




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 					snake_case_   (     unittest.TestCase	):



				'''simple docstring'''





				def __init__(      self							:   Tuple      ,							__magic_name__							:   Union[str, Any]      ,							__magic_name__							:   Any=13      ,							__magic_name__							:   Any=30      ,							__magic_name__							:   Tuple=2      ,							__magic_name__							:   List[Any]=3      ,							__magic_name__							:   Any=True      ,							__magic_name__							:   List[Any]=True      ,							__magic_name__							:   List[Any]=32      ,							__magic_name__							:   Optional[int]=5      ,							__magic_name__							:   Tuple=4      ,							__magic_name__							:   Union[str, Any]=37      ,							__magic_name__							:   List[Any]="gelu"      ,							__magic_name__							:   Optional[int]=0.1      ,							__magic_name__							:   Optional[Any]=0.1      ,							__magic_name__							:   Any=10      ,							__magic_name__							:   Union[str, Any]=0.02      ,							)							->			Tuple:
											lowerCamelCase_			:    Dict   						=      parent
											lowerCamelCase_			:    str   						=      batch_size
											lowerCamelCase_			:    Dict   						=      image_size
											lowerCamelCase_			:    List[str]   						=      patch_size
											lowerCamelCase_			:    List[Any]   						=      num_channels
											lowerCamelCase_			:    Union[str, Any]   						=      is_training
											lowerCamelCase_			:    Tuple   						=      use_labels
											lowerCamelCase_			:    int   						=      hidden_size
											lowerCamelCase_			:    Union[str, Any]   						=      num_hidden_layers
											lowerCamelCase_			:    Any   						=      num_attention_heads
											lowerCamelCase_			:    Tuple   						=      intermediate_size
											lowerCamelCase_			:    str   						=      hidden_act
											lowerCamelCase_			:    List[Any]   						=      hidden_dropout_prob
											lowerCamelCase_			:    Optional[int]   						=      attention_probs_dropout_prob
											lowerCamelCase_			:    str   						=      type_sequence_label_size
											lowerCamelCase_			:    int   						=      initializer_range

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





				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Any:
											lowerCamelCase_			:    str   						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

											lowerCamelCase_			:    Optional[int]   						=      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=__lowercase      ,							initializer_range=self.initializer_range      ,							)

											return config, pixel_values





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]      ,							__magic_name__							:   Tuple      ,							__magic_name__							:   Tuple							)							->			int:
											lowerCamelCase_			:    List[Any]   						=      FlaxViTModel(config=__lowercase							)
											lowerCamelCase_			:    Optional[Any]   						=      model(__lowercase							)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											lowerCamelCase_			:    Dict   						=      (self.image_size, self.image_size)
											lowerCamelCase_			:    Dict   						=      (self.patch_size, self.patch_size)
											lowerCamelCase_			:    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    __SCREAMING_SNAKE_CASE     (      self							:   str      ,							__magic_name__							:   List[str]      ,							__magic_name__							:   Optional[int]							)							->			Optional[Any]:
											lowerCamelCase_			:    Union[str, Any]   						=      self.type_sequence_label_size
											lowerCamelCase_			:    Tuple   						=      FlaxViTForImageClassification(config=__lowercase							)
											lowerCamelCase_			:    int   						=      model(__lowercase							)
											self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)							)

											# test greyscale images
											lowerCamelCase_			:    int   						=      1
											lowerCamelCase_			:    Optional[Any]   						=      FlaxViTForImageClassification(__lowercase							)

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





				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]							)							->			str:
											lowerCamelCase_			:    str   						=      self.prepare_config_and_inputs()
											(
											    lowerCamelCase_    
											)			:    Dict   						=      config_and_inputs
											lowerCamelCase_			:    str   						=      {'''pixel_values''': pixel_values}
											return config, inputs_dict





@require_flax
class 					snake_case_   (     snake_case__   ,	unittest.TestCase	):



				'''simple docstring'''
				lowerCamelCase       =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]							)							->			Any:
											lowerCamelCase_			:    Tuple   						=      FlaxViTModelTester(self							)
											lowerCamelCase_			:    Tuple   						=      ConfigTester(self      ,							config_class=__lowercase      ,							has_text_modality=__lowercase      ,							hidden_size=37							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			int:
											self.config_tester.run_common_tests()





				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Tuple:
											lowerCamelCase_			:    List[str]   						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__lowercase							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			Tuple:
											lowerCamelCase_			:    int   						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__lowercase							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Dict:
											lowerCamelCase_			:    int   						=      self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCamelCase_			:    List[str]   						=      ['''pixel_values''']
																		self.assertListEqual(arg_names[:1]      ,							__lowercase							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Optional[Any]							)							->			Any:
											lowerCamelCase_			:    Tuple   						=      self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__							):
																									lowerCamelCase_			:    Any   						=      self._prepare_for_class(__lowercase      ,							__lowercase							)
																									lowerCamelCase_			:    str   						=      model_class(__lowercase							)

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

																									with self.subTest("JIT Enabled"							):
																																lowerCamelCase_			:    Dict   						=      model_jitted(**__lowercase							).to_tuple()

																									with self.subTest("JIT Disabled"							):
																																with jax.disable_jit():
																																							lowerCamelCase_			:    List[str]   						=      model_jitted(**__lowercase							).to_tuple()

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





				@slow
				def    __SCREAMING_SNAKE_CASE     (      self							:   str							)							->			Dict:
											for model_class_name in self.all_model_classes:
																		lowerCamelCase_			:    Any   						=      model_class_name.from_pretrained("google/vit-base-patch16-224"							)
																		lowerCamelCase_			:    Optional[Any]   						=      model(np.ones((1, 3, 224, 224)							)							)
																		self.assertIsNotNone(__lowercase							)








"""simple docstring"""





import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.array ):


							'''simple docstring'''







							return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




