



"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__a      =				logging.get_logger(__name__)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							int						,     snake_case:							int						,     snake_case:							float						,     **snake_case:							Optional[int]						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			feature_size
											snake_case_ :Tuple						=			sampling_rate
											snake_case_ :Optional[int]						=			padding_value

											snake_case_ :Dict						=			kwargs.pop("""padding_side"""						,     """right"""						)
											snake_case_ :List[Any]						=			kwargs.pop("""return_attention_mask"""						,     snake_case						)

											super().__init__(**snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]						,     snake_case:							Union[bool, str, PaddingStrategy] = True						,     snake_case:							Optional[int] = None						,     snake_case:							bool = False						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     snake_case:							Optional[Union[str, TensorType]] = None						,     )   ->	BatchFeature:
											# If we have a list of dicts, let's convert it in a dict of lists
											# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
											if isinstance(snake_case						,     (list, tuple)						) and isinstance(processed_features[0]						,     (dict, BatchFeature)						):
																	snake_case_ :int						=			{
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

											# The model's main input name, usually `input_values`, has be passed for padding
											if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
																	    f""" to this method that includes {self.model_input_names[0]}, but you provided"""
																	    f""" {list(processed_features.keys()						)}"""						)

											snake_case_ :Union[str, Any]						=			processed_features[self.model_input_names[0]]
											snake_case_ :Optional[int]						=			(
											    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
											)

											if len(snake_case						) == 0:
																	if return_attention_mask:
																							snake_case_ :Optional[int]						=			[]
																	return processed_features

											# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
											# and rebuild them afterwards if no return_tensors is specified
											# Note that we lose the specific device the tensor may be on for PyTorch

											snake_case_ :Optional[Any]						=			required_input[0]
											if isinstance(snake_case						,     (list, tuple)						):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	snake_case_ :Tuple						=			0
																	while len(required_input[index]						) == 0:
																							index += 1
																	if index < len(snake_case						):
																							snake_case_ :Tuple						=			required_input[index][0]

											if return_tensors is None:
																	if is_tf_tensor(snake_case						):
																							snake_case_ :int						=			"""tf"""
																	elif is_torch_tensor(snake_case						):
																							snake_case_ :Union[str, Any]						=			"""pt"""
																	elif isinstance(snake_case						,     (int, float, list, tuple, np.ndarray)						):
																							snake_case_ :Optional[int]						=			"""np"""
																	else:
																							raise ValueError(
																							    f"""type of {first_element} unknown: {type(snake_case						)}. """
																							    """Should be one of a python, numpy, pytorch or tensorflow object."""						)

											for key, value in processed_features.items():
																	if isinstance(value[0]						,     (int, float)						):
																							snake_case_ :Optional[int]						=			to_numpy(snake_case						)
																	else:
																							snake_case_ :int						=			[to_numpy(snake_case						) for v in value]

        # Convert padding_strategy in PaddingStrategy
											snake_case_ :Union[str, Any]						=			self._get_padding_strategies(padding=snake_case						,     max_length=snake_case						)

											snake_case_ :List[Any]						=			processed_features[self.model_input_names[0]]

											snake_case_ :Optional[Any]						=			len(snake_case						)
											if not all(len(snake_case						) == batch_size for v in processed_features.values()						):
																	raise ValueError("""Some items in the output dictionary have a different batch size than others."""						)

											snake_case_ :Optional[Any]						=			[]
											for i in range(snake_case						):
																	snake_case_ :List[Any]						=			{k: v[i] for k, v in processed_features.items()}
																	# truncation
																	snake_case_ :Union[str, Any]						=			self._truncate(
																	    snake_case						,     max_length=snake_case						,     pad_to_multiple_of=snake_case						,     truncation=snake_case						,     )
																	truncated_inputs.append(snake_case						)

											if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	snake_case_ :Optional[Any]						=			max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
																	snake_case_ :int						=			PaddingStrategy.MAX_LENGTH

											snake_case_ :List[Any]						=			{}
											for i in range(snake_case						):
																	# padding
																	snake_case_ :Any						=			self._pad(
																	    truncated_inputs[i]						,     max_length=snake_case						,     padding_strategy=snake_case						,     pad_to_multiple_of=snake_case						,     return_attention_mask=snake_case						,     )

																	for key, value in outputs.items():
																							if key not in batch_outputs:
																													snake_case_ :Optional[int]						=			[]
																							if value.dtype is np.dtype(np.floataa						):
																													snake_case_ :Tuple						=			value.astype(np.floataa						)
																							batch_outputs[key].append(snake_case						)

											return BatchFeature(snake_case						,     tensor_type=snake_case						)






