






'''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 							__magic_name__     :







	"""simple docstring"""



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

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowercase_      :   Optional[int]        =			(image_size // patch_size) ** 2
							lowercase_      :   Any        =			num_patches + 1
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   Optional[int]        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

							lowercase_      :   str        =			self.get_config()

							return config, pixel_values, labels, pixel_labels
	def        lowerCamelCase__		(							self       )						->  Optional[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=_lowercase  ,      initializer_range=self.initializer_range  ,      out_indices=self.out_indices  ,      )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Any:
							lowercase_      :   str        =			BeitModel(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   Dict        =			model(_lowercase       )
							self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  List[str]:
							lowercase_      :   List[Any]        =			BeitForMaskedImageModeling(config=_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[str]        =			model(_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length - 1, self.vocab_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   int        =			self.type_sequence_label_size
							lowercase_      :   Optional[int]        =			BeitForImageClassification(_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[Any]        =			model(_lowercase  ,      labels=_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)       )

							# test greyscale images
							lowercase_      :   int        =			1
							lowercase_      :   Tuple        =			BeitForImageClassification(_lowercase       )
							model.to(_lowercase       )
							model.eval()

							lowercase_      :   Tuple        =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
							lowercase_      :   Any        =			model(_lowercase  ,      labels=_lowercase       )
							self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Dict:
							lowercase_      :   Dict        =			self.num_labels
							lowercase_      :   List[Any]        =			BeitForSemanticSegmentation(_lowercase       )
							model.to(_lowercase       )
							model.eval()
							lowercase_      :   List[str]        =			model(_lowercase       )
							self.parent.assertEqual(
							    result.logits.shape  ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)       )
							lowercase_      :   Any        =			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        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   str        =			self.prepare_config_and_inputs()
							lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Any        =			config_and_inputs
							lowercase_      :   Any        =			{'pixel_values': pixel_values}
							return config, inputs_dict






@require_torch
class 							__magic_name__     (	UpperCAmelCase_,						UpperCAmelCase_,						unittest.TestCase				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      List[Any]        =  (
	    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_					:      Any        =  (
	    {
	        'feature-extraction': BeitModel,
	        'image-classification': BeitForImageClassification,
	        'image-segmentation': BeitForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE_					:      Dict        =  False
	SCREAMING_SNAKE_CASE_					:      str        =  False
	SCREAMING_SNAKE_CASE_					:      Any        =  False
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Union[str, Any]        =			BeitModelTester(self       )
							lowercase_      :   Optional[Any]        =			ConfigTester(self  ,      config_class=_lowercase  ,      has_text_modality=_lowercase  ,      hidden_size=37       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							self.config_tester.run_common_tests()
	@unittest.skip(reason='BEiT does not use inputs_embeds'       )
	def        lowerCamelCase__		(							self       )						->  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        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							pass
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_  ,      lowercase_      :   Dict        =			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase_      :   Tuple        =			model_class(_lowercase       )
													self.assertIsInstance(model.get_input_embeddings()  ,      (nn.Module)       )
													lowercase_      :   Optional[int]        =			model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowercase  ,      nn.Linear       )       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							lowercase_  ,      lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase_      :   str        =			['pixel_values']
													self.assertListEqual(arg_names[:1]  ,      _lowercase       )
	def        lowerCamelCase__		(							self       )						->  str:
							lowercase_      :   str        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							lowercase_      :   List[Any]        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							lowercase_      :   Tuple        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Tuple:
							lowercase_      :   str        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							if not self.model_tester.is_training:
													return

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

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

													lowercase_      :   int        =			model_class(_lowercase       )
													model.to(_lowercase       )
													model.train()
													lowercase_      :   Union[str, Any]        =			self._prepare_for_class(_lowercase  ,      _lowercase  ,      return_labels=_lowercase       )
													lowercase_      :   Dict        =			model(**_lowercase       ).loss
													loss.backward()
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_  ,      lowercase_      :   str        =			self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
													return

							lowercase_      :   Optional[Any]        =			False
							lowercase_      :   Dict        =			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

													lowercase_      :   Dict        =			model_class(_lowercase       )
													model.gradient_checkpointing_enable()
													model.to(_lowercase       )
													model.train()
													lowercase_      :   Any        =			self._prepare_for_class(_lowercase  ,      _lowercase  ,      return_labels=_lowercase       )
													lowercase_      :   Any        =			model(**_lowercase       ).loss
													loss.backward()
	def        lowerCamelCase__		(							self       )						->  Dict:
							lowercase_  ,      lowercase_      :   List[str]        =			self.model_tester.prepare_config_and_inputs_for_common()

							lowercase_      :   str        =			_config_zero_init(_lowercase       )
							for model_class in self.all_model_classes:
													lowercase_      :   Optional[int]        =			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        lowerCamelCase__		(							self       )						->  List[Any]:
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase_      :   Union[str, Any]        =			BeitModel.from_pretrained(_lowercase       )
													self.assertIsNotNone(_lowercase       )






def 	_UpperCAmelCase    (  )			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
						return image






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







	"""simple docstring"""



	@cached_property
	def        lowerCamelCase__		(							self       )						->  Dict:
							return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'       ) if is_vision_available() else None
	@slow
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Optional[Any]        =			BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'       ).to(_lowercase       )

							lowercase_      :   int        =			self.default_image_processor
							lowercase_      :   Union[str, Any]        =			prepare_img()
							lowercase_      :   Any        =			image_processor(images=_lowercase  ,      return_tensors='pt'       ).pixel_values.to(_lowercase       )

