





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]    ,      a__	:				List[Any]    ,      a__	:				List[Any]=100    ,      a__	:				Tuple=13    ,      a__	:				Optional[int]=30    ,      a__	:				str=2    ,      a__	:				Optional[int]=3    ,      a__	:				Any=True    ,      a__	:				Tuple=True    ,      a__	:				Optional[Any]=32    ,      a__	:				Tuple=4    ,      a__	:				List[str]=4    ,      a__	:				List[str]=37    ,      a__	:				str="gelu"    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.1    ,      a__	:				Optional[Any]=10    ,      a__	:				Dict=0.0_2    ,      a__	:				Any=3    ,      a__	:				Union[str, Any]=None    ,      a__	:				int=[0, 1, 2, 3]    ,      ):





									"""simple docstring"""



									__snake_case								=       parent
									__snake_case								=       100
									__snake_case								=       batch_size
									__snake_case								=       image_size
									__snake_case								=       patch_size
									__snake_case								=       num_channels
									__snake_case								=       is_training
									__snake_case								=       use_labels
									__snake_case								=       hidden_size
									__snake_case								=       num_hidden_layers
									__snake_case								=       num_attention_heads
									__snake_case								=       intermediate_size
									__snake_case								=       hidden_act
									__snake_case								=       hidden_dropout_prob
									__snake_case								=       attention_probs_dropout_prob
									__snake_case								=       type_sequence_label_size
									__snake_case								=       initializer_range
									__snake_case								=       scope
									__snake_case								=       out_indices
									__snake_case								=       num_labels

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


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





									"""simple docstring"""



									__snake_case								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

									__snake_case								=       self.get_config()

									return config, pixel_values, labels, pixel_labels


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





									"""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=_lowerCAmelCase    ,      initializer_range=self.initializer_range    ,      out_indices=self.out_indices    ,      )


						def        a			(self	:				List[str]    ,      a__	:				List[str]    ,      a__	:				List[str]    ,      a__	:				List[str]    ,      a__	:				int				):





									"""simple docstring"""



									__snake_case								=       BeitModel(config=_lowerCAmelCase				)
									model.to(_lowerCAmelCase				)
									model.eval()
									__snake_case								=       model(_lowerCAmelCase				)
									self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)				)


						def        a			(self	:				Tuple    ,      a__	:				Union[str, Any]    ,      a__	:				Tuple    ,      a__	:				Dict    ,      a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       BeitForMaskedImageModeling(config=_lowerCAmelCase				)
									model.to(_lowerCAmelCase				)
									model.eval()
									__snake_case								=       model(_lowerCAmelCase				)
									self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length - 1, self.vocab_size)				)


						def        a			(self	:				Optional[Any]    ,      a__	:				str    ,      a__	:				Any    ,      a__	:				List[Any]    ,      a__	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       self.type_sequence_label_size
									__snake_case								=       BeitForImageClassification(_lowerCAmelCase				)
									model.to(_lowerCAmelCase				)
									model.eval()
									__snake_case								=       model(_lowerCAmelCase    ,      labels=_lowerCAmelCase				)
									self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)				)

									# test greyscale images
									__snake_case								=       1
									__snake_case								=       BeitForImageClassification(_lowerCAmelCase				)
									model.to(_lowerCAmelCase				)
									model.eval()

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


						def        a			(self	:				List[Any]    ,      a__	:				List[Any]    ,      a__	:				List[Any]    ,      a__	:				List[str]    ,      a__	:				int				):





									"""simple docstring"""



									__snake_case								=       self.num_labels
									__snake_case								=       BeitForSemanticSegmentation(_lowerCAmelCase				)
									model.to(_lowerCAmelCase				)
									model.eval()
									__snake_case								=       model(_lowerCAmelCase				)
									self.parent.assertEqual(
									    result.logits.shape    ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
									__snake_case								=       model(_lowerCAmelCase    ,      labels=_lowerCAmelCase				)
									self.parent.assertEqual(
									    result.logits.shape    ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)


						def        a			(self	:				List[str]				):





									"""simple docstring"""



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






@require_torch
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
						A_						:    List[str]							=   (
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						A_						:    Optional[int]							=   (
						    {
						        'feature-extraction': BeitModel,
						        'image-classification': BeitForImageClassification,
						        'image-segmentation': BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						A_						:    Dict							=   False
						A_						:    Any							=   False
						A_						:    Optional[int]							=   False


						def        a			(self	:				int				):





									"""simple docstring"""



									__snake_case								=       BeitModelTester(self				)
									__snake_case								=       ConfigTester(self    ,      config_class=_lowerCAmelCase    ,      has_text_modality=_lowerCAmelCase    ,      hidden_size=37				)


						def        a			(self	:				int				):





									"""simple docstring"""



									self.config_tester.run_common_tests()


						@unittest.skip(reason='''BEiT does not use inputs_embeds'''				)
						def        a			(self	:				str				):





									"""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        a			(self	:				Any				):





									"""simple docstring"""



									pass


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									__snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

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


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									__snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

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

												__snake_case								=       ['''pixel_values''']
												self.assertListEqual(arg_names[:1]    ,      _lowerCAmelCase				)


						def        a			(self	:				Dict				):





									"""simple docstring"""



									__snake_case								=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowerCAmelCase				)


						def        a			(self	:				List[str]				):





									"""simple docstring"""



									__snake_case								=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase				)


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





									"""simple docstring"""



									__snake_case								=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase				)


						def        a			(self	:				Dict				):





									"""simple docstring"""



									__snake_case								=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase				)


						def        a			(self	:				str				):





