



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 		UpperCAmelCase    :





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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         _lowerCAmelCase			      =	None
         if self.use_labels:
               _lowerCAmelCase			      =	ids_tensor([self.batch_size]							,  self.type_sequence_label_size      )

         _lowerCAmelCase			      =	self.get_config()

         return config, pixel_values, labels





   def 						__lowerCAmelCase				(						self      ):
         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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	TFSwinModel(config=_lowerCAmelCase      )
         _lowerCAmelCase			      =	model(_lowerCAmelCase      )

         _lowerCAmelCase			      =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
         _lowerCAmelCase			      =	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 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	TFSwinForMaskedImageModeling(config=_lowerCAmelCase      )
         _lowerCAmelCase			      =	model(_lowerCAmelCase      )
         self.parent.assertEqual(
             result.logits.shape							,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

         # test greyscale images
         _lowerCAmelCase			      =	1
         _lowerCAmelCase			      =	TFSwinForMaskedImageModeling(_lowerCAmelCase      )

         _lowerCAmelCase			      =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         _lowerCAmelCase			      =	model(_lowerCAmelCase      )
         self.parent.assertEqual(result.logits.shape							,  (self.batch_size, 1, self.image_size, self.image_size)      )





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	self.type_sequence_label_size
         _lowerCAmelCase			      =	TFSwinForImageClassification(_lowerCAmelCase      )
         _lowerCAmelCase			      =	model(_lowerCAmelCase							,  labels=_lowerCAmelCase      )
         self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         _lowerCAmelCase			      =	1
         _lowerCAmelCase			      =	TFSwinForImageClassification(_lowerCAmelCase      )
         _lowerCAmelCase			      =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         _lowerCAmelCase			      =	model(_lowerCAmelCase      )
         self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.prepare_config_and_inputs()
         _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase			      =	config_and_inputs
         _lowerCAmelCase			      =	{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_tf
class 		UpperCAmelCase    (    snake_case_							,snake_case_							,unittest.TestCase					):
   SCREAMING_SNAKE_CASE__					      =  (
       (
           TFSwinModel,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   SCREAMING_SNAKE_CASE__					      =  (
       {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
       if is_tf_available()
       else {}
   )

   SCREAMING_SNAKE_CASE__					      =  False
   SCREAMING_SNAKE_CASE__					      =  False
   SCREAMING_SNAKE_CASE__					      =  False
   SCREAMING_SNAKE_CASE__					      =  False





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	TFSwinModelTester(self      )
         _lowerCAmelCase			      =	ConfigTester(self							,  config_class=_lowerCAmelCase							,  embed_dim=37      )





   def 						__lowerCAmelCase				(						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 						__lowerCAmelCase				(						self      ):
         return





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase      )





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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs_for_common()

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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowerCAmelCase			      =	['''pixel_values''']
               self.assertListEqual(arg_names[:1]							,  _lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs_for_common()
         _lowerCAmelCase			      =	True

         for model_class in self.all_model_classes:
               _lowerCAmelCase			      =	True
               _lowerCAmelCase			      =	False
               _lowerCAmelCase			      =	True
               _lowerCAmelCase			      =	model_class(_lowerCAmelCase      )
               _lowerCAmelCase			      =	model(**self._prepare_for_class(_lowerCAmelCase							,  _lowerCAmelCase      )      )
               _lowerCAmelCase			      =	outputs.attentions
               _lowerCAmelCase			      =	len(self.model_tester.depths      )
               self.assertEqual(len(_lowerCAmelCase      )							,  _lowerCAmelCase      )

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _lowerCAmelCase			      =	True
               _lowerCAmelCase			      =	config.window_size**2
               _lowerCAmelCase			      =	model_class(_lowerCAmelCase      )
               _lowerCAmelCase			      =	model(**self._prepare_for_class(_lowerCAmelCase							,  _lowerCAmelCase      )      )
               _lowerCAmelCase			      =	outputs.attentions
               self.assertEqual(len(_lowerCAmelCase      )							,  _lowerCAmelCase      )

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

               # Check attention is always last and order is fine
               _lowerCAmelCase			      =	True
               _lowerCAmelCase			      =	True
               _lowerCAmelCase			      =	model_class(_lowerCAmelCase      )
               _lowerCAmelCase			      =	model(**self._prepare_for_class(_lowerCAmelCase							,  _lowerCAmelCase      )      )

               if hasattr(self.model_tester							,  '''num_hidden_states_types'''      ):
                     _lowerCAmelCase			      =	self.model_tester.num_hidden_states_types
               else:
                     # also another +1 for reshaped_hidden_states
                     _lowerCAmelCase			      =	2
               self.assertEqual(out_len + added_hidden_states							,  len(_lowerCAmelCase      )      )

