from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	8






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				List[str]=BITS			):
						UpperCamelCase_		:							str  =		x.device

						UpperCamelCase_		:							Optional[Any]  =		(x * 255).int().clamp(0		, 255			)

						UpperCamelCase_		:							int  =		2 ** torch.arange(bits - 1		, -1		, -1		, device=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Tuple  =		rearrange(_SCREAMING_SNAKE_CASE		, """d -> d 1 1"""			)
						UpperCamelCase_		:							Tuple  =		rearrange(_SCREAMING_SNAKE_CASE		, """b c h w -> b c 1 h w"""			)

						UpperCamelCase_		:							Union[str, Any]  =		((x & mask) != 0).float()
						UpperCamelCase_		:							str  =		rearrange(_SCREAMING_SNAKE_CASE		, """b c d h w -> b (c d) h w"""			)
						UpperCamelCase_		:							Optional[Any]  =		bits * 2 - 1
						return bits






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Dict=BITS			):
						UpperCamelCase_		:							str  =		x.device

						UpperCamelCase_		:							Optional[int]  =		(x > 0).int()
						UpperCamelCase_		:							Dict  =		2 ** torch.arange(bits - 1		, -1		, -1		, device=_SCREAMING_SNAKE_CASE		, dtype=torch.intaa			)

						UpperCamelCase_		:							List[str]  =		rearrange(_SCREAMING_SNAKE_CASE		, """d -> d 1 1"""			)
						UpperCamelCase_		:							List[str]  =		rearrange(_SCREAMING_SNAKE_CASE		, """b (c d) h w -> b c d h w"""		, d=8			)
						UpperCamelCase_		:							str  =		reduce(x * mask		, """b c d h w -> b c h w"""		, """sum"""			)
						return (dec / 255).clamp(0.0		, 1.0			)






def     lowerCAmelCase_			(   self     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				torch.FloatTensor		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				torch.FloatTensor		, _SCREAMING_SNAKE_CASE     :				float = 0.0		, _SCREAMING_SNAKE_CASE     :				bool = True		, _SCREAMING_SNAKE_CASE     :				List[Any]=None		, _SCREAMING_SNAKE_CASE     :				bool = True		, ):
						if self.num_inference_steps is None:
												raise ValueError(
												    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""			)

						# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
						# Ideally, read DDIM paper in-detail understanding

						# Notation (<variable name> -> <name in paper>
						# - pred_noise_t -> e_theta(x_t, t)
						# - pred_original_sample -> f_theta(x_t, t) or x_0
						# - std_dev_t -> sigma_t
						# - eta -> η
						# - pred_sample_direction -> "direction pointing to x_t"
						# - pred_prev_sample -> "x_t-1"

						# 1. get previous step value (=t-1)
						UpperCamelCase_		:							str  =		timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						UpperCamelCase_		:							List[str]  =		self.alphas_cumprod[timestep]
						UpperCamelCase_		:							Optional[Any]  =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						UpperCamelCase_		:							Optional[int]  =		1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						UpperCamelCase_		:							str  =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						UpperCamelCase_		:							int  =		self.bit_scale
						if self.config.clip_sample:
												UpperCamelCase_		:							str  =		torch.clamp(_SCREAMING_SNAKE_CASE		, -scale		, _SCREAMING_SNAKE_CASE			)

						# 5. compute variance: "sigma_t(η)" -> see formula (16)
						# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
						UpperCamelCase_		:							List[str]  =		self._get_variance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Union[str, Any]  =		eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												UpperCamelCase_		:							Dict  =		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

						# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						UpperCamelCase_		:							List[Any]  =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						UpperCamelCase_		:							int  =		alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if eta > 0:
												# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
												UpperCamelCase_		:							Optional[Any]  =		model_output.device if torch.is_tensor(_SCREAMING_SNAKE_CASE			) else """cpu"""
												UpperCamelCase_		:							Optional[Any]  =		torch.randn(model_output.shape		, dtype=model_output.dtype		, generator=_SCREAMING_SNAKE_CASE			).to(_SCREAMING_SNAKE_CASE			)
												UpperCamelCase_		:							List[str]  =		self._get_variance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			) ** 0.5 * eta * noise

												UpperCamelCase_		:							Optional[int]  =		prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		, pred_original_sample=_SCREAMING_SNAKE_CASE			)






def     lowerCAmelCase_			(   self     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				torch.FloatTensor		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				torch.FloatTensor		, _SCREAMING_SNAKE_CASE     :				int="epsilon"		, _SCREAMING_SNAKE_CASE     :				int=None		, _SCREAMING_SNAKE_CASE     :				bool = True		, ):
						UpperCamelCase_		:							Dict  =		timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												UpperCamelCase_,UpperCamelCase_		:							Any  =		torch.split(_SCREAMING_SNAKE_CASE		, sample.shape[1]		, dim=1			)
						else:
												UpperCamelCase_		:							str  =		None

