
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		:str     ,   a		:List[str]     ,   a		:Any=1_3     ,   a		:List[str]=3_2     ,   a		:Tuple=2     ,   a		:List[Any]=3     ,   a		:List[Any]=1_6     ,   a		:List[str]=[1, 2, 1]     ,   a		:Union[str, Any]=[2, 2, 4]     ,   a		:int=2     ,   a		:str=2.0     ,   a		:str=True     ,   a		:Optional[int]=0.0     ,   a		:Tuple=0.0     ,   a		:int=0.1     ,   a		:List[Any]="gelu"     ,   a		:Any=False     ,   a		:List[str]=True     ,   a		:Dict=0.02     ,   a		:List[Any]=1E-5     ,   a		:Optional[int]=True     ,   a		:int=None     ,   a		:Optional[Any]=True     ,   a		:str=1_0     ,   a		:Any=8     ,   )		-> List[Any]:
								__UpperCamelCase	:   List[Any]			   =					parent
								__UpperCamelCase	:   List[str]			   =					batch_size
								__UpperCamelCase	:   Dict			   =					image_size
								__UpperCamelCase	:   Optional[Any]			   =					patch_size
								__UpperCamelCase	:   Tuple			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					embed_dim
								__UpperCamelCase	:   Dict			   =					depths
								__UpperCamelCase	:   int			   =					num_heads
								__UpperCamelCase	:   List[str]			   =					window_size
								__UpperCamelCase	:   List[Any]			   =					mlp_ratio
								__UpperCamelCase	:   List[Any]			   =					qkv_bias
								__UpperCamelCase	:   List[str]			   =					hidden_dropout_prob
								__UpperCamelCase	:   Tuple			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   Union[str, Any]			   =					drop_path_rate
								__UpperCamelCase	:   Union[str, Any]			   =					hidden_act
								__UpperCamelCase	:   Dict			   =					use_absolute_embeddings
								__UpperCamelCase	:   int			   =					patch_norm
								__UpperCamelCase	:   Optional[int]			   =					layer_norm_eps
								__UpperCamelCase	:   Dict			   =					initializer_range
								__UpperCamelCase	:   Optional[Any]			   =					is_training
								__UpperCamelCase	:   Dict			   =					scope
								__UpperCamelCase	:   Optional[Any]			   =					use_labels
								__UpperCamelCase	:   Optional[int]			   =					type_sequence_label_size
								__UpperCamelCase	:   int			   =					encoder_stride







	def    _lowerCamelCase				(					self		:Tuple						)		-> Tuple:
								__UpperCamelCase	:   int			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

								__UpperCamelCase	:   int			   =					self.get_config()

								return config, pixel_values, labels







	def    _lowerCamelCase				(					self		:Any						)		-> 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    _lowerCamelCase				(					self		:Tuple     ,   a		:Tuple     ,   a		:Dict     ,   a		:Optional[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   Dict			   =					SwinvaModel(config=a						)
								model.to(a						)
								model.eval()
								__UpperCamelCase	:   Tuple			   =					model(a						)

								__UpperCamelCase	:   List[Any]			   =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
								__UpperCamelCase	:   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		:Dict     ,   a		:Optional[Any]     ,   a		:str     ,   a		:int						)		-> Any:
								__UpperCamelCase	:   str			   =					SwinvaForMaskedImageModeling(config=a						)
								model.to(a						)
								model.eval()
								__UpperCamelCase	:   Optional[int]			   =					model(a						)
								self.parent.assertEqual(
								    result.logits.shape     ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

								# test greyscale images
								__UpperCamelCase	:   Tuple			   =					1
								__UpperCamelCase	:   Union[str, Any]			   =					SwinvaForMaskedImageModeling(a						)
								model.to(a						)
								model.eval()

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







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[int]     ,   a		:int     ,   a		:Optional[int]						)		-> str:
								__UpperCamelCase	:   Tuple			   =					self.type_sequence_label_size
								__UpperCamelCase	:   Optional[int]			   =					SwinvaForImageClassification(a						)
								model.to(a						)
								model.eval()
								__UpperCamelCase	:   Tuple			   =					model(a     ,   labels=a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)







	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Dict			   =					self.prepare_config_and_inputs()
								__UpperCamelCase   ,						__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					config_and_inputs
								__UpperCamelCase	:   Optional[Any]			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_torch
class  lowerCamelCase__							(				__lowercase     ,						__lowercase     ,						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		:Optional[int]						)		-> int:
								__UpperCamelCase	:   Optional[Any]			   =					SwinvaModelTester(self						)
								__UpperCamelCase	:   List[Any]			   =					ConfigTester(self     ,   config_class=a     ,   embed_dim=3_7						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> List[str]:
								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:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."						)
	def    _lowerCamelCase				(					self		:Dict						)		-> Tuple:
								pass







	@unittest.skip(reason="Swinv2 does not use inputs_embeds"						)
	def    _lowerCamelCase				(					self		:Any						)		-> str:
								pass







	def    _lowerCamelCase				(					self		:Dict						)		-> List[str]:
								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.model_tester.prepare_config_and_inputs_for_common()

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







	def    _lowerCamelCase				(					self		:str						)		-> List[str]:
								__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs_for_common()

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

															__UpperCamelCase	:   Union[str, Any]			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Optional[Any]:
								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase	:   Any			   =					True

