





'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class        __a   (lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							,		unittest.TestCase  ):
							__a       : Dict          =							StableDiffusionImageVariationPipeline
							__a       : Optional[int]          =							IMAGE_VARIATION_PARAMS
							__a       : str          =							IMAGE_VARIATION_BATCH_PARAMS
							__a       : str          =							frozenset([]  )
							# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							__a       : Dict          =							frozenset([]  )

							def      UpperCAmelCase__						(				self				:  str )       ->							str:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   int      =	UNetaDConditionModel(
													    block_out_channels=(32, 64)							,  layers_per_block=2							,  sample_size=32							,  in_channels=4							,  out_channels=4							,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')							,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')							,  cross_attention_dim=32							,  )
													UpperCAmelCase_      :   List[Any]      =	PNDMScheduler(skip_prk_steps=lowercase__ )
													torch.manual_seed(0 )
													UpperCAmelCase_      :   Tuple      =	AutoencoderKL(
													    block_out_channels=[32, 64]							,  in_channels=3							,  out_channels=3							,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']							,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,  latent_channels=4							,  )
													torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[int]      =	CLIPVisionConfig(
													    hidden_size=32							,  projection_dim=32							,  intermediate_size=37							,  layer_norm_eps=1E-05							,  num_attention_heads=4							,  num_hidden_layers=5							,  image_size=32							,  patch_size=4							,  )
													UpperCAmelCase_      :   str      =	CLIPVisionModelWithProjection(lowercase__ )
													UpperCAmelCase_      :   Optional[Any]      =	CLIPImageProcessor(crop_size=32							,  size=32 )

													UpperCAmelCase_      :   str      =	{
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''image_encoder''': image_encoder,
													    '''feature_extractor''': feature_extractor,
													    '''safety_checker''': None,
													}
													return components

							def      UpperCAmelCase__						(				self				:  Union[str, Any]							,  __magic_name__				:  Tuple							,  __magic_name__				:  str=0 )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	floats_tensor((1, 3, 32, 32)							,  rng=random.Random(lowercase__ ) )
													UpperCAmelCase_      :   List[Any]      =	image.cpu().permute(0							,  2							,  3							,  1 )[0]
													UpperCAmelCase_      :   Tuple      =	Image.fromarray(np.uinta(lowercase__ ) ).convert('''RGB''' ).resize((32, 32) )
													if str(lowercase__ ).startswith('''mps''' ):
																			UpperCAmelCase_      :   Optional[int]      =	torch.manual_seed(lowercase__ )
													else:
																			UpperCAmelCase_      :   Dict      =	torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
													UpperCAmelCase_      :   Tuple      =	{
													    '''image''': image,
													    '''generator''': generator,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 6.0,
													    '''output_type''': '''numpy''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  int )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   List[Any]      =	self.get_dummy_components()
													UpperCAmelCase_      :   Optional[Any]      =	StableDiffusionImageVariationPipeline(**lowercase__ )
													UpperCAmelCase_      :   List[Any]      =	sd_pipe.to(lowercase__ )
													sd_pipe.set_progress_bar_config(disable=lowercase__ )

													UpperCAmelCase_      :   Tuple      =	self.get_dummy_inputs(lowercase__ )
													UpperCAmelCase_      :   Tuple      =	sd_pipe(**lowercase__ ).images
													UpperCAmelCase_      :   List[Any]      =	image[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)
													UpperCAmelCase_      :   Optional[Any]      =	np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1] )

													assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   List[Any]      =	self.get_dummy_components()
													UpperCAmelCase_      :   Any      =	StableDiffusionImageVariationPipeline(**lowercase__ )
													UpperCAmelCase_      :   Dict      =	sd_pipe.to(lowercase__ )
													sd_pipe.set_progress_bar_config(disable=lowercase__ )

													UpperCAmelCase_      :   Optional[Any]      =	self.get_dummy_inputs(lowercase__ )
													UpperCAmelCase_      :   Union[str, Any]      =	2 * [inputs['''image''']]
													UpperCAmelCase_      :   int      =	sd_pipe(**lowercase__ )

