





"""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       (		a__		):






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	int			=       self.config_class(**self.inputs_dict   )
								self.parent.assertTrue(hasattr(lowerCAmelCase__   ,      "hidden_sizes"   )   )
								self.parent.assertTrue(hasattr(lowerCAmelCase__   ,      "num_attention_heads"   )   )


class  __UpperCamelCase       :






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=[128, 256, 384]   ,      lowerCAmelCase__=[4, 6, 8]   ,      lowerCAmelCase__=[2, 3, 4]   ,      lowerCAmelCase__=[16, 16, 16]   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=[2, 2, 2]   ,      lowerCAmelCase__=[2, 2, 2]   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=2   ,      )			->				Union[str, Any]:
								a   :	List[str]			=       parent
								a   :	List[str]			=       batch_size
								a   :	Union[str, Any]			=       image_size
								a   :	str			=       num_channels
								a   :	Optional[Any]			=       kernel_size
								a   :	Optional[int]			=       stride
								a   :	List[Any]			=       padding
								a   :	Tuple			=       hidden_sizes
								a   :	Any			=       num_attention_heads
								a   :	Union[str, Any]			=       depths
								a   :	int			=       key_dim
								a   :	Union[str, Any]			=       drop_path_rate
								a   :	Optional[Any]			=       patch_size
								a   :	int			=       attention_ratio
								a   :	str			=       mlp_ratio
								a   :	Any			=       initializer_range
								a   :	Union[str, Any]			=       [
								    ["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],
								]
								a   :	Dict			=       is_training
								a   :	Tuple			=       use_labels
								a   :	Any			=       num_labels
								a   :	Any			=       initializer_range






							def 					__a     (     self   )			->				Tuple:
								a   :	int			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

								a   :	Tuple			=       None
								if self.use_labels:
									a   :	List[str]			=       ids_tensor([self.batch_size]   ,      self.num_labels   )

								a   :	Union[str, Any]			=       self.get_config()
								return config, pixel_values, labels






							def 					__a     (     self   )			->				Tuple:
								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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								a   :	Union[str, Any]			=       LevitModel(config=lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.eval()
								a   :	List[str]			=       model(lowerCAmelCase__   )
								a   :	int			=       (self.image_size, self.image_size)
								a, a   :	Dict			=       image_size[0], image_size[1]
								for _ in range(4   ):
									a   :	int			=       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1   )
									a   :	Optional[int]			=       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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								a   :	Optional[int]			=       self.num_labels
								a   :	str			=       LevitForImageClassification(lowerCAmelCase__   )
								model.to(lowerCAmelCase__   )
								model.eval()
								a   :	Optional[Any]			=       model(lowerCAmelCase__   ,      labels=lowerCAmelCase__   )
								self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)   )






							def 					__a     (     self   )			->				Tuple:
								a   :	List[str]			=       self.prepare_config_and_inputs()
								a, a, a   :	Optional[Any]			=       config_and_inputs
								a   :	Any			=       {"pixel_values": pixel_values}
								return config, inputs_dict


@require_torch
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Optional[Any]         =(
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							lowerCamelCase     :      Dict         =(
							    {
							        """feature-extraction""": LevitModel,
							        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCamelCase     :      Union[str, Any]         =False
							lowerCamelCase     :      int         =False
							lowerCamelCase     :      Any         =False
							lowerCamelCase     :      int         =False
							lowerCamelCase     :      str         =False






							def 					__a     (     self   )			->				Any:
								a   :	List[Any]			=       LevitModelTester(self   )
								a   :	Optional[int]			=       ConfigTester(self   ,      config_class=lowerCAmelCase__   ,      has_text_modality=lowerCAmelCase__   ,      hidden_size=37   )






							def 					__a     (     self   )			->				Optional[int]:
								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 					__a     (     self   )			->				Tuple:
								return






							@unittest.skip(reason="Levit does not use inputs_embeds"   )
							def 					__a     (     self   )			->				Optional[Any]:
								pass






