

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		)
		from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor


class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=30 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=4 ,				__UpperCamelCase=37 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=3 ,				__UpperCamelCase=None ,				__UpperCamelCase=2 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= hidden_size
				snake_case_      		= num_hidden_layers
				snake_case_      		= num_attention_heads
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= type_sequence_label_size
				snake_case_      		= initializer_range
				snake_case_      		= scope
				snake_case_      		= encoder_stride

				# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
				snake_case_      		= (image_size // patch_size) ** 2
				snake_case_      		= num_patches + 2


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return DeiTConfig(
				    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				is_decoder=__UpperCamelCase ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= TFDeiTModel(config=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= TFDeiTForMaskedImageModeling(config=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.reconstruction.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= TFDeiTForMaskedImageModeling(__UpperCamelCase							)

				snake_case_      		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.reconstruction.shape ,				(self.batch_size, 1, self.image_size, self.image_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.type_sequence_label_size
				snake_case_      		= TFDeiTForImageClassification(__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= TFDeiTForImageClassification(__UpperCamelCase							)

				snake_case_      		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







@require_tf
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




			__A						  =  (
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			__A						  =  (
			    {
			        """feature-extraction""": TFDeiTModel,
			        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFDeiTModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='DeiT does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					self.assertIsInstance(model.get_input_embeddings() ,				(tf.keras.layers.Layer)							)
					snake_case_      		= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCamelCase ,				tf.keras.layers.Dense							)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=False							):

				"""simple docstring"""


				snake_case_      		= super()._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)

				if return_labels:
					if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
						del inputs_dict["labels"]

				return inputs_dict







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= TFDeiTModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





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






@require_tf
@require_vision
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (
				    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'							)
				    if is_vision_available()
				    else None
				)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='tf'							)

				# forward pass
				snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= tf.TensorShape((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= tf.constant([-1.0266, 0.1912, -1.2861]							)

				self.assertTrue(np.allclose(outputs.logits[0, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)



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 SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=16 ,				__UpperCamelCase=[1, 2, 1] ,				__UpperCamelCase=[2, 2, 4] ,				__UpperCamelCase=2 ,				__UpperCamelCase=2.0 ,				__UpperCamelCase=True ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=False ,				__UpperCamelCase=True ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=1E-5 ,				__UpperCamelCase=True ,				__UpperCamelCase=None ,				__UpperCamelCase=True ,				__UpperCamelCase=10 ,				__UpperCamelCase=8 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= embed_dim
				snake_case_      		= depths
				snake_case_      		= num_heads
				snake_case_      		= window_size
				snake_case_      		= mlp_ratio
				snake_case_      		= qkv_bias
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= drop_path_rate
				snake_case_      		= hidden_act
				snake_case_      		= use_absolute_embeddings
				snake_case_      		= patch_norm
				snake_case_      		= layer_norm_eps
				snake_case_      		= initializer_range
				snake_case_      		= is_training
				snake_case_      		= scope
				snake_case_      		= use_labels
				snake_case_      		= type_sequence_label_size
				snake_case_      		= encoder_stride


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				snake_case_      		= None
				if self.use_labels:
					snake_case_      		= ids_tensor([self.batch_size] ,				self.type_sequence_label_size							)

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= SwinvaModel(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)

				snake_case_      		= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
				snake_case_      		= 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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= SwinvaForMaskedImageModeling(config=__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.logits.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= SwinvaForMaskedImageModeling(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()

				snake_case_      		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 1, self.image_size, self.image_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.type_sequence_label_size
				snake_case_      		= SwinvaForImageClassification(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







@require_torch
class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							,						unittest.TestCase							):






			"""simple docstring"""




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

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= SwinvaModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				embed_dim=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				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							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			@unittest.skip(reason='Swinv2 does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)							)
					snake_case_      		= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCamelCase ,				nn.Linear							)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

				for model_class in self.all_model_classes:
					snake_case_      		= True
					snake_case_      		= False
					snake_case_      		= True
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()
					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)
					snake_case_      		= outputs.attentions
					snake_case_      		= len(self.model_tester.depths							)
					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

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

					self.assertListEqual(
					    list(attentions[0].shape[-3:]							) ,				[self.model_tester.num_heads[0], window_size_squared, window_size_squared] ,				)
					snake_case_      		= len(__UpperCamelCase							)

					# Check attention is always last and order is fine
					snake_case_      		= True
					snake_case_      		= True
					snake_case_      		= model_class(__UpperCamelCase							)
					model.to(__UpperCamelCase							)
					model.eval()
					with torch.no_grad():
						snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

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

					snake_case_      		= outputs.attentions

					self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= model_class(__UpperCamelCase							)
				model.to(__UpperCamelCase							)
				model.eval()

				with torch.no_grad():
					snake_case_      		= model(**self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)

				snake_case_      		= outputs.hidden_states

				snake_case_      		= getattr(
				    self.model_tester ,				'expected_num_hidden_layers' ,				len(self.model_tester.depths							) + 1							)
				self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

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

				snake_case_      		= (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_      		= outputs.reshaped_hidden_states
				self.assertEqual(len(__UpperCamelCase							) ,				__UpperCamelCase							)

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				snake_case_      		= (
				    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_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

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

					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()
				snake_case_      		= 3

				snake_case_      		= (
				    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_      		= (
				    config.patch_size
				    if isinstance(config.patch_size ,				collections.abc.Iterable							)
				    else (config.patch_size, config.patch_size)
				)

				snake_case_      		= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				snake_case_      		= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
					snake_case_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				(padded_height, padded_width)							)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					snake_case_      		= True
					self.check_hidden_states_output(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase ,				(padded_height, padded_width)							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= SwinvaModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				snake_case_      		= _config_zero_init(__UpperCamelCase							)
				for model_class in self.all_model_classes:
					snake_case_      		= model_class(config=__UpperCamelCase							)
					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 SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return (
				    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'							)
				    if is_vision_available()
				    else None
				)







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'							).to(
				    __UpperCamelCase							)
				snake_case_      		= self.default_image_processor

				snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='pt'							).to(__UpperCamelCase							)

				# forward pass
				with torch.no_grad():
					snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= torch.Size((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)
				snake_case_      		= torch.tensor([-0.3947, -0.4306, 0.0026]							).to(__UpperCamelCase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				__UpperCamelCase ,				atol=1E-4							)							)