													UpperCAmelCase_      :   Any      =	output.images

													UpperCAmelCase_      :   Any      =	image[-1, -3:, -3:, -1]

													assert image.shape == (2, 64, 64, 3)
													UpperCAmelCase_      :   Tuple      =	np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9] )

													assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3



							def      UpperCAmelCase__						(				self				:  int )       ->							Optional[int]:







													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3E-3 )





@slow
@require_torch_gpu
class        __a   (unittest.TestCase  ):

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







													"""simple docstring"""
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def      UpperCAmelCase__						(				self				:  Union[str, Any]							,  __magic_name__				:  Any							,  __magic_name__				:  int="cpu"							,  __magic_name__				:  Union[str, Any]=torch.floataa							,  __magic_name__				:  Optional[Any]=0 )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
													UpperCAmelCase_      :   Tuple      =	load_image(
													    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
													    '''/stable_diffusion_imgvar/input_image_vermeer.png''' )
													UpperCAmelCase_      :   Union[str, Any]      =	np.random.RandomState(lowercase__ ).standard_normal((1, 4, 64, 64) )
													UpperCAmelCase_      :   int      =	torch.from_numpy(lowercase__ ).to(device=lowercase__							,  dtype=lowercase__ )
													UpperCAmelCase_      :   str      =	{
													    '''image''': init_image,
													    '''latents''': latents,
													    '''generator''': generator,
													    '''num_inference_steps''': 3,
													    '''guidance_scale''': 7.5,
													    '''output_type''': '''numpy''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	StableDiffusionImageVariationPipeline.from_pretrained(
													    '''lambdalabs/sd-image-variations-diffusers'''							,  safety_checker=lowercase__ )
													UpperCAmelCase_      :   Any      =	sd_pipe.to(lowercase__ )
													sd_pipe.set_progress_bar_config(disable=lowercase__ )

													UpperCAmelCase_      :   Optional[int]      =	self.get_inputs(lowercase__ )
													UpperCAmelCase_      :   Optional[Any]      =	sd_pipe(**lowercase__ ).images
													UpperCAmelCase_      :   List[Any]      =	image[0, -3:, -3:, -1].flatten()

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_      :   List[str]      =	np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9] )
													assert np.abs(image_slice - expected_slice ).max() < 6E-3

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							str:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	0

													def callback_fn(__magic_name__				:  List[Any]							,  __magic_name__				:  Tuple							,  __magic_name__				:  Any ) -> None:
																			UpperCAmelCase_      :   Dict      =	True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 1:
																									UpperCAmelCase_      :   str      =	latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									UpperCAmelCase_      :   Union[str, Any]      =	latents[0, -3:, -3:, -1]
																									UpperCAmelCase_      :   Dict      =	np.array(
																									    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9] )

																									assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2
																			elif step == 2:
																									UpperCAmelCase_      :   Optional[int]      =	latents.detach().cpu().numpy()
																									assert latents.shape == (1, 4, 64, 64)
																									UpperCAmelCase_      :   Tuple      =	latents[0, -3:, -3:, -1]
																									UpperCAmelCase_      :   List[Any]      =	np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3] )

																									assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2

													UpperCAmelCase_      :   Dict      =	False

													UpperCAmelCase_      :   Tuple      =	StableDiffusionImageVariationPipeline.from_pretrained(
													    '''fusing/sd-image-variations-diffusers'''							,  safety_checker=lowercase__							,  torch_dtype=torch.floataa							,  )
													pipe.to(lowercase__ )
													pipe.set_progress_bar_config(disable=lowercase__ )
													pipe.enable_attention_slicing()

													UpperCAmelCase_      :   Union[str, Any]      =	self.get_inputs(lowercase__							,  dtype=torch.floataa )
													pipe(**lowercase__							,  callback=lowercase__							,  callback_steps=1 )
													assert callback_fn.has_been_called
													assert number_of_steps == inputs["num_inference_steps"]



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







													"""simple docstring"""
													torch.cuda.empty_cache()
													torch.cuda.reset_max_memory_allocated()
													torch.cuda.reset_peak_memory_stats()

