code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import gc
  import random
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPImageProcessor, CLIPVisionConfig
  
  from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
  from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
  from diffusers.utils import floats_tensor, load_image, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
  
  
  enable_full_determinism()
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     str     						=PaintByExamplePipeline
  		a     :     List[Any]     						=IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
  		a     :     List[str]     						=IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  		a     :     int     						=frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       UNetaDConditionModel(
  					    block_out_channels=(32, 64),layers_per_block=2,sample_size=32,in_channels=9,out_channels=4,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),cross_attention_dim=32,)
  					__lowerCAmelCase      		=       PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE    )
  					torch.manual_seed(0    )
  					__lowerCAmelCase      		=       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    )
  					__lowerCAmelCase      		=       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,)
  					__lowerCAmelCase      		=       PaintByExampleImageEncoder(__SCREAMING_SNAKE_CASE,proj_size=32    )
  					__lowerCAmelCase      		=       CLIPImageProcessor(crop_size=32,size=32    )
  
  					__lowerCAmelCase      		=       {
  					    """unet""": unet,
  					    """scheduler""": scheduler,
  					    """vae""": vae,
  					    """image_encoder""": image_encoder,
  					    """safety_checker""": None,
  					    """feature_extractor""": feature_extractor,
  					}
  					return components
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       np.array(image.convert("""RGB"""    )    )
  					__lowerCAmelCase      		=       image[None].transpose(0,3,1,2    )
  					__lowerCAmelCase      		=       torch.from_numpy(__SCREAMING_SNAKE_CASE    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
  					return image
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE="cpu",__SCREAMING_SNAKE_CASE=0    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       floats_tensor((1, 3, 32, 32),rng=random.Random(__SCREAMING_SNAKE_CASE    )    ).to(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((64, 64)    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(image + 4    )    ).convert("""RGB"""    ).resize((64, 64)    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    ).resize((32, 32)    )
  
  					if str(__SCREAMING_SNAKE_CASE    ).startswith("""mps"""    ):
  								__lowerCAmelCase      		=       torch.manual_seed(__SCREAMING_SNAKE_CASE    )
  					else:
  								__lowerCAmelCase      		=       torch.Generator(device=__SCREAMING_SNAKE_CASE    ).manual_seed(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       {
  					    """example_image""": example_image,
  					    """image""": init_image,
  					    """mask_image""": mask_image,
  					    """generator""": generator,
  					    """num_inference_steps""": 2,
  					    """guidance_scale""": 6.0,
  					    """output_type""": """numpy""",
  					}
  					return inputs
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.get_dummy_components()
  
  					# make sure here that pndm scheduler skips prk
  					__lowerCAmelCase      		=       PaintByExamplePipeline(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       pipe.to("""cpu"""    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.get_dummy_inputs()
  					__lowerCAmelCase      		=       pipe(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 64, 64, 3)
  					__lowerCAmelCase      		=       np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]    )
  
  					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       """cpu"""
  					__lowerCAmelCase      		=       self.get_dummy_inputs()
  					inputs.pop("""mask_image"""    )
  					__lowerCAmelCase      		=       self.convert_to_pt(inputs.pop("""image"""    )    )
  					__lowerCAmelCase      		=       image.clamp(0,1    ) / 2
  
  					# make sure here that pndm scheduler skips prk
  					__lowerCAmelCase      		=       PaintByExamplePipeline(**self.get_dummy_components()    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       pipe(image=__SCREAMING_SNAKE_CASE,mask_image=mask_image[:, 0],**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image.cpu().permute(0,2,3,1    )[0]
  					__lowerCAmelCase      		=       mask_image.cpu().permute(0,2,3,1    )[0]
  
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    )
  					__lowerCAmelCase      		=       Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE    )    ).convert("""RGB"""    )
  
  					__lowerCAmelCase      		=       pipe(**self.get_dummy_inputs()    )
  					__lowerCAmelCase      		=       output.images
  
  					assert out_a.shape == (1, 64, 64, 3)
  					assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().test_inference_batch_single_identical(expected_max_diff=3e-3    )
  
  
  
