


"""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 		_A    :







    """simple docstring"""




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






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

      a		:						str 				=     None
      if self.use_labels:
        a		:						Dict 				=     ids_tensor([self.batch_size]							,					self.type_sequence_label_size)

      a		:						List[Any] 				=     self.get_config()

      return config, pixel_values, labels






    def       __snake_case				(   self							:       Optional[int]):
      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       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       Tuple):
      a		:						Optional[Any] 				=     TFSwinModel(config=__UpperCAmelCase)
      a		:						Dict 				=     model(__UpperCAmelCase)

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

      # test greyscale images
      a		:						Union[str, Any] 				=     1
      a		:						Optional[int] 				=     TFSwinForMaskedImageModeling(__UpperCAmelCase)

      a		:						List[str] 				=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      a		:						Tuple 				=     model(__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape							,					(self.batch_size, 1, self.image_size, self.image_size))






    def       __snake_case				(   self							:       List[str]							,					__UpperCAmelCase							:       List[Any]							,					__UpperCAmelCase							:       Optional[Any]							,					__UpperCAmelCase							:       List[str]):
      a		:						Union[str, Any] 				=     self.type_sequence_label_size
      a		:						Any 				=     TFSwinForImageClassification(__UpperCAmelCase)
      a		:						List[Any] 				=     model(__UpperCAmelCase							,					labels=__UpperCAmelCase)
      self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size))

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






    def       __snake_case				(   self							:       Any):
      a		:						Dict 				=     self.prepare_config_and_inputs()
      a				,						a				,						a		:						Optional[Any] 				=     config_and_inputs
      a		:						Union[str, Any] 				=     {"pixel_values": pixel_values}
      return config, inputs_dict






@require_tf
class 		_A    (    _a       ,_a       ,unittest.TestCase    ):







    """simple docstring"""




    UpperCAmelCase			:				Tuple      =			(
        (
            TFSwinModel,
            TFSwinForImageClassification,
            TFSwinForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    UpperCAmelCase			:				Optional[int]      =			(
        {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
        if is_tf_available()
        else {}
    )

    UpperCAmelCase			:				List[Any]      =			False
    UpperCAmelCase			:				str      =			False
    UpperCAmelCase			:				str      =			False
    UpperCAmelCase			:				Optional[int]      =			False






    def       __snake_case				(   self							:       Tuple):
      a		:						Optional[int] 				=     TFSwinModelTester(self)
      a		:						Tuple 				=     ConfigTester(self							,					config_class=__UpperCAmelCase							,					embed_dim=37)






    def       __snake_case				(   self							:       Optional[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       __snake_case				(   self							:       Dict):
      return






    def       __snake_case				(   self							:       List[str]):
      a		:						Optional[Any] 				=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase)






    def       __snake_case				(   self							:       List[str]):
      a		:						int 				=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase)






    def       __snake_case				(   self							:       List[str]):
      a		:						Optional[int] 				=     self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase)






    @unittest.skip(reason="Swin does not use inputs_embeds")
    def       __snake_case				(   self							:       Union[str, Any]):
      pass






    def       __snake_case				(   self							:       Optional[int]):
      a				,						a		:						int 				=     self.model_tester.prepare_config_and_inputs_for_common()

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






    def       __snake_case				(   self							:       List[Any]):
      a				,						a		:						List[str] 				=     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:						List[str] 				=     model_class(__UpperCAmelCase)
        a		:						Any 				=     inspect.signature(model.call)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a		:						str 				=     [*signature.parameters.keys()]

        a		:						str 				=     ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,					__UpperCAmelCase)






    def       __snake_case				(   self							:       Optional[Any]):
      a				,						a		:						Tuple 				=     self.model_tester.prepare_config_and_inputs_for_common()
      a		:						List[Any] 				=     True

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

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        a		:						Optional[Any] 				=     True
        a		:						List[Any] 				=     config.window_size**2
        a		:						Optional[int] 				=     model_class(__UpperCAmelCase)
        a		:						int 				=     model(**self._prepare_for_class(__UpperCAmelCase							,					__UpperCAmelCase))
        a		:						Any 				=     outputs.attentions
        self.assertEqual(len(__UpperCAmelCase)							,					__UpperCAmelCase)

        self.assertListEqual(
            list(attentions[0].shape[-3:])							,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,					)
        a		:						Optional[int] 				=     len(__UpperCAmelCase)

        # Check attention is always last and order is fine
        a		:						Dict 				=     True
        a		:						Union[str, Any] 				=     True
        a		:						int 				=     model_class(__UpperCAmelCase)
        a		:						Union[str, Any] 				=     model(**self._prepare_for_class(__UpperCAmelCase							,					__UpperCAmelCase))

        if hasattr(self.model_tester							,					"num_hidden_states_types"):
          a		:						List[Any] 				=     self.model_tester.num_hidden_states_types
        else:
          # also another +1 for reshaped_hidden_states
          a		:						Union[str, Any] 				=     2
        self.assertEqual(out_len + added_hidden_states							,					len(__UpperCAmelCase))

        a		:						Any 				=     outputs.attentions

        self.assertEqual(len(__UpperCAmelCase)							,					__UpperCAmelCase)