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							Union[Dict[str, np.ndarray], BatchFeature]						,     snake_case:							Optional[int] = None						,     snake_case:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     )   ->	dict:
											snake_case_ :Any						=			processed_features[self.model_input_names[0]]

											if padding_strategy == PaddingStrategy.LONGEST:
																	snake_case_ :Any						=			len(snake_case						)

											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	snake_case_ :Optional[int]						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											snake_case_ :Tuple						=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case						) < max_length

											if return_attention_mask and "attention_mask" not in processed_features:
																	snake_case_ :Union[str, Any]						=			np.ones(len(snake_case						)						,     dtype=np.intaa						)

											if needs_to_be_padded:
																	snake_case_ :Optional[int]						=			max_length - len(snake_case						)
																	if self.padding_side == "right":
																							if return_attention_mask:
																													snake_case_ :Union[str, Any]						=			np.pad(
																													    processed_features["""attention_mask"""]						,     (0, difference)						)
																							snake_case_ :Tuple						=			((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																							snake_case_ :int						=			np.pad(
																							    snake_case						,     snake_case						,     """constant"""						,     constant_values=self.padding_value						)
																	elif self.padding_side == "left":
																							if return_attention_mask:
																													snake_case_ :Dict						=			np.pad(
																													    processed_features["""attention_mask"""]						,     (difference, 0)						)
																							snake_case_ :str						=			((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																							snake_case_ :Optional[Any]						=			np.pad(
																							    snake_case						,     snake_case						,     """constant"""						,     constant_values=self.padding_value						)
																	else:
																							raise ValueError("""Invalid padding strategy:""" + str(self.padding_side						)						)

											return processed_features






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							Union[Dict[str, np.ndarray], BatchFeature]						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     )   ->	List[Any]:
											if not truncation:
																	return processed_features
											elif truncation and max_length is None:
																	raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""						)

											snake_case_ :Any						=			processed_features[self.model_input_names[0]]

											# find `max_length` that fits `pad_to_multiple_of`
											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	snake_case_ :str						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											snake_case_ :Optional[Any]						=			len(snake_case						) > max_length

											if needs_to_be_truncated:
																	snake_case_ :Union[str, Any]						=			processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																							snake_case_ :Union[str, Any]						=			processed_features["""attention_mask"""][:max_length]

											return processed_features






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple=False						,     snake_case:							Union[str, Any]=None						)   ->	Union[str, Any]:

											# Get padding strategy
											if padding is not False:
																	if padding is True:
																							snake_case_ :Optional[Any]						=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(snake_case						,     snake_case						):
																							snake_case_ :Dict						=			PaddingStrategy(snake_case						)
																	elif isinstance(snake_case						,     snake_case						):
																							snake_case_ :Optional[Any]						=			padding
											else:
																	snake_case_ :Optional[Any]						=			PaddingStrategy.DO_NOT_PAD

											# Set max length if needed
											if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																							raise ValueError(
																							    f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""						)

        # Test if we have a padding value
											if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
																	    """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""						)

