



import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







class 				a_    (						_snake_case							):

						def       __a			(       self      :Union[str, Any]  ,		_lowercase      :List[Any]  ,		_lowercase      :Dict=False)						->   str:
												UpperCAmelCase_				, UpperCAmelCase_				, UpperCAmelCase_					=	(
												    filename_skip_take['''filename'''],
												    filename_skip_take['''skip'''] if '''skip''' in filename_skip_take else None,
												    filename_skip_take['''take'''] if '''take''' in filename_skip_take else None,
												)
												open(os.path.join(a__)  ,		'''wb''').close()
												UpperCAmelCase_					=	pa.Table.from_pydict({'''filename''': [Path(a__).name] * 100})
												if take == -1:
																		UpperCAmelCase_					=	len(a__) - skip
												if skip is not None and take is not None:
																		UpperCAmelCase_					=	pa_table.slice(a__  ,		a__)
												return pa_table







class 				a_    (						_snake_case							):

						def       __a			(       self      :Optional[int])						->   int:
												UpperCAmelCase_					=	'''my_name'''
												UpperCAmelCase_					=	SplitInfo(name='''train'''  ,		num_examples=100)
												UpperCAmelCase_					=	SplitInfo(name='''test'''  ,		num_examples=100)
												UpperCAmelCase_					=	[train_info, test_info]
												UpperCAmelCase_					=	SplitDict()
												split_dict.add(a__)
												split_dict.add(a__)
												UpperCAmelCase_					=	DatasetInfo(splits=a__)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		UpperCAmelCase_					=	ReaderTest(a__  ,		a__)

																		UpperCAmelCase_					=	'''test[:33%]'''
																		UpperCAmelCase_					=	Dataset(**reader.read(a__  ,		a__  ,		a__))
																		self.assertEqual(dset['''filename'''][0]  ,		f"{name}-test")
																		self.assertEqual(dset.num_rows  ,		33)
																		self.assertEqual(dset.num_columns  ,		1)

																		UpperCAmelCase_					=	['''train''', '''test[:33%]''']
																		UpperCAmelCase_					=	[Split.TRAIN, ReadInstruction.from_spec('''test[:33%]''')]
																		for instructions in [instructionsa, instructionsa]:
																								UpperCAmelCase_					=	[reader.read(a__  ,		a__  ,		a__) for instr in instructions]
																								UpperCAmelCase_				, UpperCAmelCase_					=	(Dataset(**a__) for dataset_kwargs in datasets_kwargs)
																								self.assertEqual(train_dset['''filename'''][0]  ,		f"{name}-train")
																								self.assertEqual(train_dset.num_rows  ,		100)
																								self.assertEqual(train_dset.num_columns  ,		1)
																								self.assertIsInstance(train_dset.split  ,		a__)
																								self.assertEqual(str(train_dset.split)  ,		'''train''')
																								self.assertEqual(test_dset['''filename'''][0]  ,		f"{name}-test")
																								self.assertEqual(test_dset.num_rows  ,		33)
																								self.assertEqual(test_dset.num_columns  ,		1)
																								self.assertIsInstance(test_dset.split  ,		a__)
																								self.assertEqual(str(test_dset.split)  ,		'''test[:33%]''')
																								del train_dset, test_dset

						def       __a			(       self      :str)						->   str:
												UpperCAmelCase_					=	'''my_name'''
												UpperCAmelCase_					=	SplitInfo(name='''train'''  ,		num_examples=1000  ,		shard_lengths=[100] * 10)
												UpperCAmelCase_					=	[train_info]
												UpperCAmelCase_					=	SplitDict()
												split_dict.add(a__)
												UpperCAmelCase_					=	DatasetInfo(splits=a__)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		UpperCAmelCase_					=	ReaderTest(a__  ,		a__)

																		UpperCAmelCase_					=	'''train[:33%]'''
																		UpperCAmelCase_					=	Dataset(**reader.read(a__  ,		a__  ,		a__))
																		self.assertEqual(dset['''filename'''][0]  ,		f"{name}-train-00000-of-00010")
																		self.assertEqual(dset['''filename'''][-1]  ,		f"{name}-train-00003-of-00010")
																		self.assertEqual(dset.num_rows  ,		330)
																		self.assertEqual(dset.num_columns  ,		1)




						def       __a			(       self      :List[str])						->   Any:
												UpperCAmelCase_					=	SplitInfo(name='''train'''  ,		num_examples=100)
												UpperCAmelCase_					=	SplitInfo(name='''test'''  ,		num_examples=100)
												UpperCAmelCase_					=	SplitDict()
												split_dict.add(a__)
												split_dict.add(a__)
												UpperCAmelCase_					=	DatasetInfo(splits=a__)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		UpperCAmelCase_					=	ReaderTest(a__  ,		a__)

