

"""simple docstring"""




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image





class    lowerCamelCase						:

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						int=2    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						List[str]=False    ,				__UpperCAmelCase				:						Dict=1_0    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[str]=3_2 * 8    ,				__UpperCAmelCase				:						List[Any]=3_2 * 8    ,				__UpperCAmelCase				:						Tuple=4    ,				__UpperCAmelCase				:						Tuple=6_4    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_auxiliary_loss
						SCREAMING_SNAKE_CASE__								=      num_queries
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      min_size
						SCREAMING_SNAKE_CASE__								=      max_size
						SCREAMING_SNAKE_CASE__								=      num_labels
						SCREAMING_SNAKE_CASE__								=      hidden_dim
						SCREAMING_SNAKE_CASE__								=      hidden_dim

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
						    __UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,				device=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      (
						    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,				device=__UpperCAmelCase       ) > 0.5
						).float()
						SCREAMING_SNAKE_CASE__								=      (torch.rand((self.batch_size, self.num_labels)    ,				device=__UpperCAmelCase       ) > 0.5).long()

						SCREAMING_SNAKE_CASE__								=      self.get_config()
						return config, pixel_values, pixel_mask, mask_labels, class_labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerConfig(
						    hidden_size=self.hidden_dim    ,				)
						SCREAMING_SNAKE_CASE__								=      self.num_queries
						SCREAMING_SNAKE_CASE__								=      self.num_labels

						SCREAMING_SNAKE_CASE__								=      [1, 1, 1, 1]
						SCREAMING_SNAKE_CASE__								=      self.num_channels

						SCREAMING_SNAKE_CASE__								=      6_4
						SCREAMING_SNAKE_CASE__								=      1_2_8
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						SCREAMING_SNAKE_CASE__								=      self.hidden_dim
						return config

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
						return config, inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      output.encoder_hidden_states
						SCREAMING_SNAKE_CASE__								=      output.pixel_decoder_hidden_states
						SCREAMING_SNAKE_CASE__								=      output.transformer_decoder_hidden_states

						self.parent.assertTrue(len(__UpperCAmelCase       )    ,				len(config.backbone_config.depths       )       )
						self.parent.assertTrue(len(__UpperCAmelCase       )    ,				len(config.backbone_config.depths       )       )
						self.parent.assertTrue(len(__UpperCAmelCase       )    ,				config.decoder_layers       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=False       )   ->    Tuple:
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      MaskaFormerModel(config=__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.eval()

									SCREAMING_SNAKE_CASE__								=      model(pixel_values=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

						self.parent.assertEqual(
						    output.transformer_decoder_last_hidden_state.shape    ,				(self.batch_size, self.num_queries, self.hidden_dim)    ,				)
						# let's ensure the other two hidden state exists
						self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
						self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

						if output_hidden_states:
									self.check_output_hidden_state(__UpperCAmelCase    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Dict       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

						def comm_check_on_output(__UpperCAmelCase				:						Optional[int]       ):
									# let's still check that all the required stuff is there
									self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
									self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
									self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
									# okay, now we need to check the logits shape
									# due to the encoder compression, masks have a //4 spatial size
									self.parent.assertEqual(
									    result.masks_queries_logits.shape    ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,				)
									# + 1 for null class
									self.parent.assertEqual(
									    result.class_queries_logits.shape    ,				(self.batch_size, self.num_queries, self.num_labels + 1)       )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(pixel_values=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )

									comm_check_on_output(__UpperCAmelCase       )

									SCREAMING_SNAKE_CASE__								=      model(
									    pixel_values=__UpperCAmelCase    ,				pixel_mask=__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       )

						comm_check_on_output(__UpperCAmelCase       )

						self.parent.assertTrue(result.loss is not None       )
						self.parent.assertEqual(result.loss.shape    ,				torch.Size([1]       )       )





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Union[str, Any]							=							(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
			lowerCamelCase__   :		Any							=							{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

			lowerCamelCase__   :		Tuple							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		Dict							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase       )