  
  
  @slow
  @require_torch_gpu
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					super().tearDown()
  					gc.collect()
  					torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/dog_in_bucket.png"""    )
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/mask.png"""    )
  					__lowerCAmelCase      		=       load_image(
  					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
  					    """/paint_by_example/panda.jpg"""    )
  
  					__lowerCAmelCase      		=       PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""    )
  					__lowerCAmelCase      		=       pipe.to(__SCREAMING_SNAKE_CASE    )
  					pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.manual_seed(3_21    )
  					__lowerCAmelCase      		=       pipe(
  					    image=__SCREAMING_SNAKE_CASE,mask_image=__SCREAMING_SNAKE_CASE,example_image=__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,guidance_scale=5.0,num_inference_steps=50,output_type="""np""",)
  
  					__lowerCAmelCase      		=       output.images
  
  					__lowerCAmelCase      		=       image[0, -3:, -3:, -1]
  
  					assert image.shape == (1, 5_12, 5_12, 3)
  					__lowerCAmelCase      		=       np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]    )
  
  					assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  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 					_UpperCAmelCase							:
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 13,__SCREAMING_SNAKE_CASE = 64,__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 3,__SCREAMING_SNAKE_CASE = 3,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = 1_28,__SCREAMING_SNAKE_CASE=[16, 32, 64, 1_28],__SCREAMING_SNAKE_CASE = 7,__SCREAMING_SNAKE_CASE = 4,__SCREAMING_SNAKE_CASE = 37,__SCREAMING_SNAKE_CASE = "gelu",__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 10,__SCREAMING_SNAKE_CASE = 0.02,__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 1,__SCREAMING_SNAKE_CASE = 1_28,__SCREAMING_SNAKE_CASE = [2, 2, 2, 2],__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 2,):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       parent
  					__lowerCAmelCase      		=       batch_size
  					__lowerCAmelCase      		=       image_size
  					__lowerCAmelCase      		=       patch_size
  					__lowerCAmelCase      		=       num_channels
  					__lowerCAmelCase      		=       is_training
  					__lowerCAmelCase      		=       use_labels
  					__lowerCAmelCase      		=       hidden_size
  					__lowerCAmelCase      		=       num_hidden_layers
  					__lowerCAmelCase      		=       num_attention_heads
  					__lowerCAmelCase      		=       intermediate_size
  					__lowerCAmelCase      		=       hidden_act
  					__lowerCAmelCase      		=       hidden_dropout_prob
  					__lowerCAmelCase      		=       attention_probs_dropout_prob
  					__lowerCAmelCase      		=       type_sequence_label_size
  					__lowerCAmelCase      		=       initializer_range
  					__lowerCAmelCase      		=       encoder_stride
  					__lowerCAmelCase      		=       num_attention_outputs
  					__lowerCAmelCase      		=       embed_dim
  					__lowerCAmelCase      		=       embed_dim + 1
  					__lowerCAmelCase      		=       resolution
  					__lowerCAmelCase      		=       depths
  					__lowerCAmelCase      		=       hidden_sizes
  					__lowerCAmelCase      		=       dim
  					__lowerCAmelCase      		=       mlp_expansion_ratio
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )
  
  					__lowerCAmelCase      		=       None
  					if self.use_labels:
  								__lowerCAmelCase      		=       ids_tensor([self.batch_size],self.type_sequence_label_size    )
  
  					__lowerCAmelCase      		=       self.get_config()
  					return config, pixel_values, labels
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					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=__SCREAMING_SNAKE_CASE,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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerModel(config=__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.type_sequence_label_size
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)    )
  
  					# test greyscale images
  					__lowerCAmelCase      		=       1
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification(__SCREAMING_SNAKE_CASE    )
  					model.to(__SCREAMING_SNAKE_CASE    )
  					model.eval()
  
