'''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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def  snake_case__				(						self					:						List[str]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						self.config_class(**self.inputs_dict					)
										self.parent.assertTrue(hasattr(lowercase__       ,		"hidden_sizes"					)					)
										self.parent.assertTrue(hasattr(lowercase__       ,		"num_attention_heads"					)					)





class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						Any       ,		lowercase__					:						List[Any]=13       ,		lowercase__					:						Tuple=64       ,		lowercase__					:						Dict=3       ,		lowercase__					:						int=3       ,		lowercase__					:						int=2       ,		lowercase__					:						Tuple=1       ,		lowercase__					:						Optional[int]=16       ,		lowercase__					:						str=[128, 256, 384]       ,		lowercase__					:						List[Any]=[4, 6, 8]       ,		lowercase__					:						int=[2, 3, 4]       ,		lowercase__					:						str=[16, 16, 16]       ,		lowercase__					:						List[Any]=0       ,		lowercase__					:						Tuple=[2, 2, 2]       ,		lowercase__					:						Dict=[2, 2, 2]       ,		lowercase__					:						List[str]=0.0_2       ,		lowercase__					:						str=True       ,		lowercase__					:						Dict=True       ,		lowercase__					:						Dict=2       ,		)							->Dict:


										'''simple docstring'''




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





				def  snake_case__				(						self					:						Optional[int]					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

										_UpperCamelCase			:			Optional[int]   			=						self.get_config()
										return config, pixel_values, labels





				def  snake_case__				(						self					:						Optional[int]					)							->Optional[Any]:


										'''simple docstring'''




										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					:						List[str]       ,		lowercase__					:						Dict       ,		lowercase__					:						Any       ,		lowercase__					:						int					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						LevitModel(config=lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Optional[Any]   			=						model(lowercase__					)
										_UpperCamelCase			:			Tuple   			=						(self.image_size, self.image_size)
										_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						image_size[0], image_size[1]
										for _ in range(4					):
																_UpperCamelCase			:			List[Any]   			=						floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1					)
																_UpperCamelCase			:			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  snake_case__				(						self					:						List[str]       ,		lowercase__					:						List[str]       ,		lowercase__					:						int       ,		lowercase__					:						Tuple					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						self.num_labels
										_UpperCamelCase			:			List[str]   			=						LevitForImageClassification(lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Any   			=						model(lowercase__       ,		labels=lowercase__					)
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)







				def  snake_case__				(						self					:						int					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						config_and_inputs
										_UpperCamelCase			:			Tuple   			=						{"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				UpperCAmelCase__							=							(
				    {
				        '''feature-extraction''': LevitModel,
				        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						Dict					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						LevitModelTester(self					)
										_UpperCamelCase			:			List[str]   			=						ConfigTester(self       ,		config_class=lowercase__       ,		has_text_modality=lowercase__       ,		hidden_size=37					)





				def  snake_case__				(						self					:						Any					)							->List[Any]:


										'''simple docstring'''




										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					:						Any					)							->Tuple:


										'''simple docstring'''




										return





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


										'''simple docstring'''




										pass





				@unittest.skip(reason="Levit does not support input and output embeddings"					)
				def  snake_case__				(						self					:						List[Any]					)							->Optional[int]:


										'''simple docstring'''




										pass





				@unittest.skip(reason="Levit does not output attentions"					)
				def  snake_case__				(						self					:						List[Any]					)							->Union[str, Any]:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						List[Any]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCamelCase			:			Union[str, Any]   			=						["pixel_values"]
																self.assertListEqual(arg_names[:1]       ,		lowercase__					)





				def  snake_case__				(						self					:						int					)							->Any:


										'''simple docstring'''




										def check_hidden_states_output(lowercase__					:						Optional[Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Optional[Any]					):
																_UpperCamelCase			:			List[str]   			=						model_class(lowercase__					)
																model.to(lowercase__					)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase			:			str   			=						model(**self._prepare_for_class(lowercase__       ,		lowercase__					)					)

																_UpperCamelCase			:			List[str]   			=						outputs.hidden_states

																_UpperCamelCase			:			Union[str, Any]   			=						len(self.model_tester.depths					) + 1
																self.assertEqual(len(lowercase__					)       ,		lowercase__					)

