



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
				import torch

if is_vision_available():
				import PIL


lowerCAmelCase__					  =  logging.get_logger(__name__)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				def constraint_to_multiple_of(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=0					,			lowerCamelCase__=None						):
								lowercase__					:   Optional[int]													=					round(val / multiple						) * multiple

								if max_val is not None and x > max_val:
												lowercase__					:   str													=					math.floor(val / multiple						) * multiple

								if x < min_val:
												lowercase__					:   str													=					math.ceil(val / multiple						) * multiple

								return x

				lowercase__					:   Any													=					(output_size, output_size) if isinstance(lowerCAmelCase__					,			lowerCAmelCase__						) else output_size

				lowercase__					:   List[Any]													=					get_image_size(lowerCAmelCase__						)
				lowercase__					:   str													=					output_size

				# determine new height and width
				lowercase__					:   str													=					output_height / input_height
				lowercase__					:   Dict													=					output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width						) < abs(1 - scale_height						):
												# fit width
												lowercase__					:   List[str]													=					scale_width
								else:
												# fit height
												lowercase__					:   Dict													=					scale_height

				lowercase__					:   Optional[int]													=					constraint_to_multiple_of(scale_height * input_height					,			multiple=lowerCAmelCase__						)
				lowercase__					:   List[Any]													=					constraint_to_multiple_of(scale_width * input_width					,			multiple=lowerCAmelCase__						)

				return (new_height, new_width)

class 				snake_case__(__lowercase      ):

					"""simple docstring"""






					lowercase_								=		["pixel_values"]







					def __init__(							self :     int				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Dict[str, int] = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BILINEAR				,       SCREAMING_SNAKE_CASE :     bool = False				,       SCREAMING_SNAKE_CASE :     int = 1				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       **SCREAMING_SNAKE_CASE :     Tuple				,       ):
									super().__init__(**snake_case_					)
									lowercase__					:   str													=					size if size is not None else {'''height''': 384, '''width''': 384}
									lowercase__					:   Dict													=					get_size_dict(snake_case_					)
									lowercase__					:   Optional[int]													=					do_resize
									lowercase__					:   Dict													=					size
									lowercase__					:   Union[str, Any]													=					keep_aspect_ratio
									lowercase__					:   Optional[Any]													=					ensure_multiple_of
									lowercase__					:   Union[str, Any]													=					resample
									lowercase__					:   List[Any]													=					do_rescale
									lowercase__					:   Optional[Any]													=					rescale_factor
									lowercase__					:   List[str]													=					do_normalize
									lowercase__					:   Tuple													=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase__					:   Optional[int]													=					image_std if image_std is not None else IMAGENET_STANDARD_STD







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Dict[str, int]				,       SCREAMING_SNAKE_CASE :     bool = False				,       SCREAMING_SNAKE_CASE :     int = 1				,       SCREAMING_SNAKE_CASE :     PILImageResampling = PILImageResampling.BICUBIC				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									lowercase__					:   Union[str, Any]													=					get_size_dict(snake_case_					)
									if "height" not in size or "width" not in size:
													raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
									lowercase__					:   Optional[Any]													=					get_resize_output_image_size(
									    snake_case_				,       output_size=(size["height"], size["width"])				,       keep_aspect_ratio=snake_case_				,       multiple=snake_case_				,       )
									return resize(snake_case_				,       size=snake_case_				,       resample=snake_case_				,       data_format=snake_case_				,       **snake_case_					)







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[int, float]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
									return rescale(snake_case_				,       scale=snake_case_				,       data_format=snake_case_				,       **snake_case_					)







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Union[float, List[float]]				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     Optional[int]				,       ):
									return normalize(snake_case_				,       mean=snake_case_				,       std=snake_case_				,       data_format=snake_case_				,       **snake_case_					)







