





'''simple docstring'''


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
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 (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           DeiTForImageClassification,
           DeiTForImageClassificationWithTeacher,
           DeiTForMaskedImageModeling,
           DeiTModel,
       )
       from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DeiTImageProcessor

class  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=30      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=37      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=2      ,  )     ->				int:
     _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						      =   scope
     _lowerCAmelCase						      =   encoder_stride

     # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
     _lowerCAmelCase						      =   (image_size // patch_size) ** 2
     _lowerCAmelCase						      =   num_patches + 2




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _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 				_snake_case (						self		)     ->				Union[str, Any]:
     return DeiTConfig(
         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=_lowerCAmelCase      ,  initializer_range=self.initializer_range      ,  encoder_stride=self.encoder_stride      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				List[str]:
     _lowerCAmelCase						      =   DeiTModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   DeiTForMaskedImageModeling(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(
         result.reconstruction.shape      ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   DeiTForMaskedImageModeling(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

     _lowerCAmelCase						      =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
     _lowerCAmelCase						      =   model(_lowerCAmelCase		)
     self.parent.assertEqual(result.reconstruction.shape      ,  (self.batch_size, 1, self.image_size, self.image_size)		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   self.type_sequence_label_size
     _lowerCAmelCase						      =   DeiTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase						      =   model(_lowerCAmelCase      ,  labels=_lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.type_sequence_label_size)		)

     # test greyscale images
     _lowerCAmelCase						      =   1
     _lowerCAmelCase						      =   DeiTForImageClassification(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()

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





 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.prepare_config_and_inputs()
     (
         (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      (
         _lowerCAmelCase
     )      ,      
     )						      =   config_and_inputs
     _lowerCAmelCase						      =   {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class  lowerCAmelCase_		(		__magic_name__    ,__magic_name__    ,unittest.TestCase		):
 __lowerCamelCase    :				Tuple								=       (
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __lowerCamelCase    :				int								=       (
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __lowerCamelCase    :				Dict								=       False
 __lowerCamelCase    :				List[Any]								=       False
 __lowerCamelCase    :				Union[str, Any]								=       False




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   DeiTModelTester(self		)
     _lowerCAmelCase						      =   ConfigTester(self      ,  config_class=_lowerCAmelCase      ,  has_text_modality=_lowerCAmelCase      ,  hidden_size=37		)




 def 				_snake_case (						self		)     ->				int:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="DeiT does not use inputs_embeds"		)
 def 				_snake_case (						self		)     ->				List[Any]:
     pass




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         self.assertIsInstance(model.get_input_embeddings()      ,  (nn.Module)		)
         _lowerCAmelCase						      =   model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCAmelCase      ,  nn.Linear		)		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         _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]      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=False		)     ->				int:
     _lowerCAmelCase						      =   super()._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

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

     return inputs_dict




 def 				_snake_case (						self		)     ->				Tuple:
     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:
         # DeiTForImageClassificationWithTeacher supports inference-only
         if (
             model_class in get_values(_lowerCAmelCase		)
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
             continue
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		).loss
         loss.backward()




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
         return

     _lowerCAmelCase						      =   False
     _lowerCAmelCase						      =   True

     for model_class in self.all_model_classes:
         if model_class in get_values(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
             continue
         # DeiTForImageClassificationWithTeacher supports inference-only
         if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
             continue
         _lowerCAmelCase						      =   model_class(_lowerCAmelCase		)
         model.gradient_checkpointing_enable()
         model.to(_lowerCAmelCase		)
         model.train()
         _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)
         _lowerCAmelCase						      =   model(**_lowerCAmelCase		).loss
         loss.backward()




 def 				_snake_case (						self		)     ->				int:
     _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(_lowerCAmelCase		),
                 *get_values(_lowerCAmelCase		),
             ]
             or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
         ):
             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(_lowerCAmelCase		)
                 model.to(_lowerCAmelCase		)
                 model.train()

                 _lowerCAmelCase						      =   self._prepare_for_class(_lowerCAmelCase      ,  _lowerCAmelCase      ,  return_labels=_lowerCAmelCase		)