						# 1. compute alphas, betas
						UpperCamelCase_		:							Dict  =		self.alphas_cumprod[t]
						UpperCamelCase_		:							Optional[int]  =		self.alphas_cumprod[t - 1] if t > 0 else self.one
						UpperCamelCase_		:							Any  =		1 - alpha_prod_t
						UpperCamelCase_		:							Union[str, Any]  =		1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if prediction_type == "epsilon":
												UpperCamelCase_		:							Optional[Any]  =		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												UpperCamelCase_		:							int  =		model_output
						else:
												raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''			)

						# 3. Clip "predicted x_0"
						UpperCamelCase_		:							List[str]  =		self.bit_scale
						if self.config.clip_sample:
												UpperCamelCase_		:							Any  =		torch.clamp(_SCREAMING_SNAKE_CASE		, -scale		, _SCREAMING_SNAKE_CASE			)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						UpperCamelCase_		:							int  =		(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						UpperCamelCase_		:							str  =		self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						UpperCamelCase_		:							Optional[int]  =		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						UpperCamelCase_		:							Any  =		0
						if t > 0:
												UpperCamelCase_		:							List[str]  =		torch.randn(
												    model_output.size()		, dtype=model_output.dtype		, layout=model_output.layout		, generator=_SCREAMING_SNAKE_CASE			).to(model_output.device			)
												UpperCamelCase_		:							Optional[int]  =		(self._get_variance(_SCREAMING_SNAKE_CASE		, predicted_variance=_SCREAMING_SNAKE_CASE			) ** 0.5) * noise

						UpperCamelCase_		:							List[str]  =		pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		, pred_original_sample=_SCREAMING_SNAKE_CASE			)







class        UpperCamelCase						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase = 1.0			,     )				->				List[Any]:
												super().__init__()
												UpperCamelCase_		:							List[str]  =		bit_scale
												UpperCamelCase_		:							Optional[int]  =		(
												    ddim_bit_scheduler_step if isinstance(__UpperCamelCase			,     __UpperCamelCase						) else ddpm_bit_scheduler_step
												)

												self.register_modules(unet=__UpperCamelCase			,     scheduler=__UpperCamelCase						)





						@torch.no_grad()
						def __call__(self			,     __UpperCamelCase = 256			,     __UpperCamelCase = 256			,     __UpperCamelCase = 50			,     __UpperCamelCase = None			,     __UpperCamelCase = 1			,     __UpperCamelCase = "pil"			,     __UpperCamelCase = True			,     **__UpperCamelCase			,     )				->				Union[Tuple, ImagePipelineOutput]:
												UpperCamelCase_		:							str  =		torch.randn(
												    (batch_size, self.unet.config.in_channels, height, width)			,     generator=__UpperCamelCase			,     )
												UpperCamelCase_		:							List[str]  =		decimal_to_bits(__UpperCamelCase						) * self.bit_scale
												UpperCamelCase_		:							Tuple  =		latents.to(self.device						)

												self.scheduler.set_timesteps(__UpperCamelCase						)

												for t in self.progress_bar(self.scheduler.timesteps						):
																		# predict the noise residual
																		UpperCamelCase_		:							Tuple  =		self.unet(__UpperCamelCase			,     __UpperCamelCase						).sample

																		# compute the previous noisy sample x_t -> x_t-1
																		UpperCamelCase_		:							Dict  =		self.scheduler.step(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						).prev_sample

												UpperCamelCase_		:							Optional[int]  =		bits_to_decimal(__UpperCamelCase						)

												if output_type == "pil":
																		UpperCamelCase_		:							List[Any]  =		self.numpy_to_pil(__UpperCamelCase						)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase						)







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						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.config_class(**self.inputs_dict						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """hidden_sizes"""						)						)
												self.parent.assertTrue(hasattr(__UpperCamelCase			,     """num_attention_heads"""						)						)


class        UpperCamelCase						:





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





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							Optional[Any]  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

												UpperCamelCase_		:							Any  =		self.get_config()
												return config, pixel_values, labels





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





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		self.prepare_config_and_inputs()
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		config_and_inputs
												UpperCamelCase_		:							Tuple  =		{"""pixel_values""": pixel_values}
												return config, inputs_dict


