



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 					__lowerCAmelCase (     unittest.TestCase       ):




				def __init__(							self						,				_snake_case						,				_snake_case=100						,				_snake_case=13						,				_snake_case=30						,				_snake_case=2						,				_snake_case=3						,				_snake_case=True						,				_snake_case=True						,				_snake_case=32						,				_snake_case=5						,				_snake_case=4						,				_snake_case=37						,				_snake_case="gelu"						,				_snake_case=0.1						,				_snake_case=0.1						,				_snake_case=10						,				_snake_case=0.02						,				_snake_case=3						,				):



									"""simple docstring"""





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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





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

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

									_lowerCAmelCase						=    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 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case						,				_snake_case		):



									"""simple docstring"""





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

									# test greyscale images
									_lowerCAmelCase						=    1
									_lowerCAmelCase						=    FlaxBeitForImageClassification(_snake_case		)

									_lowerCAmelCase						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_lowerCAmelCase						=    model(_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.prepare_config_and_inputs()
									(
									    (
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	(
									    _lowerCAmelCase
									)	,	
									)						=    config_and_inputs
									_lowerCAmelCase						=    {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_flax
class 					__lowerCAmelCase (     lowerCamelCase__       ,						unittest.TestCase       ):
				__lowerCamelCase =				(
				    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
				)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    FlaxBeitModelTester(self		)
									_lowerCAmelCase						=    ConfigTester(self						,				config_class=_snake_case						,				has_text_modality=_snake_case						,				hidden_size=37		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs_for_common()

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

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




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase	,	_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__		):
																			_lowerCAmelCase						=    self._prepare_for_class(_snake_case						,				_snake_case		)
																			_lowerCAmelCase						=    model_class(_snake_case		)

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

																			with self.subTest("""JIT Enabled"""		):
																								_lowerCAmelCase						=    model_jitted(**_snake_case		).to_tuple()

																			with self.subTest("""JIT Disabled"""		):
																								with jax.disable_jit():
																													_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)




				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_snake_case		)




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





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


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

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





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




				@cached_property
				def 							snake_case   (							self		):



									"""simple docstring"""





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




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""		)

									_lowerCAmelCase						=    self.default_image_processor
									_lowerCAmelCase						=    prepare_img()
									_lowerCAmelCase						=    image_processor(images=_snake_case						,				return_tensors="""np"""		).pixel_values

									# prepare bool_masked_pos
									_lowerCAmelCase						=    np.ones((1, 196)						,				dtype=_snake_case		)

									# forward pass
									_lowerCAmelCase						=    model(pixel_values=_snake_case						,				bool_masked_pos=_snake_case		)
									_lowerCAmelCase						=    outputs.logits

									# verify the logits
									_lowerCAmelCase						=    (1, 196, 8192)
									self.assertEqual(logits.shape						,				_snake_case		)

									_lowerCAmelCase						=    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 							snake_case   (							self		):



									"""simple docstring"""





									_lowerCAmelCase						=    FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""		)

									_lowerCAmelCase						=    self.default_image_processor
									_lowerCAmelCase						=    prepare_img()
									_lowerCAmelCase						=    image_processor(images=_snake_case						,				return_tensors="""np"""		)

									# forward pass
									_lowerCAmelCase						=    model(**_snake_case		)
									_lowerCAmelCase						=    outputs.logits

									# verify the logits
									_lowerCAmelCase						=    (1, 1000)
									self.assertEqual(logits.shape						,				_snake_case		)

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

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

									_lowerCAmelCase						=    281
									self.assertEqual(logits.argmax(-1		).item()						,				_snake_case		)




				@slow
				def 							snake_case   (							self		):



									"""simple docstring"""





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

									_lowerCAmelCase						=    self.default_image_processor
									_lowerCAmelCase						=    prepare_img()
									_lowerCAmelCase						=    image_processor(images=_snake_case						,				return_tensors="""np"""		)

									# forward pass
									_lowerCAmelCase						=    model(**_snake_case		)
									_lowerCAmelCase						=    outputs.logits

									# verify the logits
									_lowerCAmelCase						=    (1, 21841)
									self.assertEqual(logits.shape						,				_snake_case		)

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

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

									_lowerCAmelCase						=    2396
									self.assertEqual(logits.argmax(-1		).item()						,				_snake_case		)





snake_case :		Optional[int]							      =  "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"






def   lowerCAmelCase_						(					_snake_case      :  bytes   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		Tuple	   =     F'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
		raise TypeError(_snake_case   )

	__magic_name__		:		Optional[int]	   =     "".join(bin(_snake_case   )[2:].zfill(8   ) for byte in data   )

	__magic_name__		:		List[Any]	   =     len(_snake_case   ) % 6 != 0

	if padding_needed:
		# The padding that will be added later
		__magic_name__		:		List[str]	   =     B"=" * ((6 - len(_snake_case   ) % 6) // 2)

		# Append binary_stream with arbitrary binary digits (0's by default) to make its
		# length a multiple of 6.
		binary_stream += "0" * (6 - len(_snake_case   ) % 6)
	else:
		__magic_name__		:		List[str]	   =     B""

	# Encode every 6 binary digits to their corresponding Base64 character
	return (
	    "".join(
	        B64_CHARSET[int(binary_stream[index : index + 6]      ,   2   )]
	        for index in range(0      ,   len(_snake_case   )      ,   6   )   ).encode()
	    + padding
	)






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		bytes:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   _snake_case   ) and not isinstance(_snake_case      ,   _snake_case   ):
		__magic_name__		:		List[str]	   =     (
		    "argument should be a bytes-like object or ASCII string, "
		    F'''not \'{encoded_data.__class__.__name__}\''''
		)
		raise TypeError(_snake_case   )

	# In case encoded_data is a bytes-like object, make sure it contains only
	# ASCII characters so we convert it to a string object
	if isinstance(_snake_case      ,   _snake_case   ):
		try:
			__magic_name__		:		List[Any]	   =     encoded_data.decode("utf-8"   )
		except UnicodeDecodeError:
			raise ValueError("base64 encoded data should only contain ASCII characters"   )

	__magic_name__		:		List[str]	   =     encoded_data.count("="   )

	# Check if the encoded string contains non base64 characters
	if padding:
		assert all(
		    char in B64_CHARSET for char in encoded_data[:-padding]   ), "Invalid base64 character(s) found."
	else:
		assert all(
		    char in B64_CHARSET for char in encoded_data   ), "Invalid base64 character(s) found."

	# Check the padding
	assert len(_snake_case   ) % 4 == 0 and padding < 3, "Incorrect padding"

	if padding:
		# Remove padding if there is one
		__magic_name__		:		Optional[int]	   =     encoded_data[:-padding]

		__magic_name__		:		Dict	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )[: -padding * 2]
	else:
		__magic_name__		:		Union[str, Any]	   =     "".join(
		    bin(B64_CHARSET.index(_snake_case   )   )[2:].zfill(6   ) for char in encoded_data   )

	__magic_name__		:		List[Any]	   =     [
	    int(binary_stream[index : index + 8]      ,   2   )
	    for index in range(0      ,   len(_snake_case   )      ,   8   )
	]

	return bytes(_snake_case   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
