


'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=13   ,							SCREAMING_SNAKE_CASE    :							List[Any]=32   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=2   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=3   ,							SCREAMING_SNAKE_CASE    :							List[str]=16   ,							SCREAMING_SNAKE_CASE    :							Dict=[1, 2, 1]   ,							SCREAMING_SNAKE_CASE    :							Dict=[2, 2, 4]   ,							SCREAMING_SNAKE_CASE    :							Tuple=2   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=2.0   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=True   ,							SCREAMING_SNAKE_CASE    :							Dict=0.0   ,							SCREAMING_SNAKE_CASE    :							List[str]=0.0   ,							SCREAMING_SNAKE_CASE    :							str=0.1   ,							SCREAMING_SNAKE_CASE    :							List[str]="gelu"   ,							SCREAMING_SNAKE_CASE    :							int=False   ,							SCREAMING_SNAKE_CASE    :							Tuple=True   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=0.02   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=1e-5   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=True   ,							SCREAMING_SNAKE_CASE    :							int=None   ,							SCREAMING_SNAKE_CASE    :							Any=True   ,							SCREAMING_SNAKE_CASE    :							int=10   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=8   ,							):
           _A  :			List[str]											=      parent
           _A  :			str											=      batch_size
           _A  :			Any											=      image_size
           _A  :			Any											=      patch_size
           _A  :			int											=      num_channels
           _A  :			Union[str, Any]											=      embed_dim
           _A  :			List[Any]											=      depths
           _A  :			List[Any]											=      num_heads
           _A  :			Union[str, Any]											=      window_size
           _A  :			List[str]											=      mlp_ratio
           _A  :			int											=      qkv_bias
           _A  :			List[str]											=      hidden_dropout_prob
           _A  :			Any											=      attention_probs_dropout_prob
           _A  :			List[Any]											=      drop_path_rate
           _A  :			Dict											=      hidden_act
           _A  :			List[str]											=      use_absolute_embeddings
           _A  :			List[str]											=      patch_norm
           _A  :			Dict											=      layer_norm_eps
           _A  :			List[Any]											=      initializer_range
           _A  :			Optional[int]											=      is_training
           _A  :			Tuple											=      scope
           _A  :			Tuple											=      use_labels
           _A  :			List[str]											=      type_sequence_label_size
           _A  :			Optional[int]											=      encoder_stride


    def      A					(						self    :							List[Any]):
           _A  :			Union[str, Any]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

           return config, pixel_values, labels


    def      A					(						self    :							Optional[Any]):
           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   ,							)


    def      A					(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							str):
           _A  :			Optional[int]											=      TFSwinModel(config=lowercase_)
           _A  :			Dict											=      model(lowercase_)

           _A  :			str											=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
           _A  :			Any											=      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      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Tuple											=      TFSwinForMaskedImageModeling(config=lowercase_)
           _A  :			Optional[Any]											=      model(lowercase_)
           self.parent.assertEqual(
               result.logits.shape   ,							(self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           _A  :			List[Any]											=      1
           _A  :			Optional[Any]											=      TFSwinForMaskedImageModeling(lowercase_)

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


    def      A					(						self    :							List[str]   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]):
           _A  :			Optional[int]											=      self.type_sequence_label_size
           _A  :			Union[str, Any]											=      TFSwinForImageClassification(lowercase_)
           _A  :			Optional[Any]											=      model(lowercase_   ,							labels=lowercase_)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _A  :			Tuple											=      1
           _A  :			Dict											=      TFSwinForImageClassification(lowercase_)
           _A  :			str											=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _A  :			List[Any]											=      model(lowercase_)
           self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size))


    def      A					(						self    :							Optional[Any]):
           _A  :			Any											=      self.prepare_config_and_inputs()
           _A  :			Any											=      config_and_inputs
           _A  :			Union[str, Any]											=      {'''pixel_values''': pixel_values}
           return config, inputs_dict




