


'''simple docstring'''



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 		_snake_case					:




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=13							,							_lowerCamelCase=3							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=224							,							_lowerCamelCase=1000							,							_lowerCamelCase=[3, 3, 6, 4]							,							_lowerCamelCase=[48, 56, 112, 220]							,							):
								UpperCAmelCase__  :					str        =     parent
								UpperCAmelCase__  :					List[str]        =     batch_size
								UpperCAmelCase__  :					str        =     num_channels
								UpperCAmelCase__  :					Union[str, Any]        =     is_training
								UpperCAmelCase__  :					List[Any]        =     use_labels
								UpperCAmelCase__  :					Optional[int]        =     hidden_dropout_prob
								UpperCAmelCase__  :					Union[str, Any]        =     attention_probs_dropout_prob
								UpperCAmelCase__  :					Tuple        =     num_labels
								UpperCAmelCase__  :					Optional[Any]        =     image_size
								UpperCAmelCase__  :					List[str]        =     layer_depths
								UpperCAmelCase__  :					Any        =     embed_dims




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

								UpperCAmelCase__  :					Optional[int]        =     None
								if self.use_labels:
											UpperCAmelCase__  :					Dict        =     ids_tensor([self.batch_size]							,							self.num_labels)

								UpperCAmelCase__  :					Any        =     self.get_config()

								return config, pixel_values, labels




					def 				snake_case__						(      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=_lowerCamelCase							,							layer_scale_init_value=1e-5							,							)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[Any]        =     SwiftFormerModel(config=_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__  :					Union[str, Any]        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.embed_dims[-1], 7, 7))




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Dict        =     self.num_labels
								UpperCAmelCase__  :					List[str]        =     SwiftFormerForImageClassification(_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()
								UpperCAmelCase__  :					int        =     model(_lowerCamelCase							,							labels=_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))

								UpperCAmelCase__  :					Tuple        =     SwiftFormerForImageClassification(_lowerCamelCase)
								model.to(_lowerCamelCase)
								model.eval()

								UpperCAmelCase__  :					Optional[Any]        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
								UpperCAmelCase__  :					Tuple        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels))




					def 				snake_case__						(      self):
								((UpperCAmelCase__)   ,       (UpperCAmelCase__)   ,       (UpperCAmelCase__))  :					List[Any]        =     self.prepare_config_and_inputs()
								UpperCAmelCase__  :					int        =     {"""pixel_values""": pixel_values}
								return config, inputs_dict


@require_torch
class 		_snake_case					(     a__       ,   a__       ,   unittest.TestCase	):
					lowerCAmelCase      :List[str]   =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					lowerCAmelCase      :Union[str, Any]   =					(
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase      :Dict   =					False
					lowerCAmelCase      :Any   =					False
					lowerCAmelCase      :Dict   =					False
					lowerCAmelCase      :Any   =					False
					lowerCAmelCase      :Union[str, Any]   =					False




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     SwiftFormerModelTester(self)
								UpperCAmelCase__  :					Optional[Any]        =     ConfigTester(
								    self							,							config_class=_lowerCamelCase							,							has_text_modality=_lowerCamelCase							,							hidden_size=37							,							num_attention_heads=12							,							num_hidden_layers=12							,							)




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




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




					def 				snake_case__						(      self):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase__  :					Dict        =     model_class(_lowerCamelCase)
											UpperCAmelCase__  :					Union[str, Any]        =     model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_lowerCamelCase							,							nn.Linear))




					def 				snake_case__						(      self):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCAmelCase__  :					int        =     ["""pixel_values"""]
											self.assertListEqual(arg_names[:1]							,							_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase)




					@slow
					def 				snake_case__						(      self):
								for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase__  :					List[Any]        =     SwiftFormerModel.from_pretrained(_lowerCamelCase)
											self.assertIsNotNone(_lowerCamelCase)