  					__lowerCAmelCase      		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
  					__lowerCAmelCase      		=       model(__SCREAMING_SNAKE_CASE    )
  					self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.prepare_config_and_inputs()
  					(
  					    (
  					    __lowerCAmelCase
  					)		, (
  					    __lowerCAmelCase
  					)		, (
  					    __lowerCAmelCase
  					)		, 
  					)      		=       config_and_inputs
  					__lowerCAmelCase      		=       {"""pixel_values""": pixel_values}
  					return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 					_UpperCAmelCase							(		lowerCAmelCase_			,      lowerCAmelCase_			,      unittest.TestCase     ):
  		a     :     List[str]     						=(
  		    (
  		        EfficientFormerModel,
  		        EfficientFormerForImageClassificationWithTeacher,
  		        EfficientFormerForImageClassification,
  		    )
  		    if is_torch_available()
  		    else ()
  		)
  		a     :     List[Any]     						=(
  		    {
  		        """feature-extraction""": EfficientFormerModel,
  		        """image-classification""": (
  		            EfficientFormerForImageClassification,
  		            EfficientFormerForImageClassificationWithTeacher,
  		        ),
  		    }
  		    if is_torch_available()
  		    else {}
  		)
  		a     :     Union[str, Any]     						=False
  
  		a     :     str     						=False
  		a     :     List[str]     						=False
  		a     :     List[Any]     						=False
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerModelTester(self    )
  					__lowerCAmelCase      		=       ConfigTester(
  					    self,config_class=__SCREAMING_SNAKE_CASE,has_text_modality=__SCREAMING_SNAKE_CASE,hidden_size=37    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					self.config_tester.run_common_tests()
  		@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					pass
  		@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					pass
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       inspect.signature(model.forward    )
  								# signature.parameters is an OrderedDict => so arg_names order is deterministic
  								__lowerCAmelCase      		=       [*signature.parameters.keys()]
  
  								__lowerCAmelCase      		=       ["""pixel_values"""]
  								self.assertListEqual(arg_names[:1],__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					def check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  
  								__lowerCAmelCase      		=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  								__lowerCAmelCase      		=       getattr(
  								    self.model_tester,"""expected_num_hidden_layers""",self.model_tester.num_hidden_layers + 1    )
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),__SCREAMING_SNAKE_CASE    )
  
  								if hasattr(self.model_tester,"""encoder_seq_length"""    ):
  											__lowerCAmelCase      		=       self.model_tester.encoder_seq_length
  											if hasattr(self.model_tester,"""chunk_length"""    ) and self.model_tester.chunk_length > 1:
  														__lowerCAmelCase      		=       seq_length * self.model_tester.chunk_length
  								else:
  											__lowerCAmelCase      		=       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:
  											__lowerCAmelCase      		=       outputs.decoder_hidden_states
  
  											self.assertIsInstance(__SCREAMING_SNAKE_CASE,(list, tuple)    )
  											self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       getattr(self.model_tester,"""seq_length""",__SCREAMING_SNAKE_CASE    )
  											__lowerCAmelCase      		=       getattr(self.model_tester,"""decoder_seq_length""",__SCREAMING_SNAKE_CASE    )
  
  											self.assertListEqual(
  											    list(hidden_states[-1].shape[-2:]    ),[decoder_seq_length, self.model_tester.hidden_size],)
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       True
  								check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  								# check that output_hidden_states also work using config
  								del inputs_dict["output_hidden_states"]
  								__lowerCAmelCase      		=       True
  
  								check_hidden_states_output(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       super()._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  
  					if return_labels:
  								if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
  											del inputs_dict["labels"]
  
  					return inputs_dict
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
  		@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs()
  					self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					if not self.model_tester.is_training:
  								return
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  					__lowerCAmelCase      		=       True
  
  					for model_class in self.all_model_classes:
  								# EfficientFormerForImageClassificationWithTeacher supports inference-only
  								if (
  								    model_class in get_values(__SCREAMING_SNAKE_CASE    )
  								    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  								):
  											continue
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.train()
  								__lowerCAmelCase      		=       self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    ).loss
  								loss.backward()
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  
  					__lowerCAmelCase      		=       [
  					    {"""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(__SCREAMING_SNAKE_CASE    ),
  								    ]
  								    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  								):
  											continue
  
  								for problem_type in problem_types:
  											with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'    ):
  														__lowerCAmelCase      		=       problem_type["""title"""]
  														__lowerCAmelCase      		=       problem_type["""num_labels"""]
  
