

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 	lowercase    :


		"""simple docstring"""




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


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

			_UpperCamelCase   :		List[str]			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				self):
			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 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[str]			=						SwinvaModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)

			_UpperCamelCase   :		List[Any]			=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
			_UpperCamelCase   :		Tuple			=						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 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Any			=						SwinvaForMaskedImageModeling(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		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
			_UpperCamelCase   :		Tuple			=						1
			_UpperCamelCase   :		Tuple			=						SwinvaForMaskedImageModeling(__snake_case)
			model.to(__snake_case)
			model.eval()

			_UpperCamelCase   :		Dict			=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
			_UpperCamelCase   :		Dict			=						model(__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, 1, self.image_size, self.image_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Union[str, Any]			=						self.type_sequence_label_size
			_UpperCamelCase   :		List[str]			=						SwinvaForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))






		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.prepare_config_and_inputs()
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		List[str]			=						config_and_inputs
			_UpperCamelCase   :		Tuple			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_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 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						SwinvaModelTester(self)
			_UpperCamelCase   :		Dict			=						ConfigTester(self	,       config_class=__snake_case	,       embed_dim=37)


		def 		A__					(				self):
			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 		A__					(				self):
			_UpperCamelCase   :		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 		A__					(				self):
			pass


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


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()

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


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Union[str, Any]			=						model_class(__snake_case)
				_UpperCamelCase   :		Optional[int]			=						inspect.signature(model.forward)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase   :		int			=						[*signature.parameters.keys()]

				_UpperCamelCase   :		Tuple			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase   :		Optional[Any]			=						True

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

				# Check attention is always last and order is fine
				_UpperCamelCase   :		int			=						True
				_UpperCamelCase   :		Any			=						True
				_UpperCamelCase   :		int			=						model_class(__snake_case)
				model.to(__snake_case)
				model.eval()
				with torch.no_grad():
					_UpperCamelCase   :		Dict			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

				if hasattr(self.model_tester	,       'num_hidden_states_types'):
					_UpperCamelCase   :		Any			=						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(__snake_case))

				_UpperCamelCase   :		Tuple			=						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 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						model_class(__snake_case)
			model.to(__snake_case)
			model.eval()

			with torch.no_grad():
				_UpperCamelCase   :		Any			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

			_UpperCamelCase   :		int			=						outputs.hidden_states

			_UpperCamelCase   :		List[str]			=						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
			_UpperCamelCase   :		Tuple			=						(
			    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   :		Optional[int]			=						outputs.reshaped_hidden_states
			self.assertEqual(len(__snake_case)	,       __snake_case)

			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						reshaped_hidden_states[0].shape
			_UpperCamelCase   :		Tuple			=						(
			    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 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		List[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   :		List[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"]
				_UpperCamelCase   :		Optional[Any]			=						True

				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase   :		List[str]			=						3

			_UpperCamelCase   :		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)
			)
			_UpperCamelCase   :		Optional[int]			=						(
			    config.patch_size
			    if isinstance(config.patch_size	,       collections.abc.Iterable)
			    else (config.patch_size, config.patch_size)
			)

			_UpperCamelCase   :		Tuple			=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			_UpperCamelCase   :		Tuple			=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		List[Any]			=						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"]
				_UpperCamelCase   :		List[str]			=						True
				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       (padded_height, padded_width))


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


		@slow
		def 		A__					(				self):
			for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		Tuple			=						SwinvaModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		str			=						self.model_tester.prepare_config_and_inputs_for_common()

			_UpperCamelCase   :		str			=						_config_zero_init(__snake_case)
			for model_class in self.all_model_classes:
				_UpperCamelCase   :		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 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		@cached_property
		def 		A__					(				self):
			return (
			    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256')
			    if is_vision_available()
			    else None
			)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256').to(
			    __snake_case)
			_UpperCamelCase   :		Any			=						self.default_image_processor

			_UpperCamelCase   :		Optional[int]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
			_UpperCamelCase   :		Dict			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		int			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		int			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)
			_UpperCamelCase   :		Any			=						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))







import functools






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     list[int]	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for day in days	):
		raise ValueError('The parameter days should be a list of integers'	)

	if len(UpperCAmelCase_	) != 3 or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for cost in costs	):
		raise ValueError('The parameter costs should be a list of three integers'	)

	if len(UpperCAmelCase_	) == 0:
		return 0

	if min(UpperCAmelCase_	) <= 0:
		raise ValueError('All days elements should be greater than 0'	)

	if max(UpperCAmelCase_	) >= 3_6_6:
		raise ValueError('All days elements should be less than 366'	)

	_UpperCamelCase   :		Union[str, Any]			=						set(UpperCAmelCase_	)

	@functools.cache
	def dynamic_programming(UpperCAmelCase_							:     int	) -> int:
		if index > 3_6_5:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1	)

		return min(
		    costs[0] + dynamic_programming(index + 1	)							,       costs[1] + dynamic_programming(index + 7	)							,       costs[2] + dynamic_programming(index + 3_0	)							,       )

	return dynamic_programming(1	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




