

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

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

						UpperCAmelCase__:	Optional[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=lowerCamelCase__				,      initializer_range=self.initializer_range				,      )

						return config, pixel_values, labels





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= FlaxBeitModel(config=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[int]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	List[Any]									= FlaxBeitForMaskedImageModeling(config=lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.seq_length - 1, self.vocab_size)						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[Any]									= self.type_sequence_label_size
						UpperCAmelCase__:	Tuple									= FlaxBeitForImageClassification(config=lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= model(lowerCamelCase__						)
						self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.type_sequence_label_size)						)

						# test greyscale images
						UpperCAmelCase__:	Optional[Any]									= 1
						UpperCAmelCase__:	Union[str, Any]									= FlaxBeitForImageClassification(lowerCamelCase__						)

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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						(
						    UpperCAmelCase__
						)							,						
						):	Dict									= config_and_inputs
						UpperCAmelCase__:	Union[str, Any]									= {"pixel_values": pixel_values}
						return config, inputs_dict

@require_flax
class __UpperCamelCase     (				_a					,unittest.TestCase		):






					'''simple docstring'''



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





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= FlaxBeitModelTester(self						)
						UpperCAmelCase__:	Optional[Any]									= ConfigTester(self				,      config_class=lowerCamelCase__				,      has_text_modality=lowerCamelCase__				,      hidden_size=3_7						)





					def    _UpperCAmelCase    (				self						):
						self.config_tester.run_common_tests()





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase__:	Tuple									= ["pixel_values"]
							self.assertListEqual(arg_names[:1]				,      lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__							,						UpperCAmelCase__:	Optional[int]									= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
								UpperCAmelCase__:	List[Any]									= self._prepare_for_class(lowerCamelCase__				,      lowerCamelCase__						)
								UpperCAmelCase__:	int									= model_class(lowerCamelCase__						)

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

								with self.subTest("JIT Enabled"						):
									UpperCAmelCase__:	Dict									= model_jitted(**lowerCamelCase__						).to_tuple()

								with self.subTest("JIT Disabled"						):
									with jax.disable_jit():
										UpperCAmelCase__:	Optional[int]									= 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    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__						)





					@slow
					def    _UpperCAmelCase    (				self						):
						for model_class_name in self.all_model_classes:
							UpperCAmelCase__:	int									= model_class_name.from_pretrained("microsoft/beit-base-patch16-224"						)
							UpperCAmelCase__:	List[str]									= model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
							self.assertIsNotNone(lowerCamelCase__						)



def 			_A	(      ):
	UpperCAmelCase__:	List[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    _UpperCAmelCase    (				self						):
						return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"						) if is_vision_available() else None





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						)

						UpperCAmelCase__:	Optional[Any]									= self.default_image_processor
						UpperCAmelCase__:	str									= prepare_img()
						UpperCAmelCase__:	Dict									= image_processor(images=lowerCamelCase__				,      return_tensors="np"						).pixel_values

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

						# forward pass
						UpperCAmelCase__:	Union[str, Any]									= model(pixel_values=lowerCamelCase__				,      bool_masked_pos=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= outputs.logits

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

						UpperCAmelCase__:	str									= 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]				,      lowerCamelCase__				,      atol=1e-2						)						)





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"						)

						UpperCAmelCase__:	Optional[Any]									= self.default_image_processor
						UpperCAmelCase__:	Optional[Any]									= prepare_img()
						UpperCAmelCase__:	Optional[int]									= image_processor(images=lowerCamelCase__				,      return_tensors="np"						)

						# forward pass
						UpperCAmelCase__:	Tuple									= model(**lowerCamelCase__						)
						UpperCAmelCase__:	Any									= outputs.logits

						# verify the logits
						UpperCAmelCase__:	str									= (1, 1_0_0_0)
						self.assertEqual(logits.shape				,      lowerCamelCase__						)

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

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

						UpperCAmelCase__:	List[Any]									= 2_8_1
						self.assertEqual(logits.argmax(-1						).item()				,      lowerCamelCase__						)





					@slow
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	str									= FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"						)

						UpperCAmelCase__:	Any									= self.default_image_processor
						UpperCAmelCase__:	Dict									= prepare_img()
						UpperCAmelCase__:	Any									= image_processor(images=lowerCamelCase__				,      return_tensors="np"						)

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

						# verify the logits
						UpperCAmelCase__:	List[str]									= (1, 2_1_8_4_1)
						self.assertEqual(logits.shape				,      lowerCamelCase__						)

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

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

						UpperCAmelCase__:	Any									= 2_3_9_6
						self.assertEqual(logits.argmax(-1						).item()				,      lowerCamelCase__						)


def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= int(SCREAMING_SNAKE_CASE      )
	if decimal in (0, 1):  # Exit cases for the recursion
		return str(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= divmod(SCREAMING_SNAKE_CASE	,2      )
	return binary_recursive(SCREAMING_SNAKE_CASE      ) + str(SCREAMING_SNAKE_CASE      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= str(SCREAMING_SNAKE_CASE      ).strip()
	if not number:
		raise ValueError("No input value was provided"      )
	UpperCAmelCase__:	Tuple									= "-" if number.startswith("-"      ) else ""
	UpperCAmelCase__:	Any									= number.lstrip("-"      )
	if not number.isnumeric():
		raise ValueError("Input value is not an integer"      )
	return f"{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE      )      )}"


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

					testmod()