

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 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_0_0       ,							_a=1_3       ,							_a=3_0       ,							_a=2       ,							_a=3       ,							_a=True       ,							_a=True       ,							_a=3_2       ,							_a=5       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_0       ,							_a=0.02       ,							_a=3       ,							)    ->    List[Any]:
												_a	:    Any         =		parent
												_a	:    Optional[Any]         =		vocab_size
												_a	:    Tuple         =		batch_size
												_a	:    Any         =		image_size
												_a	:    List[str]         =		patch_size
												_a	:    int         =		num_channels
												_a	:    Dict         =		is_training
												_a	:    List[str]         =		use_labels
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    Union[str, Any]         =		num_attention_heads
												_a	:    str         =		intermediate_size
												_a	:    Any         =		hidden_act
												_a	:    List[Any]         =		hidden_dropout_prob
												_a	:    Optional[int]         =		attention_probs_dropout_prob
												_a	:    Dict         =		type_sequence_label_size
												_a	:    Optional[int]         =		initializer_range

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


							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

												_a	:    Optional[Any]         =		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=_a       ,							initializer_range=self.initializer_range       ,							)

												return config, pixel_values, labels


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    int         =		FlaxBeitModel(config=_a       )
												_a	:    str         =		model(_a       )
												self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Optional[int]:
												_a	:    int         =		FlaxBeitForMaskedImageModeling(config=_a       )
												_a	:    Any         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length - 1, self.vocab_size)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    Any:
												_a	:    Optional[int]         =		self.type_sequence_label_size
												_a	:    Optional[int]         =		FlaxBeitForImageClassification(config=_a       )
												_a	:    List[str]         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )

												# test greyscale images
												_a	:    List[str]         =		1
												_a	:    Optional[int]         =		FlaxBeitForImageClassification(_a       )

												_a	:    Tuple         =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
												_a	:    List[str]         =		model(_a       )




							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Tuple         =		config_and_inputs
												_a	:    List[str]         =		{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_flax
class 				UpperCAmelCase_		(			__lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Tuple	    =    (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)


							def 				__lowercase				(			self       )    ->    None:
												_a	:    Dict         =		FlaxBeitModelTester(self       )
												_a	:    Union[str, Any]         =		ConfigTester(self       ,							config_class=_a       ,							has_text_modality=_a       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    Tuple:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    Dict:
												_a      ,					_a	:    Dict         =		self.model_tester.prepare_config_and_inputs_for_common()

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

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


							def 				__lowercase				(			self       )    ->    Dict:
												_a      ,					_a	:    List[Any]         =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__       ):
																						_a	:    List[str]         =		self._prepare_for_class(_a       ,							_a       )
																						_a	:    str         =		model_class(_a       )

																						@jax.jit
																						def model_jitted(_a       ,							**_a       ):
																											return model(pixel_values=_a       ,							**_a       )

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

																						with self.subTest('''JIT Disabled'''       ):
																											with jax.disable_jit():
																																_a	:    List[Any]         =		model_jitted(**_a       ).to_tuple()

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


							def 				__lowercase				(			self       )    ->    int:
												_a	:    Dict         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Dict         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a       )




							@slow
							def 				__lowercase				(			self       )    ->    str:
												for model_class_name in self.all_model_classes:
																	_a	:    List[Any]         =		model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''       )
																	_a	:    Optional[Any]         =		model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
																	self.assertIsNotNone(_a       )



def    __UpperCAmelCase (	)			->     List[str]:

					"""simple docstring"""

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







@require_vision
@require_flax
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@cached_property
							def 				__lowercase				(			self       )    ->    List[str]:
												return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''       ) if is_vision_available() else None


							@slow
							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''       )

												_a	:    List[Any]         =		self.default_image_processor
												_a	:    Tuple         =		prepare_img()
												_a	:    str         =		image_processor(images=_a       ,							return_tensors='''np'''       ).pixel_values

												# prepare bool_masked_pos
												_a	:    Any         =		np.ones((1, 1_9_6)       ,							dtype=_a       )

												# forward pass
												_a	:    Tuple         =		model(pixel_values=_a       ,							bool_masked_pos=_a       )
												_a	:    int         =		outputs.logits

												# verify the logits
												_a	:    List[Any]         =		(1, 1_9_6, 8_1_9_2)
												self.assertEqual(logits.shape       ,							_a       )

												_a	:    Optional[Any]         =		np.array(
												    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]       )

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


							@slow
							def 				__lowercase				(			self       )    ->    str:
												_a	:    int         =		FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''       )

												_a	:    int         =		self.default_image_processor
												_a	:    str         =		prepare_img()
												_a	:    Optional[Any]         =		image_processor(images=_a       ,							return_tensors='''np'''       )

												# forward pass
												_a	:    Optional[Any]         =		model(**_a       )
												_a	:    Dict         =		outputs.logits

												# verify the logits
												_a	:    Optional[Any]         =		(1, 1_0_0_0)
												self.assertEqual(logits.shape       ,							_a       )

												_a	:    Tuple         =		np.array([-1.2385, -1.0987, -1.0108]       )

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

												_a	:    Any         =		2_8_1
												self.assertEqual(logits.argmax(-1       ).item()       ,							_a       )




							@slow
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Optional[int]         =		FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''       )

												_a	:    List[str]         =		self.default_image_processor
												_a	:    str         =		prepare_img()
												_a	:    Optional[Any]         =		image_processor(images=_a       ,							return_tensors='''np'''       )

												# forward pass
												_a	:    Optional[int]         =		model(**_a       )
												_a	:    str         =		outputs.logits

												# verify the logits
												_a	:    Tuple         =		(1, 2_1_8_4_1)
												self.assertEqual(logits.shape       ,							_a       )

												_a	:    Dict         =		np.array([1.6881, -0.2787, 0.5901]       )

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

												_a	:    Union[str, Any]         =		2_3_9_6
												self.assertEqual(logits.argmax(-1       ).item()       ,							_a       )






import logging

from transformers import PretrainedConfig


a__				=		logging.getLogger(__name__)


a__				=		{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bertabs"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=5_1_2       ,							_a=6       ,							_a=5_1_2       ,							_a=8       ,							_a=5_1_2       ,							_a=0.2       ,							_a=6       ,							_a=7_6_8       ,							_a=8       ,							_a=2_0_4_8       ,							_a=0.2       ,							**_a       ,							)    ->    Any:
												super().__init__(**_a       )

												_a	:    int         =		vocab_size
												_a	:    List[str]         =		max_pos

												_a	:    Tuple         =		enc_layers
												_a	:    Optional[Any]         =		enc_hidden_size
												_a	:    int         =		enc_heads
												_a	:    Optional[Any]         =		enc_ff_size
												_a	:    List[str]         =		enc_dropout

												_a	:    Tuple         =		dec_layers
												_a	:    Optional[Any]         =		dec_hidden_size
												_a	:    Optional[Any]         =		dec_heads
												_a	:    Optional[Any]         =		dec_ff_size
												_a	:    List[Any]         =		dec_dropout