  														__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  														model.to(__SCREAMING_SNAKE_CASE    )
  														model.train()
  
  														__lowerCAmelCase      		=       self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,return_labels=__SCREAMING_SNAKE_CASE    )
  
  														if problem_type["num_labels"] > 1:
  																	__lowerCAmelCase      		=       inputs["""labels"""].unsqueeze(1    ).repeat(1,problem_type["""num_labels"""]    )
  
  														__lowerCAmelCase      		=       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=__SCREAMING_SNAKE_CASE    ) as warning_list:
  																	__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    ).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    ):
  
  
  
  					'''simple docstring'''
  
  					for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  								__lowerCAmelCase      		=       EfficientFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE    )
  								self.assertIsNotNone(__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase		, __lowerCAmelCase      		=       self.model_tester.prepare_config_and_inputs_for_common()
  					__lowerCAmelCase      		=       True
  
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""seq_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""encoder_seq_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""key_length""",__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       getattr(self.model_tester,"""chunk_length""",__SCREAMING_SNAKE_CASE    )
  
  					if chunk_length is not None and hasattr(self.model_tester,"""num_hashes"""    ):
  								__lowerCAmelCase      		=       encoder_seq_length * self.model_tester.num_hashes
  
  					for model_class in self.all_model_classes:
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       False
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),self.model_tester.num_attention_outputs    )
  
  								# check that output_attentions also work using config
  								del inputs_dict["output_attentions"]
  								__lowerCAmelCase      		=       True
  								__lowerCAmelCase      		=       model_class(__SCREAMING_SNAKE_CASE    )
  								model.to(__SCREAMING_SNAKE_CASE    )
  								model.eval()
  								with torch.no_grad():
  											__lowerCAmelCase      		=       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
  								__lowerCAmelCase      		=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  								self.assertEqual(len(__SCREAMING_SNAKE_CASE    ),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      _lowerCAmelCase		(		)      ->      List[str]:
  			__lowerCAmelCase      		=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
  			return image
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					_UpperCAmelCase							(		unittest.TestCase     ):
  		@cached_property
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					return (
  					    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""    )
  					    if is_vision_available()
  					    else None
  					)
  		@slow
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""    ).to(
  					    __SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.default_image_processor
  					__lowerCAmelCase      		=       prepare_img()
  					__lowerCAmelCase      		=       image_processor(images=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					# forward pass
  					with torch.no_grad():
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    )
  
  					# verify the logits
  					__lowerCAmelCase      		=       (1, 10_00)
  					self.assertEqual(outputs.logits.shape,__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.tensor([-0.0555, 0.4825, -0.0852]    ).to(__SCREAMING_SNAKE_CASE    )
  					self.assertTrue(torch.allclose(outputs.logits[0][:3],__SCREAMING_SNAKE_CASE,atol=1e-4    )    )
  
  
  
  
  
  
  
  		@slow
  		def 						lowerCamelCase__    (						self    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       EfficientFormerForImageClassificationWithTeacher.from_pretrained(
  					    """snap-research/efficientformer-l1-300"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       self.default_image_processor
  					__lowerCAmelCase      		=       prepare_img()
  					__lowerCAmelCase      		=       image_processor(images=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""    ).to(__SCREAMING_SNAKE_CASE    )
  
  					# forward pass
  					with torch.no_grad():
  								__lowerCAmelCase      		=       model(**__SCREAMING_SNAKE_CASE    )
  
  					# verify the logits
  					__lowerCAmelCase      		=       (1, 10_00)
  					self.assertEqual(outputs.logits.shape,__SCREAMING_SNAKE_CASE    )
  
  					__lowerCAmelCase      		=       torch.tensor([-0.1312, 0.4353, -1.0499]    ).to(__SCREAMING_SNAKE_CASE    )
  					self.assertTrue(torch.allclose(outputs.logits[0][:3],__SCREAMING_SNAKE_CASE,atol=1e-4    )    )
  
  