													UpperCAmelCase_      :   int      =	'''fusing/sd-image-variations-diffusers'''
													UpperCAmelCase_      :   Dict      =	StableDiffusionImageVariationPipeline.from_pretrained(
													    lowercase__							,  safety_checker=lowercase__							,  torch_dtype=torch.floataa )
													UpperCAmelCase_      :   str      =	pipe.to(lowercase__ )
													pipe.set_progress_bar_config(disable=lowercase__ )
													pipe.enable_attention_slicing(1 )
													pipe.enable_sequential_cpu_offload()

													UpperCAmelCase_      :   Any      =	self.get_inputs(lowercase__							,  dtype=torch.floataa )
													UpperCAmelCase_      :   int      =	pipe(**lowercase__ )

													UpperCAmelCase_      :   Any      =	torch.cuda.max_memory_allocated()
													# make sure that less than 2.6 GB is allocated
													assert mem_bytes < 2.6 * 10**9





@nightly
@require_torch_gpu
class        __a   (unittest.TestCase  ):

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







													"""simple docstring"""
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def      UpperCAmelCase__						(				self				:  List[str]							,  __magic_name__				:  Optional[Any]							,  __magic_name__				:  List[Any]="cpu"							,  __magic_name__				:  List[Any]=torch.floataa							,  __magic_name__				:  Tuple=0 )       ->							Dict:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
													UpperCAmelCase_      :   List[str]      =	load_image(
													    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
													    '''/stable_diffusion_imgvar/input_image_vermeer.png''' )
													UpperCAmelCase_      :   Optional[int]      =	np.random.RandomState(lowercase__ ).standard_normal((1, 4, 64, 64) )
													UpperCAmelCase_      :   Any      =	torch.from_numpy(lowercase__ ).to(device=lowercase__							,  dtype=lowercase__ )
													UpperCAmelCase_      :   Union[str, Any]      =	{
													    '''image''': init_image,
													    '''latents''': latents,
													    '''generator''': generator,
													    '''num_inference_steps''': 50,
													    '''guidance_scale''': 7.5,
													    '''output_type''': '''numpy''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  Dict )       ->							int:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''' )
													sd_pipe.to(lowercase__ )
													sd_pipe.set_progress_bar_config(disable=lowercase__ )

													UpperCAmelCase_      :   List[str]      =	self.get_inputs(lowercase__ )
													UpperCAmelCase_      :   List[Any]      =	sd_pipe(**lowercase__ ).images[0]

													UpperCAmelCase_      :   str      =	load_numpy(
													    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
													    '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy''' )
													UpperCAmelCase_      :   Dict      =	np.abs(expected_image - image ).max()
													assert max_diff < 1E-3



							def      UpperCAmelCase__						(				self				:  Any )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   Union[str, Any]      =	StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers''' )
													UpperCAmelCase_      :   Union[str, Any]      =	DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config )
													sd_pipe.to(lowercase__ )
													sd_pipe.set_progress_bar_config(disable=lowercase__ )

													UpperCAmelCase_      :   Optional[Any]      =	self.get_inputs(lowercase__ )
													UpperCAmelCase_      :   Optional[int]      =	25
													UpperCAmelCase_      :   Any      =	sd_pipe(**lowercase__ ).images[0]

													UpperCAmelCase_      :   Tuple      =	load_numpy(
													    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
													    '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy''' )
													UpperCAmelCase_      :   str      =	np.abs(expected_image - image ).max()
													assert max_diff < 1E-3













import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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,
					    EfficientFormerForImageClassification,
					    EfficientFormerForImageClassificationWithTeacher,
					    EfficientFormerModel,
					)
					from transformers.models.efficientformer.modeling_efficientformer import (
					    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
					)


