

from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)





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  :


						def __init__(       self						,					A_						,					A_=100						,					A_=13						,					A_=30						,					A_=2						,					A_=3						,					A_=True						,					A_=True						,					A_=32						,					A_=4						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=10						,					A_=0.02						,					A_=3						,					A_=None						,					A_=[0, 1, 2, 3]						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    parent
										_UpperCAmelCase       :		Any 		=    100
										_UpperCAmelCase       :		Optional[Any] 		=    batch_size
										_UpperCAmelCase       :		Any 		=    image_size
										_UpperCAmelCase       :		int 		=    patch_size
										_UpperCAmelCase       :		Dict 		=    num_channels
										_UpperCAmelCase       :		int 		=    is_training
										_UpperCAmelCase       :		Union[str, Any] 		=    use_labels
										_UpperCAmelCase       :		Optional[Any] 		=    hidden_size
										_UpperCAmelCase       :		str 		=    num_hidden_layers
										_UpperCAmelCase       :		Tuple 		=    num_attention_heads
										_UpperCAmelCase       :		List[str] 		=    intermediate_size
										_UpperCAmelCase       :		List[str] 		=    hidden_act
										_UpperCAmelCase       :		List[Any] 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Any 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Dict 		=    type_sequence_label_size
										_UpperCAmelCase       :		Optional[Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[int] 		=    scope
										_UpperCAmelCase       :		Any 		=    out_indices
										_UpperCAmelCase       :		List[str] 		=    num_labels

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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										_UpperCAmelCase       :		Optional[Any] 		=    None
										_UpperCAmelCase       :		str 		=    None
										if self.use_labels:
														_UpperCAmelCase       :		Union[str, Any] 		=    ids_tensor([self.batch_size]						,					self.type_sequence_label_size	)
														_UpperCAmelCase       :		Optional[int] 		=    ids_tensor([self.batch_size, self.image_size, self.image_size]						,					self.num_labels	)

										_UpperCAmelCase       :		Tuple 		=    self.get_config()

										return config, pixel_values, labels, pixel_labels


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										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=A_						,					initializer_range=self.initializer_range						,					out_indices=self.out_indices						,					)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    BeitModel(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		int 		=    model(A_	)
										self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    BeitForMaskedImageModeling(config=A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		Any 		=    model(A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length - 1, self.vocab_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.type_sequence_label_size
										_UpperCAmelCase       :		Optional[int] 		=    BeitForImageClassification(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		List[str] 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)	)

										# test greyscale images
										_UpperCAmelCase       :		Optional[Any] 		=    1
										_UpperCAmelCase       :		Tuple 		=    BeitForImageClassification(A_	)
										model.to(A_	)
										model.eval()

										_UpperCAmelCase       :		int 		=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
										_UpperCAmelCase       :		Dict 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.type_sequence_label_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.num_labels
										_UpperCAmelCase       :		Dict 		=    BeitForSemanticSegmentation(A_	)
										model.to(A_	)
										model.eval()
										_UpperCAmelCase       :		str 		=    model(A_	)
										self.parent.assertEqual(
										    result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
										_UpperCAmelCase       :		List[Any] 		=    model(A_						,					labels=A_	)
										self.parent.assertEqual(
										    result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.prepare_config_and_inputs()
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    config_and_inputs
										_UpperCAmelCase       :		int 		=    {"pixel_values": pixel_values}
										return config, inputs_dict







@require_torch
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_lowercase    						=			(
						    {
						        "feature-extraction": BeitModel,
						        "image-classification": BeitForImageClassification,
						        "image-segmentation": BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_lowercase    						=			False
						_lowercase    						=			False
						_lowercase    						=			False


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    BeitModelTester(self	)
										_UpperCAmelCase       :		List[Any] 		=    ConfigTester(self						,					config_class=A_						,					has_text_modality=A_						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						@unittest.skip(reason="BEiT does not use inputs_embeds"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						@require_torch_multi_gpu
						@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										pass


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_UpperCAmelCase       :		Union[str, Any] 		=    model_class(A_	)
														self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)	)
														_UpperCAmelCase       :		Tuple 		=    model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(A_						,					nn.Linear	)	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    self.model_tester.prepare_config_and_inputs_for_common()

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

														_UpperCAmelCase       :		int 		=    ["pixel_values"]
														self.assertListEqual(arg_names[:1]						,					A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										if not self.model_tester.is_training:
														return

										_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()
										_UpperCAmelCase       :		str 		=    True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if model_class in [*get_values(A_	), BeitForMaskedImageModeling]:
																		continue

														_UpperCAmelCase       :		Union[str, Any] 		=    model_class(A_	)
														model.to(A_	)
														model.train()
														_UpperCAmelCase       :		str 		=    self._prepare_for_class(A_						,					A_						,					return_labels=A_	)
														_UpperCAmelCase       :		Optional[Any] 		=    model(**A_	).loss
														loss.backward()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs_for_common()
										if not self.model_tester.is_training:
														return

										_UpperCAmelCase       :		Tuple 		=    False
										_UpperCAmelCase       :		Any 		=    True

										for model_class in self.all_model_classes:
														# we don't test BeitForMaskedImageModeling
														if (
														    model_class in [*get_values(A_	), BeitForMaskedImageModeling]
														    or not model_class.supports_gradient_checkpointing
														):
																		continue

