"""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 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							assert isinstance(UpperCamelCase__							,     UpperCamelCase__					)
							assert dataset.num_rows == 4
							assert dataset.num_columns == 3
							assert dataset.column_names == ["col_1", "col_2", "col_3"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize('keep_in_memory'							,     [False, True]					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														A__						  =				CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__							,     keep_in_memory=UpperCamelCase__					).read()
							_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__					)






@pytest.mark.parametrize(
    'features'							,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]							,     )
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							A__						  =				features.copy() if features else default_expected_features
							A__						  =				(
							    Features({feature: Value(UpperCamelCase__					) for feature, dtype in features.items()}					) if features is not None else None
							)
							A__						  =				CsvDatasetReader(UpperCamelCase__							,     features=UpperCamelCase__							,     cache_dir=UpperCamelCase__					).read()
							_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__					)






@pytest.mark.parametrize('split'							,     [None, NamedSplit('train'					), 'train', 'test']					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							A__						  =				CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__							,     split=UpperCamelCase__					).read()
							_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__					)
							assert dataset.split == split if split else "train"






@pytest.mark.parametrize('path_type'							,     [str, list]					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							if issubclass(UpperCamelCase__							,     UpperCamelCase__					):
														A__						  =				csv_path
							elif issubclass(UpperCamelCase__							,     UpperCamelCase__					):
														A__						  =				[csv_path]
							A__						  =				tmp_path / 'cache'
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							A__						  =				CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__					).read()
							_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=("train",)					):






							"""simple docstring"""





							assert isinstance(UpperCamelCase__							,     UpperCamelCase__					)
							for split in splits:
														A__						  =				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       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														A__						  =				CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__							,     keep_in_memory=UpperCamelCase__					).read()
							_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__					)






@pytest.mark.parametrize(
    'features'							,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]							,     )
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							A__						  =				features.copy() if features else default_expected_features
							A__						  =				(
							    Features({feature: Value(UpperCamelCase__					) for feature, dtype in features.items()}					) if features is not None else None
							)
							A__						  =				CsvDatasetReader({'train': csv_path}							,     features=UpperCamelCase__							,     cache_dir=UpperCamelCase__					).read()
							_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__					)






@pytest.mark.parametrize('split'							,     [None, NamedSplit('train'					), 'train', 'test']					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							if split:
														A__						  =				{split: csv_path}
							else:
														A__						  =				{'train': csv_path, 'test': csv_path}
							A__						  =				tmp_path / 'cache'
							A__						  =				{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
							A__						  =				CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__					).read()
							_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__							,     splits=list(path.keys()					)					)
							assert all(dataset[split].split == split for split in path.keys()					)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							with open(UpperCamelCase__							,     encoding='utf-8'					) as csvfile:
														yield from csv.reader(UpperCamelCase__					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				os.path.join(UpperCamelCase__							,     'tmp.csv'					)
							A__						  =				CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__					).read()
							CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=1					).write()

							A__						  =				iter_csv_file(UpperCamelCase__					)
							A__						  =				iter_csv_file(UpperCamelCase__					)

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				os.path.join(UpperCamelCase__							,     'tmp.csv'					)
							A__						  =				CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__					).read()
							CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=2					).write()

							A__						  =				iter_csv_file(UpperCamelCase__					)
							A__						  =				iter_csv_file(UpperCamelCase__					)

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'cache'
							A__						  =				os.path.join(UpperCamelCase__							,     'tmp.csv'					)
							A__						  =				CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__					).read()
							with pytest.raises(UpperCamelCase__					):
														CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=0					)






"""simple docstring"""




import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class      UpperCamelCase__(      __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.config_class(**self.inputs_dict  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'hidden_sizes'  )  )
													self.parent.assertTrue(hasattr(__UpperCAmelCase		,'num_encoder_blocks'  )  )






class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=64		,__UpperCAmelCase=3		,__UpperCAmelCase=4		,__UpperCAmelCase=[2, 2, 2, 2]		,__UpperCAmelCase=[8, 4, 2, 1]		,__UpperCAmelCase=[16, 32, 64, 1_28]		,__UpperCAmelCase=[1, 4, 8, 16]		,__UpperCAmelCase=False		,__UpperCAmelCase=True		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=3		,__UpperCAmelCase=None		,)       ->					Tuple:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				image_size
													A__						  =				num_channels
													A__						  =				num_encoder_blocks
													A__						  =				sr_ratios
													A__						  =				depths
													A__						  =				hidden_sizes
													A__						  =				downsampling_rates
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				initializer_range
													A__						  =				num_labels
													A__						  =				scope