@require_tf
class 				__lowerCamelCase				( __UpperCAmelCase       ,      __UpperCAmelCase       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(
        (
            TFSwinModel,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    a									=					(
        {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
        if is_tf_available()
        else {}
    )

    a									=					False
    a									=					False
    a									=					False
    a									=					False


    def      A					(						self    :							List[str]):
           _A  :			List[Any]											=      TFSwinModelTester(self)
           _A  :			List[Any]											=      ConfigTester(self   ,							config_class=lowercase_   ,							embed_dim=37)


    def      A					(						self    :							Union[str, 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      A					(						self    :							Optional[Any]):
           return


    def      A					(						self    :							Union[str, Any]):
           _A  :			int											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase_)


    def      A					(						self    :							Tuple):
           _A  :			Union[str, Any]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_)


    def      A					(						self    :							List[Any]):
           _A  :			List[str]											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase_)


    @unittest.skip(reason='Swin does not use inputs_embeds')
    def      A					(						self    :							Dict):
           pass


    def      A					(						self    :							Optional[int]):
           _A  :			Any											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			Dict											=      model_class(lowercase_)
                  self.assertIsInstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer)
                  _A  :			Tuple											=      model.get_output_embeddings()
                  self.assertTrue(x is None or isinstance(lowercase_   ,							tf.keras.layers.Dense))


    def      A					(						self    :							Optional[int]):
           _A  :			List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			Tuple											=      model_class(lowercase_)
                  _A  :			Optional[int]											=      inspect.signature(model.call)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _A  :			str											=      [*signature.parameters.keys()]

                  _A  :			Optional[int]											=      ['''pixel_values''']
                  self.assertListEqual(arg_names[:1]   ,							lowercase_)


    def      A					(						self    :							Tuple):
           _A  :			int											=      self.model_tester.prepare_config_and_inputs_for_common()
           _A  :			Optional[Any]											=      True

           for model_class in self.all_model_classes:
                  _A  :			List[Any]											=      True
                  _A  :			Any											=      False
                  _A  :			Tuple											=      True
                  _A  :			str											=      model_class(lowercase_)
                  _A  :			List[str]											=      model(**self._prepare_for_class(lowercase_   ,							lowercase_))
                  _A  :			Union[str, Any]											=      outputs.attentions
                  _A  :			Optional[int]											=      len(self.model_tester.depths)
                  self.assertEqual(len(lowercase_)   ,							lowercase_)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  _A  :			List[str]											=      True
                  _A  :			Tuple											=      config.window_size**2
                  _A  :			Optional[Any]											=      model_class(lowercase_)
                  _A  :			List[Any]											=      model(**self._prepare_for_class(lowercase_   ,							lowercase_))
                  _A  :			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]   ,							)
                  _A  :			str											=      len(lowercase_)

                  # Check attention is always last and order is fine
                  _A  :			Optional[Any]											=      True
                  _A  :			str											=      True
                  _A  :			Optional[int]											=      model_class(lowercase_)
                  _A  :			Optional[Any]											=      model(**self._prepare_for_class(lowercase_   ,							lowercase_))

                  if hasattr(self.model_tester   ,							'num_hidden_states_types'):
                         _A  :			Optional[int]											=      self.model_tester.num_hidden_states_types
                  else:
                         # also another +1 for reshaped_hidden_states
                         _A  :			Any											=      2
                  self.assertEqual(out_len + added_hidden_states   ,							len(lowercase_))

                  _A  :			Optional[Any]											=      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]   ,							)


    def      A					(						self    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							List[str]):
           _A  :			Tuple											=      model_class(lowercase_)
           _A  :			Tuple											=      model(**self._prepare_for_class(lowercase_   ,							lowercase_))
           _A  :			Tuple											=      outputs.hidden_states

           _A  :			Optional[Any]											=      getattr(
               self.model_tester   ,							'expected_num_hidden_layers'   ,							len(self.model_tester.depths) + 1)
           self.assertEqual(len(lowercase_)   ,							lowercase_)

           # Swin has a different seq_length
           _A  :			Dict											=      to_atuple(config.patch_size)

           _A  :			Dict											=      (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]   ,							)

