






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

	from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
	from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor






class 						UpperCAmelCase__ :







		def __init__(					self       ,A__       ,A__=13       ,A__=30       ,A__=2       ,A__=3       ,A__=True       ,A__=True       ,A__=32       ,A__=5       ,A__=4       ,A__=37       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=10       ,A__=0.02       ,A__=None       ,A__=2       ,):
						_A	:				List[str]   =      parent
						_A	:				Any   =      batch_size
						_A	:				List[Any]   =      image_size
						_A	:				str   =      patch_size
						_A	:				Dict   =      num_channels
						_A	:				Tuple   =      is_training
						_A	:				List[Any]   =      use_labels
						_A	:				Any   =      hidden_size
						_A	:				Tuple   =      num_hidden_layers
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				List[str]   =      intermediate_size
						_A	:				Optional[int]   =      hidden_act
						_A	:				Any   =      hidden_dropout_prob
						_A	:				List[Any]   =      attention_probs_dropout_prob
						_A	:				Any   =      type_sequence_label_size
						_A	:				Tuple   =      initializer_range
						_A	:				List[str]   =      scope
						_A	:				int   =      encoder_stride

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_A	:				Dict   =      (image_size // patch_size) ** 2
						_A	:				List[Any]   =      num_patches + 1







		def  A__      (					self		):
						_A	:				int   =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						_A	:				Tuple   =      None
						if self.use_labels:
										_A	:				Union[str, Any]   =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size		)

						_A	:				Tuple   =      self.get_config()

						return config, pixel_values, labels







		def  A__      (					self		):
						return ViTConfig(
						    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=A__       ,initializer_range=self.initializer_range       ,encoder_stride=self.encoder_stride       ,)







		def  A__      (					self       ,A__       ,A__       ,A__		):
						_A	:				Optional[int]   =      ViTModel(config=A__		)
						model.to(A__		)
						model.eval()
						_A	:				List[Any]   =      model(A__		)
						self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)		)







		def  A__      (					self       ,A__       ,A__       ,A__		):
						_A	:				List[Any]   =      ViTForMaskedImageModeling(config=A__		)
						model.to(A__		)
						model.eval()
						_A	:				Any   =      model(A__		)
						self.parent.assertEqual(
						    result.reconstruction.shape       ,(self.batch_size, self.num_channels, self.image_size, self.image_size)		)

						# test greyscale images
						_A	:				str   =      1
						_A	:				Any   =      ViTForMaskedImageModeling(A__		)
						model.to(A__		)
						model.eval()

						_A	:				int   =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						_A	:				List[str]   =      model(A__		)
						self.parent.assertEqual(result.reconstruction.shape       ,(self.batch_size, 1, self.image_size, self.image_size)		)







		def  A__      (					self       ,A__       ,A__       ,A__		):
						_A	:				Optional[int]   =      self.type_sequence_label_size
						_A	:				Dict   =      ViTForImageClassification(A__		)
						model.to(A__		)
						model.eval()
						_A	:				Any   =      model(A__       ,labels=A__		)
						self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						_A	:				Any   =      1
						_A	:				str   =      ViTForImageClassification(A__		)
						model.to(A__		)
						model.eval()

						_A	:				Optional[Any]   =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						_A	:				Optional[int]   =      model(A__		)
						self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)		)







		def  A__      (					self		):
						_A	:				List[str]   =      self.prepare_config_and_inputs()
						(
						    (
						    _A					
						)  ,				(
						    _A					
						)  ,				(
						    _A					
						)  ,									
						)	:				List[str]   =      config_and_inputs
						_A	:				Optional[Any]   =      {'''pixel_values''': pixel_values}
						return config, inputs_dict






@require_torch
class 						UpperCAmelCase__ (							__snake_case     ,				__snake_case     ,				unittest.TestCase							):
		__snake_case      :       int     =   (
		    (
		        ViTModel,
		        ViTForImageClassification,
		        ViTForMaskedImageModeling,
		    )
		    if is_torch_available()
		    else ()
		)
		__snake_case      :       int     =   (
		    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
		    if is_torch_available()
		    else {}
		)
		__snake_case      :       List[str]     =   True

