




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=13						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=0.1						,		_SCREAMING_SNAKE_CASE=224						,		_SCREAMING_SNAKE_CASE=1000						,		_SCREAMING_SNAKE_CASE=[3, 3, 6, 4]						,		_SCREAMING_SNAKE_CASE=[48, 56, 112, 220]						,		):
								_UpperCAmelCase      =			parent
								_UpperCAmelCase      =			batch_size
								_UpperCAmelCase      =			num_channels
								_UpperCAmelCase      =			is_training
								_UpperCAmelCase      =			use_labels
								_UpperCAmelCase      =			hidden_dropout_prob
								_UpperCAmelCase      =			attention_probs_dropout_prob
								_UpperCAmelCase      =			num_labels
								_UpperCAmelCase      =			image_size
								_UpperCAmelCase      =			layer_depths
								_UpperCAmelCase      =			embed_dims






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

								_UpperCAmelCase      =			self.get_config()

								return config, pixel_values, labels






		def 				UpperCAmelCase (    self			):
								return SwiftFormerConfig(
								    depths=self.layer_depths						,		embed_dims=self.embed_dims						,		mlp_ratio=4						,		downsamples=[True, True, True, True]						,		hidden_act="""gelu"""						,		num_labels=self.num_labels						,		down_patch_size=3						,		down_stride=2						,		down_pad=1						,		drop_rate=0.0						,		drop_path_rate=0.0						,		use_layer_scale=_SCREAMING_SNAKE_CASE						,		layer_scale_init_value=1e-5						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			SwiftFormerModel(config=_SCREAMING_SNAKE_CASE			)
								model.to(_SCREAMING_SNAKE_CASE			)
								model.eval()
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE			)
								self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.embed_dims[-1], 7, 7)			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.num_labels
								_UpperCAmelCase      =			SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE			)
								model.to(_SCREAMING_SNAKE_CASE			)
								model.eval()
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_SNAKE_CASE			)
								self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)			)

								_UpperCAmelCase      =			SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE			)
								model.to(_SCREAMING_SNAKE_CASE			)
								model.eval()

								_UpperCAmelCase      =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE			)
								self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)			)






		def 				UpperCAmelCase (    self			):
								((_UpperCAmelCase)    ,					(_UpperCAmelCase)    ,					(_UpperCAmelCase))      =			self.prepare_config_and_inputs()
								_UpperCAmelCase      =			{"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       _A				(		__lowercase       ,     __lowercase       ,     unittest.TestCase     ):
		__a					    =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		__a					    =       (
		    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__a					    =       False
		__a					    =       False
		__a					    =       False
		__a					    =       False
		__a					    =       False






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			SwiftFormerModelTester(self			)
								_UpperCAmelCase      =			ConfigTester(
								    self						,		config_class=_SCREAMING_SNAKE_CASE						,		has_text_modality=_SCREAMING_SNAKE_CASE						,		hidden_size=37						,		num_attention_heads=12						,		num_hidden_layers=12						,		)






		def 				UpperCAmelCase (    self			):
								self.config_tester.run_common_tests()






		@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_UpperCAmelCase      =			model_class(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE						,		nn.Linear			)			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

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

														_UpperCAmelCase      =			["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE			)






		@slow
		def 				UpperCAmelCase (    self			):
								for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase      =			SwiftFormerModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)






		@unittest.skip(reason="""SwiftFormer does not output attentions"""			)
		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self			):
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			model_class(_SCREAMING_SNAKE_CASE			)
														model.to(_SCREAMING_SNAKE_CASE			)
														model.eval()

														with torch.no_grad():
																				_UpperCAmelCase      =			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)			)

														_UpperCAmelCase      =			outputs.hidden_states

														_UpperCAmelCase      =			8
														self.assertEqual(len(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)  # TODO

														# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
														# with the width and height being successively divided by 2, after every 2 blocks
														for i in range(len(_SCREAMING_SNAKE_CASE			)			):
																				self.assertEqual(
																				    hidden_states[i].shape						,		torch.Size(
																				        [
																				            self.model_tester.batch_size,
																				            self.model_tester.embed_dims[i // 2],
																				            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																				            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																				        ]			)						,		)

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														_UpperCAmelCase      =			True
														check_hidden_states_output(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														_UpperCAmelCase      =			True

														check_hidden_states_output(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								def _config_zero_init(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			copy.deepcopy(_SCREAMING_SNAKE_CASE			)
														for key in configs_no_init.__dict__.keys():
																				if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																										setattr(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		1e-10			)
																				if isinstance(getattr(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			):
																										_UpperCAmelCase      =			_config_zero_init(getattr(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)			)
																										setattr(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														return configs_no_init

								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

								_UpperCAmelCase      =			_config_zero_init(_SCREAMING_SNAKE_CASE			)
								for model_class in self.all_model_classes:
														_UpperCAmelCase      =			model_class(config=_SCREAMING_SNAKE_CASE			)
														for name, param in model.named_parameters():
																				if param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1e9) / 1e9).round().item()						,		[0.0, 1.0]						,		msg=F"Parameter {name} of model {model_class} seems not properly initialized"						,		)






		@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
		def 				UpperCAmelCase (    self			):
								pass




def        _SCREAMING_SNAKE_CASE      (		)						->				str:
						_UpperCAmelCase      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
						return image




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






		@cached_property
		def 				UpperCAmelCase (    self			):
								return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""			) if is_vision_available() else None






		@slow
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			self.default_image_processor
								_UpperCAmelCase      =			prepare_img()
								_UpperCAmelCase      =			image_processor(images=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			).to(_SCREAMING_SNAKE_CASE			)

								# forward pass
								with torch.no_grad():
														_UpperCAmelCase      =			model(**_SCREAMING_SNAKE_CASE			)

								# verify the logits
								_UpperCAmelCase      =			torch.Size((1, 1000)			)
								self.assertEqual(outputs.logits.shape						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]			).to(_SCREAMING_SNAKE_CASE			)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]						,		_SCREAMING_SNAKE_CASE						,		atol=1e-4			)			)





from __future__ import annotations




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				list[int]:
						if len(snake_case       ) == 0:
												return array

						_UpperCAmelCase    ,					_UpperCAmelCase      =			min(snake_case       ), max(snake_case       )

						# Compute the variables
						_UpperCAmelCase      =			_max - _min + 1
						_UpperCAmelCase    ,					_UpperCAmelCase      =			[0] * holes_range, [0] * holes_range

						# Make the sorting.
						for i in array:
												_UpperCAmelCase      =			i - _min
												_UpperCAmelCase      =			i
												holes_repeat[index] += 1

						# Makes the array back by replacing the numbers.
						_UpperCAmelCase      =			0
						for i in range(snake_case       ):
												while holes_repeat[i] > 0:
																		_UpperCAmelCase      =			holes[i]
																		index += 1
																		holes_repeat[i] -= 1

    # Returns the sorted array.
						return array


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				a													=			input("Enter numbers separated by comma:\n")
				a													=			[int(x) for x in user_input.split(",")]
				print(pigeon_sort(unsorted))