




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		):



				def __init__(      self      :		Optional[int]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Dict=100		,	_UpperCAmelCase      :		List[str]=13		,	_UpperCAmelCase      :		Optional[int]=30		,	_UpperCAmelCase      :		Optional[Any]=2		,	_UpperCAmelCase      :		str=3		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		int=True		,	_UpperCAmelCase      :		Any=32		,	_UpperCAmelCase      :		str=5		,	_UpperCAmelCase      :		str=4		,	_UpperCAmelCase      :		str=37		,	_UpperCAmelCase      :		Union[str, Any]="gelu"		,	_UpperCAmelCase      :		List[Any]=0.1		,	_UpperCAmelCase      :		List[Any]=0.1		,	_UpperCAmelCase      :		Union[str, Any]=10		,	_UpperCAmelCase      :		Tuple=0.02		,	_UpperCAmelCase      :		Any=3		,	)  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      parent
								_lowerCAmelCase    :    Union[str, Any]										=      vocab_size
								_lowerCAmelCase    :    Dict										=      batch_size
								_lowerCAmelCase    :    Any										=      image_size
								_lowerCAmelCase    :    Tuple										=      patch_size
								_lowerCAmelCase    :    List[str]										=      num_channels
								_lowerCAmelCase    :    List[str]										=      is_training
								_lowerCAmelCase    :    str										=      use_labels
								_lowerCAmelCase    :    int										=      hidden_size
								_lowerCAmelCase    :    Any										=      num_hidden_layers
								_lowerCAmelCase    :    str										=      num_attention_heads
								_lowerCAmelCase    :    List[Any]										=      intermediate_size
								_lowerCAmelCase    :    List[str]										=      hidden_act
								_lowerCAmelCase    :    str										=      hidden_dropout_prob
								_lowerCAmelCase    :    Dict										=      attention_probs_dropout_prob
								_lowerCAmelCase    :    str										=      type_sequence_label_size
								_lowerCAmelCase    :    Optional[Any]										=      initializer_range

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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								_lowerCAmelCase    :    Dict										=      None
								if self.use_labels:
												_lowerCAmelCase    :    Optional[int]										=      ids_tensor([self.batch_size]		,	self.type_sequence_label_size      )

								_lowerCAmelCase    :    Union[str, 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=snake_case__		,	initializer_range=self.initializer_range		,	)

								return config, pixel_values, labels



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[int]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      FlaxBeitModel(config=snake_case__      )
								_lowerCAmelCase    :    Optional[Any]										=      model(snake_case__      )
								self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[int]      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      FlaxBeitForMaskedImageModeling(config=snake_case__      )
								_lowerCAmelCase    :    List[Any]										=      model(snake_case__      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length - 1, self.vocab_size)      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Tuple      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.type_sequence_label_size
								_lowerCAmelCase    :    int										=      FlaxBeitForImageClassification(config=snake_case__      )
								_lowerCAmelCase    :    Union[str, Any]										=      model(snake_case__      )
								self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.type_sequence_label_size)      )

								# test greyscale images
								_lowerCAmelCase    :    Any										=      1
								_lowerCAmelCase    :    Dict										=      FlaxBeitForImageClassification(snake_case__      )

								_lowerCAmelCase    :    List[str]										=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
								_lowerCAmelCase    :    Dict										=      model(snake_case__      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.prepare_config_and_inputs()
								(
								    (
								    _lowerCAmelCase						
								)					,    (
								    _lowerCAmelCase						
								)					,    (
								    _lowerCAmelCase						
								)					,    						
								)    :    Optional[int]										=      config_and_inputs
								_lowerCAmelCase    :    Union[str, Any]										=      {"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_flax
class        __snake_case  (__a						,	unittest.TestCase		):
				lowerCAmelCase__				=     (
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      FlaxBeitModelTester(self      )
								_lowerCAmelCase    :    Optional[Any]										=      ConfigTester(self		,	config_class=snake_case__		,	has_text_modality=snake_case__		,	hidden_size=37      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    str:





								'''simple docstring'''




								self.config_tester.run_common_tests()



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





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      self.model_tester.prepare_config_and_inputs_for_common()

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

												_lowerCAmelCase    :    Tuple										=      ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]		,	snake_case__      )