		__snake_case      :       Dict     =   False
		__snake_case      :       Any     =   False
		__snake_case      :       Any     =   False







		def  A__      (					self		):
						_A	:				Tuple   =      ViTModelTester(self		)
						_A	:				Union[str, Any]   =      ConfigTester(self       ,config_class=A__       ,has_text_modality=A__       ,hidden_size=37		)







		def  A__      (					self		):
						self.config_tester.run_common_tests()







		@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
		def  A__      (					self		):
						pass







		def  A__      (					self		):
						_A  ,				_A	:				Tuple   =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										_A	:				Optional[int]   =      model_class(A__		)
										self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module)		)
										_A	:				str   =      model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(A__       ,nn.Linear		)		)







		def  A__      (					self		):
						_A  ,				_A	:				Tuple   =      self.model_tester.prepare_config_and_inputs_for_common()

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

										_A	:				Union[str, Any]   =      ['''pixel_values''']
										self.assertListEqual(arg_names[:1]       ,A__		)







		def  A__      (					self		):
						_A	:				Any   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__		)







		def  A__      (					self		):
						_A	:				Any   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A__		)







		def  A__      (					self		):
						_A	:				List[Any]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__		)







		@slow
		def  A__      (					self		):







						for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A	:				Union[str, Any]   =      ViTModel.from_pretrained(A__		)
										self.assertIsNotNone(A__		)



def  a__ ()		-> Dict:
				_A	:				Optional[Any]   =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image






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







		@cached_property
		def  A__      (					self		):
						return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''		) if is_vision_available() else None







		@slow
		def  A__      (					self		):
						_A	:				Dict   =      ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''		).to(A__		)

						_A	:				Dict   =      self.default_image_processor
						_A	:				List[Any]   =      prepare_img()
						_A	:				Optional[Any]   =      image_processor(images=A__       ,return_tensors='''pt'''		).to(A__		)

						# forward pass
						with torch.no_grad():
										_A	:				Dict   =      model(**A__		)

						# verify the logits
						_A	:				List[str]   =      torch.Size((1, 1000)		)
						self.assertEqual(outputs.logits.shape       ,A__		)

						_A	:				Optional[int]   =      torch.tensor([-0.27_44, 0.82_15, -0.08_36]		).to(A__		)

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







		@slow
		def  A__      (					self		):
						_A	:				Tuple   =      ViTModel.from_pretrained('''facebook/dino-vits8'''		).to(A__		)

						_A	:				Tuple   =      ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''       ,size=480		)
						_A	:				Optional[int]   =      prepare_img()
						_A	:				int   =      image_processor(images=A__       ,return_tensors='''pt'''		)
						_A	:				Dict   =      inputs.pixel_values.to(A__		)

						# forward pass
						with torch.no_grad():
										_A	:				List[str]   =      model(A__       ,interpolate_pos_encoding=A__		)

						# verify the logits
						_A	:				Optional[int]   =      torch.Size((1, 3601, 384)		)
						self.assertEqual(outputs.last_hidden_state.shape       ,A__		)

						_A	:				Any   =      torch.tensor(
						    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]		).to(A__		)

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







		@slow
		@require_accelerate
		@require_torch_gpu
		def  A__      (					self		):
						_A	:				Dict   =      ViTModel.from_pretrained('''facebook/dino-vits8'''       ,torch_dtype=torch.floataa       ,device_map='''auto'''		)
						_A	:				str   =      self.default_image_processor

						_A	:				Optional[int]   =      prepare_img()
						_A	:				Optional[int]   =      image_processor(images=A__       ,return_tensors='''pt'''		)
						_A	:				Optional[int]   =      inputs.pixel_values.to(A__		)

						# forward pass to make sure inference works in fp16
						with torch.no_grad():
										_A	:				int   =      model(A__		)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

