


"""simple docstring"""





import inspect
import unittest

from transformers import ResNetConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
   from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  lowercase_  :







      def __init__(      self :							List[str]		,  _lowercase :							Optional[Any]		,  _lowercase :							Optional[Any]=3		,  _lowercase :							Any=3_2		,  _lowercase :							List[str]=3		,  _lowercase :							Optional[int]=1_0		,  _lowercase :							List[Any]=[1_0, 2_0, 3_0, 4_0]		,  _lowercase :							Any=[1, 1, 2, 1]		,  _lowercase :							int=True		,  _lowercase :							Union[str, Any]=True		,  _lowercase :							Optional[int]="relu"		,  _lowercase :							str=3		,  _lowercase :							Any=None		,  _lowercase :							List[str]=["stage2", "stage3", "stage4"]		,  _lowercase :							Optional[int]=[2, 3, 4]		,  ):
        lowerCAmelCase__    :    Dict								=					parent
        lowerCAmelCase__    :    Optional[int]								=					batch_size
        lowerCAmelCase__    :    Union[str, Any]								=					image_size
        lowerCAmelCase__    :    Optional[int]								=					num_channels
        lowerCAmelCase__    :    List[str]								=					embeddings_size
        lowerCAmelCase__    :    Optional[Any]								=					hidden_sizes
        lowerCAmelCase__    :    Optional[Any]								=					depths
        lowerCAmelCase__    :    Dict								=					is_training
        lowerCAmelCase__    :    int								=					use_labels
        lowerCAmelCase__    :    List[str]								=					hidden_act
        lowerCAmelCase__    :    Union[str, Any]								=					num_labels
        lowerCAmelCase__    :    Any								=					scope
        lowerCAmelCase__    :    Any								=					len(_lowercase    )
        lowerCAmelCase__    :    Optional[Any]								=					out_features
        lowerCAmelCase__    :    List[str]								=					out_indices







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

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

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

        return config, pixel_values, labels







      def 				_lowerCAmelCase       (      self :							Optional[int]    ):
        return ResNetConfig(
            num_channels=self.num_channels		,  embeddings_size=self.embeddings_size		,  hidden_sizes=self.hidden_sizes		,  depths=self.depths		,  hidden_act=self.hidden_act		,  num_labels=self.num_labels		,  out_features=self.out_features		,  out_indices=self.out_indices		,  )







      def 				_lowerCAmelCase       (      self :							Optional[Any]		,  _lowercase :							List[str]		,  _lowercase :							int		,  _lowercase :							Tuple    ):
        lowerCAmelCase__    :    Tuple								=					ResNetModel(config=_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    Optional[Any]								=					model(_lowercase    )
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape		,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)		,  )







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							Tuple		,  _lowercase :							List[str]		,  _lowercase :							Dict    ):
        lowerCAmelCase__    :    Optional[int]								=					self.num_labels
        lowerCAmelCase__    :    Optional[Any]								=					ResNetForImageClassification(_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    List[Any]								=					model(_lowercase		,  labels=_lowercase    )
        self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.num_labels)    )







      def 				_lowerCAmelCase       (      self :							int		,  _lowercase :							int		,  _lowercase :							Any		,  _lowercase :							int    ):
        lowerCAmelCase__    :    Union[str, Any]								=					ResNetBackbone(config=_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    Optional[int]								=					model(_lowercase    )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps    )		,  len(config.out_features    )    )
        self.parent.assertListEqual(list(result.feature_maps[0].shape    )		,  [self.batch_size, self.hidden_sizes[1], 4, 4]    )

        # verify channels
        self.parent.assertEqual(len(model.channels    )		,  len(config.out_features    )    )
        self.parent.assertListEqual(model.channels		,  config.hidden_sizes[1:]    )

        # verify backbone works with out_features=None
        lowerCAmelCase__    :    str								=					None
        lowerCAmelCase__    :    Any								=					ResNetBackbone(config=_lowercase    )
        model.to(_lowercase    )
        model.eval()
        lowerCAmelCase__    :    Any								=					model(_lowercase    )

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

        # verify channels
        self.parent.assertEqual(len(model.channels    )		,  1    )
        self.parent.assertListEqual(model.channels		,  [config.hidden_sizes[-1]]    )







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

@require_torch
class  lowercase_  ( UpperCAmelCase_							,							UpperCAmelCase_							,							unittest.TestCase						):
      __magic_name__     :							Tuple          =				(
          (
              ResNetModel,
              ResNetForImageClassification,
              ResNetBackbone,
          )
          if is_torch_available()
          else ()
      )
      __magic_name__     :							List[str]          =				(
          {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
          if is_torch_available()
          else {}
      )