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     int = None				,       SCREAMING_SNAKE_CASE :     PILImageResampling = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     float = None				,       SCREAMING_SNAKE_CASE :     bool = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[float, List[float]]] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     ChannelDimension = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									lowercase__					:   Optional[Any]													=					do_resize if do_resize is not None else self.do_resize
									lowercase__					:   Optional[Any]													=					size if size is not None else self.size
									lowercase__					:   Union[str, Any]													=					get_size_dict(snake_case_					)
									lowercase__					:   Union[str, Any]													=					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
									lowercase__					:   Optional[int]													=					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
									lowercase__					:   Any													=					resample if resample is not None else self.resample
									lowercase__					:   Optional[Any]													=					do_rescale if do_rescale is not None else self.do_rescale
									lowercase__					:   Dict													=					rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase__					:   Optional[int]													=					do_normalize if do_normalize is not None else self.do_normalize
									lowercase__					:   Any													=					image_mean if image_mean is not None else self.image_mean
									lowercase__					:   str													=					image_std if image_std is not None else self.image_std

									lowercase__					:   List[str]													=					make_list_of_images(snake_case_					)

									if not valid_images(snake_case_					):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."					)

									if do_resize and size is None or resample is None:
													raise ValueError("Size and resample must be specified if do_resize is True."					)

									if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."					)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."					)

									# All transformations expect numpy arrays.
									lowercase__					:   Union[str, Any]													=					[to_numpy_array(snake_case_					) for image in images]

									if do_resize:
													lowercase__					:   str													=					[self.resize(image=snake_case_				,       size=snake_case_				,       resample=snake_case_					) for image in images]

									if do_rescale:
													lowercase__					:   Optional[int]													=					[self.rescale(image=snake_case_				,       scale=snake_case_					) for image in images]

									if do_normalize:
													lowercase__					:   int													=					[self.normalize(image=snake_case_				,       mean=snake_case_				,       std=snake_case_					) for image in images]

									lowercase__					:   Optional[Any]													=					[to_channel_dimension_format(snake_case_				,       snake_case_					) for image in images]

									lowercase__					:   Union[str, Any]													=					{'''pixel_values''': images}
									return BatchFeature(data=snake_case_				,       tensor_type=snake_case_					)







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Tuple] = None					):
									lowercase__					:   List[str]													=					outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
													if len(snake_case_					) != len(snake_case_					):
																	raise ValueError(
																	    "Make sure that you pass in as many target sizes as the batch dimension of the logits"					)

													if is_torch_tensor(snake_case_					):
																	lowercase__					:   Tuple													=					target_sizes.numpy()

													lowercase__					:   Union[str, Any]													=					[]

													for idx in range(len(snake_case_					)					):
																	lowercase__					:   List[Any]													=					torch.nn.functional.interpolate(
																	    logits[idx].unsqueeze(dim=0					)				,       size=target_sizes[idx]				,       mode="bilinear"				,       align_corners=snake_case_					)
																	lowercase__					:   int													=					resized_logits[0].argmax(dim=0					)
																	semantic_segmentation.append(snake_case_					)
									else:
													lowercase__					:   Any													=					logits.argmax(dim=1					)
													lowercase__					:   Optional[int]													=					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

									return semantic_segmentation









import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=100				,       SCREAMING_SNAKE_CASE :     Tuple=13				,       SCREAMING_SNAKE_CASE :     str=30				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=2				,       SCREAMING_SNAKE_CASE :     Tuple=3				,       SCREAMING_SNAKE_CASE :     List[Any]=True				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     Dict=32				,       SCREAMING_SNAKE_CASE :     str=5				,       SCREAMING_SNAKE_CASE :     Any=4				,       SCREAMING_SNAKE_CASE :     str=37				,       SCREAMING_SNAKE_CASE :     Any="gelu"				,       SCREAMING_SNAKE_CASE :     Tuple=0.1				,       SCREAMING_SNAKE_CASE :     Optional[Any]=0.1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=10				,       SCREAMING_SNAKE_CASE :     Dict=0.02				,       SCREAMING_SNAKE_CASE :     Any=3				,       ):
									lowercase__					:   Optional[int]													=					parent
									lowercase__					:   Optional[int]													=					vocab_size
									lowercase__					:   Dict													=					batch_size
									lowercase__					:   List[Any]													=					image_size
									lowercase__					:   List[Any]													=					patch_size
									lowercase__					:   Tuple													=					num_channels
									lowercase__					:   Any													=					is_training
									lowercase__					:   str													=					use_labels
									lowercase__					:   List[Any]													=					hidden_size
									lowercase__					:   Optional[int]													=					num_hidden_layers
									lowercase__					:   Dict													=					num_attention_heads
									lowercase__					:   Optional[int]													=					intermediate_size
									lowercase__					:   int													=					hidden_act
									lowercase__					:   str													=					hidden_dropout_prob
									lowercase__					:   Union[str, Any]													=					attention_probs_dropout_prob
									lowercase__					:   int													=					type_sequence_label_size
									lowercase__					:   Optional[int]													=					initializer_range

