


import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil






@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  Any:
		"""simple docstring"""




		lowercase__					      =		tmp_path / """file.csv"""
		lowercase__					      =		textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20
        """					)
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)






@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		tmp_path / """malformed_file.csv"""
		lowercase__					      =		textwrap.dedent(
		    """\
        header1,header2
        1,2
        10,20,
        """					)
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)






@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							Optional[int]   ,   __magic_name__					:							Dict					)       ->  Dict:
		"""simple docstring"""




		lowercase__					      =		tmp_path / """csv_with_image.csv"""
		lowercase__					      =		textwrap.dedent(
		    f'''\
        image
        {image_file}
        '''					)
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)






@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							Optional[int]					)       ->  Tuple:
		"""simple docstring"""




		lowercase__					      =		tmp_path / """csv_with_label.csv"""
		lowercase__					      =		textwrap.dedent(
		    """\
        label
        good
        bad
        good
        """					)
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)






@pytest.fixture
def 			UpperCamelCase (     __magic_name__					:							List[str]					)       ->  Optional[Any]:
		"""simple docstring"""




		lowercase__					      =		tmp_path / """csv_with_int_list.csv"""
		lowercase__					      =		textwrap.dedent(
		    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """					)
		with open(__magic_name__   ,   """w"""					) as f:
				f.write(__magic_name__					)
		return str(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Union[str, Any]					)       ->  List[str]:
		"""simple docstring"""




		lowercase__					      =		Csv()
		lowercase__					      =		csv._generate_tables([[csv_file, malformed_csv_file]]					)
		with pytest.raises(__magic_name__   ,   match="""Error tokenizing data"""					):
				for _ in generator:
						pass
		assert any(
		    record.levelname == """ERROR"""
		    and """Failed to read file""" in record.message
		    and os.path.basename(__magic_name__					) in record.message
		    for record in caplog.records					)






@require_pil
def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]					)       ->  List[Any]:
		"""simple docstring"""




		with open(__magic_name__   ,   encoding="""utf-8"""					) as f:
				lowercase__					      =		f.read().splitlines()[1]
		lowercase__					      =		Csv(encoding="""utf-8"""   ,   features=Features({"""image""": Image()}					)					)
		lowercase__					      =		csv._generate_tables([[csv_file_with_image]]					)
		lowercase__					      =		pa.concat_tables([table for _, table in generator]					)
		assert pa_table.schema.field("""image"""					).type == Image()()
		lowercase__					      =		pa_table.to_pydict()["""image"""]
		assert generated_content == [{"path": image_file, "bytes": None}]






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Tuple:
		"""simple docstring"""




		with open(__magic_name__   ,   encoding="""utf-8"""					) as f:
				lowercase__					      =		f.read().splitlines()[1:]
		lowercase__					      =		Csv(encoding="""utf-8"""   ,   features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]					)}					)					)
		lowercase__					      =		csv._generate_tables([[csv_file_with_label]]					)
		lowercase__					      =		pa.concat_tables([table for _, table in generator]					)
		assert pa_table.schema.field("""label"""					).type == ClassLabel(names=["""good""", """bad"""]					)()
		lowercase__					      =		pa_table.to_pydict()["""label"""]
		assert generated_content == [ClassLabel(names=["""good""", """bad"""]					).straint(__magic_name__					) for label in labels]







def 			UpperCamelCase (     __magic_name__					:							str					)       ->  int:
		"""simple docstring"""




		lowercase__					      =		Csv(encoding="""utf-8"""   ,   sep=""","""   ,   converters={"""int_list""": lambda __magic_name__					: [int(__magic_name__					) for i in x.split()]}					)
		lowercase__					      =		csv._generate_tables([[csv_file_with_int_list]]					)
		lowercase__					      =		pa.concat_tables([table for _, table in generator]					)
		assert pa.types.is_list(pa_table.schema.field("""int_list"""					).type					)
		lowercase__					      =		pa_table.to_pydict()["""int_list"""]
		assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]





import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
	from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[int]=13				, _UpperCAmelCase   :						List[str]=32				, _UpperCAmelCase   :						Dict=3				, _UpperCAmelCase   :						Dict=4				, _UpperCAmelCase   :						Union[str, Any]=[10, 20, 30, 40]				, _UpperCAmelCase   :						Optional[int]=[2, 2, 3, 2]				, _UpperCAmelCase   :						Optional[Any]=True				, _UpperCAmelCase   :						str=True				, _UpperCAmelCase   :						Tuple=37				, _UpperCAmelCase   :						str="gelu"				, _UpperCAmelCase   :						Dict=10				, _UpperCAmelCase   :						Dict=0.02				, _UpperCAmelCase   :						List[Any]=["stage2", "stage3", "stage4"]				, _UpperCAmelCase   :						List[Any]=[2, 3, 4]				, _UpperCAmelCase   :						Optional[Any]=None				, )						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		parent
							lowercase__					      =		batch_size
							lowercase__					      =		image_size
							lowercase__					      =		num_channels
							lowercase__					      =		num_stages
							lowercase__					      =		hidden_sizes
							lowercase__					      =		depths
							lowercase__					      =		is_training
							lowercase__					      =		use_labels
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_act
							lowercase__					      =		num_labels
							lowercase__					      =		initializer_range
							lowercase__					      =		out_features
							lowercase__					      =		out_indices
							lowercase__					      =		scope

					def   lowerCamelCase__					(self   :						Any		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

							lowercase__					      =		self.get_config()

							return config, pixel_values, labels

					def   lowerCamelCase__					(self   :						int		)						->       Dict:




							"""simple docstring"""


							return ConvNextVaConfig(
							    num_channels=self.num_channels				, hidden_sizes=self.hidden_sizes				, depths=self.depths				, num_stages=self.num_stages				, hidden_act=self.hidden_act				, is_decoder=_UpperCAmelCase				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, num_labels=self.num_labels				, )

					def   lowerCamelCase__					(self   :						Union[str, Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Tuple		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModel(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)
							# expected last hidden states: B, C, H // 32, W // 32
							self.parent.assertEqual(
							    result.last_hidden_state.shape				, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				, )

					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaForImageClassification(_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase				, labels=_UpperCAmelCase		)
							self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						Dict		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)

							# 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, self.hidden_sizes[1], 4, 4]		)

							# verify channels
							self.parent.assertEqual(len(model.channels		)				, len(config.out_features		)		)
							self.parent.assertListEqual(model.channels				, config.hidden_sizes[1:]		)

							# verify backbone works with out_features=None
							lowercase__					      =		None
							lowercase__					      =		ConvNextVaBackbone(config=_UpperCAmelCase		)
							model.to(_UpperCAmelCase		)
							model.eval()
							lowercase__					      =		model(_UpperCAmelCase		)

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

							# verify channels
							self.parent.assertEqual(len(model.channels		)				, 1		)
							self.parent.assertListEqual(model.channels				, [config.hidden_sizes[-1]]		)

					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values}
							return config, inputs_dict


					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							lowercase__      ,     lowercase__      ,     lowercase__					      =		config_and_inputs
							lowercase__					      =		{"""pixel_values""": pixel_values, """labels""": labels}
							return config, inputs_dict






@require_torch
class 							A   (	UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase							):
					'''simple docstring'''






					A__      =			(
					    (
					        ConvNextVaModel,
					        ConvNextVaForImageClassification,
					        ConvNextVaBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					A__      =			(
					    {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False
					A__      =			False

					def   lowerCamelCase__					(self   :						int		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=_UpperCAmelCase				, has_text_modality=_UpperCAmelCase				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Tuple		)						->       int:




							"""simple docstring"""


							return

					@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""		)
					def   lowerCamelCase__					(self   :						List[str]		)						->       Optional[int]:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""		)
					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							pass

					@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""		)
					def   lowerCamelCase__					(self   :						int		)						->       str:




							"""simple docstring"""


							pass

					def   lowerCamelCase__					(self   :						Tuple		)						->       Any:




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

							for model_class in self.all_model_classes:
									lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_with_labels()
									lowercase__					      =		True

									if model_class.__name__ in [
									    *get_values(_UpperCAmelCase		),
									    *get_values(_UpperCAmelCase		),
									]:
											continue

									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.train()
									lowercase__					      =		self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase				, return_labels=_UpperCAmelCase		)
									lowercase__					      =		model(**_UpperCAmelCase		).loss
									loss.backward()

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       int:




							"""simple docstring"""


							if not self.model_tester.is_training:
									return

							for model_class in self.all_model_classes:
									lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_with_labels()
									lowercase__					      =		False
									lowercase__					      =		True

									if (
									    model_class.__name__
									    in [*get_values(_UpperCAmelCase		), *get_values(_UpperCAmelCase		)]
									    or not model_class.supports_gradient_checkpointing
									):
											continue

									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.gradient_checkpointing_enable()
									model.train()
									lowercase__					      =		self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase				, return_labels=_UpperCAmelCase		)
									lowercase__					      =		model(**_UpperCAmelCase		).loss
									loss.backward()

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




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						int		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase		)

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




							"""simple docstring"""


							def check_hidden_states_output(_UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						str		):
									lowercase__					      =		model_class(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()

									with torch.no_grad():
											lowercase__					      =		model(**self._prepare_for_class(_UpperCAmelCase				, _UpperCAmelCase		)		)

									lowercase__					      =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowercase__					      =		self.model_tester.num_stages
									self.assertEqual(len(_UpperCAmelCase		)				, expected_num_stages + 1		)

									# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]		)				, [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				, )

							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__					      =		True
									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

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

									check_hidden_states_output(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

					def   lowerCamelCase__					(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase		)


					@slow
					def   lowerCamelCase__					(self   :						int		)						->       Any:




							"""simple docstring"""


							for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__					      =		ConvNextVaModel.from_pretrained(_UpperCAmelCase		)
									self.assertIsNotNone(_UpperCAmelCase		)






def 			UpperCamelCase (     )       ->  int:
		"""simple docstring"""




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







@require_torch
@require_vision
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					@cached_property
					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       Optional[Any]:




							"""simple docstring"""


							return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""		) if is_vision_available() else None


					@slow
					def   lowerCamelCase__					(self   :						Any		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""		).to(_UpperCAmelCase		)

							lowercase__					      =		self.default_image_processor
							lowercase__					      =		prepare_img()
							lowercase__					      =		preprocessor(images=_UpperCAmelCase				, return_tensors="""pt"""		).to(_UpperCAmelCase		)

							# forward pass
							with torch.no_grad():
									lowercase__					      =		model(**_UpperCAmelCase		)

							# verify the logits
							lowercase__					      =		torch.Size((1, 1000)		)
							self.assertEqual(outputs.logits.shape				, _UpperCAmelCase		)

							lowercase__					      =		torch.tensor([0.9_996, 0.1_966, -0.4_386]		).to(_UpperCAmelCase		)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]				, _UpperCAmelCase				, atol=1E-4		)		)