											return padding_strategy





"""simple docstring"""
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 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Optional[int]						,     snake_case:							Any						,     snake_case:							Optional[Any]=13						,     snake_case:							Tuple=32						,     snake_case:							Optional[int]=2						,     snake_case:							Tuple=3						,     snake_case:							Tuple=16						,     snake_case:							Optional[Any]=[1, 2, 1]						,     snake_case:							Optional[int]=[2, 2, 4]						,     snake_case:							Optional[int]=2						,     snake_case:							int=2.0						,     snake_case:							Union[str, Any]=True						,     snake_case:							List[str]=0.0						,     snake_case:							List[Any]=0.0						,     snake_case:							Optional[Any]=0.1						,     snake_case:							List[Any]="gelu"						,     snake_case:							Optional[int]=False						,     snake_case:							Union[str, Any]=True						,     snake_case:							Union[str, Any]=0.0_2						,     snake_case:							Optional[int]=1E-5						,     snake_case:							Optional[Any]=True						,     snake_case:							List[Any]=None						,     snake_case:							List[Any]=True						,     snake_case:							Optional[Any]=10						,     snake_case:							str=8						,     )   ->	Tuple:
											snake_case_ :Dict						=			parent
											snake_case_ :Any						=			batch_size
											snake_case_ :List[Any]						=			image_size
											snake_case_ :List[Any]						=			patch_size
											snake_case_ :int						=			num_channels
											snake_case_ :Tuple						=			embed_dim
											snake_case_ :str						=			depths
											snake_case_ :str						=			num_heads
											snake_case_ :Optional[int]						=			window_size
											snake_case_ :Tuple						=			mlp_ratio
											snake_case_ :Any						=			qkv_bias
											snake_case_ :List[Any]						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :Union[str, Any]						=			drop_path_rate
											snake_case_ :Any						=			hidden_act
											snake_case_ :Optional[Any]						=			use_absolute_embeddings
											snake_case_ :Union[str, Any]						=			patch_norm
											snake_case_ :Dict						=			layer_norm_eps
											snake_case_ :str						=			initializer_range
											snake_case_ :Tuple						=			is_training
											snake_case_ :Tuple						=			scope
											snake_case_ :Union[str, Any]						=			use_labels
											snake_case_ :Optional[Any]						=			type_sequence_label_size
											snake_case_ :Dict						=			encoder_stride






					def    lowerCAmelCase_				(       self:							int						)   ->	int:
											snake_case_ :List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

											snake_case_ :int						=			self.get_config()

											return config, pixel_values, labels






					def    lowerCAmelCase_				(       self:							str						)   ->	Union[str, Any]:
											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:							str						,     snake_case:							Optional[int]						,     snake_case:							Dict						,     snake_case:							str						)   ->	List[Any]:
											snake_case_ :Union[str, Any]						=			SwinvaModel(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Optional[int]						=			model(snake_case						)

											snake_case_ :Optional[Any]						=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
											snake_case_ :int						=			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:							int						,     snake_case:							List[str]						,     snake_case:							Tuple						,     snake_case:							int						)   ->	Any:
											snake_case_ :Dict						=			SwinvaForMaskedImageModeling(config=snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Tuple						=			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_ :List[Any]						=			1
											snake_case_ :int						=			SwinvaForMaskedImageModeling(snake_case						)
											model.to(snake_case						)
											model.eval()

											snake_case_ :Union[str, Any]						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
											snake_case_ :int						=			model(snake_case						)
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, 1, self.image_size, self.image_size)						)






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Any						,     snake_case:							List[str]						,     snake_case:							Union[str, Any]						)   ->	Tuple:
											snake_case_ :int						=			self.type_sequence_label_size
											snake_case_ :List[Any]						=			SwinvaForImageClassification(snake_case						)
											model.to(snake_case						)
											model.eval()
											snake_case_ :Dict						=			model(snake_case						,     labels=snake_case						)
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.type_sequence_label_size)						)






					def    lowerCAmelCase_				(       self:							int						)   ->	str:
											snake_case_ :Any						=			self.prepare_config_and_inputs()
											snake_case_,       snake_case_,       snake_case_ :List[str]						=			config_and_inputs
											snake_case_ :List[Any]						=			{"""pixel_values""": pixel_values}
											return config, inputs_dict

@require_torch
class 				lowerCamelCase  (			_lowerCAmelCase    ,						_lowerCAmelCase    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Optional[Any]						=     (
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					_A		:			Any						=     (
					    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_A		:			List[Any]						=     False
					_A		:			List[str]						=     False
					_A		:			Tuple						=     False
					_A		:			List[str]						=     False






					def    lowerCAmelCase_				(       self:							Dict						)   ->	List[Any]:
											snake_case_ :Optional[int]						=			SwinvaModelTester(self						)
											snake_case_ :List[str]						=			ConfigTester(self						,     config_class=snake_case						,     embed_dim=37						)






					def    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	List[Any]:
											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:							Union[str, Any]						)   ->	Tuple:
											snake_case_ :List[str]						=			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    lowerCAmelCase_				(       self:							Union[str, Any]						)   ->	str:
											pass