																		UpperCAmelCase_					=	[
																		    {'''filename''': os.path.join(a__  ,		'''train''')},
																		    {'''filename''': os.path.join(a__  ,		'''test'''), '''skip''': 10, '''take''': 10},
																		]
																		UpperCAmelCase_					=	Dataset(**reader.read_files(a__  ,		original_instructions='''train+test[10:20]'''))
																		self.assertEqual(dset.num_rows  ,		110)
																		self.assertEqual(dset.num_columns  ,		1)
																		del dset







@pytest.mark.parametrize('''in_memory'''				,   [False, True]      )
def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			List[Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	arrow_file
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												UpperCAmelCase_					=	ArrowReader.read_table(__UpperCAmelCase				,   in_memory=__UpperCAmelCase      )
						assert table.shape == dataset.data.shape
						assert set(table.column_names      ) == set(dataset.data.column_names      )
						assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict







@pytest.mark.parametrize('''in_memory'''				,   [False, True]      )
def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )			->			Union[str, Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	arrow_file
						UpperCAmelCase_					=	ArrowReader(''''''				,   __UpperCAmelCase      )
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												UpperCAmelCase_					=	reader.read_files([{'''filename''': filename}]				,   in_memory=__UpperCAmelCase      )
						assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
						UpperCAmelCase_					=	dataset_kwargs['''arrow_table''']
						assert table.shape == dataset.data.shape
						assert set(table.column_names      ) == set(dataset.data.column_names      )
						assert dict(table.to_pydict()      ) == dict(dataset.data.to_pydict()      )  # to_pydict returns OrderedDict







def 							A     (						)			->			Optional[int]:



						'''simple docstring'''






						assert ReadInstruction('''train'''				,   to=10				,   unit='''abs'''      ).to_spec() == "train[:10]"
						assert ReadInstruction('''train'''				,   from_=-80				,   to=10				,   unit='''%'''      ).to_spec() == "train[-80%:10%]"

						UpperCAmelCase_					=	'''train+test'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == spec_train_test

						UpperCAmelCase_					=	'''train[2:10]'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == spec_train_abs

						UpperCAmelCase_					=	'''train[15%:-20%]'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == spec_train_pct

						UpperCAmelCase_					=	'''train[:10%](closest)'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == "train[:10%]"

						UpperCAmelCase_					=	'''train[:10%](pct1_dropremainder)'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == spec_train_pct_rounding

						UpperCAmelCase_					=	'''train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'''
						assert ReadInstruction.from_spec(__UpperCAmelCase      ).to_spec() == spec_train_test_pct_rounding







def 							A     (						)			->			Dict:



						'''simple docstring'''






						UpperCAmelCase_					=	'''dummy'''
						UpperCAmelCase_					=	[SplitInfo(name='''train'''				,   num_examples=100      )]
						UpperCAmelCase_					=	'''train[:33%]'''
						UpperCAmelCase_					=	'''arrow'''
						UpperCAmelCase_					=	'''prefix'''

						UpperCAmelCase_					=	make_file_instructions(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
						assert isinstance(__UpperCAmelCase				,   __UpperCAmelCase      )
						assert file_instructions.num_examples == 33
						assert file_instructions.file_instructions == [
						    {"filename": os.path.join(__UpperCAmelCase				,   f"{name}-train.arrow"      ), "skip": 0, "take": 33}
						]

						UpperCAmelCase_					=	[SplitInfo(name='''train'''				,   num_examples=100				,   shard_lengths=[10] * 10      )]
						UpperCAmelCase_					=	make_file_instructions(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
						assert isinstance(__UpperCAmelCase				,   __UpperCAmelCase      )
						assert file_instructions.num_examples == 33
						assert file_instructions.file_instructions == [
						    {"filename": os.path.join(__UpperCAmelCase				,   f"{name}-train-00000-of-00010.arrow"      ), "skip": 0, "take": -1},
						    {"filename": os.path.join(__UpperCAmelCase				,   f"{name}-train-00001-of-00010.arrow"      ), "skip": 0, "take": -1},
						    {"filename": os.path.join(__UpperCAmelCase				,   f"{name}-train-00002-of-00010.arrow"      ), "skip": 0, "take": -1},
						    {"filename": os.path.join(__UpperCAmelCase				,   f"{name}-train-00003-of-00010.arrow"      ), "skip": 0, "take": 3},
						]







