


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






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




		try:
				lowercase__					      =		os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				lowercase__					      =		default
		else:
				# KEY is set, convert it to True or False.
				try:
						lowercase__					      =		strtobool(a_					)
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(f'''If set, {key} must be yes or no.'''					)
		return _value


A	:		Optional[int]    =       parse_flag_from_env('RUN_SLOW', default=False)






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




		return unittest.skip("""Test was skipped"""					)(a_					)






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




		return unittest.skipUnless(_run_slow_tests   ,   """test is slow"""					)(a_					)






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




		return unittest.skipUnless(not torch.cuda.is_available()   ,   """test requires only a CPU"""					)(a_					)






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




		return unittest.skipUnless(torch.cuda.is_available()   ,   """test requires a GPU"""					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							Any					)       ->  Any:
		"""simple docstring"""




		return unittest.skipUnless(is_xpu_available()   ,   """test requires a XPU"""					)(a_					)






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




		return unittest.skipUnless(is_mps_available()   ,   """test requires a `mps` backend support in `torch`"""					)(a_					)






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




		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available()   ,   """test requires the Hugging Face suite"""					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							Any					)       ->  Dict:
		"""simple docstring"""




		return unittest.skipUnless(is_bnb_available()   ,   """test requires the bitsandbytes library"""					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  List[str]:
		"""simple docstring"""




		return unittest.skipUnless(is_tpu_available()   ,   """test requires TPU"""					)(a_					)






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




		return unittest.skipUnless(torch.cuda.device_count() == 1   ,   """test requires a GPU"""					)(a_					)






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




		return unittest.skipUnless(torch.xpu.device_count() == 1   ,   """test requires a XPU"""					)(a_					)






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




		return unittest.skipUnless(torch.cuda.device_count() > 1   ,   """test requires multiple GPUs"""					)(a_					)






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




		return unittest.skipUnless(torch.xpu.device_count() > 1   ,   """test requires multiple XPUs"""					)(a_					)






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




		return unittest.skipUnless(is_safetensors_available()   ,   """test requires safetensors"""					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  Dict:
		"""simple docstring"""




		return unittest.skipUnless(is_deepspeed_available()   ,   """test requires DeepSpeed"""					)(a_					)






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




		return unittest.skipUnless(is_torch_version(""">="""   ,   """1.12.0"""					)   ,   """test requires torch version >= 1.12.0"""					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							int=None   ,   __magic_name__					:							Union[str, Any]=None					)       ->  List[Any]:
		"""simple docstring"""




		if test_case is None:
				return partial(a_   ,   version=a_					)
		return unittest.skipUnless(is_torch_version(""">="""   ,   a_					)   ,   f'''test requires torch version >= {version}'''					)(a_					)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  List[str]:
		"""simple docstring"""




		return unittest.skipUnless(is_tensorboard_available()   ,   """test requires Tensorboard"""					)(a_					)






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




		return unittest.skipUnless(is_wandb_available()   ,   """test requires wandb"""					)(a_					)






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




		return unittest.skipUnless(is_comet_ml_available()   ,   """test requires comet_ml"""					)(a_					)


A	:		List[Any]    =       (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 			UpperCamelCase (     __magic_name__					:							Dict					)       ->  Optional[int]:
		"""simple docstring"""




		return unittest.skipUnless(
		    _atleast_one_tracker_available   ,   """test requires at least one tracker to be available and for `comet_ml` to not be installed"""   ,   )(a_					)



class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					A__      =			True

					@classmethod
					def   lowerCamelCase__					(cls   :						str		)						->       str:




							"""simple docstring"""


							lowercase__					      =		tempfile.mkdtemp()

					@classmethod
					def   lowerCamelCase__					(cls   :						int		)						->       Tuple:




							"""simple docstring"""


							if os.path.exists(cls.tmpdir		):
									shutil.rmtree(cls.tmpdir		)


