



import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
 import onnxruntime as ort


a_   :Union[str, Any]        =      logging.get_logger(__name__)

a_   :str        =      {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Any, _snake_case  :  Tuple=None, **_snake_case  :  Optional[int]					)   ->Any:
            logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'					)
            snake_case__							:	Union[str, Any]   	=	model
            snake_case__							:	Tuple   	=	kwargs.get('model_save_dir', _snake_case					)
            snake_case__							:	str   	=	kwargs.get('latest_model_name', _snake_case					)






     def __call__(       self  :  Optional[Any], **_snake_case  :  str					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	{k: np.array(_snake_case					) for k, v in kwargs.items()}
            return self.model.run(_snake_case, _snake_case					)






     @staticmethod
     def    lowercase_   (       _snake_case  :  Union[str, Path], _snake_case  :  str=None, _snake_case  :  Dict=None					)   ->Optional[int]:
            if provider is None:
                   logger.info('No onnxruntime provider specified, using CPUExecutionProvider'					)
                   snake_case__							:	Dict   	=	'CPUExecutionProvider'

            return ort.InferenceSession(_snake_case, providers=[provider], sess_options=_snake_case					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  Union[str, Path], _snake_case  :  Optional[str] = None, **_snake_case  :  Optional[Any]					)   ->List[Any]:
            snake_case__							:	Dict   	=	file_name if file_name is not None else ONNX_WEIGHTS_NAME

            snake_case__							:	List[str]   	=	self.model_save_dir.joinpath(self.latest_model_name					)
            snake_case__							:	Tuple   	=	Path(_snake_case					).joinpath(_snake_case					)
            try:
                   shutil.copyfile(_snake_case, _snake_case					)
            except shutil.SameFileError:
                   pass

            # copy external weights (for models >2GB)
            snake_case__							:	List[Any]   	=	self.model_save_dir.joinpath(_snake_case					)
            if src_path.exists():
                   snake_case__							:	str   	=	Path(_snake_case					).joinpath(_snake_case					)
                   try:
                          shutil.copyfile(_snake_case, _snake_case					)
                   except shutil.SameFileError:
                          pass






     def    lowercase_   (       self  :  Dict, _snake_case  :  Union[str, os.PathLike], **_snake_case  :  Optional[int], )   ->Union[str, Any]:
            if os.path.isfile(_snake_case					):
                   logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''					)
                   return

            os.makedirs(_snake_case, exist_ok=_snake_case					)

            # saving model weights/files
            self._save_pretrained(_snake_case, **_snake_case					)






     @classmethod
     def    lowercase_   (       cls  :  str, _snake_case  :  Union[str, Path], _snake_case  :  Optional[Union[bool, str, None]] = None, _snake_case  :  Optional[Union[str, None]] = None, _snake_case  :  bool = False, _snake_case  :  Optional[str] = None, _snake_case  :  Optional[str] = None, _snake_case  :  Optional[str] = None, _snake_case  :  Optional["ort.SessionOptions"] = None, **_snake_case  :  Dict, )   ->int:
            snake_case__							:	Any   	=	file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(_snake_case					):
                   snake_case__							:	Optional[int]   	=	OnnxRuntimeModel.load_model(
                       os.path.join(_snake_case, _snake_case					), provider=_snake_case, sess_options=_snake_case					)
                   snake_case__							:	int   	=	Path(_snake_case					)
            # load model from hub
            else:
                   # download model
                   snake_case__							:	Optional[Any]   	=	hf_hub_download(
                       repo_id=_snake_case, filename=_snake_case, use_auth_token=_snake_case, revision=_snake_case, cache_dir=_snake_case, force_download=_snake_case, )
                   snake_case__							:	List[str]   	=	Path(_snake_case					).parent
                   snake_case__							:	Optional[int]   	=	Path(_snake_case					).name
                   snake_case__							:	Union[str, Any]   	=	OnnxRuntimeModel.load_model(_snake_case, provider=_snake_case, sess_options=_snake_case					)
            return cls(model=_snake_case, **_snake_case					)