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







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   List[Any]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									lowercase__					:   Union[str, Any]													=					None
									if self.use_labels:
													lowercase__					:   List[Any]													=					ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)

									lowercase__					:   int													=					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=SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       )

									return config, pixel_values, labels







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Optional[Any]													=					FlaxBeitModel(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   int													=					FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length - 1, self.vocab_size)					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Any					):
									lowercase__					:   Tuple													=					self.type_sequence_label_size
									lowercase__					:   Optional[int]													=					FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)					)

									# test greyscale images
									lowercase__					:   int													=					1
									lowercase__					:   List[str]													=					FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
									lowercase__					:   List[str]													=					model(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   str													=					config_and_inputs
									lowercase__					:   Dict													=					{"pixel_values": pixel_values}
									return config, inputs_dict






@require_flax
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   List[Any]													=					FlaxBeitModelTester(self					)
									lowercase__					:   str													=					ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE				,       has_text_modality=SCREAMING_SNAKE_CASE				,       hidden_size=37					)







					def 			snake_case					(							self :     int					):
									self.config_tester.run_common_tests()







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)
													lowercase__					:   str													=					inspect.signature(model.__call__					)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase__					:   Optional[int]													=					[*signature.parameters.keys()]

													lowercase__					:   str													=					["pixel_values"]
													self.assertListEqual(arg_names[:1]				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Optional[int]													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

																	@jax.jit
																	def model_jitted(SCREAMING_SNAKE_CASE :     Optional[Any]				,       **SCREAMING_SNAKE_CASE :     List[Any]					):
																					return model(pixel_values=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   Union[str, Any]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Optional[int]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       len(SCREAMING_SNAKE_CASE					)					)
																	for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																					self.assertEqual(jitted_output.shape				,       output.shape					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   List[Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     Optional[int]					):
									for model_class_name in self.all_model_classes:
													lowercase__					:   Any													=					model_class_name.from_pretrained("microsoft/beit-base-patch16-224"					)
													lowercase__					:   Optional[int]													=					model(np.ones((1, 3, 224, 224)					)					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       ):

				"""simple docstring"""


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

@require_vision
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@cached_property
					def 			snake_case					(							self :     int					):
									return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"					) if is_vision_available() else None







					@slow
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Tuple													=					FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"					)

									lowercase__					:   int													=					self.default_image_processor
									lowercase__					:   Union[str, Any]													=					prepare_img()
									lowercase__					:   str													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					).pixel_values

									# prepare bool_masked_pos
									lowercase__					:   Optional[Any]													=					np.ones((1, 196)				,       dtype=SCREAMING_SNAKE_CASE					)

									# forward pass
									lowercase__					:   Any													=					model(pixel_values=SCREAMING_SNAKE_CASE				,       bool_masked_pos=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Tuple													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 196, 8_192)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					np.array(
									    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]					)

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







					@slow
					def 			snake_case					(							self :     Any					):
									lowercase__					:   Union[str, Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"					)

									lowercase__					:   Tuple													=					self.default_image_processor
									lowercase__					:   List[Any]													=					prepare_img()
									lowercase__					:   Optional[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   str													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 1_000)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Tuple													=					np.array([-1.2_385, -1.0_987, -1.0_108]					)

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

									lowercase__					:   str													=					281
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     str					):
									lowercase__					:   List[Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"					)

									lowercase__					:   Dict													=					self.default_image_processor
									lowercase__					:   Dict													=					prepare_img()
									lowercase__					:   List[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   Union[str, Any]													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					outputs.logits

									# verify the logits
									lowercase__					:   int													=					(1, 21_841)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					np.array([1.6_881, -0.2_787, 0.5_901]					)

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

									lowercase__					:   Union[str, Any]													=					2_396
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)




