
import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, 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 NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor




class    _SCREAMING_SNAKE_CASE					:
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase=13						,		lowerCamelCase=64						,		lowerCamelCase=4						,		lowerCamelCase=3						,		lowerCamelCase=16						,		lowerCamelCase=[1, 2, 1]						,		lowerCamelCase=[2, 4, 8]						,		lowerCamelCase=3						,		lowerCamelCase=2.0						,		lowerCamelCase=True						,		lowerCamelCase=0.0						,		lowerCamelCase=0.0						,		lowerCamelCase=0.1						,		lowerCamelCase="gelu"						,		lowerCamelCase=True						,		lowerCamelCase=0.0_2						,		lowerCamelCase=1e-5						,		lowerCamelCase=True						,		lowerCamelCase=None						,		lowerCamelCase=True						,		lowerCamelCase=10						,		lowerCamelCase=["stage1", "stage2"]						,		lowerCamelCase=[1, 2]						,		):
          snake_case__  =			parent
          snake_case__  =			batch_size
          snake_case__  =			image_size
          snake_case__  =			patch_size
          snake_case__  =			num_channels
          snake_case__  =			embed_dim
          snake_case__  =			depths
          snake_case__  =			num_heads
          snake_case__  =			kernel_size
          snake_case__  =			mlp_ratio
          snake_case__  =			qkv_bias
          snake_case__  =			hidden_dropout_prob
          snake_case__  =			attention_probs_dropout_prob
          snake_case__  =			drop_path_rate
          snake_case__  =			hidden_act
          snake_case__  =			patch_norm
          snake_case__  =			layer_norm_eps
          snake_case__  =			initializer_range
          snake_case__  =			is_training
          snake_case__  =			scope
          snake_case__  =			use_labels
          snake_case__  =			num_labels
          snake_case__  =			out_features
          snake_case__  =			out_indices
      def A_     (     self	):
          snake_case__  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

          snake_case__  =			None
          if self.use_labels:
              snake_case__  =			ids_tensor([self.batch_size]						,		self.num_labels	)

          snake_case__  =			self.get_config()

          return config, pixel_values, labels
      def A_     (     self	):
          return NatConfig(
              num_labels=self.num_labels						,		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						,		kernel_size=self.kernel_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						,		patch_norm=self.patch_norm						,		layer_norm_eps=self.layer_norm_eps						,		initializer_range=self.initializer_range						,		out_features=self.out_features						,		out_indices=self.out_indices						,		)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			NatModel(config=lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()
          snake_case__  =			model(lowerCamelCase	)

          snake_case__  =			snake_case__  =			(config.image_size // config.patch_size) // (2 ** (len(config.depths	) - 1))
          snake_case__  =			int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

          self.parent.assertEqual(
              result.last_hidden_state.shape						,		(self.batch_size, expected_height, expected_width, expected_dim)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			NatForImageClassification(lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()
          snake_case__  =			model(lowerCamelCase						,		labels=lowerCamelCase	)
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)	)

          # test greyscale images
          snake_case__  =			1
          snake_case__  =			NatForImageClassification(lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()

          snake_case__  =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
          snake_case__  =			model(lowerCamelCase	)
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			NatBackbone(config=lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()
          snake_case__  =			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], 16, 16]	)

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

          # verify backbone works with out_features=None
          snake_case__  =			None
          snake_case__  =			NatBackbone(config=lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()
          snake_case__  =			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 A_     (     self	):
          snake_case__  =			self.prepare_config_and_inputs()
          snake_case__  ,   snake_case__  ,   snake_case__  =			config_and_inputs
          snake_case__  =			{"pixel_values": pixel_values}
          return config, inputs_dict





@require_natten
@require_torch
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						Optional[Any]      		=       (
          (
              NatModel,
              NatForImageClassification,
              NatBackbone,
          )
          if is_torch_available()
          else ()
      )
      _A  :						Tuple      		=       (
          {'feature-extraction': NatModel, 'image-classification': NatForImageClassification}
          if is_torch_available()
          else {}
      )
      _A  :						List[str]      		=       False