                 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=_lowerCAmelCase		) as warning_list:
                     _lowerCAmelCase						      =   model(**_lowerCAmelCase		).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 				_snake_case (						self		)     ->				Any:
     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						      =   DeiTModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)







def 						__a():



    '''simple docstring'''





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





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




 @cached_property
 def 				_snake_case (						self		)     ->				Any:
     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"		)
         if is_vision_available()
         else None
     )




 @slow
 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"		).to(
         _lowerCAmelCase		)

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

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

     # verify the logits
     _lowerCAmelCase						      =   torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   torch.tensor([-1.0266, 0.1912, -1.2861]		).to(_lowerCAmelCase		)

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





 @slow
 @require_accelerate
 @require_torch_gpu
 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   DeiTModel.from_pretrained(
         "facebook/deit-base-distilled-patch16-224"      ,  torch_dtype=torch.floataa      ,  device_map="auto"		)
     _lowerCAmelCase						      =   self.default_image_processor

     _lowerCAmelCase						      =   prepare_img()
     _lowerCAmelCase						      =   image_processor(images=_lowerCAmelCase      ,  return_tensors="pt"		)
     _lowerCAmelCase						      =   inputs.pixel_values.to(_lowerCAmelCase		)

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





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							int:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()



				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				1
											SCREAMING_SNAKE_CASE_				=				3
											SCREAMING_SNAKE_CASE_				=				(32, 32)

											SCREAMING_SNAKE_CASE_				=				floats_tensor((batch_size, num_channels) + sizes,     rng=random.Random(0       )       ).to(_lowercase       )
											return image



				@property
				def   a__					(    self       )     ->							Any:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				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,     )
											return model



				@property
				def   a__					(    self       )     ->							Tuple:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				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,     )
											return model



				@property
				def   a__					(    self       )     ->							Tuple:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				RobertaSeriesConfig(
											    hidden_size=32,     project_dim=32,     intermediate_size=37,     layer_norm_eps=1E-05,     num_attention_heads=4,     num_hidden_layers=5,     pad_token_id=1,     vocab_size=5006,     )
											return RobertaSeriesModelWithTransformation(_lowercase       )



				@property
				def   a__					(    self       )     ->							List[str]:
											def extract(*_lowercase,     **_lowercase       ):






																		class    snake_case     :






																						"""simple docstring"""



																						def __init__(    self       )     ->							Dict:
																													SCREAMING_SNAKE_CASE_				=				torch.ones([0]       )





																						def   a__					(    self,     _lowercase       )     ->							List[str]:
																													self.pixel_values.to(_lowercase       )
																													return self


																		return Out()

											return extract



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				'cpu'  # ensure determinism for the device-dependent torch.Generator
											SCREAMING_SNAKE_CASE_				=				self.dummy_cond_unet
											SCREAMING_SNAKE_CASE_				=				PNDMScheduler(skip_prk_steps=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.dummy_vae
											SCREAMING_SNAKE_CASE_				=				self.dummy_text_encoder
											SCREAMING_SNAKE_CASE_				=				XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											SCREAMING_SNAKE_CASE_				=				77

											SCREAMING_SNAKE_CASE_				=				self.dummy_image.to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				init_image / 2 + 0.5

											# make sure here that pndm scheduler skips prk
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline(
											    unet=_lowercase,     scheduler=_lowercase,     vae=_lowercase,     text_encoder=_lowercase,     tokenizer=_lowercase,     safety_checker=_lowercase,     feature_extractor=self.dummy_extractor,     )
											SCREAMING_SNAKE_CASE_				=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,     do_normalize=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe.to(_lowercase       )
											alt_pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A painting of a squirrel eating a burger'
											SCREAMING_SNAKE_CASE_				=				torch.Generator(device=_lowercase       ).manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     guidance_scale=6.0,     num_inference_steps=2,     output_type='np',     image=_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				output.images

											SCREAMING_SNAKE_CASE_				=				torch.Generator(device=_lowercase       ).manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     guidance_scale=6.0,     num_inference_steps=2,     output_type='np',     image=_lowercase,     return_dict=_lowercase,     )[0]