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




					def 				snake_case__						(      self):
								def check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											UpperCAmelCase__  :					Any        =     model_class(_lowerCamelCase)
											model.to(_lowerCamelCase)
											model.eval()

											with torch.no_grad():
														UpperCAmelCase__  :					Optional[Any]        =     model(**self._prepare_for_class(_lowerCamelCase							,							_lowerCamelCase))

											UpperCAmelCase__  :					Any        =     outputs.hidden_states

											UpperCAmelCase__  :					Any        =     8
											self.assertEqual(len(_lowerCamelCase)							,							_lowerCamelCase)  # 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(_lowerCamelCase)):
														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__  :					Any        =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase__  :					str        =     True
											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)

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

											check_hidden_states_output(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)




					def 				snake_case__						(      self):
								def _config_zero_init(_lowerCamelCase):
											UpperCAmelCase__  :					List[str]        =     copy.deepcopy(_lowerCamelCase)
											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(_lowerCamelCase							,							_lowerCamelCase							,							1e-1_0)
														if isinstance(getattr(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)							,							_lowerCamelCase):
																	UpperCAmelCase__  :					List[Any]        =     _config_zero_init(getattr(_lowerCamelCase							,							_lowerCamelCase))
																	setattr(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase)
											return configs_no_init

								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase__  :					int        =     _config_zero_init(_lowerCamelCase)
								for model_class in self.all_model_classes:
											UpperCAmelCase__  :					Dict        =     model_class(config=_lowerCamelCase)
											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 				snake_case__						(      self):
								pass







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[Any]        =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
			return image


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




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




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""").to(_lowerCamelCase)

								UpperCAmelCase__  :					Tuple        =     self.default_image_processor
								UpperCAmelCase__  :					Optional[Any]        =     prepare_img()
								UpperCAmelCase__  :					List[Any]        =     image_processor(images=_lowerCamelCase							,							return_tensors="""pt""").to(_lowerCamelCase)

								# forward pass
								with torch.no_grad():
											UpperCAmelCase__  :					List[Any]        =     model(**_lowerCamelCase)

								# verify the logits
								UpperCAmelCase__  :					str        =     torch.Size((1, 1000))
								self.assertEqual(outputs.logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__  :					str        =     torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]).to(_lowerCamelCase)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]							,							_lowerCamelCase							,							atol=1e-4))



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__					):
			# if the collection is empty, returns empty
			if collection == []:
						return []

			# get some information about the collection
			UpperCAmelCase__  :					List[str]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Union[str, Any]        =     max(UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[Any]        =     min(UpperCamelCase__					)

			# create the counting array
			UpperCAmelCase__  :					Dict        =     coll_max + 1 - coll_min
			UpperCAmelCase__  :					Optional[int]        =     [0] * counting_arr_length

			# count how much a number appears in the collection
			for number in collection:
						counting_arr[number - coll_min] += 1

			# sum each position with it's predecessors. now, counting_arr[i] tells
			# us how many elements <= i has in the collection
			for i in range(1 ,      UpperCamelCase__					):
						UpperCAmelCase__  :					List[str]        =     counting_arr[i] + counting_arr[i - 1]

			# create the output collection
			UpperCAmelCase__  :					Tuple        =     [0] * coll_len

			# place the elements in the output, respecting the original order (stable
			# sort) from end to begin, updating counting_arr
			for i in reversed(range(0 ,      UpperCamelCase__					)					):
						UpperCAmelCase__  :					Any        =     collection[i]
						counting_arr[collection[i] - coll_min] -= 1

			return ordered







def     _UpperCamelCase      (		UpperCamelCase__					):
			return "".join([chr(UpperCamelCase__					) for i in counting_sort([ord(UpperCamelCase__					) for c in string]					)]					)


if __name__ == "__main__":
		# Test string sort
		assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

		__A	=input('Enter numbers separated by a comma:\n').strip()
		__A	=[int(item) for item in user_input.split(',')]
		print(counting_sort(unsorted))