


'''simple docstring'''







def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	Dict  )  ->			str:


       if number < 0 or shift_amount < 0:
              raise ValueError("""both inputs must be positive integers"""  )

       _a    : Union[str, Any]     					=str(bin(_lowercase  )  )
       binary_number += "0" * shift_amount
       return binary_number


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	List[Any]  )  ->			Optional[int]:


       if number < 0 or shift_amount < 0:
              raise ValueError("""both inputs must be positive integers"""  )

       _a    : Optional[int]     					=str(bin(_lowercase  )  )[2:]
       if shift_amount >= len(_lowercase  ):
              return "0b0"
       _a    : Optional[int]     					=binary_number[: len(_lowercase  ) - shift_amount]
       return "0b" + shifted_binary_number


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Optional[Any]  )  ->			Dict:


       if number >= 0:  # Get binary representation of positive number
              _a    : Any     					="""0""" + str(bin(_lowercase  )  ).strip("""-"""  )[2:]
       else:  # Get binary (2's complement) representation of negative number
              _a    : Dict     					=len(bin(_lowercase  )[3:]  )  # Find 2's complement of number
              _a    : Optional[int]     					=bin(abs(_lowercase  ) - (1 << binary_number_length)  )[3:]
              _a    : Any     					=(
                  """1""" + """0""" * (binary_number_length - len(_lowercase  )) + binary_number
              )

       if shift_amount >= len(_lowercase  ):
              return "0b" + binary_number[0] * len(_lowercase  )
       return (
           "0b"
           + binary_number[0] * shift_amount
           + binary_number[: len(_lowercase  ) - shift_amount]
       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()







"""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						)						)
