


import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
			import torch
			from torch import nn

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


if is_vision_available():
			import PIL
			from PIL import Image

			from transformers import BeitImageProcessor

class     a		:




							"""simple docstring"""


							def __init__(							self	:  int   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Optional[Any]=100   ,     lowerCamelCase	:  Any=13   ,     lowerCamelCase	:  Optional[int]=30   ,     lowerCamelCase	:  Tuple=2   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  List[Any]=True   ,     lowerCamelCase	:  Any=True   ,     lowerCamelCase	:  Optional[Any]=32   ,     lowerCamelCase	:  Any=4   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  str=37   ,     lowerCamelCase	:  Dict="gelu"   ,     lowerCamelCase	:  Optional[int]=0.1   ,     lowerCamelCase	:  Optional[Any]=0.1   ,     lowerCamelCase	:  Any=10   ,     lowerCamelCase	:  Optional[int]=0.02   ,     lowerCamelCase	:  Optional[int]=3   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  str=[0, 1, 2, 3]   ,     )     ->   int:
														__snake_case		:      List[Any]										=       parent
														__snake_case		:      int										=       100
														__snake_case		:      str										=       batch_size
														__snake_case		:      Optional[int]										=       image_size
														__snake_case		:      Tuple										=       patch_size
														__snake_case		:      int										=       num_channels
														__snake_case		:      List[Any]										=       is_training
														__snake_case		:      Optional[Any]										=       use_labels
														__snake_case		:      Tuple										=       hidden_size
														__snake_case		:      List[Any]										=       num_hidden_layers
														__snake_case		:      Optional[Any]										=       num_attention_heads
														__snake_case		:      int										=       intermediate_size
														__snake_case		:      str										=       hidden_act
														__snake_case		:      Optional[int]										=       hidden_dropout_prob
														__snake_case		:      int										=       attention_probs_dropout_prob
														__snake_case		:      int										=       type_sequence_label_size
														__snake_case		:      Tuple										=       initializer_range
														__snake_case		:      Dict										=       scope
														__snake_case		:      int										=       out_indices
														__snake_case		:      Any										=       num_labels

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

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

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

														__snake_case		:      List[str]										=       self.get_config()

														return config, pixel_values, labels, pixel_labels

							def    __snake_case (							self	:  Any    )     ->   Any:
														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    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  Tuple    )     ->   Dict:
														__snake_case		:      Optional[int]										=       BeitModel(config=lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Dict										=       model(lowerCamelCase    )
														self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)    )

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

							def    __snake_case (							self	:  Union[str, Any]   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  Dict    )     ->   int:
														__snake_case		:      Tuple										=       self.type_sequence_label_size
														__snake_case		:      Optional[int]										=       BeitForImageClassification(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      Optional[Any]										=       model(lowerCamelCase   ,     labels=lowerCamelCase    )
														self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)    )

														# test greyscale images
														__snake_case		:      str										=       1
														__snake_case		:      Any										=       BeitForImageClassification(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()

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

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Optional[int]   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  List[Any]    )     ->   str:
														__snake_case		:      Tuple										=       self.num_labels
														__snake_case		:      Tuple										=       BeitForSemanticSegmentation(lowerCamelCase    )
														model.to(lowerCamelCase    )
														model.eval()
														__snake_case		:      List[Any]										=       model(lowerCamelCase    )
														self.parent.assertEqual(
														    result.logits.shape   ,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)    )
														__snake_case		:      int										=       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    __snake_case (							self	:  List[str]    )     ->   Any:
														__snake_case		:      int										=       self.prepare_config_and_inputs()
														__snake_case			,      __snake_case			,      __snake_case			,      __snake_case		:      Any										=       config_and_inputs
														__snake_case		:      Any										=       {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class     a		(_lowerCAmelCase					,    _lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


