
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


lowerCamelCase__     :		List[str]        = logging.get_logger(__name__)  # pylint: disable=invalid-name



class    _snake_case (						__a				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__()

   if safety_checker is None:
     logger.warning(
         f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
         """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
         """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
         """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
         """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
         """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""")

   self.register_modules(
       speech_model=__UpperCamelCase       ,	speech_processor=__UpperCamelCase       ,	vae=__UpperCamelCase       ,	text_encoder=__UpperCamelCase       ,	tokenizer=__UpperCamelCase       ,	unet=__UpperCamelCase       ,	scheduler=__UpperCamelCase       ,	feature_extractor=__UpperCamelCase       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_ = "auto"):


   '''simple docstring'''




   if slice_size == "auto":
     lowercase__	: Any     		=   self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(__UpperCamelCase)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.enable_attention_slicing(__UpperCamelCase)



 @torch.no_grad()
 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=1_60_00       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 50       ,	SCREAMING_SNAKE_CASE_ = 7.5       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	SCREAMING_SNAKE_CASE_ = 0.0       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "pil"       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   self.speech_processor.feature_extractor(
       __UpperCamelCase       ,	return_tensors="""pt"""       ,	sampling_rate=__UpperCamelCase).input_features.to(self.device)
   lowercase__	: List[Any]     		=   self.speech_model.generate(__UpperCamelCase       ,	max_length=48_00_00)

   lowercase__	: List[Any]     		=   self.speech_processor.tokenizer.batch_decode(__UpperCamelCase       ,	skip_special_tokens=__UpperCamelCase       ,	normalize=__UpperCamelCase)[
       0
   ]

   if isinstance(__UpperCamelCase       ,	__UpperCamelCase):
     lowercase__	: List[Any]     		=   1
   elif isinstance(__UpperCamelCase       ,	__UpperCamelCase):
     lowercase__	: Optional[int]     		=   len(__UpperCamelCase)
   else:
     raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase)}')

   if height % 8 != 0 or width % 8 != 0:
     raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

   if (callback_steps is None) or (
       callback_steps is not None and (not isinstance(__UpperCamelCase       ,	__UpperCamelCase) or callback_steps <= 0)
   ):
     raise ValueError(
         f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
         f' {type(__UpperCamelCase)}.')

   # get prompt text embeddings
   lowercase__	: List[Any]     		=   self.tokenizer(
       __UpperCamelCase       ,	padding="""max_length"""       ,	max_length=self.tokenizer.model_max_length       ,	return_tensors="""pt"""       ,	)
   lowercase__	: Dict     		=   text_inputs.input_ids

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     lowercase__	: List[str]     		=   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
     logger.warning(
         """The following part of your input was truncated because CLIP can only handle sequences up to"""
         f' {self.tokenizer.model_max_length} tokens: {removed_text}')
     lowercase__	: Optional[int]     		=   text_input_ids[:, : self.tokenizer.model_max_length]
   lowercase__	: Tuple     		=   self.text_encoder(text_input_ids.to(self.device))[0]

   # duplicate text embeddings for each generation per prompt, using mps friendly method
   lowercase__	: Any     		=   text_embeddings.shape
   lowercase__	: Union[str, Any]     		=   text_embeddings.repeat(1       ,	__UpperCamelCase       ,	1)
   lowercase__	: Optional[Any]     		=   text_embeddings.view(bs_embed * num_images_per_prompt       ,	__UpperCamelCase       ,	-1)

   # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
   # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
   # corresponds to doing no classifier free guidance.
   lowercase__	: List[Any]     		=   guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     lowercase__	: List[str]
     if negative_prompt is None:
       lowercase__	: Optional[Any]     		=   [""""""] * batch_size
     elif type(__UpperCamelCase) is not type(__UpperCamelCase):
       raise TypeError(
           f'`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCamelCase)} !='
           f' {type(__UpperCamelCase)}.')
     elif isinstance(__UpperCamelCase       ,	__UpperCamelCase):
       lowercase__	: Any     		=   [negative_prompt]
     elif batch_size != len(__UpperCamelCase):
       raise ValueError(
           f'`negative_prompt`: {negative_prompt} has batch size {len(__UpperCamelCase)}, but `prompt`:'
           f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
           """ the batch size of `prompt`.""")
     else:
       lowercase__	: Optional[int]     		=   negative_prompt

     lowercase__	: List[Any]     		=   text_input_ids.shape[-1]
     lowercase__	: Any     		=   self.tokenizer(
         __UpperCamelCase       ,	padding="""max_length"""       ,	max_length=__UpperCamelCase       ,	truncation=__UpperCamelCase       ,	return_tensors="""pt"""       ,	)
     lowercase__	: Union[str, Any]     		=   self.text_encoder(uncond_input.input_ids.to(self.device))[0]

