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				,      ):
            _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=	scope
            _A=	encoder_stride

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



     def 			a__  (					self       ):
            _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 			a__  (					self       ):
            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 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	DeiTModel(config=lowerCAmelCase__       )
            model.to(lowerCAmelCase__       )
            model.eval()
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	DeiTForMaskedImageModeling(config=lowerCAmelCase__       )
            model.to(lowerCAmelCase__       )
            model.eval()
            _A=	model(lowerCAmelCase__       )
            self.parent.assertEqual(
                result.reconstruction.shape				,      (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

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

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



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            _A=	self.type_sequence_label_size
            _A=	DeiTForImageClassification(lowerCAmelCase__       )
            model.to(lowerCAmelCase__       )
            model.eval()
            _A=	model(lowerCAmelCase__				,      labels=lowerCAmelCase__       )
            self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.type_sequence_label_size)       )

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

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





     def 			a__  (					self       ):
            _A=	self.prepare_config_and_inputs()
            (
                (
                _A
            ),			(
                _A
            ),			(
                _A
            ),			
            )=	config_and_inputs
            _A=	{'pixel_values': pixel_values}
            return config, inputs_dict





@require_torch
class      lowerCAmelCase						(					_a			,		_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     int		=(
         (
             DeiTModel,
             DeiTForImageClassification,
             DeiTForImageClassificationWithTeacher,
             DeiTForMaskedImageModeling,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE     :     Dict		=(
         {
             """feature-extraction""": DeiTModel,
             """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE     :     str		=False
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=False
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		=False



     def 			a__  (					self       ):
            _A=	DeiTModelTester(self       )
            _A=	ConfigTester(self				,      config_class=lowerCAmelCase__				,      has_text_modality=lowerCAmelCase__				,      hidden_size=37       )



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



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



     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

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



     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()

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



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__=False       ):
            _A=	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 			a__  (					self       ):
            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:
                   # DeiTForImageClassificationWithTeacher supports inference-only
                   if (
                       model_class in get_values(lowerCAmelCase__       )
                       or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                   ):
                          continue
                   _A=	model_class(lowerCAmelCase__       )
                   model.to(lowerCAmelCase__       )
                   model.train()
                   _A=	self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__				,      return_labels=lowerCAmelCase__       )
                   _A=	model(**lowerCAmelCase__       ).loss
                   loss.backward()



     def 			a__  (					self       ):
            _A,			_A=	self.model_tester.prepare_config_and_inputs_for_common()
            if not self.model_tester.is_training:
                   return

            _A=	False
            _A=	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
                   _A=	model_class(lowerCAmelCase__       )
                   model.gradient_checkpointing_enable()
                   model.to(lowerCAmelCase__       )
                   model.train()
                   _A=	self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__				,      return_labels=lowerCAmelCase__       )
                   _A=	model(**lowerCAmelCase__       ).loss
                   loss.backward()



     def 			a__  (					self       ):
            _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(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']}"       ):
                                 _A=	problem_type['title']
                                 _A=	problem_type['num_labels']

                                 _A=	model_class(lowerCAmelCase__       )
                                 model.to(lowerCAmelCase__       )
                                 model.train()

                                 _A=	self._prepare_for_class(lowerCAmelCase__				,      lowerCAmelCase__				,      return_labels=lowerCAmelCase__       )

                                 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=lowerCAmelCase__       ) as warning_list:
                                        _A=	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 			a__  (					self       ):
            for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=	DeiTModel.from_pretrained(lowerCAmelCase__       )
                   self.assertIsNotNone(lowerCAmelCase__       )


def 							UpperCamelCase				(    )		->						Optional[int]:




       '''simple docstring'''






       _A=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
       return image





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



     @cached_property
     def 			a__  (					self       ):
            return (
                DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'       )
                if is_vision_available()
                else None
            )



     @slow
     def 			a__  (					self       ):
            _A=	DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'       ).to(
                lowerCAmelCase__       )

            _A=	self.default_image_processor
            _A=	prepare_img()
            _A=	image_processor(images=lowerCAmelCase__				,      return_tensors='pt'       ).to(lowerCAmelCase__       )

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

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

            _A=	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 			a__  (					self       ):
            _A=	DeiTModel.from_pretrained(
                'facebook/deit-base-distilled-patch16-224'				,      torch_dtype=torch.floataa				,      device_map='auto'       )
            _A=	self.default_image_processor

            _A=	prepare_img()
            _A=	image_processor(images=lowerCAmelCase__				,      return_tensors='pt'       )
            _A=	inputs.pixel_values.to(lowerCAmelCase__       )

            # forward pass to make sure inference works in fp16
            with torch.no_grad():
                   _A=	model(lowerCAmelCase__       )
import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=UnCLIPImageVariationPipeline
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=IMAGE_VARIATION_BATCH_PARAMS

