


'''simple docstring'''






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    MODEL_MAPPING,
		    BeitForImageClassification,
		    BeitForMaskedImageModeling,
		    BeitForSemanticSegmentation,
		    BeitModel,
		)
		from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		import PIL
		from PIL import Image

		from transformers import BeitImageProcessor




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=1_00   ,       lowerCamelCase_=13   ,       lowerCamelCase_=30   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=True   ,       lowerCamelCase_=True   ,       lowerCamelCase_=32   ,       lowerCamelCase_=4   ,       lowerCamelCase_=4   ,       lowerCamelCase_=37   ,       lowerCamelCase_="gelu"   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=0.1   ,       lowerCamelCase_=10   ,       lowerCamelCase_=0.02   ,       lowerCamelCase_=3   ,       lowerCamelCase_=None   ,       lowerCamelCase_=[0, 1, 2, 3]   ,       )			->				List[str]:
														lowerCAmelCase__			  =      parent
														lowerCAmelCase__			  =      1_00
														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__			  =      out_indices
														lowerCAmelCase__			  =      num_labels

														# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
														lowerCAmelCase__			  =      (image_size // patch_size) ** 2
														lowerCAmelCase__			  =      num_patches + 1







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      None
														if self.use_labels:
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size]   ,       self.type_sequence_label_size  )
																					lowerCAmelCase__			  =      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,       self.num_labels  )

														lowerCAmelCase__			  =      self.get_config()

														return config, pixel_values, labels, pixel_labels







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														return BeitConfig(
														    vocab_size=self.vocab_size   ,       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   ,       out_indices=self.out_indices   ,       )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      BeitModel(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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      BeitForMaskedImageModeling(config=lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length - 1, self.vocab_size)  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.type_sequence_label_size
														lowerCAmelCase__			  =      BeitForImageClassification(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__			  =      BeitForImageClassification(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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				List[Any]:
														lowerCAmelCase__			  =      self.num_labels
														lowerCAmelCase__			  =      BeitForSemanticSegmentation(lowerCamelCase_  )
														model.to(lowerCamelCase_  )
														model.eval()
														lowerCAmelCase__			  =      model(lowerCamelCase_  )
														self.parent.assertEqual(
														    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
														lowerCAmelCase__			  =      model(lowerCamelCase_   ,       labels=lowerCamelCase_  )
														self.parent.assertEqual(
														    result.logits.shape   ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      self.prepare_config_and_inputs()
														lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__			  =      config_and_inputs
														lowerCAmelCase__			  =      {'''pixel_values''': pixel_values}
														return config, inputs_dict




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





							'''simple docstring'''






							lowercase__     :  str      		= (
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							lowercase__     :  List[Any]      		= (
							    {
							        "feature-extraction": BeitModel,
							        "image-classification": BeitForImageClassification,
							        "image-segmentation": BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase__     :  Tuple      		= False
							lowercase__     :  Dict      		= False
							lowercase__     :  Optional[int]      		= False







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      BeitModelTester(self  )
														lowerCAmelCase__			  =      ConfigTester(self   ,       config_class=lowerCamelCase_   ,       has_text_modality=lowerCamelCase_   ,       hidden_size=37  )







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







							@unittest.skip(reason='''BEiT does not use inputs_embeds'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														pass







							@require_torch_multi_gpu
							@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''  )
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														pass







							def     __SCREAMING_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_  )
																					self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)  )
																					lowerCAmelCase__			  =      model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(lowerCamelCase_   ,       nn.Linear  )  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														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:
																					# we don't test BeitForMaskedImageModeling
																					if model_class in [*get_values(lowerCamelCase_  ), BeitForMaskedImageModeling]:
																												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     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														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:
																					# we don't test BeitForMaskedImageModeling
																					if (
																					    model_class in [*get_values(lowerCamelCase_  ), BeitForMaskedImageModeling]
																					    or not model_class.supports_gradient_checkpointing
																					):
																												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     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      self.model_tester.prepare_config_and_inputs_for_common()

														lowerCAmelCase__			  =      _config_zero_init(lowerCamelCase_  )
														for model_class in self.all_model_classes:
																					lowerCAmelCase__			  =      model_class(config=lowerCamelCase_  )
																					for name, param in model.named_parameters():
																												# we skip lambda parameters as these require special initial values
																												# determined by config.layer_scale_init_value
																												if "lambda" in name:
																																			continue
																												if param.requires_grad:
																																			self.assertIn(
																																			    ((param.data.mean() * 1e9).round() / 1e9).item()   ,       [0.0, 1.0]   ,       msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""   ,       )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCAmelCase__			  =      BeitModel.from_pretrained(lowerCamelCase_  )
																					self.assertIsNotNone(lowerCamelCase_  )


def    _snake_case  (	)		->       Optional[int]:
							lowerCAmelCase__			  =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
							return image