@require_torch
class        UpperCamelCase						(				__a	,	__a	,	unittest.TestCase				):
						a__		:Any      =			(
						    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
						    if is_torch_available()
						    else ()
						)
						a__		:str      =			(
						    {
						        '''feature-extraction''': LevitModel,
						        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
						    }
						    if is_torch_available()
						    else {}
						)

						a__		:Optional[Any]      =			False
						a__		:Optional[int]      =			False
						a__		:Tuple      =			False
						a__		:List[str]      =			False
						a__		:Dict      =			False





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		LevitModelTester(self						)
												UpperCamelCase_		:							Union[str, Any]  =		ConfigTester(self			,     config_class=__UpperCamelCase			,     has_text_modality=__UpperCamelCase			,     hidden_size=37						)





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





						@unittest.skip(reason="""Levit does not use inputs_embeds"""						)
						def        A_							(self						)				->				int:
												pass





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





						@unittest.skip(reason="""Levit does not output attentions"""						)
						def        A_							(self						)				->				List[str]:
												pass





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Any  =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCamelCase_		:							Tuple  =		["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]			,     __UpperCamelCase						)





						def        A_							(self						)				->				Optional[Any]:
												def check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							List[str]  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.eval()

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

																		UpperCamelCase_		:							Optional[Any]  =		outputs.hidden_states

																		UpperCamelCase_		:							Optional[int]  =		len(self.model_tester.depths						) + 1
																		self.assertEqual(len(__UpperCamelCase						)			,     __UpperCamelCase						)

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

												for model_class in self.all_model_classes:
																		UpperCamelCase_		:							Union[str, Any]  =		True
																		check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)

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

																		check_hidden_states_output(__UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase						)





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





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=False						)				->				Tuple:
												UpperCamelCase_		:							List[str]  =		super()._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)

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

												return inputs_dict





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Tuple  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							int  =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)





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

												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		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(__UpperCamelCase						)
																		    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																		):
																								continue
																		UpperCamelCase_		:							int  =		model_class(__UpperCamelCase						)
																		model.to(__UpperCamelCase						)
																		model.train()
																		UpperCamelCase_		:							List[str]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)
																		UpperCamelCase_		:							int  =		model(**__UpperCamelCase						).loss
																		loss.backward()





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																		return

												UpperCamelCase_		:							Tuple  =		False
												UpperCamelCase_		:							str  =		True

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





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_,UpperCamelCase_		:							Optional[Any]  =		self.model_tester.prepare_config_and_inputs_for_common()

												UpperCamelCase_		:							Any  =		[
												    {"""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(__UpperCamelCase						),
																		    ]
																		    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_		:							Any  =		problem_type["""title"""]
																														UpperCamelCase_		:							Dict  =		problem_type["""num_labels"""]

																														UpperCamelCase_		:							Dict  =		model_class(__UpperCamelCase						)
																														model.to(__UpperCamelCase						)
																														model.train()

																														UpperCamelCase_		:							Optional[int]  =		self._prepare_for_class(__UpperCamelCase			,     __UpperCamelCase			,     return_labels=__UpperCamelCase						)

																														if problem_type["num_labels"] > 1:
																																				UpperCamelCase_		:							Union[str, Any]  =		inputs["""labels"""].unsqueeze(1						).repeat(1			,     problem_type["""num_labels"""]						)

																														UpperCamelCase_		:							Tuple  =		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=__UpperCamelCase						) as warning_list:
																																				UpperCamelCase_		:							str  =		model(**__UpperCamelCase						).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]:
																		UpperCamelCase_		:							Any  =		LevitModel.from_pretrained(__UpperCamelCase						)
																		self.assertIsNotNone(__UpperCamelCase						)






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							str  =		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						)				->				str:
												UpperCamelCase_		:							Tuple  =		LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
												    __UpperCamelCase						)

												UpperCamelCase_		:							Optional[Any]  =		self.default_image_processor
												UpperCamelCase_		:							List[str]  =		prepare_img()
												UpperCamelCase_		:							Optional[int]  =		image_processor(images=__UpperCamelCase			,     return_tensors="""pt"""						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																		UpperCamelCase_		:							Union[str, Any]  =		model(**__UpperCamelCase						)

												# verify the logits
												UpperCamelCase_		:							List[str]  =		torch.Size((1, 1_000)						)
												self.assertEqual(outputs.logits.shape			,     __UpperCamelCase						)

												UpperCamelCase_		:							Any  =		torch.tensor([1.0_448, -0.3_745, -1.8_317]						).to(__UpperCamelCase						)

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