      _A  :						Union[str, Any]      		=       False
      _A  :						Dict      		=       False
      _A  :						Optional[Any]      		=       False
      _A  :						str      		=       False
      def A_     (     self	):
          snake_case__  =			NatModelTester(self	)
          snake_case__  =			ConfigTester(self						,		config_class=lowerCamelCase						,		embed_dim=37	)
      def A_     (     self	):
          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 A_     (     self	):
          return
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowerCamelCase	)
      @unittest.skip(reason="Nat does not use inputs_embeds"	)
      def A_     (     self	):
          pass
      @unittest.skip(reason="Nat does not use feedforward chunking"	)
      def A_     (     self	):
          pass
      def A_     (     self	):
          snake_case__  ,   snake_case__  =			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              snake_case__  =			model_class(lowerCamelCase	)
              self.assertIsInstance(model.get_input_embeddings()						,		(nn.Module)	)
              snake_case__  =			model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCamelCase						,		nn.Linear	)	)
      def A_     (     self	):
          snake_case__  ,   snake_case__  =			self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case__  =			["pixel_values"]
              self.assertListEqual(arg_names[:1]						,		lowerCamelCase	)
      def A_     (     self	):
          self.skipTest("Nat's attention operation is handled entirely by NATTEN."	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			model_class(lowerCamelCase	)
          model.to(lowerCamelCase	)
          model.eval()

          with torch.no_grad():
              snake_case__  =			model(**self._prepare_for_class(lowerCamelCase						,		lowerCamelCase	)	)

          snake_case__  =			outputs.hidden_states

          snake_case__  =			getattr(
              self.model_tester						,		"expected_num_hidden_layers"						,		len(self.model_tester.depths	) + 1	)
          self.assertEqual(len(lowerCamelCase	)						,		lowerCamelCase	)

          # Nat has a different seq_length
          snake_case__  =			(
              config.patch_size
              if isinstance(config.patch_size						,		collections.abc.Iterable	)
              else (config.patch_size, config.patch_size)
          )

          snake_case__  =			image_size[0] // patch_size[0]
          snake_case__  =			image_size[1] // patch_size[1]

          self.assertListEqual(
              list(hidden_states[0].shape[-3:]	)						,		[height, width, self.model_tester.embed_dim]						,		)

          if model_class.__name__ != "NatBackbone":
              snake_case__  =			outputs.reshaped_hidden_states
              self.assertEqual(len(lowerCamelCase	)						,		lowerCamelCase	)

              snake_case__  ,   snake_case__  ,   snake_case__  ,   snake_case__  =			reshaped_hidden_states[0].shape
              snake_case__  =			(
                  reshaped_hidden_states[0].view(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	).permute(0						,		2						,		3						,		1	)
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-3:]	)						,		[height, width, self.model_tester.embed_dim]						,		)
      def A_     (     self	):
          snake_case__  ,   snake_case__  =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__  =			(
              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__  =			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"]
              snake_case__  =			True

              self.check_hidden_states_output(lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	)
      @slow
      def A_     (     self	):
          for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__  =			NatModel.from_pretrained(lowerCamelCase	)
              self.assertIsNotNone(lowerCamelCase	)



      def A_     (     self	):
          snake_case__  ,   snake_case__  =			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__  =			_config_zero_init(lowerCamelCase	)
          for model_class in self.all_model_classes:
              snake_case__  =			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_natten
@require_vision
@require_torch
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      @cached_property
      def A_     (     self	):
          return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"	) if is_vision_available() else None



      @slow
      def A_     (     self	):
          snake_case__  =			NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"	).to(lowerCamelCase	)
          snake_case__  =			self.default_image_processor

          snake_case__  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
          snake_case__  =			image_processor(images=lowerCamelCase						,		return_tensors="pt"	).to(lowerCamelCase	)