							# prepare bool_masked_pos
							lowercase_      :   List[Any]        =			torch.ones((1, 196)  ,      dtype=torch.bool       ).to(_lowercase       )

							# forward pass
							with torch.no_grad():
													lowercase_      :   Any        =			model(pixel_values=_lowercase  ,      bool_masked_pos=_lowercase       )
							lowercase_      :   Union[str, Any]        =			outputs.logits

							# verify the logits
							lowercase_      :   Union[str, Any]        =			torch.Size((1, 196, 8192)       )
							self.assertEqual(logits.shape  ,      _lowercase       )

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

							self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]  ,      _lowercase  ,      atol=1E-2       )       )
	@slow
	def        lowerCamelCase__		(							self       )						->  List[str]:
							lowercase_      :   int        =			BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'       ).to(_lowercase       )

							lowercase_      :   Dict        =			self.default_image_processor
							lowercase_      :   Optional[int]        =			prepare_img()
							lowercase_      :   Tuple        =			image_processor(images=_lowercase  ,      return_tensors='pt'       ).to(_lowercase       )

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

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

							lowercase_      :   Optional[Any]        =			torch.tensor([-1.23_85, -1.09_87, -1.01_08]       ).to(_lowercase       )

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

							lowercase_      :   Union[str, Any]        =			281
							self.assertEqual(logits.argmax(-1       ).item()  ,      _lowercase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   List[Any]        =			BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'       ).to(
							    _lowercase       )

							lowercase_      :   Dict        =			self.default_image_processor
							lowercase_      :   Optional[Any]        =			prepare_img()
							lowercase_      :   List[Any]        =			image_processor(images=_lowercase  ,      return_tensors='pt'       ).to(_lowercase       )

							# forward pass
							with torch.no_grad():
													lowercase_      :   str        =			model(**_lowercase       )
							lowercase_      :   Any        =			outputs.logits

							# verify the logits
							lowercase_      :   Dict        =			torch.Size((1, 2_1841)       )
							self.assertEqual(logits.shape  ,      _lowercase       )

							lowercase_      :   Tuple        =			torch.tensor([1.68_81, -0.27_87, 0.59_01]       ).to(_lowercase       )

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

							lowercase_      :   str        =			2396
							self.assertEqual(logits.argmax(-1       ).item()  ,      _lowercase       )
	@slow
	def        lowerCamelCase__		(							self       )						->  int:
							lowercase_      :   Tuple        =			BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'       )
							lowercase_      :   Optional[int]        =			model.to(_lowercase       )

							lowercase_      :   str        =			BeitImageProcessor(do_resize=_lowercase  ,      size=640  ,      do_center_crop=_lowercase       )

							lowercase_      :   Tuple        =			load_dataset('hf-internal-testing/fixtures_ade20k'  ,      split='test'       )
							lowercase_      :   Dict        =			Image.open(ds[0]['file']       )
							lowercase_      :   Optional[Any]        =			image_processor(images=_lowercase  ,      return_tensors='pt'       ).to(_lowercase       )

							# forward pass
							with torch.no_grad():
													lowercase_      :   List[Any]        =			model(**_lowercase       )
							lowercase_      :   Union[str, Any]        =			outputs.logits

							# verify the logits
							lowercase_      :   Dict        =			torch.Size((1, 150, 160, 160)       )
							self.assertEqual(logits.shape  ,      _lowercase       )

							lowercase_      :   Union[str, Any]        =			version.parse(PIL.__version__       ) < version.parse('9.0.0'       )

							if is_pillow_less_than_a:
													lowercase_      :   Optional[Any]        =			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=_lowercase  ,      )
							else:
													lowercase_      :   Optional[int]        =			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=_lowercase  ,      )

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







	@slow
	def        lowerCamelCase__		(							self       )						->  Optional[int]:
							lowercase_      :   List[str]        =			BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'       )
							lowercase_      :   List[str]        =			model.to(_lowercase       )

							lowercase_      :   Tuple        =			BeitImageProcessor(do_resize=_lowercase  ,      size=640  ,      do_center_crop=_lowercase       )

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

							# forward pass
							with torch.no_grad():
													lowercase_      :   Optional[Any]        =			model(**_lowercase       )

							lowercase_      :   Dict        =			outputs.logits.detach().cpu()

							lowercase_      :   int        =			image_processor.post_process_semantic_segmentation(outputs=_lowercase  ,      target_sizes=[(500, 300)]       )
							lowercase_      :   Optional[Any]        =			torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape  ,      _lowercase       )

							lowercase_      :   List[str]        =			image_processor.post_process_semantic_segmentation(outputs=_lowercase       )
							lowercase_      :   List[Any]        =			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


A:      Dict  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  'vit'
	def __init__(							self  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=3072  ,      _lowercase="gelu"  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=224  ,      _lowercase=16  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=16  ,      **_lowercase  ,      )						->  List[str]:
							super().__init__(**_lowercase       )

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






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  version.parse('1.11'				)
	@property
	def        lowerCamelCase__		(							self       )						->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]       )







	@property
	def        lowerCamelCase__		(							self       )						->  float:
							return 1E-4