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

													A__						  =				None
													if self.use_labels:
																				A__						  =				ids_tensor([self.batch_size, self.image_size, self.image_size]		,self.num_labels  )

													A__						  =				PoolFormerConfig(
													    image_size=self.image_size		,num_channels=self.num_channels		,num_encoder_blocks=self.num_encoder_blocks		,depths=self.depths		,hidden_sizes=self.hidden_sizes		,hidden_act=self.hidden_act		,hidden_dropout_prob=self.hidden_dropout_prob		,initializer_range=self.initializer_range		,)

													return config, pixel_values, labels





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				PoolFormerModel(config=__UpperCAmelCase  )
													model.to(__UpperCAmelCase  )
													model.eval()
													A__						  =				model(__UpperCAmelCase  )
													A__						  =				A__						  =				self.image_size // 3_2.0
													self.parent.assertEqual(
													    result.last_hidden_state.shape		,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				self.prepare_config_and_inputs()
													A__  ,	A__  ,	A__						  =				config_and_inputs
													A__						  =				{'pixel_values': pixel_values}
													return config, inputs_dict






@require_torch
class      UpperCamelCase__(      __A		,    __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Optional[int]    				=		(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
						lowerCAmelCase__	:    Optional[Any]    				=		(
						    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Any    				=		False
						lowerCAmelCase__	:    str    				=		False
						lowerCAmelCase__	:    Optional[int]    				=		False





						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				PoolFormerModelTester(self  )
													A__						  =				PoolFormerConfigTester(self		,config_class=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													self.config_tester.run_common_tests()





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase  )





						@unittest.skip('PoolFormer does not use inputs_embeds'  )
						def 					snake_case__						(							self  )       ->					int:
													pass





						@unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'  )
						def 					snake_case__						(							self  )       ->					List[str]:
													pass





						def 					snake_case__						(							self  )       ->					List[Any]:
													def check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.eval()

																				with torch.no_grad():
																											A__						  =				model(**self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )  )

																				A__						  =				outputs.hidden_states

																				A__						  =				self.model_tester.num_encoder_blocks
																				self.assertEqual(len(__UpperCAmelCase  )		,__UpperCAmelCase  )

																				# verify the first hidden states (first block)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-3:]  )		,[
																				        self.model_tester.hidden_sizes[0],
																				        self.model_tester.image_size // 4,
																				        self.model_tester.image_size // 4,
																				    ]		,)

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				True
																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				A__						  =				True

																				check_hidden_states_output(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													if not self.model_tester.is_training:
																				return

													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()
													A__						  =				True

													for model_class in self.all_model_classes:
																				if model_class in get_values(__UpperCAmelCase  ):
																											continue
																				A__						  =				model_class(__UpperCAmelCase  )
																				model.to(__UpperCAmelCase  )
																				model.train()
																				A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase		,return_labels=__UpperCAmelCase  )
																				A__						  =				model(**__UpperCAmelCase  ).loss
																				loss.backward()





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				A__						  =				model_class(__UpperCAmelCase  )
																				A__						  =				inspect.signature(model.forward  )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				A__						  =				[*signature.parameters.keys()]

																				A__						  =				['pixel_values']
																				self.assertListEqual(arg_names[:1]		,__UpperCAmelCase  )





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__						  =				PoolFormerModel.from_pretrained(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							return image


@require_torch
class      UpperCamelCase__(      unittest.TestCase			):





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				PoolFormerImageProcessor()
													A__						  =				PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'  ).to(__UpperCAmelCase  )

													A__						  =				image_processor(images=prepare_img()		,return_tensors='pt'  ).to(__UpperCAmelCase  )

													# forward pass
													with torch.no_grad():
																				A__						  =				model(**__UpperCAmelCase  )

													# verify the logits
													A__						  =				torch.Size((1, 10_00)  )
													self.assertEqual(outputs.logits.shape		,__UpperCAmelCase  )

													A__						  =				torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]  ).to(__UpperCAmelCase  )
													self.assertTrue(torch.allclose(outputs.logits[0, :3]		,__UpperCAmelCase		,atol=1e-4  )  )