@pytest.mark.parametrize('''name, expected_exception'''				,   [(None, TypeError), ('''''', ValueError)]      )
def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			Any:



						'''simple docstring'''






						UpperCAmelCase_					=	[SplitInfo(name='''train'''				,   num_examples=100      )]
						UpperCAmelCase_					=	'''train'''
						UpperCAmelCase_					=	'''arrow'''
						UpperCAmelCase_					=	'''prefix_path'''
						with pytest.raises(__UpperCAmelCase      ):
												UpperCAmelCase_					=	make_file_instructions(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )












"""simple docstring"""






import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType



@require_torch
class 						lowerCAmelCase       :
       lowerCAmelCase__    			=	()
       lowerCAmelCase__    			=	True






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.config_class

              # test default config
              _UpperCAmelCase			     =  config_class()
              self.assertIsNotNone(a__  )
              _UpperCAmelCase			     =  ['stem'] + [f"""stage{idx}""" for idx in range(1     ,	len(config.depths  ) + 1  )]
              self.assertEqual(config.stage_names     ,	a__  )
              self.assertTrue(set(config.out_features  ).issubset(set(config.stage_names  )  )  )

              # Test out_features and out_indices are correctly set
              # out_features and out_indices both None
              _UpperCAmelCase			     =  config_class(out_features=a__     ,	out_indices=a__  )
              self.assertEqual(config.out_features     ,	[config.stage_names[-1]]  )
              self.assertEqual(config.out_indices     ,	[len(config.stage_names  ) - 1]  )

              # out_features and out_indices both set
              _UpperCAmelCase			     =  config_class(out_features=['stem', 'stage1']     ,	out_indices=[0, 1]  )
              self.assertEqual(config.out_features     ,	['stem', 'stage1']  )
              self.assertEqual(config.out_indices     ,	[0, 1]  )

              # Only out_features set
              _UpperCAmelCase			     =  config_class(out_features=['stage1', 'stage3']  )
              self.assertEqual(config.out_features     ,	['stage1', 'stage3']  )
              self.assertEqual(config.out_indices     ,	[1, 3]  )

              # Only out_indices set
              _UpperCAmelCase			     =  config_class(out_indices=[0, 2]  )
              self.assertEqual(config.out_features     ,	[config.stage_names[0], config.stage_names[2]]  )
              self.assertEqual(config.out_indices     ,	[0, 2]  )

              # Error raised when out_indices do not correspond to out_features
              with self.assertRaises(a__  ):
                     _UpperCAmelCase			     =  config_class(out_features=['stage1', 'stage2']     ,	out_indices=[0, 2]  )






       def 				__A			( self  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase			     =  model_class(a__  )
                     _UpperCAmelCase			     =  inspect.signature(model.forward  )
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     _UpperCAmelCase			     =  [*signature.parameters.keys()]
                     _UpperCAmelCase			     =  ['pixel_values']
                     self.assertListEqual(arg_names[:1]     ,	a__  )






       def 				__A			( self  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase			     =  model_class(a__  )
                     self.assertEqual(len(model.channels  )     ,	len(config.out_features  )  )
                     _UpperCAmelCase			     =  model.num_features
                     _UpperCAmelCase			     =  [config.stage_names.index(a__  ) for feat in config.out_features]
                     _UpperCAmelCase			     =  [num_features[idx] for idx in out_indices]
                     self.assertListEqual(model.channels     ,	a__  )

                     _UpperCAmelCase			     =  copy.deepcopy(a__  )
                     _UpperCAmelCase			     =  None
                     _UpperCAmelCase			     =  model_class(a__  )
                     self.assertEqual(len(model.channels  )     ,	1  )
                     self.assertListEqual(model.channels     ,	[num_features[-1]]  )

                     _UpperCAmelCase			     =  copy.deepcopy(a__  )
                     _UpperCAmelCase			     =  None
                     _UpperCAmelCase			     =  model_class(a__  )
                     self.assertEqual(len(model.channels  )     ,	1  )
                     self.assertListEqual(model.channels     ,	[num_features[-1]]  )






       def 				__A			( self  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     _UpperCAmelCase			     =  model_class(a__  )
                     model.to(a__  )
                     model.eval()
                     _UpperCAmelCase			     =  model(**a__  )

                     self.assertEqual(len(result.feature_maps  )     ,	len(config.out_features  )  )
                     self.assertEqual(len(model.channels  )     ,	len(config.out_features  )  )
                     self.assertEqual(len(result.feature_maps  )     ,	len(config.out_indices  )  )
                     self.assertEqual(len(model.channels  )     ,	len(config.out_indices  )  )