     @classmethod
     def    lowercase_   (       cls  :  Dict, _snake_case  :  Union[str, Path], _snake_case  :  bool = True, _snake_case  :  Optional[str] = None, _snake_case  :  Optional[str] = None, **_snake_case  :  Optional[int], )   ->Optional[Any]:
            snake_case__							:	int   	=	None
            if len(str(_snake_case					).split('@'					)					) == 2:
                   snake_case__	,				snake_case__							:	str   	=	model_id.split('@'					)

            return cls._from_pretrained(
                model_id=_snake_case, revision=_snake_case, cache_dir=_snake_case, force_download=_snake_case, use_auth_token=_snake_case, **_snake_case, )











import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
 from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  Tuple, _snake_case  :  Any, _snake_case  :  int=1_3, _snake_case  :  Optional[int]=3_2, _snake_case  :  Tuple=2, _snake_case  :  Any=3, _snake_case  :  Tuple=1_6, _snake_case  :  Tuple=[1, 2, 1], _snake_case  :  Dict=[2, 2, 4], _snake_case  :  str=2, _snake_case  :  Union[str, Any]=2.0, _snake_case  :  Dict=True, _snake_case  :  Dict=0.0, _snake_case  :  str=0.0, _snake_case  :  str=0.1, _snake_case  :  List[str]="gelu", _snake_case  :  int=False, _snake_case  :  Optional[Any]=True, _snake_case  :  List[Any]=0.0_2, _snake_case  :  Union[str, Any]=1e-5, _snake_case  :  Union[str, Any]=True, _snake_case  :  List[Any]=None, _snake_case  :  Any=True, _snake_case  :  List[Any]=1_0, _snake_case  :  str=8, )   ->Union[str, Any]:
            snake_case__							:	Any   	=	parent
            snake_case__							:	Tuple   	=	batch_size
            snake_case__							:	Tuple   	=	image_size
            snake_case__							:	Any   	=	patch_size
            snake_case__							:	Optional[int]   	=	num_channels
            snake_case__							:	Tuple   	=	embed_dim
            snake_case__							:	Any   	=	depths
            snake_case__							:	Any   	=	num_heads
            snake_case__							:	List[str]   	=	window_size
            snake_case__							:	Dict   	=	mlp_ratio
            snake_case__							:	Optional[int]   	=	qkv_bias
            snake_case__							:	Optional[Any]   	=	hidden_dropout_prob
            snake_case__							:	List[str]   	=	attention_probs_dropout_prob
            snake_case__							:	Union[str, Any]   	=	drop_path_rate
            snake_case__							:	str   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	use_absolute_embeddings
            snake_case__							:	Union[str, Any]   	=	patch_norm
            snake_case__							:	Any   	=	layer_norm_eps
            snake_case__							:	Tuple   	=	initializer_range
            snake_case__							:	Dict   	=	is_training
            snake_case__							:	Any   	=	scope
            snake_case__							:	Optional[Any]   	=	use_labels
            snake_case__							:	str   	=	type_sequence_label_size
            snake_case__							:	List[Any]   	=	encoder_stride






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

            snake_case__							:	List[Any]   	=	None
            if self.use_labels:
                   snake_case__							:	Optional[Any]   	=	ids_tensor([self.batch_size], self.type_sequence_label_size					)

            snake_case__							:	Any   	=	self.get_config()

            return config, pixel_values, labels






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[int]:
            return SwinvaConfig(
                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, window_size=self.window_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, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  str, _snake_case  :  List[str], _snake_case  :  int					)   ->Dict:
            snake_case__							:	List[Any]   	=	SwinvaModel(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	model(_snake_case					)

            snake_case__							:	List[Any]   	=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
            snake_case__							:	List[Any]   	=	int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

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






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Any, _snake_case  :  List[str], _snake_case  :  Dict					)   ->List[Any]:
            snake_case__							:	List[str]   	=	SwinvaForMaskedImageModeling(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Union[str, Any]   	=	model(_snake_case					)
            self.parent.assertEqual(
                result.logits.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size)					)

