


"""simple docstring"""


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										self.parent.assertTrue(hasattr(__a    ,   "hidden_sizes"	)	)
										self.parent.assertTrue(hasattr(__a    ,   "num_attention_heads"	)	)
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=13    ,   __a=64    ,   __a=3    ,   __a=3    ,   __a=2    ,   __a=1    ,   __a=16    ,   __a=[1_28, 2_56, 3_84]    ,   __a=[4, 6, 8]    ,   __a=[2, 3, 4]    ,   __a=[16, 16, 16]    ,   __a=0    ,   __a=[2, 2, 2]    ,   __a=[2, 2, 2]    ,   __a=0.0_2    ,   __a=True    ,   __a=True    ,   __a=2    ,   ):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			batch_size
										__lowerCAmelCase				    =			image_size
										__lowerCAmelCase				    =			num_channels
										__lowerCAmelCase				    =			kernel_size
										__lowerCAmelCase				    =			stride
										__lowerCAmelCase				    =			padding
										__lowerCAmelCase				    =			hidden_sizes
										__lowerCAmelCase				    =			num_attention_heads
										__lowerCAmelCase				    =			depths
										__lowerCAmelCase				    =			key_dim
										__lowerCAmelCase				    =			drop_path_rate
										__lowerCAmelCase				    =			patch_size
										__lowerCAmelCase				    =			attention_ratio
										__lowerCAmelCase				    =			mlp_ratio
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			[
										    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
										    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
										]
										__lowerCAmelCase				    =			is_training
										__lowerCAmelCase				    =			use_labels
										__lowerCAmelCase				    =			num_labels
										__lowerCAmelCase				    =			initializer_range

							def 			snake_case				(     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.num_labels	)

										__lowerCAmelCase				    =			self.get_config()
										return config, pixel_values, labels

							def 			snake_case				(     self	):
										return LevitConfig(
										    image_size=self.image_size    ,   num_channels=self.num_channels    ,   kernel_size=self.kernel_size    ,   stride=self.stride    ,   padding=self.padding    ,   patch_size=self.patch_size    ,   hidden_sizes=self.hidden_sizes    ,   num_attention_heads=self.num_attention_heads    ,   depths=self.depths    ,   key_dim=self.key_dim    ,   drop_path_rate=self.drop_path_rate    ,   mlp_ratio=self.mlp_ratio    ,   attention_ratio=self.attention_ratio    ,   initializer_range=self.initializer_range    ,   down_ops=self.down_ops    ,   )

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			LevitModel(config=__a	)
										model.to(__a	)
										model.eval()
										__lowerCAmelCase				    =			model(__a	)
										__lowerCAmelCase				    =			(self.image_size, self.image_size)
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			image_size[0], image_size[1]
										for _ in range(4	):
													__lowerCAmelCase				    =			floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
													__lowerCAmelCase				    =			floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1	)
										self.parent.assertEqual(
										    result.last_hidden_state.shape    ,   (self.batch_size, ceil(height / 4	) * ceil(width / 4	), self.hidden_sizes[-1])    ,   )

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			LevitForImageClassification(__a	)
										model.to(__a	)
										model.eval()
										__lowerCAmelCase				    =			model(__a    ,   labels=__a	)
										self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)	)



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.prepare_config_and_inputs()
										__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			config_and_inputs
										__lowerCAmelCase				    =			{"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      =(
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase						:    Tuple				      =(
							    {
							        """feature-extraction""": LevitModel,
							        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase						:    int				      =False
							__UpperCAmelCase						:    str				      =False
							__UpperCAmelCase						:    Optional[Any]				      =False
							__UpperCAmelCase						:    Any				      =False
							__UpperCAmelCase						:    int				      =False

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			LevitModelTester(self	)
										__lowerCAmelCase				    =			ConfigTester(self    ,   config_class=__a    ,   has_text_modality=__a    ,   hidden_size=37	)

							def 			snake_case				(     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 			snake_case				(     self	):
										return

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

							@unittest.skip(reason="Levit does not support input and output embeddings"	)
							def 			snake_case				(     self	):
										pass

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

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

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

													__lowerCAmelCase				    =			["pixel_values"]
													self.assertListEqual(arg_names[:1]    ,   __a	)

							def 			snake_case				(     self	):
										def check_hidden_states_output(__a    ,   __a    ,   __a	):
													__lowerCAmelCase				    =			model_class(__a	)
													model.to(__a	)
													model.eval()

													with torch.no_grad():
																__lowerCAmelCase				    =			model(**self._prepare_for_class(__a    ,   __a	)	)

													__lowerCAmelCase				    =			outputs.hidden_states

													__lowerCAmelCase				    =			len(self.model_tester.depths	) + 1
													self.assertEqual(len(__a	)    ,   __a	)

													__lowerCAmelCase				    =			(self.model_tester.image_size, self.model_tester.image_size)
													__lowerCAmelCase    ,	__lowerCAmelCase				    =			image_size[0], image_size[1]
													for _ in range(4	):
																__lowerCAmelCase				    =			floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1	)
																__lowerCAmelCase				    =			floor(
																    (
																        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1	)
													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]	)    ,   [
													        height * width,
													        self.model_tester.hidden_sizes[0],
													    ]    ,   )