     _SCREAMING_SNAKE_CASE     :     int		=[
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=False



     @property
     def 			a__  (					self       ):
            return 32



     @property
     def 			a__  (					self       ):
            return 32



     @property
     def 			a__  (					self       ):
            return self.time_input_dim



     @property
     def 			a__  (					self       ):
            return self.time_input_dim * 4



     @property
     def 			a__  (					self       ):
            return 100



     @property
     def 			a__  (					self       ):
            _A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
            return tokenizer



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	CLIPTextConfig(
                bos_token_id=0				,      eos_token_id=2				,      hidden_size=self.text_embedder_hidden_size				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
            return CLIPTextModelWithProjection(lowerCAmelCase__       )



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	CLIPVisionConfig(
                hidden_size=self.text_embedder_hidden_size				,      projection_dim=self.text_embedder_hidden_size				,      num_hidden_layers=5				,      num_attention_heads=4				,      image_size=32				,      intermediate_size=37				,      patch_size=1				,      )
            return CLIPVisionModelWithProjection(lowerCAmelCase__       )



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )

            _A=	{
                'clip_embeddings_dim': self.text_embedder_hidden_size,
                'time_embed_dim': self.time_embed_dim,
                'cross_attention_dim': self.cross_attention_dim,
            }

            _A=	UnCLIPTextProjModel(**lowerCAmelCase__       )
            return model



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )

            _A=	{
                'sample_size': 32,
                # RGB in channels
                'in_channels': 3,
                # Out channels is double in channels because predicts mean and variance
                'out_channels': 6,
                'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                'layers_per_block': 1,
                'cross_attention_dim': self.cross_attention_dim,
                'attention_head_dim': 4,
                'resnet_time_scale_shift': 'scale_shift',
                'class_embed_type': 'identity',
            }

            _A=	UNetaDConditionModel(**lowerCAmelCase__       )
            return model



     @property
     def 			a__  (					self       ):
            return {
                "sample_size": 64,
                "layers_per_block": 1,
                "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                "in_channels": 6,
                "out_channels": 3,
            }



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )

            _A=	UNetaDModel(**self.dummy_super_res_kwargs       )
            return model



     @property
     def 			a__  (					self       ):
            # seeded differently to get different unet than `self.dummy_super_res_first`
            torch.manual_seed(1       )

            _A=	UNetaDModel(**self.dummy_super_res_kwargs       )
            return model



     def 			a__  (					self       ):
            _A=	self.dummy_decoder
            _A=	self.dummy_text_proj
            _A=	self.dummy_text_encoder
            _A=	self.dummy_tokenizer
            _A=	self.dummy_super_res_first
            _A=	self.dummy_super_res_last

            _A=	UnCLIPScheduler(
                variance_type='learned_range'				,      prediction_type='epsilon'				,      num_train_timesteps=1000				,      )

            _A=	UnCLIPScheduler(
                variance_type='fixed_small_log'				,      prediction_type='epsilon'				,      num_train_timesteps=1000				,      )

            _A=	CLIPImageProcessor(crop_size=32				,      size=32       )

            _A=	self.dummy_image_encoder

            return {
                "decoder": decoder,
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "text_proj": text_proj,
                "feature_extractor": feature_extractor,
                "image_encoder": image_encoder,
                "super_res_first": super_res_first,
                "super_res_last": super_res_last,
                "decoder_scheduler": decoder_scheduler,
                "super_res_scheduler": super_res_scheduler,
            }



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0				,      lowerCAmelCase__=True       ):
            _A=	floats_tensor((1, 3, 32, 32)				,      rng=random.Random(lowerCAmelCase__       )       ).to(lowerCAmelCase__       )
            if str(lowerCAmelCase__       ).startswith('mps'       ):
                   _A=	torch.manual_seed(lowerCAmelCase__       )
            else:
                   _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )

            if pil_image:
                   _A=	input_image * 0.5 + 0.5
                   _A=	input_image.clamp(0				,      1       )
                   _A=	input_image.cpu().permute(0				,      2				,      3				,      1       ).float().numpy()
                   _A=	DiffusionPipeline.numpy_to_pil(lowerCAmelCase__       )[0]

            return {
                "image": input_image,
                "generator": generator,
                "decoder_num_inference_steps": 2,
                "super_res_num_inference_steps": 2,
                "output_type": "np",
            }



     def 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

            assert image.shape == (1, 64, 64, 3)

            _A=	np.array(
                [
                    0.9997,
                    0.0002,
                    0.9997,
                    0.9997,
                    0.9969,
                    0.0023,
                    0.9997,
                    0.9969,
                    0.9970,
                ]       )

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



     def 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

            assert image.shape == (1, 64, 64, 3)

            _A=	np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]       )

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



     def 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            _A=	[
                pipeline_inputs['image'],
                pipeline_inputs['image'],
            ]