            # test greyscale images
            snake_case__							:	Optional[Any]   	=	1
            snake_case__							:	Optional[int]   	=	SwinvaForMaskedImageModeling(_snake_case					)
            model.to(_snake_case					)
            model.eval()

            snake_case__							:	Tuple   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
            snake_case__							:	Any   	=	model(_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.image_size, self.image_size)					)






     def    lowercase_   (       self  :  List[str], _snake_case  :  int, _snake_case  :  List[Any], _snake_case  :  Optional[int]					)   ->Any:
            snake_case__							:	Tuple   	=	self.type_sequence_label_size
            snake_case__							:	int   	=	SwinvaForImageClassification(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Tuple   	=	model(_snake_case, labels=_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)					)






     def    lowercase_   (       self  :  Any					)   ->Dict:
            snake_case__							:	str   	=	self.prepare_config_and_inputs()
            snake_case__	,				snake_case__	,				snake_case__							:	List[str]   	=	config_and_inputs
            snake_case__							:	Union[str, Any]   	=	{'pixel_values': pixel_values}
            return config, inputs_dict




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	(
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     _SCREAMING_SNAKE_CASE						=	(
         {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Optional[int]   	=	SwinvaModelTester(self					)
            snake_case__							:	int   	=	ConfigTester(self, config_class=_snake_case, embed_dim=3_7					)






     def    lowercase_   (       self  :  Tuple					)   ->int:
            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    lowercase_   (       self  :  Any					)   ->str:
            snake_case__							:	int   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_snake_case					)






     @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'					)
     def    lowercase_   (       self  :  Any					)   ->Union[str, Any]:
            pass






     @unittest.skip(reason='Swinv2 does not use inputs_embeds'					)
     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            pass






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

            for model_class in self.all_model_classes:
                   snake_case__							:	Union[str, Any]   	=	model_class(_snake_case					)
                   self.assertIsInstance(model.get_input_embeddings(), (nn.Module)					)
                   snake_case__							:	Union[str, Any]   	=	model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(_snake_case, nn.Linear					)					)






     def    lowercase_   (       self  :  List[str]					)   ->Optional[int]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

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

                   snake_case__							:	List[Any]   	=	['pixel_values']
                   self.assertListEqual(arg_names[:1], _snake_case					)






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

            for model_class in self.all_model_classes:
                   snake_case__							:	str   	=	True
                   snake_case__							:	Union[str, Any]   	=	False
                   snake_case__							:	Tuple   	=	True
                   snake_case__							:	int   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	Optional[int]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)
                   snake_case__							:	List[str]   	=	outputs.attentions
                   snake_case__							:	List[Any]   	=	len(self.model_tester.depths					)
                   self.assertEqual(len(_snake_case					), _snake_case					)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   snake_case__							:	str   	=	True
                   snake_case__							:	Tuple   	=	config.window_size**2
                   snake_case__							:	Optional[int]   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	str   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)
                   snake_case__							:	Tuple   	=	outputs.attentions
                   self.assertEqual(len(_snake_case					), _snake_case					)

                   self.assertListEqual(
                       list(attentions[0].shape[-3:]					), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )
                   snake_case__							:	Optional[Any]   	=	len(_snake_case					)

                   # Check attention is always last and order is fine
                   snake_case__							:	Optional[int]   	=	True
                   snake_case__							:	Dict   	=	True
                   snake_case__							:	List[Any]   	=	model_class(_snake_case					)
                   model.to(_snake_case					)
                   model.eval()
                   with torch.no_grad():
                          snake_case__							:	Optional[int]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)

                   if hasattr(self.model_tester, 'num_hidden_states_types'					):
                          snake_case__							:	str   	=	self.model_tester.num_hidden_states_types
                   else:
                          # also another +1 for reshaped_hidden_states
                          snake_case__							:	Dict   	=	2
                   self.assertEqual(out_len + added_hidden_states, len(_snake_case					)					)

                   snake_case__							:	Any   	=	outputs.attentions

                   self.assertEqual(len(_snake_case					), _snake_case					)

