



'''simple docstring'''




import os
import sys
import time

from . import transposition_cipher as trans_cipher


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		'Prehistoric Men.txt'
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		'Output.txt'
		__SCREAMING_SNAKE_CASE	:		str   	=		int(input("""Enter key: """ ) )
		__SCREAMING_SNAKE_CASE	:		Any   	=		input("""Encrypt/Decrypt [e/d]: """ )

		if not os.path.exists(SCREAMING_SNAKE_CASE__ ):
				print(F"File {input_file} does not exist. Quitting..." )
				sys.exit()
		if os.path.exists(SCREAMING_SNAKE_CASE__ ):
				print(F"Overwrite {output_file}? [y/n]" )
				__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		input("""> """ )
				if not response.lower().startswith("""y""" ):
						sys.exit()

		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		time.time()
		if mode.lower().startswith("""e""" ):
				with open(SCREAMING_SNAKE_CASE__ ) as f:
						__SCREAMING_SNAKE_CASE	:		Dict   	=		f.read()
				__SCREAMING_SNAKE_CASE	:		Any   	=		trans_cipher.encrypt_message(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__ )
		elif mode.lower().startswith("""d""" ):
				with open(SCREAMING_SNAKE_CASE__ ) as f:
						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		f.read()
				__SCREAMING_SNAKE_CASE	:		Dict   	=		trans_cipher.decrypt_message(SCREAMING_SNAKE_CASE__	,				SCREAMING_SNAKE_CASE__ )

		with open(SCREAMING_SNAKE_CASE__	,				"""w""" ) as output_obj:
				output_obj.write(SCREAMING_SNAKE_CASE__ )

		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		round(time.time() - start_time	,				2 )
		print(("""Done (""", total_time, """seconds )""") )


if __name__ == "__main__":
						main()




'''simple docstring'''




import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
						from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Dict							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			Tuple=1_3							,					lowerCAmelCase__					:			Tuple=3_2							,					lowerCAmelCase__					:			Optional[int]=2							,					lowerCAmelCase__					:			str=3							,					lowerCAmelCase__					:			Optional[int]=1_6							,					lowerCAmelCase__					:			List[str]=[1, 2, 1]							,					lowerCAmelCase__					:			Dict=[2, 2, 4]							,					lowerCAmelCase__					:			Any=2							,					lowerCAmelCase__					:			str=2.0							,					lowerCAmelCase__					:			List[Any]=True							,					lowerCAmelCase__					:			Any=0.0							,					lowerCAmelCase__					:			List[Any]=0.0							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Optional[int]="gelu"							,					lowerCAmelCase__					:			Dict=False							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			Any=0.02							,					lowerCAmelCase__					:			List[str]=1E-5							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			int=None							,					lowerCAmelCase__					:			Optional[Any]=True							,					lowerCAmelCase__					:			int=1_0							,					lowerCAmelCase__					:			List[Any]=8							,					lowerCAmelCase__					:			Union[str, Any]=["stage1", "stage2"]							,					lowerCAmelCase__					:			Any=[1, 2]							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		parent
					__SCREAMING_SNAKE_CASE	:		Any   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		str   	=		image_size
					__SCREAMING_SNAKE_CASE	:		Any   	=		patch_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		embed_dim
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		depths
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_heads
					__SCREAMING_SNAKE_CASE	:		Any   	=		window_size
					__SCREAMING_SNAKE_CASE	:		Dict   	=		mlp_ratio
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		qkv_bias
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		str   	=		drop_path_rate
					__SCREAMING_SNAKE_CASE	:		Any   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		use_absolute_embeddings
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		patch_norm
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		layer_norm_eps
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		is_training
					__SCREAMING_SNAKE_CASE	:		int   	=		scope
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		encoder_stride
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		out_features
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		out_indices

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					return SwinConfig(
					    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							,					out_features=self.out_features							,					out_indices=self.out_indices							,					)

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		SwinModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
					__SCREAMING_SNAKE_CASE	:		Dict   	=		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 UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		SwinBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Any   	=		model(lowerCAmelCase__						)

					# verify hidden states
					self.parent.assertEqual(len(result.feature_maps						)							,					len(config.out_features						)						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,					[self.batch_size, model.channels[0], 1_6, 1_6]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,					len(config.out_features						)						)

					# verify backbone works with out_features=None
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		None
					__SCREAMING_SNAKE_CASE	:		str   	=		SwinBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(lowerCAmelCase__						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)							,					1						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,					[self.batch_size, model.channels[-1], 4, 4]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,					1						)

			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		SwinForMaskedImageModeling(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(
					    result.logits.shape							,					(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		1
					__SCREAMING_SNAKE_CASE	:		Dict   	=		SwinForMaskedImageModeling(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, 1, self.image_size, self.image_size)						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		Dict   	=		SwinForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(lowerCAmelCase__							,					labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)

					# test greyscale images
					__SCREAMING_SNAKE_CASE	:		Dict   	=		1
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		SwinForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		model(lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)						)



			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.prepare_config_and_inputs()
					(
					    (
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						
					)	:		Union[str, Any]   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		Any   	=		{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		Dict         =		(
			    (
			        SwinModel,
			        SwinBackbone,
			        SwinForImageClassification,
			        SwinForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			_A				:		List[str]         =		(
			    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_A				:		int         =		True

			_A				:		str         =		False
			_A				:		str         =		False
			_A				:		Optional[int]         =		False

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		SwinModelTester(self						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		ConfigTester(self							,					config_class=lowerCAmelCase__							,					embed_dim=3_7						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					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 UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					return

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""						)
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					super().test_training_gradient_checkpointing()

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)

			@unittest.skip(reason="""Swin does not use inputs_embeds"""						)
			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""						)
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()

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

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		self.model_tester.prepare_config_and_inputs_for_common()

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

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

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True

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

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

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

							# Check attention is always last and order is fine
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
							__SCREAMING_SNAKE_CASE	:		str   	=		True
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

							# also another +1 for reshaped_hidden_states
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		1 if model_class.__name__ == """SwinBackbone""" else 2
							self.assertEqual(out_len + added_hidden_states							,					len(lowerCAmelCase__						)						)

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.attentions

							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

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

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			int							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model_class(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		outputs.hidden_states

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		getattr(
					    self.model_tester							,					"""expected_num_hidden_layers"""							,					len(self.model_tester.depths						) + 1						)
					self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

					# Swin has a different seq_length
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    config.patch_size
					    if isinstance(config.patch_size							,					collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE	:		Any   	=		(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]							,					)

					if not model_class.__name__ == "SwinBackbone":
							__SCREAMING_SNAKE_CASE	:		int   	=		outputs.reshaped_hidden_states
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

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

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    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:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True

							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		3

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

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

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		int   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		SwinModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		Any   	=		_config_zero_init(lowerCAmelCase__						)
					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model_class(config=lowerCAmelCase__						)
							for name, param in model.named_parameters():
									if "embeddings" 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 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					return (
					    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						)
					    if is_vision_available()
					    else None
					)



			@slow
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""						).to(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.default_image_processor

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

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(**lowerCAmelCase__						)

					# verify the logits
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.Size((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.tensor([-0.09_48, -0.64_54, -0.09_21]						).to(lowerCAmelCase__						)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,					lowerCAmelCase__							,					atol=1E-4						)						)






@require_torch
class 						_UpperCamelCase  (							unittest.TestCase   , lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Tuple         =		(SwinBackbone,) if is_torch_available() else ()
			_A				:		Optional[int]         =		SwinConfig

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		SwinModelTester(self						)