											SCREAMING_SNAKE_CASE_				=				image[0, -3:, -3:, -1]
											SCREAMING_SNAKE_CASE_				=				image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											SCREAMING_SNAKE_CASE_				=				np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3
											assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 5E-3



				@unittest.skipIf(torch_device != 'cuda',     'This test requires a GPU'       )
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				self.dummy_cond_unet
											SCREAMING_SNAKE_CASE_				=				PNDMScheduler(skip_prk_steps=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.dummy_vae
											SCREAMING_SNAKE_CASE_				=				self.dummy_text_encoder
											SCREAMING_SNAKE_CASE_				=				XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'       )
											SCREAMING_SNAKE_CASE_				=				77

											SCREAMING_SNAKE_CASE_				=				self.dummy_image.to(_lowercase       )

											# put models in fp16
											SCREAMING_SNAKE_CASE_				=				unet.half()
											SCREAMING_SNAKE_CASE_				=				vae.half()
											SCREAMING_SNAKE_CASE_				=				bert.half()

											# make sure here that pndm scheduler skips prk
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline(
											    unet=_lowercase,     scheduler=_lowercase,     vae=_lowercase,     text_encoder=_lowercase,     tokenizer=_lowercase,     safety_checker=_lowercase,     feature_extractor=self.dummy_extractor,     )
											SCREAMING_SNAKE_CASE_				=				VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,     do_normalize=_lowercase       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe.to(_lowercase       )
											alt_pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				'A painting of a squirrel eating a burger'
											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				alt_pipe(
											    [prompt],     generator=_lowercase,     num_inference_steps=2,     output_type='np',     image=_lowercase,     ).images

											assert image.shape == (1, 32, 32, 3)





				@unittest.skipIf(torch_device != 'cuda',     'This test requires a GPU'       )
				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'       )
											# resize to resolution that is divisible by 8 but not 16 or 32
											SCREAMING_SNAKE_CASE_				=				init_image.resize((760, 504)       )

											SCREAMING_SNAKE_CASE_				=				'BAAI/AltDiffusion'
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline.from_pretrained(
											    _lowercase,     safety_checker=_lowercase,     )
											pipe.to(_lowercase       )
											pipe.set_progress_bar_config(disable=_lowercase       )
											pipe.enable_attention_slicing()

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     strength=0.75,     guidance_scale=7.5,     generator=_lowercase,     output_type='np',     )
											SCREAMING_SNAKE_CASE_				=				output.images[0]

											SCREAMING_SNAKE_CASE_				=				image[255:258, 383:386, -1]

											assert image.shape == (504, 760, 3)
											SCREAMING_SNAKE_CASE_				=				np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]       )

											assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2






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






				"""simple docstring"""



				def   a__					(    self       )     ->							List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/img2img/sketch-mountains-input.jpg'       )
											SCREAMING_SNAKE_CASE_				=				init_image.resize((768, 512)       )
											SCREAMING_SNAKE_CASE_				=				load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'       )

											SCREAMING_SNAKE_CASE_				=				'BAAI/AltDiffusion'
											SCREAMING_SNAKE_CASE_				=				AltDiffusionImgaImgPipeline.from_pretrained(
											    _lowercase,     safety_checker=_lowercase,     )
											pipe.to(_lowercase       )
											pipe.set_progress_bar_config(disable=_lowercase       )
											pipe.enable_attention_slicing()

											SCREAMING_SNAKE_CASE_				=				'A fantasy landscape, trending on artstation'

											SCREAMING_SNAKE_CASE_				=				torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				pipe(
											    prompt=_lowercase,     image=_lowercase,     strength=0.75,     guidance_scale=7.5,     generator=_lowercase,     output_type='np',     )
											SCREAMING_SNAKE_CASE_				=				output.images[0]

											assert image.shape == (512, 768, 3)
											# img2img is flaky across GPUs even in fp32, so using MAE here
											assert np.abs(expected_image - image       ).max() < 1E-2





