




"""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    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      Optional[int]    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      Optional[int]=100    ,    lowerCAmelCase					:      Union[str, Any]=13    ,    lowerCAmelCase					:      str=30    ,    lowerCAmelCase					:      List[Any]=2    ,    lowerCAmelCase					:      int=3    ,    lowerCAmelCase					:      Union[str, Any]=True    ,    lowerCAmelCase					:      List[str]=True    ,    lowerCAmelCase					:      Dict=32    ,    lowerCAmelCase					:      str=4    ,    lowerCAmelCase					:      int=4    ,    lowerCAmelCase					:      List[str]=37    ,    lowerCAmelCase					:      Optional[int]="gelu"    ,    lowerCAmelCase					:      List[str]=0.1    ,    lowerCAmelCase					:      int=0.1    ,    lowerCAmelCase					:      int=10    ,    lowerCAmelCase					:      Any=0.02    ,    lowerCAmelCase					:      List[Any]=3    ,    lowerCAmelCase					:      Dict=None    ,    lowerCAmelCase					:      int=[0, 1, 2, 3]    ,    ):
										lowerCAmelCase						=					parent
										lowerCAmelCase						=					100
										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 	__lowercase			(				self					:      Optional[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 	__lowercase			(				self					:      int       ):
										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=__lowercase    ,    initializer_range=self.initializer_range    ,    out_indices=self.out_indices    ,    )


						def 	__lowercase			(				self					:      List[Any]    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      List[str]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      Tuple       ):
										lowerCAmelCase						=					BeitModel(config=__lowercase       )
										model.to(__lowercase       )
										model.eval()
										lowerCAmelCase						=					model(__lowercase       )
										self.parent.assertEqual(result.last_hidden_state.shape    ,    (self.batch_size, self.seq_length, self.hidden_size)       )


						def 	__lowercase			(				self					:      List[str]    ,    lowerCAmelCase					:      Optional[Any]    ,    lowerCAmelCase					:      Optional[int]    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      Optional[int]       ):
										lowerCAmelCase						=					BeitForMaskedImageModeling(config=__lowercase       )
										model.to(__lowercase       )
										model.eval()
										lowerCAmelCase						=					model(__lowercase       )
										self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.seq_length - 1, self.vocab_size)       )


						def 	__lowercase			(				self					:      List[str]    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      Any       ):
										lowerCAmelCase						=					self.type_sequence_label_size
										lowerCAmelCase						=					BeitForImageClassification(__lowercase       )
										model.to(__lowercase       )
										model.eval()
										lowerCAmelCase						=					model(__lowercase    ,    labels=__lowercase       )
										self.parent.assertEqual(result.logits.shape    ,    (self.batch_size, self.type_sequence_label_size)       )

										# test greyscale images
										lowerCAmelCase						=					1
										lowerCAmelCase						=					BeitForImageClassification(__lowercase       )
										model.to(__lowercase       )
										model.eval()

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


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




						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					self.prepare_config_and_inputs()
										lowerCAmelCase						=					config_and_inputs
										lowerCAmelCase						=					{'pixel_values': pixel_values}
										return config, inputs_dict






@require_torch
class    SCREAMING_SNAKE_CASE__       (       snake_case_    ,					snake_case_    ,					unittest.TestCase       ):
						_a         =    (
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						_a         =    (
						    {
						        '''feature-extraction''': BeitModel,
						        '''image-classification''': BeitForImageClassification,
						        '''image-segmentation''': BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_a         =    False
						_a         =    False
						_a         =    False


						def 	__lowercase			(				self					:      str       ):
										lowerCAmelCase						=					BeitModelTester(self       )
										lowerCAmelCase						=					ConfigTester(self    ,    config_class=__lowercase    ,    has_text_modality=__lowercase    ,    hidden_size=37       )


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										self.config_tester.run_common_tests()


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


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


						def 	__lowercase			(				self					:      Optional[int]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()

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


						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase						=					model_class(__lowercase       )
														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]    ,    __lowercase       )


						def 	__lowercase			(				self					:      Tuple       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase       )


						def 	__lowercase			(				self					:      Dict       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__lowercase       )


						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowercase       )


						def 	__lowercase			(				self					:      Union[str, Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase       )


						def 	__lowercase			(				self					:      Union[str, Any]       ):







										if not self.model_tester.is_training:
														return

										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(__lowercase       ), BeitForMaskedImageModeling]:
																		continue

														lowerCAmelCase						=					model_class(__lowercase       )
														model.to(__lowercase       )
														model.train()
														lowerCAmelCase						=					self._prepare_for_class(__lowercase    ,    __lowercase    ,    return_labels=__lowercase       )
														lowerCAmelCase						=					model(**__lowercase       ).loss
														loss.backward()


						def 	__lowercase			(				self					:      str       ):
										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(__lowercase       ), BeitForMaskedImageModeling]
														    or not model_class.supports_gradient_checkpointing
														):
																		continue

														lowerCAmelCase						=					model_class(__lowercase       )
														model.gradient_checkpointing_enable()
														model.to(__lowercase       )
														model.train()
														lowerCAmelCase						=					self._prepare_for_class(__lowercase    ,    __lowercase    ,    return_labels=__lowercase       )
														lowerCAmelCase						=					model(**__lowercase       ).loss
														loss.backward()