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




							"""simple docstring"""


							if self.clear_on_setup:
									for path in Path(self.tmpdir		).glob("""**/*"""		):
											if path.is_file():
													path.unlink()
											elif path.is_dir():
													shutil.rmtree(lowerCAmelCase__		)






class 							A   (	unittest.TestCase							):
					'''simple docstring'''






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




							"""simple docstring"""


							super().tearDown()
							# Reset the state of the AcceleratorState singleton.
							AcceleratorState._reset_state()
							PartialState._reset_state()






class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Union[mock.Mock, List[mock.Mock]]		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__					      =		mocks if isinstance(lowerCAmelCase__				, (tuple, list)		) else [mocks]
							for m in self.mocks:
									m.start()
									self.addCleanup(m.stop		)






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




		lowercase__					      =		AcceleratorState()
		lowercase__					      =		tensor[None].clone().to(state.device					)
		lowercase__					      =		gather(a_					).cpu()
		lowercase__					      =		tensor[0].cpu()
		for i in range(tensors.shape[0]					):
				if not torch.equal(tensors[i]   ,   a_					):
						return False
		return True



class 							A   :
					'''simple docstring'''






					def __init__(self   :						Dict				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[Any]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		returncode
							lowercase__					      =		stdout
							lowercase__					      =		stderr






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




		while True:
				lowercase__					      =		await stream.readline()
				if line:
						callback(a_					)
				else:
						break






async def 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Dict=None   ,   __magic_name__					:							Dict=None   ,   __magic_name__					:							List[Any]=None   ,   __magic_name__					:							Optional[Any]=False   ,   __magic_name__					:							Optional[Any]=False					)       ->  List[Any]:
		"""simple docstring"""




		if echo:
				print("""\nRunning: """   ,   """ """.join(a_					)					)

		lowercase__					      =		await asyncio.create_subprocess_exec(
		    cmd[0]   ,   *cmd[1:]   ,   stdin=a_   ,   stdout=asyncio.subprocess.PIPE   ,   stderr=asyncio.subprocess.PIPE   ,   env=a_   ,   )

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		lowercase__					      =		[]
		lowercase__					      =		[]

		def tee(__magic_name__					:							Union[str, Any]   ,   __magic_name__					:							str   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Union[str, Any]=""					):
				lowercase__					      =		line.decode("""utf-8"""					).rstrip()
				sink.append(a_					)
				if not quiet:
						print(a_   ,   a_   ,   file=a_					)

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        asyncio.create_task(_read_stream(p.stdout   ,   lambda __magic_name__					: tee(a_   ,   a_   ,   sys.stdout   ,   label="""stdout:"""					)					)					),
		        asyncio.create_task(_read_stream(p.stderr   ,   lambda __magic_name__					: tee(a_   ,   a_   ,   sys.stderr   ,   label="""stderr:"""					)					)					),
		    ]   ,   timeout=a_   ,   )
		return _RunOutput(await p.wait()   ,   a_   ,   a_					)






def 			UpperCamelCase (     __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[Any]=None   ,   __magic_name__					:							int=None   ,   __magic_name__					:							Any=180   ,   __magic_name__					:							Optional[int]=False   ,   __magic_name__					:							int=True					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		asyncio.get_event_loop()
		lowercase__					      =		loop.run_until_complete(
		    _stream_subprocess(a_   ,   env=a_   ,   stdin=a_   ,   timeout=a_   ,   quiet=a_   ,   echo=a_					)					)

		lowercase__					      =		" ".join(a_					)
		if result.returncode > 0:
				lowercase__					      =		"\n".join(result.stderr					)
				raise RuntimeError(
				    f'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
				    f'''The combined stderr from workers follows:\n{stderr}'''					)

		return result



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					pass






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




		try:
				lowercase__					      =		subprocess.check_output(a_   ,   stderr=subprocess.STDOUT					)
				if return_stdout:
						if hasattr(a_   ,   """decode"""					):
								lowercase__					      =		output.decode("""utf-8"""					)
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    f'''Command `{" ".join(a_					)}` failed with the following error:\n\n{e.output.decode()}'''					) from e





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		)		)