            _A=	pipe(**lowerCAmelCase__       )
            _A=	output.images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            _A=	[
                tuple_pipeline_inputs['image'],
                tuple_pipeline_inputs['image'],
            ]

            _A=	pipe(
                **lowerCAmelCase__				,      return_dict=lowerCAmelCase__				,      )[0]

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

            assert image.shape == (2, 64, 64, 3)

            _A=	np.array(
                [
                    0.9997,
                    0.9989,
                    0.0008,
                    0.0021,
                    0.9960,
                    0.0018,
                    0.0014,
                    0.0002,
                    0.9933,
                ]       )

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



     def 			a__  (					self       ):
            _A=	torch.device('cpu'       )





            class      lowerCAmelCase						:
                 _SCREAMING_SNAKE_CASE     :     List[str]		=1


            _A=	self.get_dummy_components()

            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
            _A=	pipe.decoder.dtype
            _A=	1

            _A=	(
                batch_size,
                pipe.decoder.config.in_channels,
                pipe.decoder.config.sample_size,
                pipe.decoder.config.sample_size,
            )
            _A=	pipe.prepare_latents(
                lowerCAmelCase__				,      dtype=lowerCAmelCase__				,      device=lowerCAmelCase__				,      generator=lowerCAmelCase__				,      latents=lowerCAmelCase__				,      scheduler=DummyScheduler()       )

            _A=	(
                batch_size,
                pipe.super_res_first.config.in_channels // 2,
                pipe.super_res_first.config.sample_size,
                pipe.super_res_first.config.sample_size,
            )
            _A=	pipe.prepare_latents(
                lowerCAmelCase__				,      dtype=lowerCAmelCase__				,      device=lowerCAmelCase__				,      generator=lowerCAmelCase__				,      latents=lowerCAmelCase__				,      scheduler=DummyScheduler()       )

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )

            _A=	pipe(
                **lowerCAmelCase__				,      decoder_latents=lowerCAmelCase__				,      super_res_latents=lowerCAmelCase__       ).images

            _A=	self.get_dummy_inputs(lowerCAmelCase__				,      pil_image=lowerCAmelCase__       )
            # Don't pass image, instead pass embedding
            _A=	pipeline_inputs.pop('image'       )
            _A=	pipe.image_encoder(lowerCAmelCase__       ).image_embeds

            _A=	pipe(
                **lowerCAmelCase__				,      decoder_latents=lowerCAmelCase__				,      super_res_latents=lowerCAmelCase__				,      image_embeddings=lowerCAmelCase__				,      ).images

            # make sure passing text embeddings manually is identical
            assert np.abs(img_out_a - img_out_a       ).max() < 1E-4



     @skip_mps
     def 			a__  (					self       ):
            _A=	torch_device == 'cpu'

            # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
            _A=	1E-2

            self._test_attention_slicing_forward_pass(
                test_max_difference=lowerCAmelCase__				,      expected_max_diff=lowerCAmelCase__       )



     @skip_mps
     def 			a__  (					self       ):
            _A=	torch_device == 'cpu'
            _A=	True
            _A=	[
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

            self._test_inference_batch_single_identical(
                test_max_difference=lowerCAmelCase__				,      relax_max_difference=lowerCAmelCase__				,      additional_params_copy_to_batched_inputs=lowerCAmelCase__				,      )



     def 			a__  (					self       ):
            _A=	[
                'decoder_num_inference_steps',
                'super_res_num_inference_steps',
            ]

            if torch_device == "mps":
                   # TODO: MPS errors with larger batch sizes
                   _A=	[2, 3]
                   self._test_inference_batch_consistent(
                       batch_sizes=lowerCAmelCase__				,      additional_params_copy_to_batched_inputs=lowerCAmelCase__				,      )
            else:
                   self._test_inference_batch_consistent(
                       additional_params_copy_to_batched_inputs=lowerCAmelCase__       )



     @skip_mps
     def 			a__  (					self       ):
            return super().test_dict_tuple_outputs_equivalent()



     @skip_mps
     def 			a__  (					self       ):
            return super().test_save_load_local()





     @skip_mps
     def 			a__  (					self       ):
            return super().test_save_load_optional_components()





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



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





     def 			a__  (					self       ):
            _A=	load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'       )
            _A=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'       )

            _A=	UnCLIPImageVariationPipeline.from_pretrained(
                'kakaobrain/karlo-v1-alpha-image-variations'				,      torch_dtype=torch.floataa       )
            _A=	pipeline.to(lowerCAmelCase__       )
            pipeline.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.Generator(device='cpu'       ).manual_seed(0       )
            _A=	pipeline(
                lowerCAmelCase__				,      generator=lowerCAmelCase__				,      output_type='np'				,      )

            _A=	output.images[0]

            assert image.shape == (256, 256, 3)

            assert_mean_pixel_difference(lowerCAmelCase__				,      lowerCAmelCase__				,      15       )