									"""simple docstring"""



									if not self.model_tester.is_training:
												return

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

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

												__snake_case								=       model_class(_lowerCAmelCase				)
												model.to(_lowerCAmelCase				)
												model.train()
												__snake_case								=       self._prepare_for_class(_lowerCAmelCase    ,      _lowerCAmelCase    ,      return_labels=_lowerCAmelCase				)
												__snake_case								=       model(**_lowerCAmelCase				).loss
												loss.backward()


						def        a			(self	:				int				):





									"""simple docstring"""



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

									__snake_case								=       False
									__snake_case								=       True

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

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


						def        a			(self	:				str				):





									"""simple docstring"""



									__snake_case	,     __snake_case								=       self.model_tester.prepare_config_and_inputs_for_common()

									__snake_case								=       _config_zero_init(_lowerCAmelCase				)
									for model_class in self.all_model_classes:
												__snake_case								=       model_class(config=_lowerCAmelCase				)
												for name, param in model.named_parameters():
															# we skip lambda parameters as these require special initial values
															# determined by config.layer_scale_init_value
															if "lambda" in name:
																		continue
															if param.requires_grad:
																		self.assertIn(
																		    ((param.data.mean() * 1E9).round() / 1E9).item()    ,      [0.0, 1.0]    ,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,      )


						@slow
						def        a			(self	:				int				):





									"""simple docstring"""



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






def lowerCamelCase__   (							)				->    List[Any]:
			__snake_case								=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
			return image







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


						@cached_property
						def        a			(self	:				str				):





									"""simple docstring"""



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


						@slow
						def        a			(self	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''				).to(_lowerCAmelCase				)

									__snake_case								=       self.default_image_processor
									__snake_case								=       prepare_img()
									__snake_case								=       image_processor(images=_lowerCAmelCase    ,      return_tensors='''pt'''				).pixel_values.to(_lowerCAmelCase				)

									# prepare bool_masked_pos
									__snake_case								=       torch.ones((1, 196)    ,      dtype=torch.bool				).to(_lowerCAmelCase				)

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

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

									__snake_case								=       torch.tensor(
									    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]				).to(_lowerCAmelCase				)

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


						@slow
						def        a			(self	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''				).to(_lowerCAmelCase				)

									__snake_case								=       self.default_image_processor
									__snake_case								=       prepare_img()
									__snake_case								=       image_processor(images=_lowerCAmelCase    ,      return_tensors='''pt'''				).to(_lowerCAmelCase				)

									# forward pass
									with torch.no_grad():
												__snake_case								=       model(**_lowerCAmelCase				)
									__snake_case								=       outputs.logits

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

									__snake_case								=       torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]				).to(_lowerCAmelCase				)

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

									__snake_case								=       281
									self.assertEqual(logits.argmax(-1				).item()    ,      _lowerCAmelCase				)


						@slow
						def        a			(self	:				List[Any]				):





									"""simple docstring"""



									__snake_case								=       BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''				).to(
									    _lowerCAmelCase				)

									__snake_case								=       self.default_image_processor
									__snake_case								=       prepare_img()
									__snake_case								=       image_processor(images=_lowerCAmelCase    ,      return_tensors='''pt'''				).to(_lowerCAmelCase				)

									# forward pass
									with torch.no_grad():
												__snake_case								=       model(**_lowerCAmelCase				)
									__snake_case								=       outputs.logits

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

									__snake_case								=       torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]				).to(_lowerCAmelCase				)

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

									__snake_case								=       2396
									self.assertEqual(logits.argmax(-1				).item()    ,      _lowerCAmelCase				)


						@slow
						def        a			(self	:				Tuple				):





									"""simple docstring"""



									__snake_case								=       BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
									__snake_case								=       model.to(_lowerCAmelCase				)

									__snake_case								=       BeitImageProcessor(do_resize=_lowerCAmelCase    ,      size=640    ,      do_center_crop=_lowerCAmelCase				)

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

									# forward pass
									with torch.no_grad():
												__snake_case								=       model(**_lowerCAmelCase				)
									__snake_case								=       outputs.logits

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

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

									if is_pillow_less_than_a:
												__snake_case								=       torch.tensor(
												    [
												        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
												        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
												        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
												    ]    ,      device=_lowerCAmelCase    ,      )
									else:
												__snake_case								=       torch.tensor(
												    [
												        [[-4.8_9_6_0, -2.3_6_8_8, -3.0_3_5_5], [-2.8_4_7_8, -0.9_8_3_6, -1.7_4_1_8], [-2.9_4_4_9, -1.3_3_3_2, -2.1_4_5_6]],
												        [[-5.8_0_8_1, -3.4_1_2_4, -4.1_0_0_6], [-3.8_5_6_1, -2.2_0_8_1, -3.0_3_2_3], [-3.8_3_6_5, -2.4_6_0_1, -3.3_6_6_9]],
												        [[-0.0_3_0_9, 3.9_8_6_8, 4.0_5_4_0], [2.9_6_4_0, 4.6_8_7_7, 4.9_9_7_6], [3.2_0_8_1, 4.7_6_9_0, 4.9_9_4_2]],
												    ]    ,      device=_lowerCAmelCase    ,      )

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


						@slow
						def        a			(self	:				List[str]				):





									"""simple docstring"""



									__snake_case								=       BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
									__snake_case								=       model.to(_lowerCAmelCase				)

									__snake_case								=       BeitImageProcessor(do_resize=_lowerCAmelCase    ,      size=640    ,      do_center_crop=_lowerCAmelCase				)

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

									# forward pass
									with torch.no_grad():
												__snake_case								=       model(**_lowerCAmelCase				)

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

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

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_           =      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