														_UpperCAmelCase       :		Optional[Any] 		=    model_class(A_	)
														model.gradient_checkpointing_enable()
														model.to(A_	)
														model.train()
														_UpperCAmelCase       :		Union[str, Any] 		=    self._prepare_for_class(A_						,					A_						,					return_labels=A_	)
														_UpperCAmelCase       :		Optional[Any] 		=    model(**A_	).loss
														loss.backward()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCAmelCase       :		Optional[Any] 		=    _config_zero_init(A_	)
										for model_class in self.all_model_classes:
														_UpperCAmelCase       :		Tuple 		=    model_class(config=A_	)
														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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase       :		Dict 		=    BeitModel.from_pretrained(A_	)
														self.assertIsNotNone(A_	)







def 							__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		Optional[int] 		=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
				return image





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


						@cached_property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"	) if is_vision_available() else None


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"	).to(A_	)

										_UpperCAmelCase       :		Dict 		=    self.default_image_processor
										_UpperCAmelCase       :		Optional[Any] 		=    prepare_img()
										_UpperCAmelCase       :		Any 		=    image_processor(images=A_						,					return_tensors="pt"	).pixel_values.to(A_	)

										# prepare bool_masked_pos
										_UpperCAmelCase       :		Dict 		=    torch.ones((1, 196)						,					dtype=torch.bool	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		List[str] 		=    model(pixel_values=A_						,					bool_masked_pos=A_	)
										_UpperCAmelCase       :		Optional[int] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		Optional[int] 		=    torch.Size((1, 196, 8192)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		Tuple 		=    torch.tensor(
										    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]	).to(A_	)

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


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"	).to(A_	)

										_UpperCAmelCase       :		str 		=    self.default_image_processor
										_UpperCAmelCase       :		Union[str, Any] 		=    prepare_img()
										_UpperCAmelCase       :		List[Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Tuple 		=    model(**A_	)
										_UpperCAmelCase       :		Dict 		=    outputs.logits

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

										_UpperCAmelCase       :		int 		=    torch.tensor([-1.23_85, -1.09_87, -1.01_08]	).to(A_	)

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

										_UpperCAmelCase       :		List[Any] 		=    281
										self.assertEqual(logits.argmax(-1	).item()						,					A_	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"	).to(
										    A_	)

										_UpperCAmelCase       :		str 		=    self.default_image_processor
										_UpperCAmelCase       :		List[str] 		=    prepare_img()
										_UpperCAmelCase       :		List[Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Any 		=    model(**A_	)
										_UpperCAmelCase       :		List[Any] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		List[str] 		=    torch.Size((1, 21841)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor([1.68_81, -0.27_87, 0.59_01]	).to(A_	)

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

										_UpperCAmelCase       :		List[str] 		=    2396
										self.assertEqual(logits.argmax(-1	).item()						,					A_	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"	)
										_UpperCAmelCase       :		str 		=    model.to(A_	)

										_UpperCAmelCase       :		int 		=    BeitImageProcessor(do_resize=A_						,					size=640						,					do_center_crop=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    load_dataset("hf-internal-testing/fixtures_ade20k"						,					split="test"	)
										_UpperCAmelCase       :		Any 		=    Image.open(ds[0]["file"]	)
										_UpperCAmelCase       :		int 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		Dict 		=    model(**A_	)
										_UpperCAmelCase       :		Optional[int] 		=    outputs.logits

										# verify the logits
										_UpperCAmelCase       :		str 		=    torch.Size((1, 150, 160, 160)	)
										self.assertEqual(logits.shape						,					A_	)

										_UpperCAmelCase       :		str 		=    version.parse(PIL.__version__	) < version.parse("9.0.0"	)

										if is_pillow_less_than_a:
														_UpperCAmelCase       :		int 		=    torch.tensor(
														    [
														        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
														        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
														        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
														    ]						,					device=A_						,					)
										else:
														_UpperCAmelCase       :		Any 		=    torch.tensor(
														    [
														        [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
														        [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
														        [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
														    ]						,					device=A_						,					)

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





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"	)
										_UpperCAmelCase       :		Dict 		=    model.to(A_	)

										_UpperCAmelCase       :		Dict 		=    BeitImageProcessor(do_resize=A_						,					size=640						,					do_center_crop=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    load_dataset("hf-internal-testing/fixtures_ade20k"						,					split="test"	)
										_UpperCAmelCase       :		List[str] 		=    Image.open(ds[0]["file"]	)
										_UpperCAmelCase       :		Union[str, Any] 		=    image_processor(images=A_						,					return_tensors="pt"	).to(A_	)

										# forward pass
										with torch.no_grad():
														_UpperCAmelCase       :		List[Any] 		=    model(**A_	)

										_UpperCAmelCase       :		Tuple 		=    outputs.logits.detach().cpu()

										_UpperCAmelCase       :		str 		=    image_processor.post_process_semantic_segmentation(outputs=A_						,					target_sizes=[(500, 300)]	)
										_UpperCAmelCase       :		Dict 		=    torch.Size((500, 300)	)
										self.assertEqual(segmentation[0].shape						,					A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    image_processor.post_process_semantic_segmentation(outputs=A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Size((160, 160)	)
										self.assertEqual(segmentation[0].shape						,					A_	)