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase    ,				**__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCAmelCase       )

			@unittest.skip(reason="""Mask2Former does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
						pass

			@unittest.skip(reason="""Mask2Former does not have a get_input_embeddings method"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Any:
						pass

			@unittest.skip(reason="""Mask2Former is not a generative model"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Optional[Any]:
						pass

			@unittest.skip(reason="""Mask2Former does not use token embeddings"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Tuple:
						pass

			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
						pass

			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    int:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__								=      [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
						for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
									SCREAMING_SNAKE_CASE__								=      MaskaFormerModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      (self.model_tester.min_size,) * 2
						SCREAMING_SNAKE_CASE__								=      {
						    """pixel_values""": torch.randn((2, 3, *size)    ,				device=__UpperCAmelCase       ),
						    """mask_labels""": torch.randn((2, 1_0, *size)    ,				device=__UpperCAmelCase       ),
						    """class_labels""": torch.zeros(2    ,				1_0    ,				device=__UpperCAmelCase       ).long(),
						}
						SCREAMING_SNAKE_CASE__								=      self.model_tester.get_config()

						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						self.assertTrue(outputs.loss is not None       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase    ,				**__UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       ).to(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase    ,				output_attentions=__UpperCAmelCase       )
									self.assertTrue(outputs.attentions is not None       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__								=      self.all_model_classes[1]
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()

						SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.train()

						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       ).loss
						loss.backward()





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      self.all_model_classes[1]
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__								=      True
						SCREAMING_SNAKE_CASE__								=      True

						SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						model.train()

						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				mask_labels=__UpperCAmelCase    ,				class_labels=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      outputs.encoder_hidden_states[0]
						encoder_hidden_states.retain_grad()

						SCREAMING_SNAKE_CASE__								=      outputs.pixel_decoder_hidden_states[0]
						pixel_decoder_hidden_states.retain_grad()

						SCREAMING_SNAKE_CASE__								=      outputs.transformer_decoder_hidden_states[0]
						transformer_decoder_hidden_states.retain_grad()

						SCREAMING_SNAKE_CASE__								=      outputs.attentions[0]
						attentions.retain_grad()

						outputs.loss.backward(retain_graph=__UpperCAmelCase       )

						self.assertIsNotNone(encoder_hidden_states.grad       )
						self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
						self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
						self.assertIsNotNone(attentions.grad       )
A_							:			Optional[Any]											= 1E-4

def 	A       (     ):




			'''simple docstring'''


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





@require_vision
@slow
class    lowerCamelCase						(unittest.TestCase			):

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    int:
						return "facebook/mask2former-swin-small-coco-instance"

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
						return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
						# check size
						self.assertEqual(__UpperCAmelCase    ,				(1, 3, 3_8_4, 3_8_4)       )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]       ).to(__UpperCAmelCase       )
						self.assertTrue(
						    torch.allclose(
						        outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       ).eval()
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].shape
						# check size is divisible by 32
						self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0       )
						# check size
						self.assertEqual(__UpperCAmelCase    ,				(1, 3, 3_8_4, 3_8_4)       )

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )
						# masks_queries_logits
						SCREAMING_SNAKE_CASE__								=      outputs.masks_queries_logits
						self.assertEqual(
						    masks_queries_logits.shape    ,				(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
						SCREAMING_SNAKE_CASE__								=      [
						    [-8.7_839, -9.0_056, -8.8_121],
						    [-7.4_104, -7.0_313, -6.5_401],
						    [-6.6_105, -6.3_427, -6.4_675],
						]
						SCREAMING_SNAKE_CASE__								=      torch.tensor(__UpperCAmelCase       ).to(__UpperCAmelCase       )
						self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )
						# class_queries_logits
						SCREAMING_SNAKE_CASE__								=      outputs.class_queries_logits
						self.assertEqual(class_queries_logits.shape    ,				(1, model.config.num_queries, model.config.num_labels + 1)       )
						SCREAMING_SNAKE_CASE__								=      torch.tensor(
						    [
						        [1.8_324, -8.0_835, -4.1_922],
						        [0.8_450, -9.0_050, -3.6_053],
						        [0.3_045, -7.7_293, -3.0_275],
						    ]       ).to(__UpperCAmelCase       )
						self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,				__UpperCAmelCase    ,				atol=__UpperCAmelCase       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(__UpperCAmelCase       ).eval()
						SCREAMING_SNAKE_CASE__								=      self.default_image_processor