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





							'''simple docstring'''






							@cached_property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''  ) if is_vision_available() else None







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''  ).to(lowerCamelCase_  )

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

														# prepare bool_masked_pos
														lowerCAmelCase__			  =      torch.ones((1, 1_96)   ,       dtype=torch.bool  ).to(lowerCamelCase_  )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__			  =      model(pixel_values=lowerCamelCase_   ,       bool_masked_pos=lowerCamelCase_  )
														lowerCAmelCase__			  =      outputs.logits

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 1_96, 81_92)  )
														self.assertEqual(logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor(
														    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]  ).to(lowerCamelCase_  )

														self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]   ,       lowerCamelCase_   ,       atol=1e-2  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      BeitForImageClassification.from_pretrained('''microsoft/beit-base-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_  )
														lowerCAmelCase__			  =      outputs.logits

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 10_00)  )
														self.assertEqual(logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor([-1.2_385, -1.0_987, -1.0_108]  ).to(lowerCamelCase_  )

														self.assertTrue(torch.allclose(logits[0, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )

														lowerCAmelCase__			  =      2_81
														self.assertEqual(logits.argmax(-1  ).item()   ,       lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''  ).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_  )
														lowerCAmelCase__			  =      outputs.logits

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 2_18_41)  )
														self.assertEqual(logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      torch.tensor([1.6_881, -0.2_787, 0.5_901]  ).to(lowerCamelCase_  )

														self.assertTrue(torch.allclose(logits[0, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )

														lowerCAmelCase__			  =      23_96
														self.assertEqual(logits.argmax(-1  ).item()   ,       lowerCamelCase_  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''  )
														lowerCAmelCase__			  =      model.to(lowerCamelCase_  )

														lowerCAmelCase__			  =      BeitImageProcessor(do_resize=lowerCamelCase_   ,       size=6_40   ,       do_center_crop=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,       split='''test'''  )
														lowerCAmelCase__			  =      Image.open(ds[0]['''file''']  )
														lowerCAmelCase__			  =      image_processor(images=lowerCamelCase_   ,       return_tensors='''pt'''  ).to(lowerCamelCase_  )

														# forward pass
														with torch.no_grad():
																					lowerCAmelCase__			  =      model(**lowerCamelCase_  )
														lowerCAmelCase__			  =      outputs.logits

														# verify the logits
														lowerCAmelCase__			  =      torch.Size((1, 1_50, 1_60, 1_60)  )
														self.assertEqual(logits.shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      version.parse(PIL.__version__  ) < version.parse('''9.0.0'''  )

														if is_pillow_less_than_a:
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
																					        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
																					        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
																					    ]   ,       device=lowerCamelCase_   ,       )
														else:
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
																					        [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
																					        [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
																					    ]   ,       device=lowerCamelCase_   ,       )

														self.assertTrue(torch.allclose(logits[0, :3, :3, :3]   ,       lowerCamelCase_   ,       atol=1e-4  )  )







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''  )
														lowerCAmelCase__			  =      model.to(lowerCamelCase_  )

														lowerCAmelCase__			  =      BeitImageProcessor(do_resize=lowerCamelCase_   ,       size=6_40   ,       do_center_crop=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_dataset('''hf-internal-testing/fixtures_ade20k'''   ,       split='''test'''  )
														lowerCAmelCase__			  =      Image.open(ds[0]['''file''']  )
														lowerCAmelCase__			  =      image_processor(images=lowerCamelCase_   ,       return_tensors='''pt'''  ).to(lowerCamelCase_  )

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

														lowerCAmelCase__			  =      outputs.logits.detach().cpu()

														lowerCAmelCase__			  =      image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_   ,       target_sizes=[(5_00, 3_00)]  )
														lowerCAmelCase__			  =      torch.Size((5_00, 3_00)  )
														self.assertEqual(segmentation[0].shape   ,       lowerCamelCase_  )

														lowerCAmelCase__			  =      image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_  )
														lowerCAmelCase__			  =      torch.Size((1_60, 1_60)  )
														self.assertEqual(segmentation[0].shape   ,       lowerCamelCase_  )



'''simple docstring'''






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 						a__							(				a__							,							a__							,							a__							,							unittest.TestCase    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= AltDiffusionPipeline
							lowercase__     :  Dict      		= TEXT_TO_IMAGE_PARAMS
							lowercase__     :  str      		= TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__     :  Any      		= TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__     :  List[str]      		= TEXT_TO_IMAGE_IMAGE_PARAMS