          # forward pass
          with torch.no_grad():
              snake_case__  =			model(**lowerCamelCase	)

          # verify the logits
          snake_case__  =			torch.Size((1, 10_00)	)
          self.assertEqual(outputs.logits.shape						,		lowerCamelCase	)
          snake_case__  =			torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]	).to(lowerCamelCase	)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		lowerCamelCase						,		atol=1e-4	)	)





@require_torch
@require_natten
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      _A  :						int      		=       (NatBackbone,) if is_torch_available() else ()
      _A  :						Optional[Any]      		=       NatConfig
      def A_     (     self	):
          snake_case__  =			NatModelTester(self	)





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      @abstractmethod
      def __lt__(     self						,		lowerCamelCase	):
          pass
      @abstractmethod
      def __gt__(     self						,		lowerCamelCase	):
          pass



      @abstractmethod
      def __eq__(     self						,		lowerCamelCase	):
          pass





__magic_name__           =					TypeVar('''T''', bound=Comparable)




class    _SCREAMING_SNAKE_CASE					( Generic[T]	):
      def __init__(     self	):
          snake_case__  =			[]
          snake_case__  =			0
      def __repr__(     self	):
          return str(self.h	)
      def A_     (     self						,		lowerCamelCase	):
          if child_idx > 0:
              return (child_idx - 1) // 2
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 1
          if left_child_index < self.heap_size:
              return left_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 2
          if right_child_index < self.heap_size:
              return right_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          if index < self.heap_size:
              snake_case__  =			index
              snake_case__  =			self.left_child_idx(lowerCamelCase	)
              snake_case__  =			self.right_child_idx(lowerCamelCase	)
              # check which child is larger than its parent
              if left_child is not None and self.h[left_child] > self.h[violation]:
                  snake_case__  =			left_child
              if right_child is not None and self.h[right_child] > self.h[violation]:
                  snake_case__  =			right_child
              # if violation indeed exists
              if violation != index:
                  # swap to fix the violation
                  snake_case__  ,   snake_case__  =			self.h[index], self.h[violation]
                  # fix the subsequent violation recursively if any
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			len(self.h	)
          if self.heap_size > 1:
              # max_heapify from right to left but exclude leaves (last level)
              for i in range(self.heap_size // 2 - 1						,		-1						,		-1	):
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self	):
          if self.heap_size >= 2:
              snake_case__  =			self.h[0]
              snake_case__  =			self.h.pop(-1	)
              self.heap_size -= 1
              self.max_heapify(0	)
              return me
          elif self.heap_size == 1:
              self.heap_size -= 1
              return self.h.pop(-1	)
          else:
              raise Exception("Empty heap"	)
      def A_     (     self						,		lowerCamelCase	):
          self.h.append(lowerCamelCase	)
          snake_case__  =			(self.heap_size - 1) // 2
          self.heap_size += 1
          while idx >= 0:
              self.max_heapify(lowerCamelCase	)
              snake_case__  =			(idx - 1) // 2



      def A_     (     self	):
          snake_case__  =			self.heap_size
          for j in range(size - 1						,		0						,		-1	):
              snake_case__  ,   snake_case__  =			self.h[j], self.h[0]
              self.heap_size -= 1
              self.max_heapify(0	)
          snake_case__  =			size


if __name__ == "__main__":
     import doctest

     # run doc test
     doctest.testmod()

     # demo
     for unsorted in [
         [0],
         [2],
         [3, 5],
         [5, 3],
         [5, 5],
         [0, 0, 0, 0],
         [1, 1, 1, 1],
         [2, 2, 3, 5],
         [0, 2, 2, 3, 5],
         [2, 5, 3, 0, 2, 3, 0, 3],
         [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
         [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
         [-45, -2, -5],
     ]:
          print(F'''unsorted array: {unsorted}''')

          __magic_name__           =					Heap()
          heap.build_max_heap(unsorted)
          print(F'''after build heap: {heap}''')

          print(F'''max value: {heap.extract_max()}''')
          print(F'''after max value removed: {heap}''')

          heap.insert(100)
          print(F'''after new value 100 inserted: {heap}''')

          heap.heap_sort()
          print(F'''heap-sorted array: {heap}\n''')



