





"""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[int]:
								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 ,   )      ->					int:
								a :						Dict      =							parent
								a :						str      =							batch_size
								a :						Dict      =							image_size
								a :						Dict      =							num_channels
								a :						Optional[Any]      =							kernel_size
								a :						Optional[Any]      =							stride
								a :						str      =							padding
								a :						Optional[int]      =							hidden_sizes
								a :						List[Any]      =							num_attention_heads
								a :						Optional[Any]      =							depths
								a :						str      =							key_dim
								a :						Optional[Any]      =							drop_path_rate
								a :						str      =							patch_size
								a :						Union[str, Any]      =							attention_ratio
								a :						Optional[int]      =							mlp_ratio
								a :						int      =							initializer_range
								a :						Dict      =							[
								    ["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 :						int      =							is_training
								a :						Optional[Any]      =							use_labels
								a :						str      =							num_labels
								a :						Optional[int]      =							initializer_range






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

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

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






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






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


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

							lowerCamelCase  :   List[str]    =False
							lowerCamelCase  :   Tuple    =False
							lowerCamelCase  :   Dict    =False
							lowerCamelCase  :   Optional[Any]    =False
							lowerCamelCase  :   Dict    =False






							def        __a  (  self )      ->					Any:
								a :						int      =							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 )      ->					Dict:
								return






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






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

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

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






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

									with torch.no_grad():
										a :						Union[str, Any]      =							model(**self._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ) )

									a :						Optional[Any]      =							outputs.hidden_states

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

									a :						str      =							(self.model_tester.image_size, self.model_tester.image_size)
									a,			a :						str      =							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 :						Optional[Any]      =							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 :						Tuple      =							self.model_tester.prepare_config_and_inputs_for_common()

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

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									a :						Optional[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 )      ->					str:
								pass






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__=False )      ->					Union[str, Any]:
								a :						Tuple      =							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 )      ->					Tuple:
								a :						Optional[int]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Dict      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )






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

								a,			a :						str      =							self.model_tester.prepare_config_and_inputs_for_common()
								a :						List[str]      =							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 :						Tuple      =							model_class(lowerCAmelCase__ )
									model.to(lowerCAmelCase__ )
									model.train()
									a :						Optional[Any]      =							self._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ,   return_labels=lowerCAmelCase__ )
									a :						Dict      =							model(**lowerCAmelCase__ ).loss
									loss.backward()






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

								a :						Tuple      =							False
								a :						Optional[int]      =							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 :						Optional[int]      =							model_class(lowerCAmelCase__ )
									model.gradient_checkpointing_enable()
									model.to(lowerCAmelCase__ )
									model.train()
									a :						Union[str, Any]      =							self._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ,   return_labels=lowerCAmelCase__ )
									a :						int      =							model(**lowerCAmelCase__ ).loss
									loss.backward()






							def        __a  (  self )      ->					Optional[Any]:
								a,			a :						Tuple      =							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 :						Union[str, Any]      =							problem_type["title"]
											a :						str      =							problem_type["num_labels"]

											a :						Optional[int]      =							model_class(lowerCAmelCase__ )
											model.to(lowerCAmelCase__ )
											model.train()

											a :						int      =							self._prepare_for_class(lowerCAmelCase__ ,   lowerCAmelCase__ ,   return_labels=lowerCAmelCase__ )

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

											a :						int      =							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 :						List[Any]      =							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 )      ->					Union[str, Any]:
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Optional[Any]      =							LevitModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






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







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






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






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

								a :						str      =							self.default_image_processor
								a :						List[str]      =							prepare_img()
								a :						Dict      =							image_processor(images=lowerCAmelCase__ ,   return_tensors="pt" ).to(lowerCAmelCase__ )

								# forward pass
								with torch.no_grad():
									a :						Tuple      =							model(**lowerCAmelCase__ )

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

								a :						Dict      =							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		:       list[int]   )					->float:






	'''simple docstring'''






	if not nums:  # Makes sure that the list is not empty
		raise ValueError("List is empty"   )

	a :						Dict      =							sum(_lowercase   ) / len(_lowercase   )  # Calculate the average
	return sum(abs(x - average   ) for x in nums   ) / len(_lowercase   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