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				str:
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      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   ,       )
														lowerCAmelCase__			  =      DDIMScheduler(
														    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=lowerCamelCase_   ,       set_alpha_to_one=lowerCamelCase_   ,       )
														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   ,       )

														# TODO: address the non-deterministic text encoder (fails for save-load tests)
														# torch.manual_seed(0)
														# text_encoder_config = RobertaSeriesConfig(
														#     hidden_size=32,
														#     project_dim=32,
														#     intermediate_size=37,
														#     layer_norm_eps=1e-05,
														#     num_attention_heads=4,
														#     num_hidden_layers=5,
														#     vocab_size=5002,
														# )
														# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

														torch.manual_seed(0  )
														lowerCAmelCase__			  =      CLIPTextConfig(
														    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       projection_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=50_02   ,       )
														lowerCAmelCase__			  =      CLIPTextModel(lowerCamelCase_  )

														lowerCAmelCase__			  =      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''  )
														lowerCAmelCase__			  =      77

														lowerCAmelCase__			  =      {
														    '''unet''': unet,
														    '''scheduler''': scheduler,
														    '''vae''': vae,
														    '''text_encoder''': text_encoder,
														    '''tokenizer''': tokenizer,
														    '''safety_checker''': None,
														    '''feature_extractor''': None,
														}
														return components







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_=0  )			->				List[str]:
														if str(lowerCamelCase_  ).startswith('''mps'''  ):
																					lowerCAmelCase__			  =      torch.manual_seed(lowerCamelCase_  )
														else:
																					lowerCAmelCase__			  =      torch.Generator(device=lowerCamelCase_  ).manual_seed(lowerCamelCase_  )
														lowerCAmelCase__			  =      {
														    '''prompt''': '''A painting of a squirrel eating a burger''',
														    '''generator''': generator,
														    '''num_inference_steps''': 2,
														    '''guidance_scale''': 6.0,
														    '''output_type''': '''numpy''',
														}
														return inputs







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Union[str, Any]:
														super().test_attention_slicing_forward_pass(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[str]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder

														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      '''A photo of an astronaut'''
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.5_748_162, 0.60_447_145, 0.48_821_217, 0.50_100_636, 0.5_431_185, 0.45_763_683, 0.49_657_696, 0.48_132_733, 0.47_573_093]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														lowerCAmelCase__			  =      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

														lowerCAmelCase__			  =      self.get_dummy_components()
														lowerCAmelCase__			  =      PNDMScheduler(skip_prk_steps=lowerCamelCase_  )
														torch.manual_seed(0  )
														lowerCAmelCase__			  =      RobertaSeriesConfig(
														    hidden_size=32   ,       project_dim=32   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       vocab_size=50_02   ,       )
														# TODO: remove after fixing the non-deterministic text encoder
														lowerCAmelCase__			  =      RobertaSeriesModelWithTransformation(lowerCamelCase_  )
														lowerCAmelCase__			  =      text_encoder
														lowerCAmelCase__			  =      AltDiffusionPipeline(**lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      self.get_dummy_inputs(lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe(**lowerCamelCase_  )
														lowerCAmelCase__			  =      output.images
														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														lowerCAmelCase__			  =      np.array(
														    [0.51_605_093, 0.5_707_241, 0.47_365_507, 0.50_578_886, 0.5_633_877, 0.4_642_503, 0.5_182_081, 0.48_763_484, 0.49_084_237]  )

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




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





							'''simple docstring'''






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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														# make sure here that pndm scheduler skips prk
														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       guidance_scale=6.0   ,       num_inference_steps=20   ,       output_type='''np'''  )

														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_010, 0.0_800, 0.0_794, 0.0_885, 0.0_843, 0.0_762, 0.0_769, 0.0_729, 0.0_586]  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''   ,       subfolder='''scheduler'''  )

														lowerCAmelCase__			  =      AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''   ,       scheduler=lowerCamelCase_   ,       safety_checker=lowerCamelCase_  )
														lowerCAmelCase__			  =      alt_pipe.to(lowerCamelCase_  )
														alt_pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger'''
														lowerCAmelCase__			  =      torch.manual_seed(0  )

														lowerCAmelCase__			  =      alt_pipe([prompt]   ,       generator=lowerCamelCase_   ,       num_inference_steps=2   ,       output_type='''numpy'''  )
														lowerCAmelCase__			  =      output.images

														lowerCAmelCase__			  =      image[0, -3:, -3:, -1]

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.4_019, 0.4_052, 0.3_810, 0.4_119, 0.3_916, 0.3_982, 0.4_651, 0.4_195, 0.5_323]  )

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