if is_vision_available():
					from PIL import Image

					from transformers import EfficientFormerImageProcessor





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__ = 13							, lowercase__ = 64							, lowercase__ = 2							, lowercase__ = 3							, lowercase__ = 3							, lowercase__ = True							, lowercase__ = True							, lowercase__ = 128							, lowercase__=[16, 32, 64, 128]							, lowercase__ = 7							, lowercase__ = 4							, lowercase__ = 37							, lowercase__ = "gelu"							, lowercase__ = 0.1							, lowercase__ = 0.1							, lowercase__ = 10							, lowercase__ = 0.02							, lowercase__ = 2							, lowercase__ = 1							, lowercase__ = 128							, lowercase__ = [2, 2, 2, 2]							, lowercase__ = 2							, lowercase__ = 2							, )				->				Optional[int]:
				__A    =							parent
				__A    =							batch_size
				__A    =							image_size
				__A    =							patch_size
				__A    =							num_channels
				__A    =							is_training
				__A    =							use_labels
				__A    =							hidden_size
				__A    =							num_hidden_layers
				__A    =							num_attention_heads
				__A    =							intermediate_size
				__A    =							hidden_act
				__A    =							hidden_dropout_prob
				__A    =							attention_probs_dropout_prob
				__A    =							type_sequence_label_size
				__A    =							initializer_range
				__A    =							encoder_stride
				__A    =							num_attention_outputs
				__A    =							embed_dim
				__A    =							embed_dim + 1
				__A    =							resolution
				__A    =							depths
				__A    =							hidden_sizes
				__A    =							dim
				__A    =							mlp_expansion_ratio






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				__A    =							None
				if self.use_labels:
						__A    =							ids_tensor([self.batch_size]							, self.type_sequence_label_size						)

				__A    =							self.get_config()
				return config, pixel_values, labels






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return EfficientFormerConfig(
				    image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, is_decoder=lowercase__							, initializer_range=self.initializer_range							, encoder_stride=self.encoder_stride							, resolution=self.resolution							, depths=self.depths							, hidden_sizes=self.hidden_sizes							, dim=self.dim							, mlp_expansion_ratio=self.mlp_expansion_ratio							, )






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				str:
				__A    =							EfficientFormerModel(config=lowercase__						)
				model.to(lowercase__						)
				model.eval()
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				Dict:
				__A    =							self.type_sequence_label_size
				__A    =							EfficientFormerForImageClassification(lowercase__						)
				model.to(lowercase__						)
				model.eval()
				__A    =							model(lowercase__							, labels=lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)

				# test greyscale images
				__A    =							1
				__A    =							EfficientFormerForImageClassification(lowercase__						)
				model.to(lowercase__						)
				model.eval()

				__A    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)






		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							self.prepare_config_and_inputs()
				(
				    (
				    __A
				) ,   (
				    __A
				) ,   (
				    __A
				) ,   
				)    =							config_and_inputs
				__A    =							{"pixel_values": pixel_values}
				return config, inputs_dict





@require_torch
class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Dict 					= (
		    (
		        EfficientFormerModel,
		        EfficientFormerForImageClassificationWithTeacher,
		        EfficientFormerForImageClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		UpperCAmelCase_							:       str 					= (
		    {
		        'feature-extraction': EfficientFormerModel,
		        'image-classification': (
		            EfficientFormerForImageClassification,
		            EfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_torch_available()
		    else {}
		)
		UpperCAmelCase_							:       List[str] 					= False

		UpperCAmelCase_							:       Any 					= False
		UpperCAmelCase_							:       Union[str, Any] 					= False
		UpperCAmelCase_							:       Optional[int] 					= False






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							EfficientFormerModelTester(self						)
				__A    =							ConfigTester(
				    self							, config_class=lowercase__							, has_text_modality=lowercase__							, hidden_size=37						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				self.config_tester.run_common_tests()






		@unittest.skip(reason="EfficientFormer does not use inputs_embeds"						)
		def 					_lowerCamelCase					(      self						)				->				Tuple:
				pass






		@unittest.skip(reason="EfficientFormer does not support input and output embeddings"						)
		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				pass






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

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

						__A    =							["pixel_values"]
						self.assertListEqual(arg_names[:1]							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				def check_hidden_states_output(lowercase__							, lowercase__							, lowercase__						):
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()