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

							__UpperCAmelCase   :							Optional[int]					  =  False
							__UpperCAmelCase   :							Tuple					  =  False
							__UpperCAmelCase   :							Tuple					  =  False

							def    __snake_case (							self	:  Optional[int]    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       BeitModelTester(self    )
														__snake_case		:      str										=       ConfigTester(self   ,     config_class=lowerCamelCase   ,     has_text_modality=lowerCamelCase   ,     hidden_size=37    )

							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														self.config_tester.run_common_tests()

							@unittest.skip(reason="BEiT does not use inputs_embeds"    )
							def    __snake_case (							self	:  List[str]    )     ->   Union[str, Any]:
														pass

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

							def    __snake_case (							self	:  Dict    )     ->   Optional[int]:
														__snake_case			,      __snake_case		:      int										=       self.model_tester.prepare_config_and_inputs_for_common()

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

							def    __snake_case (							self	:  Optional[Any]    )     ->   Dict:
														__snake_case			,      __snake_case		:      Union[str, Any]										=       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case		:      Dict										=       model_class(lowerCamelCase    )
																					__snake_case		:      int										=       inspect.signature(model.forward    )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case		:      Union[str, Any]										=       [*signature.parameters.keys()]

																					__snake_case		:      Optional[Any]										=       ["pixel_values"]
																					self.assertListEqual(arg_names[:1]   ,     lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														__snake_case		:      Any										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   Union[str, Any]:
														__snake_case		:      Optional[int]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Any:
														__snake_case		:      Optional[int]										=       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase    )

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

														__snake_case			,      __snake_case		:      Dict										=       self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case		:      str										=       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		:      str										=       model_class(lowerCamelCase    )
																					model.to(lowerCamelCase    )
																					model.train()
																					__snake_case		:      Tuple										=       self._prepare_for_class(lowerCamelCase   ,     lowerCamelCase   ,     return_labels=lowerCamelCase    )
																					__snake_case		:      Optional[Any]										=       model(**lowerCamelCase    ).loss
																					loss.backward()

							def    __snake_case (							self	:  List[Any]    )     ->   Optional[int]:
														__snake_case			,      __snake_case		:      List[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()
														if not self.model_tester.is_training:
																					return

														__snake_case		:      int										=       False
														__snake_case		:      Dict										=       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		:      int										=       model_class(lowerCamelCase    )
																					model.gradient_checkpointing_enable()
																					model.to(lowerCamelCase    )
																					model.train()
																					__snake_case		:      str										=       self._prepare_for_class(lowerCamelCase   ,     lowerCamelCase   ,     return_labels=lowerCamelCase    )
																					__snake_case		:      Any										=       model(**lowerCamelCase    ).loss
																					loss.backward()

							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[str]:
														__snake_case			,      __snake_case		:      int										=       self.model_tester.prepare_config_and_inputs_for_common()

														__snake_case		:      Tuple										=       _config_zero_init(lowerCamelCase    )
														for model_class in self.all_model_classes:
																					__snake_case		:      Optional[int]										=       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    __snake_case (							self	:  str    )     ->   List[str]:
														for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case		:      Dict										=       BeitModel.from_pretrained(lowerCamelCase    )
																					self.assertIsNotNone(lowerCamelCase    )







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[int]										=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
							return image

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




							"""simple docstring"""


							@cached_property
							def    __snake_case (							self	:  str    )     ->   Dict:
														return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"    ) if is_vision_available() else None

							@slow
							def    __snake_case (							self	:  Union[str, Any]    )     ->   List[str]:
														__snake_case		:      List[Any]										=       BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"    ).to(lowerCamelCase    )

														__snake_case		:      List[str]										=       self.default_image_processor
														__snake_case		:      Tuple										=       prepare_img()
														__snake_case		:      Any										=       image_processor(images=lowerCamelCase   ,     return_tensors="pt"    ).pixel_values.to(lowerCamelCase    )

