




'''simple docstring'''


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     A					(	unittest.TestCase					):







							def __init__(						self :						Optional[int]							,    lowerCAmelCase_ :						Any							,    lowerCAmelCase_ :						Optional[Any]=1_00							,    lowerCAmelCase_ :						List[str]=13							,    lowerCAmelCase_ :						List[Any]=30							,    lowerCAmelCase_ :						List[Any]=2							,    lowerCAmelCase_ :						Any=3							,    lowerCAmelCase_ :						Any=True							,    lowerCAmelCase_ :						Tuple=True							,    lowerCAmelCase_ :						Optional[Any]=32							,    lowerCAmelCase_ :						List[str]=5							,    lowerCAmelCase_ :						Any=4							,    lowerCAmelCase_ :						Dict=37							,    lowerCAmelCase_ :						str="gelu"							,    lowerCAmelCase_ :						Tuple=0.1							,    lowerCAmelCase_ :						int=0.1							,    lowerCAmelCase_ :						Tuple=10							,    lowerCAmelCase_ :						str=0.0_2							,    lowerCAmelCase_ :						Optional[int]=3							,    )    ->							Tuple:


														"""simple docstring"""


														_a			  =       parent
														_a			  =       vocab_size
														_a			  =       batch_size
														_a			  =       image_size
														_a			  =       patch_size
														_a			  =       num_channels
														_a			  =       is_training
														_a			  =       use_labels
														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       intermediate_size
														_a			  =       hidden_act
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       type_sequence_label_size
														_a			  =       initializer_range

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







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							str:


														"""simple docstring"""


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

														_a			  =       None
														if self.use_labels:
																					_a			  =       ids_tensor([self.batch_size]							,    self.type_sequence_label_size      )

														_a			  =       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							,    )

														return config, pixel_values, labels







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						List[Any]							,    lowerCAmelCase_ :						Tuple      )    ->							int:


														"""simple docstring"""


														_a			  =       FlaxBeitModel(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)      )







							def      __lowerCAmelCase    (						self :						Union[str, Any]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						Optional[int]							,    lowerCAmelCase_ :						List[str]      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       FlaxBeitForMaskedImageModeling(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length - 1, self.vocab_size)      )







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						List[Any]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       self.type_sequence_label_size
														_a			  =       FlaxBeitForImageClassification(config=lowerCAmelCase_      )
														_a			  =       model(lowerCAmelCase_      )
														self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.type_sequence_label_size)      )

														# test greyscale images
														_a			  =       1
														_a			  =       FlaxBeitForImageClassification(lowerCAmelCase_      )

														_a			  =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
														_a			  =       model(lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


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



@require_flax
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							None:


														"""simple docstring"""


														_a			  =       FlaxBeitModelTester(self      )
														_a			  =       ConfigTester(self							,    config_class=lowerCAmelCase_							,    has_text_modality=lowerCAmelCase_							,    hidden_size=37      )







							def      __lowerCAmelCase    (						self :						int      )    ->							str:


														"""simple docstring"""


														self.config_tester.run_common_tests()







							def      __lowerCAmelCase    (						self :						Any      )    ->							List[str]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

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

																					_a			  =       ['''pixel_values''']
																					self.assertListEqual(arg_names[:1]							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Optional[Any]:


														"""simple docstring"""


														_a  ,							_a			  =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					with self.subTest(model_class.__name__      ):
																												_a			  =       self._prepare_for_class(lowerCAmelCase_							,    lowerCAmelCase_      )
																												_a			  =       model_class(lowerCAmelCase_      )

																												@jax.jit
																												def model_jitted(lowerCAmelCase_ :						int							,    **lowerCAmelCase_ :						Dict      ):
																																			return model(pixel_values=lowerCAmelCase_							,    **lowerCAmelCase_      )

																												with self.subTest('''JIT Enabled'''      ):
																																			_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

																												with self.subTest('''JIT Disabled'''      ):
																																			with jax.disable_jit():
																																										_a			  =       model_jitted(**lowerCAmelCase_      ).to_tuple()

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







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_      )







							@slow
							def      __lowerCAmelCase    (						self :						Dict      )    ->							List[str]:


														"""simple docstring"""


														for model_class_name in self.all_model_classes:
																					_a			  =       model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
																					_a			  =       model(np.ones((1, 3, 2_24, 2_24)      )      )
																					self.assertIsNotNone(lowerCAmelCase_      )



def 	snake_case_					():





							'''simple docstring'''


							_a			  =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
							return image

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







							@cached_property
							def      __lowerCAmelCase    (						self :						int      )    ->							Optional[int]:


														"""simple docstring"""


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







							@slow
							def      __lowerCAmelCase    (						self :						Any      )    ->							Dict:


														"""simple docstring"""


														_a			  =       FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      ).pixel_values

														# prepare bool_masked_pos
														_a			  =       np.ones((1, 1_96)							,    dtype=lowerCAmelCase_      )

														# forward pass
														_a			  =       model(pixel_values=lowerCAmelCase_							,    bool_masked_pos=lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 1_96, 81_92)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array(
														    [[-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]]      )

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







							@slow
							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Dict:


														"""simple docstring"""


														_a			  =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

														# forward pass
														_a			  =       model(**lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 10_00)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]      )

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

														_a			  =       2_81
														self.assertEqual(logits.argmax(-1      ).item()							,    lowerCAmelCase_      )







							@slow
							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

														_a			  =       self.default_image_processor
														_a			  =       prepare_img()
														_a			  =       image_processor(images=lowerCAmelCase_							,    return_tensors='''np'''      )

														# forward pass
														_a			  =       model(**lowerCAmelCase_      )
														_a			  =       outputs.logits

														# verify the logits
														_a			  =       (1, 2_18_41)
														self.assertEqual(logits.shape							,    lowerCAmelCase_      )

														_a			  =       np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]      )

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

														_a			  =       23_96
														self.assertEqual(logits.argmax(-1      ).item()							,    lowerCAmelCase_      )







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


_snake_case   :   List[Any]     							=	{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	['BeitFeatureExtractor']
				_snake_case   :   Any     							=	['BeitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BeitForImageClassification',
				    'BeitForMaskedImageModeling',
				    'BeitForSemanticSegmentation',
				    'BeitModel',
				    'BeitPreTrainedModel',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'FlaxBeitForImageClassification',
				    'FlaxBeitForMaskedImageModeling',
				    'FlaxBeitModel',
				    'FlaxBeitPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_beit import BeitFeatureExtractor
								from .image_processing_beit import BeitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_beit import (
								    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BeitForImageClassification,
								    BeitForMaskedImageModeling,
								    BeitForSemanticSegmentation,
								    BeitModel,
								    BeitPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_beit import (
								    FlaxBeitForImageClassification,
								    FlaxBeitForMaskedImageModeling,
								    FlaxBeitModel,
								    FlaxBeitPreTrainedModel,
								)


else:
				import sys

				_snake_case   :   Union[str, Any]     							=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