      __magic_name__     :							int          =				True
      __magic_name__     :							str          =				False
      __magic_name__     :							int          =				False
      __magic_name__     :							Optional[int]          =				False
      __magic_name__     :							Tuple          =				False







      def 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    Any								=					ResNetModelTester(self    )
        lowerCAmelCase__    :    Any								=					ConfigTester(self		,  config_class=_lowercase		,  has_text_modality=_lowercase    )







      def 				_lowerCAmelCase       (      self :							List[Any]    ):
        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 				_lowerCAmelCase       (      self :							Dict    ):
        return







      @unittest.skip(reason="ResNet does not use inputs_embeds"    )
      def 				_lowerCAmelCase       (      self :							Dict    ):
        pass







      @unittest.skip(reason="ResNet does not support input and output embeddings"    )
      def 				_lowerCAmelCase       (      self :							Tuple    ):
        pass







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

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

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







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







      def 				_lowerCAmelCase       (      self :							int    ):
        lowerCAmelCase__    :    int								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*_lowercase    )







      def 				_lowerCAmelCase       (      self :							str    ):
        lowerCAmelCase__				,		lowerCAmelCase__    :    Tuple								=					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase__    :    List[str]								=					model_class(config=_lowercase    )
          for name, module in model.named_modules():
            if isinstance(_lowercase		,  (nn.BatchNormad, nn.GroupNorm)    ):
              self.assertTrue(
                  torch.all(module.weight == 1    )		,  msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,  )
              self.assertTrue(
                  torch.all(module.bias == 0    )		,  msg=f"Parameter {name} of model {model_class} seems not properly initialized"		,  )







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

          with torch.no_grad():
            lowerCAmelCase__    :    Optional[int]								=					model(**self._prepare_for_class(_lowercase		,  _lowercase    )    )

          lowerCAmelCase__    :    Union[str, Any]								=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase__    :    Optional[int]								=					self.model_tester.num_stages
          self.assertEqual(len(_lowercase    )		,  expected_num_stages + 1    )

          # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]    )		,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]		,  )

        lowerCAmelCase__				,		lowerCAmelCase__    :    Optional[int]								=					self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase__    :    List[str]								=					["basic", "bottleneck"]
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            lowerCAmelCase__    :    Optional[int]								=					layer_type
            lowerCAmelCase__    :    Optional[Any]								=					True
            check_hidden_states_output(_lowercase		,  _lowercase		,  _lowercase    )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            lowerCAmelCase__    :    Dict								=					True

            check_hidden_states_output(_lowercase		,  _lowercase		,  _lowercase    )







      @unittest.skip(reason="ResNet does not use feedforward chunking"    )
      def 				_lowerCAmelCase       (      self :							str    ):
        pass







      def 				_lowerCAmelCase       (      self :							Dict    ):
        lowerCAmelCase__    :    str								=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_lowercase    )







      @slow
      def 				_lowerCAmelCase       (      self :							Union[str, Any]    ):
        for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__    :    Union[str, Any]								=					ResNetModel.from_pretrained(_lowercase    )
          self.assertIsNotNone(_lowercase    )





def     lowercase__	(  )					->     Optional[Any]:
  lowerCAmelCase__    :    Optional[int]								=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
  return image

@require_torch
@require_vision
class  lowercase_  ( unittest.TestCase						):







      @cached_property
      def 				_lowerCAmelCase       (      self :							str    ):
        return (
            AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
            if is_vision_available()
            else None
        )







      @slow
      def 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    List[Any]								=					ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(_lowercase    )

        lowerCAmelCase__    :    List[str]								=					self.default_image_processor
        lowerCAmelCase__    :    str								=					prepare_img()
        lowerCAmelCase__    :    Union[str, Any]								=					image_processor(images=_lowercase		,  return_tensors="pt"    ).to(_lowercase    )

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

        # verify the logits
        lowerCAmelCase__    :    List[str]								=					torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape		,  _lowercase    )

        lowerCAmelCase__    :    List[str]								=					torch.tensor([-11.1069, -9.7877, -8.3777]    ).to(_lowercase    )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]		,  _lowercase		,  atol=1e-4    )    )

@require_torch
class  lowercase_  ( UpperCAmelCase_							,							unittest.TestCase						):
      __magic_name__     :							str          =				(ResNetBackbone,) if is_torch_available() else ()
      __magic_name__     :							Union[str, Any]          =				False
      __magic_name__     :							Dict          =				ResNetConfig







      def 				_lowerCAmelCase       (      self :							Dict    ):
        lowerCAmelCase__    :    str								=					ResNetModelTester(self    )




"""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()
