


"""simple docstring"""





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  lowercase_  :







      def __init__(      self :							List[str]		,  _lowercase :							List[str]		,  _lowercase :							Tuple=1_3		,  _lowercase :							Optional[Any]=3_2		,  _lowercase :							List[str]=1		,  _lowercase :							Dict=3		,  _lowercase :							Dict=1_6		,  _lowercase :							Any=[1, 2, 1]		,  _lowercase :							Union[str, Any]=[2, 2, 4]		,  _lowercase :							int=2		,  _lowercase :							Union[str, Any]=2.0		,  _lowercase :							int=True		,  _lowercase :							Optional[int]=0.0		,  _lowercase :							str=0.0		,  _lowercase :							Optional[Any]=0.1		,  _lowercase :							Tuple="gelu"		,  _lowercase :							Dict=False		,  _lowercase :							Tuple=True		,  _lowercase :							List[str]=0.02		,  _lowercase :							Union[str, Any]=1e-5		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Optional[Any]=None		,  _lowercase :							Optional[Any]=False		,  _lowercase :							Any=2		,  ):
        lowerCAmelCase__    :    Union[str, Any]								=					parent
        lowerCAmelCase__    :    Optional[Any]								=					batch_size
        lowerCAmelCase__    :    Union[str, Any]								=					image_size
        lowerCAmelCase__    :    str								=					patch_size
        lowerCAmelCase__    :    List[Any]								=					num_channels
        lowerCAmelCase__    :    Dict								=					embed_dim
        lowerCAmelCase__    :    Optional[Any]								=					depths
        lowerCAmelCase__    :    Dict								=					num_heads
        lowerCAmelCase__    :    List[Any]								=					window_size
        lowerCAmelCase__    :    List[str]								=					mlp_ratio
        lowerCAmelCase__    :    List[Any]								=					qkv_bias
        lowerCAmelCase__    :    Optional[Any]								=					hidden_dropout_prob
        lowerCAmelCase__    :    Optional[Any]								=					attention_probs_dropout_prob
        lowerCAmelCase__    :    Dict								=					drop_path_rate
        lowerCAmelCase__    :    List[str]								=					hidden_act
        lowerCAmelCase__    :    Optional[int]								=					use_absolute_embeddings
        lowerCAmelCase__    :    Optional[int]								=					patch_norm
        lowerCAmelCase__    :    List[Any]								=					layer_norm_eps
        lowerCAmelCase__    :    Optional[Any]								=					initializer_range
        lowerCAmelCase__    :    List[str]								=					is_training
        lowerCAmelCase__    :    List[str]								=					scope
        lowerCAmelCase__    :    List[Any]								=					use_labels
        lowerCAmelCase__    :    str								=					upscale

        # here we set some attributes to make tests pass
        lowerCAmelCase__    :    Union[str, Any]								=					len(_lowercase    )
        lowerCAmelCase__    :    Optional[int]								=					embed_dim
        lowerCAmelCase__    :    str								=					(image_size // patch_size) ** 2







      def 				_lowerCAmelCase       (      self :							Optional[int]    ):
        lowerCAmelCase__    :    str								=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

        lowerCAmelCase__    :    str								=					self.get_config()

        return config, pixel_values, labels







      def 				_lowerCAmelCase       (      self :							Tuple    ):
        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 				_lowerCAmelCase       (      self :							int		,  _lowercase :							Tuple		,  _lowercase :							List[str]		,  _lowercase :							List[Any]    ):
        lowerCAmelCase__    :    Dict								=					SwinaSRModel(config=_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    Any								=					model(_lowercase    )

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







      def 				_lowerCAmelCase       (      self :							Optional[Any]		,  _lowercase :							List[str]		,  _lowercase :							Dict		,  _lowercase :							Tuple    ):
        lowerCAmelCase__    :    List[str]								=					SwinaSRForImageSuperResolution(_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    Union[str, Any]								=					model(_lowercase    )

        lowerCAmelCase__    :    List[Any]								=					self.image_size * self.upscale
        self.parent.assertEqual(
            result.reconstruction.shape		,  (self.batch_size, self.num_channels, expected_image_size, expected_image_size)    )







      def 				_lowerCAmelCase       (      self :							Tuple    ):
        lowerCAmelCase__    :    Dict								=					self.prepare_config_and_inputs()
        lowerCAmelCase__				,		lowerCAmelCase__				,		lowerCAmelCase__    :    List[Any]								=					config_and_inputs
        lowerCAmelCase__    :    Any								=					{"pixel_values": pixel_values}
        return config, inputs_dict

@require_torch
class  lowercase_  ( a_							,							a_							,							unittest.TestCase						):
      __magic_name__     :							Tuple          =				(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
      __magic_name__     :							List[Any]          =				{"""feature-extraction""": SwinaSRModel} if is_torch_available() else {}

      __magic_name__     :							int          =				False
      __magic_name__     :							Union[str, Any]          =				False
      __magic_name__     :							Optional[int]          =				False
      __magic_name__     :							List[str]          =				False
      __magic_name__     :							Any          =				False







      def 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    Union[str, Any]								=					SwinaSRModelTester(self    )
        lowerCAmelCase__    :    str								=					ConfigTester(self		,  config_class=_lowercase		,  embed_dim=3_7    )







      def 				_lowerCAmelCase       (      self :							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 :							Optional[Any]    ):
        lowerCAmelCase__    :    str								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_lowercase    )







      def 				_lowerCAmelCase       (      self :							Union[str, Any]    ):
        lowerCAmelCase__    :    Dict								=					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 				_lowerCAmelCase       (      self :							int    ):
        pass