           _A  :			Optional[int]											=      outputs.reshaped_hidden_states
           self.assertEqual(len(lowercase_)   ,							lowercase_)

           _A  :			Optional[int]											=      reshaped_hidden_states[0].shape

           _A  :			Optional[Any]											=      tf.reshape(reshaped_hidden_states[0]   ,							(batch_size, num_channels, height * width))
           _A  :			List[str]											=      tf.transpose(lowercase_   ,							(0, 2, 1))

           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:])   ,							[num_patches, self.model_tester.embed_dim]   ,							)


    def      A					(						self    :							int):
           _A  :			List[str]											=      self.model_tester.prepare_config_and_inputs_for_common()

           _A  :			Union[str, Any]											=      to_atuple(self.model_tester.image_size)

           for model_class in self.all_model_classes:
                  _A  :			Optional[Any]											=      True
                  self.check_hidden_states_output(lowercase_   ,							lowercase_   ,							lowercase_   ,							lowercase_)

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

                  self.check_hidden_states_output(lowercase_   ,							lowercase_   ,							lowercase_   ,							lowercase_)


    def      A					(						self    :							List[str]):
           _A  :			Union[str, Any]											=      self.model_tester.prepare_config_and_inputs_for_common()
           _A  :			Union[str, Any]											=      3

           _A  :			Optional[int]											=      to_atuple(self.model_tester.image_size)
           _A  :			int											=      to_atuple(config.patch_size)

           _A  :			Optional[int]											=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           _A  :			int											=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  _A  :			Tuple											=      True
                  self.check_hidden_states_output(lowercase_   ,							lowercase_   ,							lowercase_   ,							(padded_height, padded_width))


    @slow
    def      A					(						self    :							int):

           for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A  :			List[str]											=      TFSwinModel.from_pretrained(lowercase_)
                  self.assertIsNotNone(lowercase_)




@require_vision
@require_tf
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @cached_property
    def      A					(						self    :							List[Any]):
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224')
               if is_vision_available()
               else None
           )


    @slow
    def      A					(						self    :							Dict):
           _A  :			Optional[Any]											=      TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224')
           _A  :			Optional[int]											=      self.default_image_processor

           _A  :			Any											=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
           _A  :			Tuple											=      image_processor(images=lowercase_   ,							return_tensors='tf')

           # forward pass
           _A  :			Dict											=      model(lowercase_)

           # verify the logits
           _A  :			List[str]											=      tf.TensorShape((1, 1000))
           self.assertEqual(outputs.logits.shape   ,							lowercase_)
           _A  :			Optional[int]											=      tf.constant([-0.0948, -0.6454, -0.0921])
           self.assertTrue(np.allclose(outputs.logits[0, :3]   ,							lowercase_   ,							atol=1e-4))










'''simple docstring'''







class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Optional[Any]):
           _A  :			Optional[Any]											=      [0]
           _A  :			Optional[Any]											=      0


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           _A  :			Optional[Any]											=      self.__heap[i]
           while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                         _A  :			Dict											=      self.__heap[i // 2]
                         _A  :			Optional[Any]											=      temporary
                  i //= 2


    def      A					(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int):
           self.__heap.append(SCREAMING_SNAKE_CASE)
           self.__size += 1
           self.__swap_up(self.__size)


    def      A					(						self    :							int   ,							SCREAMING_SNAKE_CASE    :							int):
           while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                         _A  :			Optional[int]											=      2 * i
                  else:
                         if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                                _A  :			Any											=      2 * i
                         else:
                                _A  :			Optional[Any]											=      2 * i + 1
                  _A  :			List[str]											=      self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                         _A  :			Union[str, Any]											=      self.__heap[bigger_child]
                         _A  :			Optional[int]											=      temporary
                  _A  :			Any											=      bigger_child


    def      A					(						self    :							List[str]):
           _A  :			str											=      self.__heap[1]
           _A  :			Tuple											=      self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1)
           return max_value


    @property
    def      A					(						self    :							int):
           return self.__heap[1:]


    def __len__(						self    :							List[str]):
           return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   A   :  Dict       		=				BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))