                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]					), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], )






     def    lowercase_   (       self  :  Dict, _snake_case  :  Tuple, _snake_case  :  Any, _snake_case  :  int, _snake_case  :  Optional[int]					)   ->str:
            snake_case__							:	Dict   	=	model_class(_snake_case					)
            model.to(_snake_case					)
            model.eval()

            with torch.no_grad():
                   snake_case__							:	List[Any]   	=	model(**self._prepare_for_class(_snake_case, _snake_case					)					)

            snake_case__							:	Dict   	=	outputs.hidden_states

            snake_case__							:	int   	=	getattr(
                self.model_tester, 'expected_num_hidden_layers', len(self.model_tester.depths					) + 1					)
            self.assertEqual(len(_snake_case					), _snake_case					)

            # Swinv2 has a different seq_length
            snake_case__							:	int   	=	(
                config.patch_size
                if isinstance(config.patch_size, collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            snake_case__							:	Optional[Any]   	=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]					), [num_patches, self.model_tester.embed_dim], )

            snake_case__							:	Union[str, Any]   	=	outputs.reshaped_hidden_states
            self.assertEqual(len(_snake_case					), _snake_case					)

            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	str   	=	reshaped_hidden_states[0].shape
            snake_case__							:	Any   	=	(
                reshaped_hidden_states[0].view(_snake_case, _snake_case, height * width					).permute(0, 2, 1					)
            )
            self.assertListEqual(
                list(reshaped_hidden_states.shape[-2:]					), [num_patches, self.model_tester.embed_dim], )






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

            snake_case__							:	List[Any]   	=	(
                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:
                   snake_case__							:	Optional[int]   	=	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"]
                   snake_case__							:	Dict   	=	True

                   self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->str:
            snake_case__	,				snake_case__							:	Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:	List[str]   	=	3

            snake_case__							:	Union[str, Any]   	=	(
                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)
            )
            snake_case__							:	str   	=	(
                config.patch_size
                if isinstance(config.patch_size, collections.abc.Iterable					)
                else (config.patch_size, config.patch_size)
            )

            snake_case__							:	Tuple   	=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
            snake_case__							:	Optional[Any]   	=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

            for model_class in self.all_model_classes:
                   snake_case__							:	int   	=	True
                   self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width)					)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   snake_case__							:	List[str]   	=	True
                   self.check_hidden_states_output(_snake_case, _snake_case, _snake_case, (padded_height, padded_width)					)






     def    lowercase_   (       self  :  List[str]					)   ->Optional[int]:
            snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case					)






     def    lowercase_   (       self  :  List[Any]					)   ->str:
            snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*_snake_case					)






     @slow
     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   snake_case__							:	Dict   	=	SwinvaModel.from_pretrained(_snake_case					)
                   self.assertIsNotNone(_snake_case					)






     def    lowercase_   (       self  :  Optional[int]					)   ->List[str]:
            snake_case__	,				snake_case__							:	Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

            snake_case__							:	List[Any]   	=	_config_zero_init(_snake_case					)
            for model_class in self.all_model_classes:
                   snake_case__							:	List[str]   	=	model_class(config=_snake_case					)
                   for name, param in model.named_parameters():
                          if "embeddings" not in name and "logit_scale" 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_vision
@require_torch
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     @cached_property
     def    lowercase_   (       self  :  Union[str, Any]					)   ->List[str]:
            return (
                AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'					)
                if is_vision_available()
                else None
            )






     @slow
     def    lowercase_   (       self  :  int					)   ->List[Any]:
            snake_case__							:	Any   	=	SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'					).to(
                _snake_case					)
            snake_case__							:	int   	=	self.default_image_processor

            snake_case__							:	Union[str, Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
            snake_case__							:	Optional[Any]   	=	image_processor(images=_snake_case, return_tensors='pt'					).to(_snake_case					)

            # forward pass
            with torch.no_grad():
                   snake_case__							:	List[str]   	=	model(**_snake_case					)

            # verify the logits
            snake_case__							:	int   	=	torch.Size((1, 1_0_0_0)					)
            self.assertEqual(outputs.logits.shape, _snake_case					)
            snake_case__							:	Optional[int]   	=	torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]					).to(_snake_case					)
            self.assertTrue(torch.allclose(outputs.logits[0, :3], _snake_case, atol=1e-4					)					)