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

														# forward pass
														with torch.no_grad():
																					__snake_case		:      Union[str, Any]										=       model(pixel_values=lowerCamelCase   ,     bool_masked_pos=lowerCamelCase    )
														__snake_case		:      Optional[int]										=       outputs.logits

														# verify the logits
														__snake_case		:      List[Any]										=       torch.Size((1, 196, 8192)    )
														self.assertEqual(logits.shape   ,     lowerCamelCase    )

														__snake_case		:      Union[str, Any]										=       torch.tensor(
														    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]    ).to(lowerCamelCase    )

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

							@slow
							def    __snake_case (							self	:  Any    )     ->   Optional[int]:
														__snake_case		:      List[Any]										=       BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"    ).to(lowerCamelCase    )

														__snake_case		:      int										=       self.default_image_processor
														__snake_case		:      Dict										=       prepare_img()
														__snake_case		:      List[Any]										=       image_processor(images=lowerCamelCase   ,     return_tensors="pt"    ).to(lowerCamelCase    )

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

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

														__snake_case		:      Any										=       torch.tensor([-1.23_85, -1.09_87, -1.01_08]    ).to(lowerCamelCase    )

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

														__snake_case		:      Tuple										=       281
														self.assertEqual(logits.argmax(-1    ).item()   ,     lowerCamelCase    )

							@slow
							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														__snake_case		:      Dict										=       BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"    ).to(
														    lowerCamelCase    )

														__snake_case		:      Optional[int]										=       self.default_image_processor
														__snake_case		:      Dict										=       prepare_img()
														__snake_case		:      Tuple										=       image_processor(images=lowerCamelCase   ,     return_tensors="pt"    ).to(lowerCamelCase    )

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

														# verify the logits
														__snake_case		:      int										=       torch.Size((1, 21841)    )
														self.assertEqual(logits.shape   ,     lowerCamelCase    )

														__snake_case		:      int										=       torch.tensor([1.68_81, -0.27_87, 0.59_01]    ).to(lowerCamelCase    )

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

														__snake_case		:      int										=       2396
														self.assertEqual(logits.argmax(-1    ).item()   ,     lowerCamelCase    )

							@slow
							def    __snake_case (							self	:  List[str]    )     ->   Optional[int]:
														__snake_case		:      Tuple										=       BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"    )
														__snake_case		:      List[str]										=       model.to(lowerCamelCase    )

														__snake_case		:      Optional[int]										=       BeitImageProcessor(do_resize=lowerCamelCase   ,     size=640   ,     do_center_crop=lowerCamelCase    )

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

														# forward pass
														with torch.no_grad():
																					__snake_case		:      List[str]										=       model(**lowerCamelCase    )
														__snake_case		:      str										=       outputs.logits

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

														__snake_case		:      List[Any]										=       version.parse(PIL.__version__    ) < version.parse("9.0.0"    )

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

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






							@slow
							def    __snake_case (							self	:  List[str]    )     ->   List[Any]:
														__snake_case		:      Tuple										=       BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"    )
														__snake_case		:      List[Any]										=       model.to(lowerCamelCase    )

														__snake_case		:      Dict										=       BeitImageProcessor(do_resize=lowerCamelCase   ,     size=640   ,     do_center_crop=lowerCamelCase    )

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

														# forward pass
														with torch.no_grad():
																					__snake_case		:      Optional[int]										=       model(**lowerCamelCase    )

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

														__snake_case		:      Union[str, Any]										=       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase   ,     target_sizes=[(500, 300)]    )
														__snake_case		:      Tuple										=       torch.Size((500, 300)    )
														self.assertEqual(segmentation[0].shape   ,     lowerCamelCase    )

														__snake_case		:      Dict										=       image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase    )
														__snake_case		:      Optional[Any]										=       torch.Size((160, 160)    )
														self.assertEqual(segmentation[0].shape   ,     lowerCamelCase    )