      @unittest.skip(reason="Swin2SR does not use inputs_embeds"    )
      def 				_lowerCAmelCase       (      self :							List[str]    ):
        pass







      @unittest.skip(reason="Swin2SR does not support training yet"    )
      def 				_lowerCAmelCase       (      self :							int    ):
        pass







      @unittest.skip(reason="Swin2SR does not support training yet"    )
      def 				_lowerCAmelCase       (      self :							int    ):
        pass







      def 				_lowerCAmelCase       (      self :							Union[str, Any]    ):
        lowerCAmelCase__				,		lowerCAmelCase__    :    List[str]								=					self.model_tester.prepare_config_and_inputs_for_common()

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







      def 				_lowerCAmelCase       (      self :							Optional[int]    ):
        lowerCAmelCase__				,		lowerCAmelCase__    :    List[str]								=					self.model_tester.prepare_config_and_inputs_for_common()

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

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







      @slow
      def 				_lowerCAmelCase       (      self :							Any    ):
        for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__    :    List[str]								=					SwinaSRModel.from_pretrained(_lowercase    )
          self.assertIsNotNone(_lowercase    )







      def 				_lowerCAmelCase       (      self :							Dict    ):
        lowerCAmelCase__				,		lowerCAmelCase__    :    Optional[int]								=					self.model_tester.prepare_config_and_inputs_for_common()

        lowerCAmelCase__    :    Tuple								=					_config_zero_init(_lowercase    )
        for model_class in self.all_model_classes:
          lowerCAmelCase__    :    Optional[int]								=					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 				_lowerCAmelCase       (      self :							int    ):
        lowerCAmelCase__				,		lowerCAmelCase__    :    Optional[Any]								=					self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__    :    Optional[Any]								=					True

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

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

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

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

          lowerCAmelCase__    :    int								=					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  lowercase_  ( unittest.TestCase						):







      def 				_lowerCAmelCase       (      self :							str    ):
        lowerCAmelCase__    :    str								=					SwinaSRImageProcessor()
        lowerCAmelCase__    :    List[Any]								=					SwinaSRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64"    ).to(_lowercase    )

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

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

        # verify the logits
        lowerCAmelCase__    :    Optional[int]								=					torch.Size([1, 3, 9_7_6, 1_2_9_6]    )
        self.assertEqual(outputs.reconstruction.shape		,  _lowercase    )
        lowerCAmelCase__    :    List[str]								=					torch.tensor(
            [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]]    ).to(_lowercase    )
        self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,  _lowercase		,  atol=1e-4    )    )




"""simple docstring"""





from __future__ import annotations

__UpperCAmelCase    				=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]

class  lowercase_  :







      def __init__(      self :							int		,  _lowercase :							int    ):
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    int								=					-1
        lowerCAmelCase__    :    list[int]								=					[-1] * length
        lowerCAmelCase__    :    Node | None								=					None
        lowerCAmelCase__    :    Node | None								=					None







      def __repr__(      self :							List[Any]    ):
        return f"Node(min_value={self.minn} max_value={self.maxx})"





def     lowercase__	(  lowerCamelCase     :  list[int]				)					->     Node | None:
  lowerCAmelCase__    :    Any								=					Node(len(lowerCamelCase				)				)
  lowerCAmelCase__				,		lowerCAmelCase__    :    Any								=					min(lowerCamelCase				), max(lowerCamelCase				)
  # Leaf node case where the node contains only one unique value
  if root.minn == root.maxx:
    return root
  lowerCAmelCase__    :    str								=					(root.minn + root.maxx) // 2

  lowerCAmelCase__    :    list[int]								=					[]
  lowerCAmelCase__    :    list[int]								=					[]

  for index, num in enumerate(lowerCamelCase				):
    if num <= pivot:
      left_arr.append(lowerCamelCase				)
    else:
      right_arr.append(lowerCamelCase				)
    lowerCAmelCase__    :    Optional[Any]								=					len(lowerCamelCase				)
  lowerCAmelCase__    :    List[str]								=					build_tree(lowerCamelCase				)
  lowerCAmelCase__    :    List[Any]								=					build_tree(lowerCamelCase				)
  return root





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     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
  lowerCAmelCase__    :    int								=					(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				,	lowerCamelCase				,	node.map_left[index] - 1				)
  else:
    # go to the right subtree and map index to the right subtree
    return rank_till_index(node.right				,	lowerCamelCase				,	index - node.map_left[index]				)





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     int:
  if start > end:
    return 0
  lowerCAmelCase__    :    int								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				)
  lowerCAmelCase__    :    Optional[Any]								=					rank_till_index(lowerCamelCase				,	lowerCamelCase				,	start - 1				)
  return rank_till_end - rank_before_start





def     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     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]
  lowerCAmelCase__    :    List[str]								=					node.map_left[end] - (
      node.map_left[start - 1] if start else 0
  )
  if num_elements_in_left_tree > index:
    return quantile(
        node.left				,	lowerCamelCase				,	(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     lowercase__	(  lowerCamelCase     :  Node | None				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				,	lowerCamelCase     :  int				)					->     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
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.left				,	(node.map_left[start - 1] if start else 0)				,	node.map_left[end] - 1				,	lowerCamelCase				,	lowerCamelCase				,	)
  lowerCAmelCase__    :    List[str]								=					range_counting(
      node.right				,	start - (node.map_left[start - 1] if start else 0)				,	end - node.map_left[end]				,	lowerCamelCase				,	lowerCamelCase				,	)
  return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
