

import inspect
import unittest

from transformers import SwinaSRConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import 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 SwinaSRForImageSuperResolution, SwinaSRModel
 from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import SwinaSRImageProcessor






class 		__magic_name__ :






     def __init__(							self						,		_lowercase						,		_lowercase=13						,		_lowercase=32						,		_lowercase=1						,		_lowercase=3						,		_lowercase=16						,		_lowercase=[1, 2, 1]						,		_lowercase=[2, 2, 4]						,		_lowercase=2						,		_lowercase=2.0						,		_lowercase=True						,		_lowercase=0.0						,		_lowercase=0.0						,		_lowercase=0.1						,		_lowercase="gelu"						,		_lowercase=False						,		_lowercase=True						,		_lowercase=0.02						,		_lowercase=1e-5						,		_lowercase=True						,		_lowercase=None						,		_lowercase=False						,		_lowercase=2						,		)->			List[Any]:
         UpperCamelCase_          =		parent
         UpperCamelCase_          =		batch_size
         UpperCamelCase_          =		image_size
         UpperCamelCase_          =		patch_size
         UpperCamelCase_          =		num_channels
         UpperCamelCase_          =		embed_dim
         UpperCamelCase_          =		depths
         UpperCamelCase_          =		num_heads
         UpperCamelCase_          =		window_size
         UpperCamelCase_          =		mlp_ratio
         UpperCamelCase_          =		qkv_bias
         UpperCamelCase_          =		hidden_dropout_prob
         UpperCamelCase_          =		attention_probs_dropout_prob
         UpperCamelCase_          =		drop_path_rate
         UpperCamelCase_          =		hidden_act
         UpperCamelCase_          =		use_absolute_embeddings
         UpperCamelCase_          =		patch_norm
         UpperCamelCase_          =		layer_norm_eps
         UpperCamelCase_          =		initializer_range
         UpperCamelCase_          =		is_training
         UpperCamelCase_          =		scope
         UpperCamelCase_          =		use_labels
         UpperCamelCase_          =		upscale

         # here we set some attributes to make tests pass
         UpperCamelCase_          =		len(_lowercase     )
         UpperCamelCase_          =		embed_dim
         UpperCamelCase_          =		(image_size // patch_size) ** 2






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

         UpperCamelCase_          =		None
         if self.use_labels:
             UpperCamelCase_          =		ids_tensor([self.batch_size]						,		self.type_sequence_label_size     )

         UpperCamelCase_          =		self.get_config()

         return config, pixel_values, labels






     def      UpperCAmelCase_							(							self     )->			str:
         return SwinaSRConfig(
             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						,		upscale=self.upscale						,		)






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			Union[str, Any]:
         UpperCamelCase_          =		SwinaSRModel(config=_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )

         self.parent.assertEqual(
             result.last_hidden_state.shape						,		(self.batch_size, self.embed_dim, self.image_size, self.image_size)     )






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			Optional[Any]:
         UpperCamelCase_          =		SwinaSRForImageSuperResolution(_lowercase     )
         model.to(_lowercase     )
         model.eval()
         UpperCamelCase_          =		model(_lowercase     )

         UpperCamelCase_          =		self.image_size * self.upscale
         self.parent.assertEqual(
             result.reconstruction.shape						,		(self.batch_size, self.num_channels, expected_image_size, expected_image_size)     )






     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		self.prepare_config_and_inputs()
         UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		config_and_inputs
         UpperCamelCase_          =		{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 		__magic_name__ (				snake_case       ,	snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :Any									=	(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
     UpperCamelCase_     :Optional[Any]									=	{"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

     UpperCamelCase_     :List[Any]									=	False
     UpperCamelCase_     :int									=	False
     UpperCamelCase_     :Optional[Any]									=	False
     UpperCamelCase_     :Optional[Any]									=	False
     UpperCamelCase_     :Tuple									=	False






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		SwinaSRModelTester(self     )
         UpperCamelCase_          =		ConfigTester(self						,		config_class=_lowercase						,		embed_dim=37     )






     def      UpperCAmelCase_							(							self     )->			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      UpperCAmelCase_							(							self     )->			List[Any]:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_super_resolution(*_lowercase     )






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






     @unittest.skip(reason="Swin2SR does not use inputs_embeds"     )
     def      UpperCAmelCase_							(							self     )->			int:
         pass






     @unittest.skip(reason="Swin2SR does not support training yet"     )
     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         pass






     @unittest.skip(reason="Swin2SR does not support training yet"     )
     def      UpperCAmelCase_							(							self     )->			Dict:
         pass






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

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






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

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

             UpperCamelCase_          =		["pixel_values"]
             self.assertListEqual(arg_names[:1]						,		_lowercase     )






     @slow
     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_          =		SwinaSRModel.from_pretrained(_lowercase     )
             self.assertIsNotNone(_lowercase     )






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()

         UpperCamelCase_          =		_config_zero_init(_lowercase     )
         for model_class in self.all_model_classes:
             UpperCamelCase_          =		model_class(config=_lowercase     )
             for name, param in model.named_parameters():
                 if "logit_scale" in name:
                     continue
                 if 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"						,		)






     def      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_   ,    UpperCamelCase_          =		self.model_tester.prepare_config_and_inputs_for_common()
         UpperCamelCase_          =		True

         for model_class in self.all_model_classes:
             UpperCamelCase_          =		True
             UpperCamelCase_          =		False
             UpperCamelCase_          =		True
             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )
             UpperCamelCase_          =		outputs.attentions
             UpperCamelCase_          =		len(self.model_tester.depths     )
             self.assertEqual(len(_lowercase     )						,		_lowercase     )

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

             self.assertListEqual(
                 list(attentions[0].shape[-3:]     )						,		[self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,		)
             UpperCamelCase_          =		len(_lowercase     )

             # Check attention is always last and order is fine
             UpperCamelCase_          =		True
             UpperCamelCase_          =		True
             UpperCamelCase_          =		model_class(_lowercase     )
             model.to(_lowercase     )
             model.eval()
             with torch.no_grad():
                 UpperCamelCase_          =		model(**self._prepare_for_class(_lowercase						,		_lowercase     )     )

             self.assertEqual(out_len + 1						,		len(_lowercase     )     )

             UpperCamelCase_          =		outputs.attentions

             self.assertEqual(len(_lowercase     )						,		_lowercase     )

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






@require_vision
@require_torch
@slow
class 		__magic_name__ (				unittest.TestCase							):






     def      UpperCAmelCase_							(							self     )->			Optional[int]:
         UpperCamelCase_          =		SwinaSRImageProcessor()
         UpperCamelCase_          =		SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"     ).to(_lowercase     )

         UpperCamelCase_          =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
         UpperCamelCase_          =		processor(images=_lowercase						,		return_tensors="pt"     ).to(_lowercase     )

         # forward pass
         with torch.no_grad():
             UpperCamelCase_          =		model(**_lowercase     )

         # verify the logits
         UpperCamelCase_          =		torch.Size([1, 3, 976, 1_296]     )
         self.assertEqual(outputs.reconstruction.shape						,		_lowercase     )
         UpperCamelCase_          =		torch.tensor(
             [[0.5_458, 0.5_546, 0.5_638], [0.5_526, 0.5_565, 0.5_651], [0.5_396, 0.5_426, 0.5_621]]     ).to(_lowercase     )
         self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]						,		_lowercase						,		atol=1e-4     )     )




"""simple docstring"""


from __future__ import annotations

a_											=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class snake_case					:







		def __init__(    self					:				Optional[int]			,      a__					:				int						)				->  None:





						'''simple docstring'''







						_A         =	-1
						_A         =	-1
						_A         =	[-1] * length
						_A         =	None
						_A         =	None







		def __repr__(    self					:				Any						)				->  str:





						'''simple docstring'''







						return F"""Node(min_value={self.minn} max_value={self.maxx})"""






def    a__       ( __lowercase      )		->		Node | None:
				_A         =	Node(len(__lowercase      )      )
				_A     ,						_A         =	min(__lowercase      ), max(__lowercase      )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_A         =	(root.minn + root.maxx) // 2

				_A         =	[]
				_A         =	[]

				for index, num in enumerate(__lowercase      ):
								if num <= pivot:
												left_arr.append(__lowercase      )
								else:
												right_arr.append(__lowercase      )
								_A         =	len(__lowercase      )
				_A         =	build_tree(__lowercase      )
				_A         =	build_tree(__lowercase      )
				return root






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_A         =	(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left   ,	__lowercase   ,	node.map_left[index] - 1      )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right   ,	__lowercase   ,	index - node.map_left[index]      )






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if start > end:
								return 0
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	__lowercase      )
				_A         =	rank_till_index(__lowercase   ,	__lowercase   ,	start - 1      )
				return rank_till_end - rank_before_start






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_A         =	node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left   ,	__lowercase   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	)
				else:
								return quantile(
								    node.right   ,	index - num_elements_in_left_tree   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	)






def    a__       ( __lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase   ,	__lowercase      )		->		int:
				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_A         =	range_counting(
				    node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowercase   ,	__lowercase   ,	)
				_A         =	range_counting(
				    node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowercase   ,	__lowercase   ,	)
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()