch_size, model.channels[-1], 4, 4]					)

           # verify channels
           self.parent.assertEqual(len(model.channels					)       ,					1					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						List[str]							= self.prepare_config_and_inputs()
           lowerCAmelCase       :						Any							= config_and_inputs
           lowerCAmelCase       :						int							= {'pixel_values': pixel_values}
           return config, inputs_dict



@require_natten
@require_torch
class 		snake_case_(							__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase      ):
     __UpperCamelCase	=       (
         (
             NatModel,
             NatForImageClassification,
             NatBackbone,
         )
         if is_torch_available()
         else ()
     )
     __UpperCamelCase	=       (
         {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
         if is_torch_available()
         else {}
     )
     __UpperCamelCase	=       False

     __UpperCamelCase	=       False
     __UpperCamelCase	=       False
     __UpperCamelCase	=       False
     __UpperCamelCase	=       False






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						str							= NatModelTester(self					)
           lowerCAmelCase       :						Tuple							= ConfigTester(self       ,					config_class=__snake_case       ,					embed_dim=3_7					)






     def   lowerCamelCase__  (						self	:       Dict					):
           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   lowerCamelCase__  (						self	:       List[Any]					):
           return






     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						Union[str, Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__snake_case					)






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						Optional[Any]							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__snake_case					)






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						Tuple							= self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__snake_case					)






     @unittest.skip(reason='''Nat does not use inputs_embeds'''					)
     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           pass






     @unittest.skip(reason='''Nat does not use feedforward chunking'''					)
     def   lowerCamelCase__  (						self	:       Optional[int]					):
           pass






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase       :						Optional[Any]							= self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase       :						Any							= model_class(__snake_case					)
                 self.assertIsInstance(model.get_input_embeddings()       ,					(nn.Module)					)
                 lowerCAmelCase       :						int							= model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__snake_case       ,					nn.Linear					)					)






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase       :						Tuple							= self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 lowerCAmelCase       :						Tuple							= model_class(__snake_case					)
                 lowerCAmelCase       :						Union[str, Any]							= inspect.signature(model.forward					)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCAmelCase       :						Optional[int]							= [*signature.parameters.keys()]

                 lowerCAmelCase       :						Tuple							= ['pixel_values']
                 self.assertListEqual(arg_names[:1]       ,					__snake_case					)






     def   lowerCamelCase__  (						self	:       List[str]					):
           self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''					)






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       Optional[Any]       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       Any       ,					UpperCamelCase_	:       Union[str, Any]					):
           lowerCAmelCase       :						Optional[Any]							= model_class(__snake_case					)
           model.to(__snake_case					)
           model.eval()

           with torch.no_grad():
                 lowerCAmelCase       :						Optional[int]							= model(**self._prepare_for_class(__snake_case       ,					__snake_case					)					)

           lowerCAmelCase       :						Any							= outputs.hidden_states

           lowerCAmelCase       :						Tuple							= getattr(
               self.model_tester       ,					'''expected_num_hidden_layers'''       ,					len(self.model_tester.depths					) + 1					)
           self.assertEqual(len(__snake_case					)       ,					__snake_case					)

           # Nat has a different seq_length
           lowerCAmelCase       :						int							= (
               config.patch_size
               if isinstance(config.patch_size       ,					collections.abc.Iterable					)
               else (config.patch_size, config.patch_size)
           )

           lowerCAmelCase       :						Optional[Any]							= image_size[0] // patch_size[0]
           lowerCAmelCase       :						Optional[int]							= image_size[1] // patch_size[1]

           self.assertListEqual(
               list(hidden_states[0].shape[-3:]					)       ,					[height, width, self.model_tester.embed_dim]       ,					)

           if model_class.__name__ != "NatBackbone":
                 lowerCAmelCase       :						List[str]							= outputs.reshaped_hidden_states
                 self.assertEqual(len(__snake_case					)       ,					__snake_case					)

                 lowerCAmelCase       :						Dict							= reshaped_hidden_states[0].shape
                 lowerCAmelCase       :						List[Any]							= (
                     reshaped_hidden_states[0].view(__snake_case       ,					__snake_case       ,					__snake_case       ,					__snake_case					).permute(0       ,					2       ,					3       ,					1					)
                 )
                 self.assertListEqual(
                     list(reshaped_hidden_states.shape[-3:]					)       ,					[height, width, self.model_tester.embed_dim]       ,					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						Dict							= self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase       :						str							= (
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size       ,					collections.abc.Iterable					)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                 lowerCAmelCase       :						Optional[Any]							= True
                 self.check_hidden_states_output(__snake_case       ,					__snake_case       ,					__snake_case       ,					__snake_case					)

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

                 self.check_hidden_states_output(__snake_case       ,					__snake_case       ,					__snake_case       ,					__snake_case					)






     @slow
     def   lowerCamelCase__  (						self	:       List[Any]					):





           for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase       :						List[Any]							= NatModel.from_pretrained(__snake_case					)
                 self.assertIsNotNone(__snake_case					)






     def   lowerCamelCase__  (						self	:       Any					):
           lowerCAmelCase       :						List[str]							= self.model_tester.prepare_config_and_inputs_for_common()

           lowerCAmelCase       :						Tuple							= _config_zero_init(__snake_case					)
           for model_class in self.all_model_classes:
                 lowerCAmelCase       :						Union[str, Any]							= model_class(config=__snake_case					)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()       ,					[0.0, 1.0]       ,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''       ,					)