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






    def       __snake_case				(   self							:       int							,					__UpperCAmelCase							:       Any							,					__UpperCAmelCase							:       str							,					__UpperCAmelCase							:       int							,					__UpperCAmelCase							:       Optional[int]):
      a		:						Optional[int] 				=     model_class(__UpperCAmelCase)
      a		:						int 				=     model(**self._prepare_for_class(__UpperCAmelCase							,					__UpperCAmelCase))
      a		:						str 				=     outputs.hidden_states

      a		:						Dict 				=     getattr(
          self.model_tester							,					"expected_num_hidden_layers"							,					len(self.model_tester.depths) + 1)
      self.assertEqual(len(__UpperCAmelCase)							,					__UpperCAmelCase)

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

      a		:						str 				=     (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[Any] 				=     outputs.reshaped_hidden_states
      self.assertEqual(len(__UpperCAmelCase)							,					__UpperCAmelCase)

      a				,						a				,						a				,						a		:						Union[str, Any] 				=     reshaped_hidden_states[0].shape

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

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






    def       __snake_case				(   self							:       Dict):
      a				,						a		:						List[str] 				=     self.model_tester.prepare_config_and_inputs_for_common()

      a		:						Any 				=     to_atuple(self.model_tester.image_size)

      for model_class in self.all_model_classes:
        a		:						List[Any] 				=     True
        self.check_hidden_states_output(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)

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

        self.check_hidden_states_output(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase)






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

      a		:						str 				=     to_atuple(self.model_tester.image_size)
      a		:						List[str] 				=     to_atuple(config.patch_size)

      a		:						Dict 				=     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		:						Optional[Any] 				=     True
        self.check_hidden_states_output(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					(padded_height, padded_width))

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a		:						List[str] 				=     True
        self.check_hidden_states_output(__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					(padded_height, padded_width))






    @slow
    def       __snake_case				(   self							:       Dict):
      for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:						Union[str, Any] 				=     TFSwinModel.from_pretrained(__UpperCAmelCase)
        self.assertIsNotNone(__UpperCAmelCase)






@require_vision
@require_tf
class 		_A    (    unittest.TestCase    ):







    """simple docstring"""




    @cached_property
    def       __snake_case				(   self							:       Union[str, Any]):
      return (
          AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
          if is_vision_available()
          else None
      )






    @slow
    def       __snake_case				(   self							:       str):
      a		:						Any 				=     TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224")
      a		:						List[Any] 				=     self.default_image_processor

      a		:						str 				=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
      a		:						Union[str, Any] 				=     image_processor(images=__UpperCAmelCase							,					return_tensors="tf")

      # forward pass
      a		:						Optional[Any] 				=     model(__UpperCAmelCase)

      # verify the logits
      a		:						Union[str, Any] 				=     tf.TensorShape((1, 1000))
      self.assertEqual(outputs.logits.shape							,					__UpperCAmelCase)
      a		:						Union[str, Any] 				=     tf.constant([-0.0_948, -0.6_454, -0.0_921])
      self.assertTrue(np.allclose(outputs.logits[0, :3]							,					__UpperCAmelCase							,					atol=1e-4))










"""simple docstring"""







class 		_A    :







    """simple docstring"""




    def __init__(   self							:       Any):
      a		:						Tuple 				=     [0]
      a		:						str 				=     0






    def       __snake_case				(   self							:       Optional[Any]							,					__UpperCAmelCase							:       int):
      a		:						Any 				=     self.__heap[i]
      while i // 2 > 0:
        if self.__heap[i] > self.__heap[i // 2]:
          a		:						Tuple 				=     self.__heap[i // 2]
          a		:						Any 				=     temporary
        i //= 2






    def       __snake_case				(   self							:       Dict							,					__UpperCAmelCase							:       int):
      self.__heap.append(__UpperCAmelCase)
      self.__size += 1
      self.__swap_up(self.__size)






    def       __snake_case				(   self							:       str							,					__UpperCAmelCase							:       int):
      while self.__size >= 2 * i:
        if 2 * i + 1 > self.__size:
          a		:						List[Any] 				=     2 * i
        else:
          if self.__heap[2 * i] > self.__heap[2 * i + 1]:
            a		:						Tuple 				=     2 * i
          else:
            a		:						str 				=     2 * i + 1
        a		:						Optional[int] 				=     self.__heap[i]
        if self.__heap[i] < self.__heap[bigger_child]:
          a		:						Any 				=     self.__heap[bigger_child]
          a		:						Union[str, Any] 				=     temporary
        a		:						Dict 				=     bigger_child






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






    @property
    def       __snake_case				(   self							:       List[str]):
      return self.__heap[1:]






    def __len__(   self							:       Optional[int]):
      return self.__size


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # create an instance of BinaryHeap
 __lowercase          = 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))