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase    :    str										=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__      ):
																_lowerCAmelCase    :    Optional[Any]										=      self._prepare_for_class(snake_case__		,	snake_case__      )
																_lowerCAmelCase    :    Tuple										=      model_class(snake_case__      )

																@jax.jit
																def model_jitted(_UpperCAmelCase      :		Optional[int]		,	**_UpperCAmelCase      :		str      ):
																				return model(pixel_values=snake_case__		,	**snake_case__      )

																with self.subTest("""JIT Enabled"""      ):
																				_lowerCAmelCase    :    Optional[Any]										=      model_jitted(**snake_case__      ).to_tuple()

																with self.subTest("""JIT Disabled"""      ):
																				with jax.disable_jit():
																								_lowerCAmelCase    :    Any										=      model_jitted(**snake_case__      ).to_tuple()

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*snake_case__      )



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												_lowerCAmelCase    :    Any										=      model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""      )
												_lowerCAmelCase    :    Dict										=      model(np.ones((1, 3, 224, 224)      )      )
												self.assertIsNotNone(snake_case__      )






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
				return image





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



				@cached_property
				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




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



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""      )

								_lowerCAmelCase    :    List[Any]										=      self.default_image_processor
								_lowerCAmelCase    :    List[str]										=      prepare_img()
								_lowerCAmelCase    :    List[Any]										=      image_processor(images=snake_case__		,	return_tensors="""np"""      ).pixel_values

								# prepare bool_masked_pos
								_lowerCAmelCase    :    str										=      np.ones((1, 196)		,	dtype=snake_case__      )

								# forward pass
								_lowerCAmelCase    :    Tuple										=      model(pixel_values=snake_case__		,	bool_masked_pos=snake_case__      )
								_lowerCAmelCase    :    Optional[Any]										=      outputs.logits

								# verify the logits
								_lowerCAmelCase    :    Union[str, Any]										=      (1, 196, 8192)
								self.assertEqual(logits.shape		,	snake_case__      )

								_lowerCAmelCase    :    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]		,	snake_case__		,	atol=1E-2      )      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""      )

								_lowerCAmelCase    :    List[str]										=      self.default_image_processor
								_lowerCAmelCase    :    Tuple										=      prepare_img()
								_lowerCAmelCase    :    Dict										=      image_processor(images=snake_case__		,	return_tensors="""np"""      )

								# forward pass
								_lowerCAmelCase    :    Tuple										=      model(**snake_case__      )
								_lowerCAmelCase    :    str										=      outputs.logits

								# verify the logits
								_lowerCAmelCase    :    Tuple										=      (1, 1000)
								self.assertEqual(logits.shape		,	snake_case__      )

								_lowerCAmelCase    :    int										=      np.array([-1.2385, -1.0987, -1.0108]      )

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

								_lowerCAmelCase    :    Dict										=      281
								self.assertEqual(logits.argmax(-1      ).item()		,	snake_case__      )



				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""      )

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

								# forward pass
								_lowerCAmelCase    :    Dict										=      model(**snake_case__      )
								_lowerCAmelCase    :    Dict										=      outputs.logits

								# verify the logits
								_lowerCAmelCase    :    Any										=      (1, 2_1841)
								self.assertEqual(logits.shape		,	snake_case__      )

								_lowerCAmelCase    :    List[str]										=      np.array([1.6881, -0.2787, 0.5901]      )

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

								_lowerCAmelCase    :    Optional[Any]										=      2396
								self.assertEqual(logits.argmax(-1      ).item()		,	snake_case__      )











def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : bool = False       ):
				'''simple docstring'''

				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Tuple										=      F"Expected string as input, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )
				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Optional[Any]										=      F"Expected boolean as use_pascal parameter, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )

				_lowerCAmelCase    :    Any										=      input_str.split("""_"""       )

				_lowerCAmelCase    :    str										=      0 if use_pascal else 1

				_lowerCAmelCase    :    Any										=      words[start_index:]

				_lowerCAmelCase    :    List[str]										=      [word[0].upper() + word[1:] for word in words_to_capitalize]

				_lowerCAmelCase    :    Any										=      """""" if use_pascal else words[0]

				return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
	from doctest import testmod

	testmod()