@require_natten
@require_vision
@require_torch
class 		snake_case_(							unittest.TestCase      ):






     @cached_property
     def   lowerCamelCase__  (						self	:       List[str]					):
           return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''					) if is_vision_available() else None






     @slow
     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase       :						List[str]							= NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''					).to(__snake_case					)
           lowerCAmelCase       :						Union[str, Any]							= self.default_image_processor

           lowerCAmelCase       :						Optional[int]							= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
           lowerCAmelCase       :						Dict							= image_processor(images=__snake_case       ,					return_tensors='''pt'''					).to(__snake_case					)

           # forward pass
           with torch.no_grad():
                 lowerCAmelCase       :						Tuple							= model(**__snake_case					)

           # verify the logits
           lowerCAmelCase       :						Optional[int]							= torch.Size((1, 1_0_0_0)					)
           self.assertEqual(outputs.logits.shape       ,					__snake_case					)
           lowerCAmelCase       :						Optional[Any]							= torch.tensor([0.3_805, -0.8_676, -0.3_912]					).to(__snake_case					)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,					__snake_case       ,					atol=1E-4					)					)



@require_torch
@require_natten
class 		snake_case_(							unittest.TestCase				,       __UpperCAmelCase      ):
     __UpperCamelCase	=       (NatBackbone,) if is_torch_available() else ()
     __UpperCamelCase	=       NatConfig






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase       :						int							= NatModelTester(self					)









'''simple docstring'''






import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def    lowercase_   (		lowercase__					,	lowercase__    ) ->List[str]:
						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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->str:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						_snake_case: str	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: Union[str, Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: List[str]	       =		features.copy() if features else default_expected_features
						_snake_case: str	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Optional[int]	       =		CsvDatasetReader(lowercase__					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						_snake_case: Dict	       =		tmp_path / 'cache'
						_snake_case: int	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__					,	split=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )
						assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'					,	[str, list]    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[str]:
						if issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Optional[Any]	       =		csv_path
						elif issubclass(lowercase__					,	lowercase__    ):
												_snake_case: Tuple	       =		[csv_path]
						_snake_case: Tuple	       =		tmp_path / 'cache'
						_snake_case: Any	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Union[str, Any]	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_dataset(lowercase__					,	lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__=("train",)    ) ->str:
						assert isinstance(lowercase__					,	lowercase__    )
						for split in splits:
												_snake_case: Optional[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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: Optional[Any]	       =		tmp_path / 'cache'
						_snake_case: Optional[Any]	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_snake_case: Dict	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__					,	keep_in_memory=lowercase__    ).read()
						_check_csv_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    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->Optional[int]:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
						_snake_case: str	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: str	       =		features.copy() if features else default_expected_features
						_snake_case: int	       =		(
						    Features({feature: Value(lowercase__    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						_snake_case: Tuple	       =		CsvDatasetReader({'train': csv_path}					,	features=lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__    )





@pytest.mark.parametrize('split'					,	[None, NamedSplit('train'    ), 'train', 'test']    )
def    lowercase_   (		lowercase__					,	lowercase__					,	lowercase__    ) ->List[Any]:
						if split:
												_snake_case: Optional[Any]	       =		{split: csv_path}
						else:
												_snake_case: Tuple	       =		{'train': csv_path, 'test': csv_path}
						_snake_case: Union[str, Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
						_snake_case: Any	       =		CsvDatasetReader(lowercase__					,	cache_dir=lowercase__    ).read()
						_check_csv_datasetdict(lowercase__					,	lowercase__					,	splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )





def    lowercase_   (		lowercase__    ) ->Union[str, Any]:
						with open(lowercase__					,	encoding='utf-8'    ) as csvfile:
												yield from csv.reader(lowercase__    )





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[Any]	       =		tmp_path / 'cache'
						_snake_case: Dict	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: Optional[Any]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=1    ).write()

						_snake_case: Optional[int]	       =		iter_csv_file(lowercase__    )
						_snake_case: List[Any]	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->str:
						_snake_case: List[str]	       =		tmp_path / 'cache'
						_snake_case: List[str]	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: List[str]	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=2    ).write()

						_snake_case: Optional[Any]	       =		iter_csv_file(lowercase__    )
						_snake_case: Tuple	       =		iter_csv_file(lowercase__    )

						for rowa, rowa in zip(lowercase__					,	lowercase__    ):
												assert rowa == rowa





def    lowercase_   (		lowercase__					,	lowercase__    ) ->Any:
						_snake_case: Optional[int]	       =		tmp_path / 'cache'
						_snake_case: Any	       =		os.path.join(lowercase__					,	'tmp.csv'    )
						_snake_case: str	       =		CsvDatasetReader({'train': csv_path}					,	cache_dir=lowercase__    ).read()
						with pytest.raises(lowercase__    ):
												CsvDatasetWriter(dataset['train']					,	lowercase__					,	num_proc=0    )