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






							@unittest.skip(reason="Levit does not output attentions"   )
							def 					__a     (     self   )			->				Any:
								pass






							def 					__a     (     self   )			->				Dict:
								a, a   :	Optional[Any]			=       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a   :	List[Any]			=       model_class(lowerCAmelCase__   )
									a   :	Dict			=       inspect.signature(model.forward   )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									a   :	Optional[int]			=       [*signature.parameters.keys()]

									a   :	Dict			=       ["pixel_values"]
									self.assertListEqual(arg_names[:1]   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				int:
								def check_hidden_states_output(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Optional[Any]			=       model_class(lowerCAmelCase__   )
									model.to(lowerCAmelCase__   )
									model.eval()

									with torch.no_grad():
										a   :	Optional[int]			=       model(**self._prepare_for_class(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

									a   :	Tuple			=       outputs.hidden_states

									a   :	Any			=       len(self.model_tester.depths   ) + 1
									self.assertEqual(len(lowerCAmelCase__   )   ,      lowerCAmelCase__   )

									a   :	List[Any]			=       (self.model_tester.image_size, self.model_tester.image_size)
									a, a   :	Optional[int]			=       image_size[0], image_size[1]
									for _ in range(4   ):
										a   :	Optional[int]			=       floor(
										    (
										        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
										        / self.model_tester.stride
										    )
										    + 1   )
										a   :	str			=       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],
									    ]   ,      )

								a, a   :	str			=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

									check_hidden_states_output(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
							def 					__a     (     self   )			->				List[Any]:
								pass






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=False   )			->				Union[str, Any]:
								a   :	Optional[int]			=       super()._prepare_for_class(lowerCAmelCase__   ,      lowerCAmelCase__   ,      return_labels=lowerCAmelCase__   )

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

								return inputs_dict






							def 					__a     (     self   )			->				Any:
								a   :	Dict			=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__   )






							def 					__a     (     self   )			->				str:
								a   :	Optional[int]			=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__   )






							def 					__a     (     self   )			->				Optional[Any]:
								if not self.model_tester.is_training:
									return

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

								for model_class in self.all_model_classes:
									# LevitForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(lowerCAmelCase__   )
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
										continue
									a   :	Any			=       model_class(lowerCAmelCase__   )
									model.to(lowerCAmelCase__   )
									model.train()
									a   :	int			=       self._prepare_for_class(lowerCAmelCase__   ,      lowerCAmelCase__   ,      return_labels=lowerCAmelCase__   )
									a   :	Union[str, Any]			=       model(**lowerCAmelCase__   ).loss
									loss.backward()






							def 					__a     (     self   )			->				List[str]:
								a, a   :	str			=       self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
									return

								a   :	Any			=       False
								a   :	str			=       True

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






							def 					__a     (     self   )			->				Dict:
								a, a   :	str			=       self.model_tester.prepare_config_and_inputs_for_common()

								a   :	str			=       [
								    {"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(lowerCAmelCase__   ),
									    ]
									    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
									):
										continue

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

											a   :	Union[str, Any]			=       model_class(lowerCAmelCase__   )
											model.to(lowerCAmelCase__   )
											model.train()

											a   :	List[Any]			=       self._prepare_for_class(lowerCAmelCase__   ,      lowerCAmelCase__   ,      return_labels=lowerCAmelCase__   )

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

											a   :	Optional[Any]			=       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=lowerCAmelCase__   ) as warning_list:
												a   :	Optional[int]			=       model(**lowerCAmelCase__   ).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 					__a     (     self   )			->				Dict:
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a   :	List[str]			=       LevitModel.from_pretrained(lowerCAmelCase__   )
									self.assertIsNotNone(lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->List[str]:






	'''simple docstring'''






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







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






							@cached_property
							def 					__a     (     self   )			->				Union[str, Any]:
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )






							@slow
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Optional[int]			=       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(
								    lowerCAmelCase__   )

								a   :	Optional[Any]			=       self.default_image_processor
								a   :	int			=       prepare_img()
								a   :	Any			=       image_processor(images=lowerCAmelCase__   ,      return_tensors="pt"   ).to(lowerCAmelCase__   )

								# forward pass
								with torch.no_grad():
									a   :	Optional[Any]			=       model(**lowerCAmelCase__   )

								# verify the logits
								a   :	Dict			=       torch.Size((1, 1000)   )
								self.assertEqual(outputs.logits.shape   ,      lowerCAmelCase__   )

								a   :	Optional[Any]			=       torch.tensor([1.0_448, -0.3_745, -1.8_317]   ).to(lowerCAmelCase__   )

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









"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 200      )				->int:






	'''simple docstring'''






	a   :	Dict			=       [1, 2, 5, 10, 20, 50, 100, 200]
	a   :	Optional[Any]			=       [0] * (pence + 1)
	a   :	List[Any]			=       1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(_lowercase					, pence + 1					, 1      ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682