					@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""						)
					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											pass






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Union[str, Any]:
											snake_case_,       snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	snake_case_ :Optional[int]						=			model_class(snake_case						)
																	self.assertIsInstance(model.get_input_embeddings()						,     (nn.Module)						)
																	snake_case_ :List[Any]						=			model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(snake_case						,     nn.Linear						)						)






					def    lowerCAmelCase_				(       self:							Dict						)   ->	Optional[int]:
											snake_case_,       snake_case_ :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

																	snake_case_ :List[Any]						=			["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]						,     snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						)   ->	Optional[Any]:
											snake_case_,       snake_case_ :List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :List[str]						=			True

											for model_class in self.all_model_classes:
																	snake_case_ :List[Any]						=			True
																	snake_case_ :Any						=			False
																	snake_case_ :Optional[int]						=			True
																	snake_case_ :Tuple						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()
																	with torch.no_grad():
																							snake_case_ :Any						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)
																	snake_case_ :str						=			outputs.attentions
																	snake_case_ :Dict						=			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_ :Union[str, Any]						=			True
																	snake_case_ :Tuple						=			config.window_size**2
																	snake_case_ :Any						=			model_class(snake_case						)
																	model.to(snake_case						)
																	model.eval()
																	with torch.no_grad():
																							snake_case_ :Union[str, Any]						=			model(**self._prepare_for_class(snake_case						,     snake_case						)						)
																	snake_case_ :int						=			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_ :Any						=			len(snake_case						)

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

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

																	snake_case_ :str						=			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    lowerCAmelCase_				(       self:							int						,     snake_case:							Dict						,     snake_case:							Dict						,     snake_case:							Optional[Any]						,     snake_case:							Dict						)   ->	List[str]:
											snake_case_ :Dict						=			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_ :str						=			outputs.hidden_states

											snake_case_ :List[Any]						=			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_ :List[Any]						=			(
											    config.patch_size
											    if isinstance(config.patch_size						,     collections.abc.Iterable						)
											    else (config.patch_size, config.patch_size)
											)

											snake_case_ :Optional[int]						=			(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_ :str						=			outputs.reshaped_hidden_states
											self.assertEqual(len(snake_case						)						,     snake_case						)

											snake_case_,       snake_case_,       snake_case_,       snake_case_ :Any						=			reshaped_hidden_states[0].shape
											snake_case_ :int						=			(
											    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    lowerCAmelCase_				(       self:							Any						)   ->	Any:
											snake_case_,       snake_case_ :List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											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)
											)

											for model_class in self.all_model_classes:
																	snake_case_ :Union[str, Any]						=			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_ :List[str]						=			True

																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     snake_case						)






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :Union[str, Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_ :Optional[int]						=			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_ :Any						=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											snake_case_ :int						=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

											for model_class in self.all_model_classes:
																	snake_case_ :str						=			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_ :Tuple						=			True
																	self.check_hidden_states_output(snake_case						,     snake_case						,     snake_case						,     (padded_height, padded_width)						)






					def    lowerCAmelCase_				(       self:							Any						)   ->	Tuple:
											snake_case_ :int						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Dict:
											snake_case_ :Any						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case						)






					@slow
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Dict:
											for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	snake_case_ :List[str]						=			SwinvaModel.from_pretrained(snake_case						)
																	self.assertIsNotNone(snake_case						)






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

											snake_case_ :Optional[int]						=			_config_zero_init(snake_case						)
											for model_class in self.all_model_classes:
																	snake_case_ :Tuple						=			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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					@cached_property
					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	List[Any]:
											return (
											    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""						)
											    if is_vision_available()
											    else None
											)






					@slow
					def    lowerCAmelCase_				(       self:							List[str]						)   ->	List[str]:
											snake_case_ :Tuple						=			SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""						).to(
											    snake_case						)
											snake_case_ :str						=			self.default_image_processor

											snake_case_ :List[Any]						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
											snake_case_ :str						=			image_processor(images=snake_case						,     return_tensors="""pt"""						).to(snake_case						)

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

											# verify the logits
											snake_case_ :Dict						=			torch.Size((1, 1_000)						)
											self.assertEqual(outputs.logits.shape						,     snake_case						)
											snake_case_ :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						)						)