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase				    =			True
													check_hidden_states_output(__a    ,   __a    ,   __a	)

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

													check_hidden_states_output(__a    ,   __a    ,   __a	)

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

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a=False	):
										__lowerCAmelCase				    =			super()._prepare_for_class(__a    ,   __a    ,   return_labels=__a	)

										if return_labels:
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																del inputs_dict["labels"]

										return inputs_dict

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

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

							def 			snake_case				(     self	):
										if not self.model_tester.is_training:
													return

										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()
										__lowerCAmelCase				    =			True

										for model_class in self.all_model_classes:
													# LevitForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(__a	)
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																continue
													__lowerCAmelCase				    =			model_class(__a	)
													model.to(__a	)
													model.train()
													__lowerCAmelCase				    =			self._prepare_for_class(__a    ,   __a    ,   return_labels=__a	)
													__lowerCAmelCase				    =			model(**__a	).loss
													loss.backward()

							def 			snake_case				(     self	):
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
													return

										__lowerCAmelCase				    =			False
										__lowerCAmelCase				    =			True

										for model_class in self.all_model_classes:
													if model_class in get_values(__a	) or not model_class.supports_gradient_checkpointing:
																continue
													# LevitForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																continue
													__lowerCAmelCase				    =			model_class(__a	)
													model.gradient_checkpointing_enable()
													model.to(__a	)
													model.train()
													__lowerCAmelCase				    =			self._prepare_for_class(__a    ,   __a    ,   return_labels=__a	)
													__lowerCAmelCase				    =			model(**__a	).loss
													loss.backward()

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

										__lowerCAmelCase				    =			[
										    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
										    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
										    {"title": "regression", "num_labels": 1, "dtype": torch.float},
										]

										for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(__a	),
													    ]
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																continue

													for problem_type in problem_types:
																with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"	):
																			__lowerCAmelCase				    =			problem_type["title"]
																			__lowerCAmelCase				    =			problem_type["num_labels"]

																			__lowerCAmelCase				    =			model_class(__a	)
																			model.to(__a	)
																			model.train()

																			__lowerCAmelCase				    =			self._prepare_for_class(__a    ,   __a    ,   return_labels=__a	)

																			if problem_type["num_labels"] > 1:
																						__lowerCAmelCase				    =			inputs["labels"].unsqueeze(1	).repeat(1    ,   problem_type["num_labels"]	)

																			__lowerCAmelCase				    =			inputs["labels"].to(problem_type["dtype"]	)

																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																			# they have the same size." which is a symptom something in wrong for the regression problem.
																			# See https://github.com/huggingface/transformers/issues/11780
																			with warnings.catch_warnings(record=__a	) as warning_list:
																						__lowerCAmelCase				    =			model(**__a	).loss
																			for w in warning_list:
																						if "Using a target size that is different to the input size" in str(w.message	):
																									raise ValueError(
																									    f"Something is going wrong in the regression problem: intercepted {w.message}"	)

																			loss.backward()



							@slow
							def 			snake_case				(     self	):
										for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCAmelCase				    =			LevitModel.from_pretrained(__a	)
													self.assertIsNotNone(__a	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
			return image


@require_torch
@require_vision
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							@cached_property
							def 			snake_case				(     self	):
										return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)



							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(
										    __a	)

										__lowerCAmelCase				    =			self.default_image_processor
										__lowerCAmelCase				    =			prepare_img()
										__lowerCAmelCase				    =			image_processor(images=__a    ,   return_tensors="pt"	).to(__a	)

										# forward pass
										with torch.no_grad():
													__lowerCAmelCase				    =			model(**__a	)

										# verify the logits
										__lowerCAmelCase				    =			torch.Size((1, 10_00)	)
										self.assertEqual(outputs.logits.shape    ,   __a	)

										__lowerCAmelCase				    =			torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]	).to(__a	)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,   __a    ,   atol=1e-4	)	)






"""simple docstring"""




from __future__ import annotations


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

       lowercase		,    lowercase    :				Tuple       		=							min(__snake_case  ), max(__snake_case  )

       # Compute the variables
       lowercase    :				Optional[Any]       		=							_max - _min + 1
       lowercase		,    lowercase    :				List[str]       		=							[0] * holes_range, [0] * holes_range

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

       # Makes the array back by replacing the numbers.
       lowercase    :				Union[str, Any]       		=							0
       for i in range(__snake_case  ):
              while holes_repeat[i] > 0:
                     lowercase    :				Tuple       		=							holes[i]
                     index += 1
                     holes_repeat[i] -= 1

    # Returns the sorted array.
       return array


if __name__ == "__main__":
   import doctest

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