						SCREAMING_SNAKE_CASE__								=      image_processor(
						    [np.zeros((3, 8_0_0, 1_3_3_3)       ), np.zeros((3, 8_0_0, 1_3_3_3)       )]    ,				segmentation_maps=[np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       ), np.zeros((3_8_4, 3_8_4)       ).astype(np.floataa       )]    ,				return_tensors="""pt"""    ,				)

						SCREAMING_SNAKE_CASE__								=      inputs["""pixel_values"""].to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      [el.to(__UpperCAmelCase       ) for el in inputs["""mask_labels"""]]
						SCREAMING_SNAKE_CASE__								=      [el.to(__UpperCAmelCase       ) for el in inputs["""class_labels"""]]

						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						self.assertTrue(outputs.loss is not None       )





"""simple docstring"""




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

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


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor





class    lowerCamelCase						:

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						Optional[Any]=5    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						int=3_7    ,				__UpperCAmelCase				:						str="gelu"    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=1_0    ,				__UpperCAmelCase				:						Dict=0.02    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						str=2    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      encoder_stride

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

						SCREAMING_SNAKE_CASE__								=      None
						if self.use_labels:
									SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size]    ,				self.type_sequence_label_size       )

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						return DeiTConfig(
						    image_size=self.image_size    ,				patch_size=self.patch_size    ,				num_channels=self.num_channels    ,				hidden_size=self.hidden_size    ,				num_hidden_layers=self.num_hidden_layers    ,				num_attention_heads=self.num_attention_heads    ,				intermediate_size=self.intermediate_size    ,				hidden_act=self.hidden_act    ,				hidden_dropout_prob=self.hidden_dropout_prob    ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,				is_decoder=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      DeiTModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.reconstruction.shape    ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

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

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		
						)								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Tuple							=							(
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		Any							=							(
			    {
			        'feature-extraction': DeiTModel,
			        'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Any							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      DeiTModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						self.config_tester.run_common_tests()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      inspect.signature(model.forward       )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE__								=      [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=False       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__								=      True

						for model_class in self.all_model_classes:
									# DeiTForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(__UpperCAmelCase       )
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue
									SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
									model.to(__UpperCAmelCase       )
									model.train()
									SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).loss
									loss.backward()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__								=      False
						SCREAMING_SNAKE_CASE__								=      True

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE__								=      [
						    {"""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(__UpperCAmelCase       ),
									        *get_values(__UpperCAmelCase       ),
									    ]
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
												continue

									for problem_type in problem_types:
												with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""       ):
															SCREAMING_SNAKE_CASE__								=      problem_type["""title"""]
															SCREAMING_SNAKE_CASE__								=      problem_type["""num_labels"""]

															SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
															model.to(__UpperCAmelCase       )
															model.train()

															SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

															if problem_type["num_labels"] > 1:
																		SCREAMING_SNAKE_CASE__								=      inputs["""labels"""].unsqueeze(1       ).repeat(1    ,				problem_type["""num_labels"""]       )

															SCREAMING_SNAKE_CASE__								=      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=__UpperCAmelCase       ) as warning_list:
																		SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).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 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


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





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

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

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       ).to(
						    __UpperCAmelCase       )

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

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

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([-1.0_266, 0.1_912, -1.2_861]       ).to(__UpperCAmelCase       )

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





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

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

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