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

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

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

															# Check attention is always last and order is fine
															__UpperCamelCase	:   Any			   =					True
															__UpperCamelCase	:   Tuple			   =					True
															__UpperCamelCase	:   Any			   =					model_class(a						)
															model.to(a						)
															model.eval()
															with torch.no_grad():
																						__UpperCamelCase	:   Optional[Any]			   =					model(**self._prepare_for_class(a     ,   a						)						)

															if hasattr(self.model_tester     ,   "num_hidden_states_types"						):
																						__UpperCamelCase	:   Optional[int]			   =					self.model_tester.num_hidden_states_types
															else:
																						# also another +1 for reshaped_hidden_states
																						__UpperCamelCase	:   Optional[Any]			   =					2
															self.assertEqual(out_len + added_hidden_states     ,   len(a						)						)

															__UpperCamelCase	:   List[str]			   =					outputs.attentions

															self.assertEqual(len(a						)     ,   a						)

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







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:List[Any]     ,   a		:str     ,   a		:List[str]						)		-> Tuple:
								__UpperCamelCase	:   Union[str, Any]			   =					model_class(a						)
								model.to(a						)
								model.eval()

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

								__UpperCamelCase	:   Union[str, Any]			   =					outputs.hidden_states

								__UpperCamelCase	:   Tuple			   =					getattr(
								    self.model_tester     ,   "expected_num_hidden_layers"     ,   len(self.model_tester.depths						) + 1						)
								self.assertEqual(len(a						)     ,   a						)

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

								__UpperCamelCase	:   str			   =					(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]     ,   )

								__UpperCamelCase	:   str			   =					outputs.reshaped_hidden_states
								self.assertEqual(len(a						)     ,   a						)

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







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

								__UpperCamelCase	:   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:
															__UpperCamelCase	:   Optional[int]			   =					True
															self.check_hidden_states_output(a     ,   a     ,   a     ,   a						)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__UpperCamelCase	:   str			   =					True

															self.check_hidden_states_output(a     ,   a     ,   a     ,   a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.model_tester.prepare_config_and_inputs_for_common()
								__UpperCamelCase	:   List[Any]			   =					3

								__UpperCamelCase	:   Tuple			   =					(
								    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)
								)
								__UpperCamelCase	:   List[str]			   =					(
								    config.patch_size
								    if isinstance(config.patch_size     ,   collections.abc.Iterable						)
								    else (config.patch_size, config.patch_size)
								)

								__UpperCamelCase	:   Union[str, Any]			   =					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								__UpperCamelCase	:   Dict			   =					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   Optional[int]			   =					True
															self.check_hidden_states_output(a     ,   a     ,   a     ,   (padded_height, padded_width)						)

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







	def    _lowerCamelCase				(					self		:Tuple						)		-> Union[str, Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*a						)







	def    _lowerCamelCase				(					self		:str						)		-> Tuple:
								__UpperCamelCase	:   Union[str, Any]			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	@slow
	def    _lowerCamelCase				(					self		:int						)		-> Tuple:
								for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__UpperCamelCase	:   Union[str, Any]			   =					SwinvaModel.from_pretrained(a						)
															self.assertIsNotNone(a						)







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   str			   =					self.model_tester.prepare_config_and_inputs_for_common()

								__UpperCamelCase	:   Tuple			   =					_config_zero_init(a						)
								for model_class in self.all_model_classes:
															__UpperCamelCase	:   str			   =					model_class(config=a						)
															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		:Union[str, Any]						)		-> List[Any]:
								return (
								    AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"						)
								    if is_vision_available()
								    else None
								)







	@slow
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Optional[Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256"						).to(
								    a						)
								__UpperCamelCase	:   Optional[int]			   =					self.default_image_processor

								__UpperCamelCase	:   Optional[Any]			   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
								__UpperCamelCase	:   Dict			   =					image_processor(images=a     ,   return_tensors="pt"						).to(a						)

								# forward pass
								with torch.no_grad():
															__UpperCamelCase	:   Optional[Any]			   =					model(**a						)

								# verify the logits
								__UpperCamelCase	:   Optional[Any]			   =					torch.Size((1, 1_0_0_0)						)
								self.assertEqual(outputs.logits.shape     ,   a						)
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor([-0.3947, -0.4306, 0.0026]						).to(a						)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,   a     ,   atol=1E-4						)						)

from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase  :					Optional[Any]													=       TypeVar('T')




class  lowerCamelCase__							(				Generic[T]):




	'''simple docstring'''




	_A								=     42  # Cache store of keys
	_A								=     42  # References of the keys in cache
	_A								=     1_0  # Maximum capacity of cache







	def __init__(					self		:Optional[Any]     ,   a		:int						)		-> None:
								__UpperCamelCase	:   Union[str, Any]			   =					deque()
								__UpperCamelCase	:   str			   =					set()
								if not n:
															__UpperCamelCase	:   Union[str, Any]			   =					sys.maxsize
								elif n < 0:
															raise ValueError("n should be an integer greater than 0."						)
								else:
															__UpperCamelCase	:   Any			   =					n







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:T						)		-> None:
								if x not in self.key_reference:
															if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
																						__UpperCamelCase	:   int			   =					self.dq_store.pop()
																						self.key_reference.remove(a						)
								else:
															self.dq_store.remove(a						)

								self.dq_store.appendleft(a						)
								self.key_reference.add(a						)







	def    _lowerCamelCase				(					self		:Any						)		-> None:
								for k in self.dq_store:
															print(a						)







	def __repr__(					self		:Tuple						)		-> str:
								return f'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase  :					LRUCache[str | int]													=       LRUCache(4)
		lru_cache.refer('A')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('A')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"