               _lowerCAmelCase			      =	outputs.attentions

               self.assertEqual(len(_lowerCAmelCase      )							,  _lowerCAmelCase      )

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





   def 						__lowerCAmelCase				(						self							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      ):
         _lowerCAmelCase			      =	model_class(_lowerCAmelCase      )
         _lowerCAmelCase			      =	model(**self._prepare_for_class(_lowerCAmelCase							,  _lowerCAmelCase      )      )
         _lowerCAmelCase			      =	outputs.hidden_states

         _lowerCAmelCase			      =	getattr(
             self.model_tester							,  '''expected_num_hidden_layers'''							,  len(self.model_tester.depths      ) + 1      )
         self.assertEqual(len(_lowerCAmelCase      )							,  _lowerCAmelCase      )

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

         _lowerCAmelCase			      =	(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]							,  )

         _lowerCAmelCase			      =	outputs.reshaped_hidden_states
         self.assertEqual(len(_lowerCAmelCase      )							,  _lowerCAmelCase      )

         _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase      , _lowerCAmelCase			      =	reshaped_hidden_states[0].shape

         _lowerCAmelCase			      =	tf.reshape(reshaped_hidden_states[0]							,  (batch_size, num_channels, height * width)      )
         _lowerCAmelCase			      =	tf.transpose(_lowerCAmelCase							,  (0, 2, 1)      )

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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs_for_common()

         _lowerCAmelCase			      =	to_atuple(self.model_tester.image_size      )

         for model_class in self.all_model_classes:
               _lowerCAmelCase			      =	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"]
               _lowerCAmelCase			      =	True

               self.check_hidden_states_output(_lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase      , _lowerCAmelCase			      =	self.model_tester.prepare_config_and_inputs_for_common()
         _lowerCAmelCase			      =	3

         _lowerCAmelCase			      =	to_atuple(self.model_tester.image_size      )
         _lowerCAmelCase			      =	to_atuple(config.patch_size      )

         _lowerCAmelCase			      =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         _lowerCAmelCase			      =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
               _lowerCAmelCase			      =	True
               self.check_hidden_states_output(_lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  (padded_height, padded_width)      )

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





   @slow
   def 						__lowerCAmelCase				(						self      ):
         for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowerCAmelCase			      =	TFSwinModel.from_pretrained(_lowerCAmelCase      )
               self.assertIsNotNone(_lowerCAmelCase      )






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





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





   @slow
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''      )
         _lowerCAmelCase			      =	self.default_image_processor

         _lowerCAmelCase			      =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
         _lowerCAmelCase			      =	image_processor(images=_lowerCAmelCase							,  return_tensors='''tf'''      )

         # forward pass
         _lowerCAmelCase			      =	model(_lowerCAmelCase      )

         # verify the logits
         _lowerCAmelCase			      =	tf.TensorShape((1, 1_000)      )
         self.assertEqual(outputs.logits.shape							,  _lowerCAmelCase      )
         _lowerCAmelCase			      =	tf.constant([-0.0_948, -0.6_454, -0.0_921]      )
         self.assertTrue(np.allclose(outputs.logits[0, :3]							,  _lowerCAmelCase							,  atol=1E-4      )      )




from __future__ import annotations

from random import randrange
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						list					)->list:
      if len(_SCREAMING_SNAKE_CASE					) < 2:
            return collection
      _lowerCAmelCase			      =	randrange(len(_SCREAMING_SNAKE_CASE					)					)  # Use random element as pivot
      _lowerCAmelCase			      =	collection[pivot_index]
      _lowerCAmelCase			      =	[]  # All elements greater than pivot
      _lowerCAmelCase			      =	[]  # All elements less than or equal to pivot

      for element in collection[:pivot_index]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      for element in collection[pivot_index + 1 :]:
            (greater if element > pivot else lesser).append(_SCREAMING_SNAKE_CASE					)

      return [*quick_sort(_SCREAMING_SNAKE_CASE					), pivot, *quick_sort(_SCREAMING_SNAKE_CASE					)]


if __name__ == "__main__":
    UpperCAmelCase_          =				input("Enter numbers separated by a comma:\n").strip()
    UpperCAmelCase_          =				[int(item) for item in user_input.split(",")]
    print(quick_sort(unsorted))