





'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase=13	,  _lowerCAmelCase=30	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  _lowerCAmelCase=True	,  _lowerCAmelCase=True	,  _lowerCAmelCase=32	,  _lowerCAmelCase=5	,  _lowerCAmelCase=4	,  _lowerCAmelCase=37	,  _lowerCAmelCase="gelu"	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase=10	,  _lowerCAmelCase=0.02	,  ):
										lowerCamelCase__       =       parent
										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 ViT, 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 					__magic_name__ ( self	):
										lowerCamelCase__       =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

										lowerCamelCase__       =       ViTConfig(
										    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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       FlaxViTModel(config=_A	)
										lowerCamelCase__       =       model(_A	)
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
										lowerCamelCase__       =       (self.image_size, self.image_size)
										lowerCamelCase__       =       (self.patch_size, self.patch_size)
										lowerCamelCase__       =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
										self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, num_patches + 1, self.hidden_size)	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.type_sequence_label_size
										lowerCamelCase__       =       FlaxViTForImageClassification(config=_A	)
										lowerCamelCase__       =       model(_A	)
										self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)	)

										# test greyscale images
										lowerCamelCase__       =       1
										lowerCamelCase__       =       FlaxViTForImageClassification(_A	)

										lowerCamelCase__       =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
										lowerCamelCase__       =       model(_A	)






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

@require_flax
class SCREAMING_SNAKE_CASE_ (					a__							,       unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       FlaxViTModelTester(self	)
										lowerCamelCase__       =       ConfigTester(self	,  config_class=_A	,  has_text_modality=_A	,  hidden_size=37	)





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





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_A	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_A	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															lowerCamelCase__       =       model_class(_A	)
															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]	,  _A	)





					def 					__magic_name__ ( self	):
										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(_A	,  _A	)
																				lowerCamelCase__       =       model_class(_A	)

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

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

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






					@slow
					def 					__magic_name__ ( self	):

										for model_class_name in self.all_model_classes:
															lowerCamelCase__       =       model_class_name.from_pretrained("google/vit-base-patch16-224"	)
															lowerCamelCase__       =       model(np.ones((1, 3, 224, 224)	)	)
															self.assertIsNotNone(_A	)








def    __UpperCamelCase      (    a) ->str:

					if isinstance(a,     a):
										raise TypeError("'float' object cannot be interpreted as an integer")
					if isinstance(a,     a):
										raise TypeError("'str' object cannot be interpreted as an integer")

					if num == 0:
										return "0b0"

					lowerCamelCase__       =       False

					if num < 0:
										lowerCamelCase__       =       True
										lowerCamelCase__       =       -num

					lowerCamelCase__       =       []
					while num > 0:
										binary.insert(0,     num % 2)
										num >>= 1

					if negative:
										return "-0b" + "".join(str(a) for e in binary)

					return "0b" + "".join(str(a) for e in binary)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