						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					self.model_tester.prepare_config_and_inputs_for_common()

										lowerCAmelCase						=					_config_zero_init(__lowercase       )
										for model_class in self.all_model_classes:
														lowerCAmelCase						=					model_class(config=__lowercase       )
														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 	__lowercase			(				self					:      Tuple       ):







										for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase						=					BeitModel.from_pretrained(__lowercase       )
														self.assertIsNotNone(__lowercase       )





def 				lowercase       ()  ->	int:


				'''simple docstring'''






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






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


						@cached_property
						def 	__lowercase			(				self					:      Union[str, Any]       ):
										return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""       ) if is_vision_available() else None


						@slow
						def 	__lowercase			(				self					:      List[str]       ):
										lowerCAmelCase						=					BeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""       ).to(__lowercase       )

										lowerCAmelCase						=					self.default_image_processor
										lowerCAmelCase						=					prepare_img()
										lowerCAmelCase						=					image_processor(images=__lowercase    ,    return_tensors="""pt"""       ).pixel_values.to(__lowercase       )

										# prepare bool_masked_pos
										lowerCAmelCase						=					torch.ones((1, 196)    ,    dtype=torch.bool       ).to(__lowercase       )

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

										# verify the logits
										lowerCAmelCase						=					torch.Size((1, 196, 8192)       )
										self.assertEqual(logits.shape    ,    __lowercase       )

										lowerCAmelCase						=					torch.tensor(
										    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]       ).to(__lowercase       )

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


						@slow
						def 	__lowercase			(				self					:      Any       ):
										lowerCAmelCase						=					BeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""       ).to(__lowercase       )

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

										# forward pass
										with torch.no_grad():
														lowerCAmelCase						=					model(**__lowercase       )
										lowerCAmelCase						=					outputs.logits

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

										lowerCAmelCase						=					torch.tensor([-1.2385, -1.0987, -1.0108]       ).to(__lowercase       )

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

										lowerCAmelCase						=					281
										self.assertEqual(logits.argmax(-1       ).item()    ,    __lowercase       )


						@slow
						def 	__lowercase			(				self					:      List[Any]       ):
										lowerCAmelCase						=					BeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""       ).to(
										    __lowercase       )

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

										# forward pass
										with torch.no_grad():
														lowerCAmelCase						=					model(**__lowercase       )
										lowerCAmelCase						=					outputs.logits

										# verify the logits
										lowerCAmelCase						=					torch.Size((1, 2_1841)       )
										self.assertEqual(logits.shape    ,    __lowercase       )

										lowerCAmelCase						=					torch.tensor([1.6881, -0.2787, 0.5901]       ).to(__lowercase       )

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

										lowerCAmelCase						=					2396
										self.assertEqual(logits.argmax(-1       ).item()    ,    __lowercase       )


						@slow
						def 	__lowercase			(				self					:      Tuple       ):
										lowerCAmelCase						=					BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""       )
										lowerCAmelCase						=					model.to(__lowercase       )

										lowerCAmelCase						=					BeitImageProcessor(do_resize=__lowercase    ,    size=640    ,    do_center_crop=__lowercase       )

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

										# forward pass
										with torch.no_grad():
														lowerCAmelCase						=					model(**__lowercase       )
										lowerCAmelCase						=					outputs.logits

										# verify the logits
										lowerCAmelCase						=					torch.Size((1, 150, 160, 160)       )
										self.assertEqual(logits.shape    ,    __lowercase       )

										lowerCAmelCase						=					version.parse(PIL.__version__       ) < version.parse("""9.0.0"""       )

										if is_pillow_less_than_a:
														lowerCAmelCase						=					torch.tensor(
														    [
														        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
														        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
														        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
														    ]    ,    device=__lowercase    ,    )
										else:
														lowerCAmelCase						=					torch.tensor(
														    [
														        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
														        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
														        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
														    ]    ,    device=__lowercase    ,    )

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




						@slow
						def 	__lowercase			(				self					:      Tuple       ):
										lowerCAmelCase						=					BeitForSemanticSegmentation.from_pretrained("""microsoft/beit-base-finetuned-ade-640-640"""       )
										lowerCAmelCase						=					model.to(__lowercase       )

										lowerCAmelCase						=					BeitImageProcessor(do_resize=__lowercase    ,    size=640    ,    do_center_crop=__lowercase       )

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

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

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

										lowerCAmelCase						=					image_processor.post_process_semantic_segmentation(outputs=__lowercase    ,    target_sizes=[(500, 300)]       )
										lowerCAmelCase						=					torch.Size((500, 300)       )
										self.assertEqual(segmentation[0].shape    ,    __lowercase       )

										lowerCAmelCase						=					image_processor.post_process_semantic_segmentation(outputs=__lowercase       )
										lowerCAmelCase						=					torch.Size((160, 160)       )
										self.assertEqual(segmentation[0].shape    ,    __lowercase       )









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