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

										_UpperCamelCase			,       _UpperCamelCase			:			int   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Dict   			=						True
																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)

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

																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)





				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
				def  snake_case__				(						self					:						Any					)							->Tuple:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Optional[Any]=False					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						super()._prepare_for_class(lowercase__       ,		lowercase__       ,		return_labels=lowercase__					)

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

										return inputs_dict





				def  snake_case__				(						self					:						Any					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__					)





				def  snake_case__				(						self					:						Any					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__					)





				def  snake_case__				(						self					:						Optional[Any]					)							->Any:


										'''simple docstring'''




										if not self.model_tester.is_training:
																return

										_UpperCamelCase			,       _UpperCamelCase			:			int   			=						self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCamelCase			:			List[Any]   			=						True

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





				def  snake_case__				(						self					:						List[Any]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			str   			=						self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
																return

										_UpperCamelCase			:			List[str]   			=						False
										_UpperCamelCase			:			Union[str, Any]   			=						True

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





				def  snake_case__				(						self					:						Optional[Any]					)							->Tuple:


										'''simple docstring'''




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

										_UpperCamelCase			:			Dict   			=						[
										    {"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(lowercase__					),
																    ]
																    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																):
																						continue

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

																												_UpperCamelCase			:			int   			=						model_class(lowercase__					)
																												model.to(lowercase__					)
																												model.train()

																												_UpperCamelCase			:			Optional[Any]   			=						self._prepare_for_class(lowercase__       ,		lowercase__       ,		return_labels=lowercase__					)

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

																												_UpperCamelCase			:			List[str]   			=						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=lowercase__					) as warning_list:
																																		_UpperCamelCase			:			Optional[int]   			=						model(**lowercase__					).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					:						List[str]					)							->int:


										'''simple docstring'''




										for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase			:			Any   			=						LevitModel.from_pretrained(lowercase__					)
																self.assertIsNotNone(lowercase__					)





def 						__A							(     )     ->			List[str]:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[Any]   			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
						return image






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







				'''simple docstring'''





				@cached_property
				def  snake_case__				(						self					:						List[str]					)							->Any:


										'''simple docstring'''




										return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)







				@slow
				def  snake_case__				(						self					:						int					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(
										    lowercase__					)

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

										# forward pass
										with torch.no_grad():
																_UpperCamelCase			:			str   			=						model(**lowercase__					)

										# verify the logits
										_UpperCamelCase			:			Optional[int]   			=						torch.Size((1, 1_000)					)
										self.assertEqual(outputs.logits.shape       ,		lowercase__					)

										_UpperCamelCase			:			List[str]   			=						torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]					).to(lowercase__					)

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



'''simple docstring'''





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase=False					)     ->			Optional[int]:




						'''simple docstring'''

						if isinstance(UpperCAmelCase     ,UpperCAmelCase					) and isinstance(UpperCAmelCase     ,UpperCAmelCase					):
												_UpperCamelCase			:			Any   			=						len(set_a.intersection(UpperCAmelCase					)					)

												if alternative_union:
																		_UpperCamelCase			:			List[str]   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
												else:
																		_UpperCamelCase			:			List[str]   			=						len(set_a.union(UpperCAmelCase					)					)

												return intersection / union

						if isinstance(UpperCAmelCase     ,(list, tuple)					) and isinstance(UpperCAmelCase     ,(list, tuple)					):
												_UpperCamelCase			:			List[Any]   			=						[element for element in set_a if element in set_b]

												if alternative_union:
																		_UpperCamelCase			:			str   			=						len(UpperCAmelCase					) + len(UpperCAmelCase					)
																		return len(UpperCAmelCase					) / union
												else:
																		_UpperCamelCase			:			int   			=						set_a + [element for element in set_b if element not in set_a]
																		return len(UpperCAmelCase					) / len(UpperCAmelCase					)

												return len(UpperCAmelCase					) / len(UpperCAmelCase					)
						return None


if __name__ == "__main__":
			lowerCAmelCase_	:		List[str]			=     {"""a""", """b""", """c""", """d""", """e"""}
			lowerCAmelCase_	:		Dict			=     {"""c""", """d""", """e""", """f""", """h""", """i"""}
			print(jaccard_similarity(set_a, set_b))