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     lowercase__	: List[str]     		=   uncond_embeddings.shape[1]
     lowercase__	: List[str]     		=   uncond_embeddings.repeat(1       ,	__UpperCamelCase       ,	1)
     lowercase__	: Tuple     		=   uncond_embeddings.view(batch_size * num_images_per_prompt       ,	__UpperCamelCase       ,	-1)

     # For classifier free guidance, we need to do two forward passes.
     # Here we concatenate the unconditional and text embeddings into a single batch
     # to avoid doing two forward passes
     lowercase__	: str     		=   torch.cat([uncond_embeddings, text_embeddings])

   # get the initial random noise unless the user supplied it

   # Unlike in other pipelines, latents need to be generated in the target device
   # for 1-to-1 results reproducibility with the CompVis implementation.
   # However this currently doesn't work in `mps`.
   lowercase__	: Union[str, Any]     		=   (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
   lowercase__	: Optional[int]     		=   text_embeddings.dtype
   if latents is None:
     if self.device.type == "mps":
       # randn does not exist on mps
       lowercase__	: str     		=   torch.randn(__UpperCamelCase       ,	generator=__UpperCamelCase       ,	device="""cpu"""       ,	dtype=__UpperCamelCase).to(
           self.device)
     else:
       lowercase__	: Optional[Any]     		=   torch.randn(__UpperCamelCase       ,	generator=__UpperCamelCase       ,	device=self.device       ,	dtype=__UpperCamelCase)
   else:
     if latents.shape != latents_shape:
       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
     lowercase__	: Optional[int]     		=   latents.to(self.device)

   # set timesteps
   self.scheduler.set_timesteps(__UpperCamelCase)

   # Some schedulers like PNDM have timesteps as arrays
   # It's more optimized to move all timesteps to correct device beforehand
   lowercase__	: Any     		=   self.scheduler.timesteps.to(self.device)

   # scale the initial noise by the standard deviation required by the scheduler
   lowercase__	: List[str]     		=   latents * self.scheduler.init_noise_sigma

   # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
   # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
   # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
   # and should be between [0, 1]
   lowercase__	: Optional[Any]     		=   """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
   lowercase__	: Union[str, Any]     		=   {}
   if accepts_eta:
     lowercase__	: Any     		=   eta

   for i, t in enumerate(self.progress_bar(__UpperCamelCase)):
     # expand the latents if we are doing classifier free guidance
     lowercase__	: Optional[int]     		=   torch.cat([latents] * 2) if do_classifier_free_guidance else latents
     lowercase__	: int     		=   self.scheduler.scale_model_input(__UpperCamelCase       ,	__UpperCamelCase)

     # predict the noise residual
     lowercase__	: Tuple     		=   self.unet(__UpperCamelCase       ,	__UpperCamelCase       ,	encoder_hidden_states=__UpperCamelCase).sample

     # perform guidance
     if do_classifier_free_guidance:
       lowercase__	: Union[str, Any]     		=   noise_pred.chunk(2)
       lowercase__	: List[str]     		=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     # compute the previous noisy sample x_t -> x_t-1
     lowercase__	: List[Any]     		=   self.scheduler.step(__UpperCamelCase       ,	__UpperCamelCase       ,	__UpperCamelCase       ,	**__UpperCamelCase).prev_sample

     # call the callback, if provided
     if callback is not None and i % callback_steps == 0:
       callback(__UpperCamelCase       ,	__UpperCamelCase       ,	__UpperCamelCase)

   lowercase__	: List[str]     		=   1 / 0.1_8_2_1_5 * latents
   lowercase__	: List[Any]     		=   self.vae.decode(__UpperCamelCase).sample

   lowercase__	: Dict     		=   (image / 2 + 0.5).clamp(0       ,	1)

   # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
   lowercase__	: Dict     		=   image.cpu().permute(0       ,	2       ,	3       ,	1).float().numpy()

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

   if not return_dict:
     return image

   return StableDiffusionPipelineOutput(images=__UpperCamelCase       ,	nsfw_content_detected=__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						)						)