                     # Check output of last stage is taken if out_features=None, out_indices=None
                     _UpperCAmelCase			     =  copy.deepcopy(a__  )
                     _UpperCAmelCase			     =  None
                     _UpperCAmelCase			     =  model_class(a__  )
                     model.to(a__  )
                     model.eval()
                     _UpperCAmelCase			     =  model(**a__  )

                     self.assertEqual(len(result.feature_maps  )     ,	1  )
                     self.assertEqual(len(model.channels  )     ,	1  )

                     _UpperCAmelCase			     =  copy.deepcopy(a__  )
                     _UpperCAmelCase			     =  None
                     _UpperCAmelCase			     =  model_class(a__  )
                     model.to(a__  )
                     model.eval()
                     _UpperCAmelCase			     =  model(**a__  )

                     self.assertEqual(len(result.feature_maps  )     ,	1  )
                     self.assertEqual(len(model.channels  )     ,	1  )

                     # Check backbone can be initialized with fresh weights
                     _UpperCAmelCase			     =  copy.deepcopy(a__  )
                     _UpperCAmelCase			     =  False
                     _UpperCAmelCase			     =  model_class(a__  )
                     model.to(a__  )
                     model.eval()
                     _UpperCAmelCase			     =  model(**a__  )






       def 				__A			( self  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs_for_common()

              for backbone_class in self.all_model_classes:
                     _UpperCAmelCase			     =  backbone_class(a__  )

                     self.assertTrue(hasattr(a__     ,	'backbone_type'  )  )
                     self.assertTrue(hasattr(a__     ,	'stage_names'  )  )
                     self.assertTrue(hasattr(a__     ,	'num_features'  )  )
                     self.assertTrue(hasattr(a__     ,	'out_indices'  )  )
                     self.assertTrue(hasattr(a__     ,	'out_features'  )  )
                     self.assertTrue(hasattr(a__     ,	'out_feature_channels'  )  )
                     self.assertTrue(hasattr(a__     ,	'channels'  )  )

                     self.assertIsInstance(backbone.backbone_type     ,	a__  )
                     # Verify num_features has been initialized in the backbone init
                     self.assertIsNotNone(backbone.num_features  )
                     self.assertTrue(len(backbone.channels  ) == len(backbone.out_indices  )  )
                     self.assertTrue(len(backbone.stage_names  ) == len(backbone.num_features  )  )
                     self.assertTrue(len(backbone.channels  ) <= len(backbone.num_features  )  )
                     self.assertTrue(len(backbone.out_feature_channels  ) == len(backbone.stage_names  )  )






       def 				__A			( self  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase			     =  inputs_dict['pixel_values'].shape[0]

              for backbone_class in self.all_model_classes:
                     _UpperCAmelCase			     =  backbone_class(a__  )
                     backbone.to(a__  )
                     backbone.eval()

                     _UpperCAmelCase			     =  backbone(**a__  )

                     # Test default outputs and verify feature maps
                     self.assertIsInstance(outputs.feature_maps     ,	a__  )
                     self.assertTrue(len(outputs.feature_maps  ) == len(backbone.channels  )  )
                     for feature_map, n_channels in zip(outputs.feature_maps     ,	backbone.channels  ):
                            self.assertTrue(feature_map.shape[:2]     ,	(batch_size, n_channels)  )
                     self.assertIsNone(outputs.hidden_states  )
                     self.assertIsNone(outputs.attentions  )

                     # Test output_hidden_states=True
                     _UpperCAmelCase			     =  backbone(**a__     ,	output_hidden_states=a__  )
                     self.assertIsNotNone(outputs.hidden_states  )
                     self.assertTrue(len(outputs.hidden_states  )     ,	len(backbone.stage_names  )  )
                     for hidden_state, n_channels in zip(outputs.hidden_states     ,	backbone.channels  ):
                            self.assertTrue(hidden_state.shape[:2]     ,	(batch_size, n_channels)  )

                     # Test output_attentions=True
                     if self.has_attentions:
                            _UpperCAmelCase			     =  backbone(**a__     ,	output_attentions=a__  )
                            self.assertIsNotNone(outputs.attentions  )