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							str					  =  "OwlViTImageProcessor"
							__UpperCAmelCase   :							Dict					  =  ("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(							self	:  str   ,     lowerCamelCase	:  Any=None   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																					    " instead."   ,     lowerCamelCase   ,     )
																					__snake_case		:      List[Any]										=       kwargs.pop("feature_extractor"    )

														__snake_case		:      str										=       image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("You need to specify an `image_processor`."    )
														if tokenizer is None:
																					raise ValueError("You need to specify a `tokenizer`."    )

														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  List[str]="max_length"   ,     lowerCamelCase	:  Dict="np"   ,     **lowerCamelCase	:  str    )     ->   List[Any]:

														if text is None and query_images is None and images is None:
																					raise ValueError(
																					    "You have to specify at least one text or query image or image. All three cannot be none."    )

														if text is not None:
																					if isinstance(lowerCamelCase   ,     lowerCamelCase    ) or (isinstance(lowerCamelCase   ,     lowerCamelCase    ) and not isinstance(text[0]   ,     lowerCamelCase    )):
																												__snake_case		:      Union[str, Any]										=       [self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )]

																					elif isinstance(lowerCamelCase   ,     lowerCamelCase    ) and isinstance(text[0]   ,     lowerCamelCase    ):
																												__snake_case		:      Tuple										=       []

																												# Maximum number of queries across batch
																												__snake_case		:      str										=       max([len(lowerCamelCase    ) for t in text]    )

																												# Pad all batch samples to max number of text queries
																												for t in text:
																																			if len(lowerCamelCase    ) != max_num_queries:
																																										__snake_case		:      Dict										=       t + [" "] * (max_num_queries - len(lowerCamelCase    ))

																																			__snake_case		:      int										=       self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )
																																			encodings.append(lowerCamelCase    )
																					else:
																												raise TypeError("Input text should be a string, a list of strings or a nested list of strings"    )

																					if return_tensors == "np":
																												__snake_case		:      Any										=       np.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Tuple										=       np.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "jax" and is_flax_available():
																												import jax.numpy as jnp

																												__snake_case		:      List[Any]										=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Any										=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "pt" and is_torch_available():
																												import torch

																												__snake_case		:      int										=       torch.cat([encoding["input_ids"] for encoding in encodings]   ,     dim=0    )
																												__snake_case		:      int										=       torch.cat([encoding["attention_mask"] for encoding in encodings]   ,     dim=0    )

																					elif return_tensors == "tf" and is_tf_available():
																												import tensorflow as tf

																												__snake_case		:      int										=       tf.stack([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Dict										=       tf.stack([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					else:
																												raise ValueError("Target return tensor type could not be returned"    )

																					__snake_case		:      Any										=       BatchEncoding()
																					__snake_case		:      Tuple										=       input_ids
																					__snake_case		:      int										=       attention_mask

														if query_images is not None:
																					__snake_case		:      List[Any]										=       BatchEncoding()
																					__snake_case		:      Union[str, Any]										=       self.image_processor(
																					    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    ).pixel_values
																					__snake_case		:      str										=       query_pixel_values

														if images is not None:
																					__snake_case		:      Optional[int]										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )

														if text is not None and images is not None:
																					__snake_case		:      List[str]										=       image_features.pixel_values
																					return encoding
														elif query_images is not None and images is not None:
																					__snake_case		:      int										=       image_features.pixel_values
																					return encoding
														elif text is not None or query_images is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**lowerCamelCase    )   ,     tensor_type=lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   str:
														return self.image_processor.post_process(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  str   ,     **lowerCamelCase	:  List[str]    )     ->   Tuple:
														return self.image_processor.post_process_object_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     *lowerCamelCase	:  Optional[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Any:
														return self.image_processor.post_process_image_guided_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  Optional[int]    )     ->   str:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  List[Any]    )     ->   Tuple:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Any    )     ->   Dict:
														warnings.warn(
														    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor_class






							@property
							def    __snake_case (							self	:  List[str]    )     ->   Union[str, Any]:
														warnings.warn(
														    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor