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

						__A    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						__A    =							getattr(
						    self.model_tester							, "expected_num_hidden_layers"							, self.model_tester.num_hidden_layers + 1						)
						self.assertEqual(len(lowercase__						)							, lowercase__						)

						if hasattr(self.model_tester							, "encoder_seq_length"						):
								__A    =							self.model_tester.encoder_seq_length
								if hasattr(self.model_tester							, "chunk_length"						) and self.model_tester.chunk_length > 1:
										__A    =							seq_length * self.model_tester.chunk_length
						else:
								__A    =							self.model_tester.seq_length

						self.assertListEqual(
						    list(hidden_states[-1].shape[-2:]						)							, [seq_length, self.model_tester.hidden_size]							, )

						if config.is_encoder_decoder:
								__A    =							outputs.decoder_hidden_states

								self.assertIsInstance(lowercase__							, (list, tuple)						)
								self.assertEqual(len(lowercase__						)							, lowercase__						)
								__A    =							getattr(self.model_tester							, "seq_length"							, lowercase__						)
								__A    =							getattr(self.model_tester							, "decoder_seq_length"							, lowercase__						)

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:]						)							, [decoder_seq_length, self.model_tester.hidden_size]							, )

				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

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

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__A    =							True

						check_hidden_states_output(lowercase__							, lowercase__							, lowercase__						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__=False						)				->				List[str]:
				__A    =							super()._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)

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

				return inputs_dict






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase__						)






		@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"						)
		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase__						)






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

				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				for model_class in self.all_model_classes:
						# EfficientFormerForImageClassificationWithTeacher supports inference-only
						if (
						    model_class in get_values(lowercase__						)
						    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
						):
								continue
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.train()
						__A    =							self._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)
						__A    =							model(**lowercase__						).loss
						loss.backward()






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

				__A    =							[
				    {"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__ == "EfficientFormerForImageClassificationWithTeacher"
						):
								continue

						for problem_type in problem_types:
								with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""						):
										__A    =							problem_type["title"]
										__A    =							problem_type["num_labels"]

										__A    =							model_class(lowercase__						)
										model.to(lowercase__						)
										model.train()

										__A    =							self._prepare_for_class(lowercase__							, lowercase__							, return_labels=lowercase__						)

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

										__A    =							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:
												__A    =							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 					_lowerCamelCase					(      self						)				->				List[Any]:
				for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__A    =							EfficientFormerModel.from_pretrained(lowercase__						)
						self.assertIsNotNone(lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				__A    =							getattr(self.model_tester							, "seq_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "encoder_seq_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "key_length"							, lowercase__						)
				__A    =							getattr(self.model_tester							, "chunk_length"							, lowercase__						)

				if chunk_length is not None and hasattr(self.model_tester							, "num_hashes"						):
						__A    =							encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
						__A    =							True
						__A    =							False
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						with torch.no_grad():
								__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_attention_outputs						)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__A    =							True
						__A    =							model_class(lowercase__						)
						model.to(lowercase__						)
						model.eval()
						with torch.no_grad():
								__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(lowercase__						)							, self.model_tester.num_attention_outputs						)

						if chunk_length is not None:
								self.assertListEqual(
								    list(attentions[0].shape[-4:]						)							, [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]							, )
						else:
								self.assertListEqual(
								    list(attentions[0].shape[-3:]						)							, [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]							, )



def      UpperCAmelCase						( ):


		'''simple docstring'''

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





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






		@cached_property
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				return (
				    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"						)
				    if is_vision_available()
				    else None
				)






		@slow
		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"						).to(
				    lowercase__						)

				__A    =							self.default_image_processor
				__A    =							prepare_img()
				__A    =							image_processor(images=lowercase__							, return_tensors="pt"						).to(lowercase__						)

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

				# verify the logits
				__A    =							(1, 1000)
				self.assertEqual(outputs.logits.shape							, lowercase__						)

				__A    =							torch.tensor([-0.0555, 0.4825, -0.0852]						).to(lowercase__						)
				self.assertTrue(torch.allclose(outputs.logits[0][:3]							, lowercase__							, atol=1e-4						)						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							EfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    "snap-research/efficientformer-l1-300"						).to(lowercase__						)

				__A    =							self.default_image_processor
				__A    =							prepare_img()
				__A    =							image_processor(images=lowercase__							, return_tensors="pt"						).to(lowercase__						)

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

				# verify the logits
				__A    =							(1, 1000)
				self.assertEqual(outputs.logits.shape							, lowercase__						)

				__A    =							torch.tensor([-0.1312, 0.4353, -1.0499]						).to(lowercase__						)
				self.assertTrue(torch.allclose(outputs.logits[0][:3]							, lowercase__							, atol=1e-4						)						)

