


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   ):






						def __init__( self : str						,				_A : str						,				_A : List[str]=13						,				_A : Union[str, Any]=30						,				_A : Dict=2						,				_A : str=3						,				_A : List[Any]=True						,				_A : Any=True						,				_A : Tuple=32						,				_A : List[Any]=5						,				_A : Any=4						,				_A : Optional[int]=37						,				_A : Tuple="gelu"						,				_A : Optional[int]=0.1						,				_A : Optional[Any]=0.1						,				_A : Dict=10						,				_A : int=0.0_2						,				)							->      Optional[int]:







							"""simple docstring"""


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

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							snake_case_						:     Optional[Any]     =		(image_size // patch_size) ** 2
							snake_case_						:     Optional[int]     =		num_patches + 1






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Any     =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

							snake_case_						:     Dict     =		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 UpperCAmelCase_			( self : str						,				_A : Any						,				_A : Tuple		)							->      Any:







							"""simple docstring"""


							snake_case_						:     str     =		FlaxViTModel(config=_A		)
							snake_case_						:     Dict     =		model(_A		)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							snake_case_						:     Any     =		(self.image_size, self.image_size)
							snake_case_						:     List[str]     =		(self.patch_size, self.patch_size)
							snake_case_						:     List[str]     =		(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 UpperCAmelCase_			( self : Union[str, Any]						,				_A : Tuple						,				_A : Optional[Any]		)							->      str:







							"""simple docstring"""


							snake_case_						:     str     =		self.type_sequence_label_size
							snake_case_						:     List[Any]     =		FlaxViTForImageClassification(config=_A		)
							snake_case_						:     Dict     =		model(_A		)
							self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.type_sequence_label_size)		)

							# test greyscale images
							snake_case_						:     List[Any]     =		1
							snake_case_						:     Dict     =		FlaxViTForImageClassification(_A		)

							snake_case_						:     List[str]     =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
							snake_case_						:     Optional[int]     =		model(_A		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.prepare_config_and_inputs()
							(
							    snake_case_
							)						:     Optional[int]     =		config_and_inputs
							snake_case_						:     List[str]     =		{'pixel_values': pixel_values}
							return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     int	 =    (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






						def UpperCAmelCase_			( self : Dict		)							->      None:







							"""simple docstring"""


							snake_case_						:     int     =		FlaxViTModelTester(self		)
							snake_case_						:     Dict     =		ConfigTester(self						,				config_class=_A						,				has_text_modality=_A						,				hidden_size=37		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      str:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     int     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A		)






						def UpperCAmelCase_			( self : Tuple		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A		)






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







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		self.model_tester.prepare_config_and_inputs_for_common()

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

								snake_case_						:     int     =		['pixel_values']
								self.assertListEqual(arg_names[:1]						,				_A		)






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     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__		):
									snake_case_						:     Optional[Any]     =		self._prepare_for_class(_A						,				_A		)
									snake_case_						:     List[str]     =		model_class(_A		)

									@jax.jit
									def model_jitted(_A : int						,				**_A : Optional[Any]		):
										return model(pixel_values=_A						,				**_A		)

									with self.subTest('JIT Enabled'		):
										snake_case_						:     List[Any]     =		model_jitted(**_A		).to_tuple()

									with self.subTest('JIT Disabled'		):
										with jax.disable_jit():
											snake_case_						:     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		)






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







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_						:     Optional[Any]     =		model_class_name.from_pretrained('google/vit-base-patch16-224'		)
								snake_case_						:     Optional[Any]     =		model(np.ones((1, 3, 224, 224)		)		)
								self.assertIsNotNone(_A		)








def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a = False				):

	if not isinstance(__a				,	__a				):
		snake_case_						:     str     =		f"""Expected string as input, found {type(__a				)}"""
		raise ValueError(__a				)
	if not isinstance(__a				,	__a				):
		snake_case_						:     int     =		f"""Expected boolean as use_pascal parameter, found {type(__a				)}"""
		raise ValueError(__a				)

	snake_case_						:     Union[str, Any]     =		input_str.split('_'				)

	snake_case_						:     int     =		0 if use_pascal else 1

	snake_case_						:     List[Any]     =		words[start_index:]

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

	snake_case_						:     Optional[Any]     =		'' if use_pascal else words[0]

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


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

						testmod()

