
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					):
    def __init__(  self		,snake_case		,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		,):



           '''simple docstring'''







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

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



           '''simple docstring'''







           lowercase							:					Union[str, Any]	     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           lowercase							:					Tuple	     =				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=snake_case		,initializer_range=self.initializer_range		,)

           return config, pixel_values
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				FlaxViTModel(config=snake_case				)
           lowercase							:					str	     =				model(snake_case				)
           # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
           lowercase							:					List[Any]	     =				(self.image_size, self.image_size)
           lowercase							:					Tuple	     =				(self.patch_size, self.patch_size)
           lowercase							:					Union[str, Any]	     =				(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		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					int	     =				self.type_sequence_label_size
           lowercase							:					Any	     =				FlaxViTForImageClassification(config=snake_case				)
           lowercase							:					str	     =				model(snake_case				)
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size)				)

           # test greyscale images
           lowercase							:					int	     =				1
           lowercase							:					Dict	     =				FlaxViTForImageClassification(snake_case				)

           lowercase							:					Dict	     =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
           lowercase							:					List[str]	     =				model(snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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






@require_flax
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						List[str]=    (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				FlaxViTModelTester(self				)
           lowercase							:					int	     =				ConfigTester(self		,config_class=snake_case		,has_text_modality=snake_case		,hidden_size=37				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.model_tester.prepare_config_and_inputs_for_common()

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

                  lowercase							:					List[str]	     =				["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Any	     =				self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__				):
                         lowercase							:					List[str]	     =				self._prepare_for_class(snake_case		,snake_case				)
                         lowercase							:					Optional[int]	     =				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"""				):
                                lowercase							:					List[Any]	     =				model_jitted(**snake_case				).to_tuple()

                         with self.subTest("""JIT Disabled"""				):
                                with jax.disable_jit():
                                       lowercase							:					Union[str, Any]	     =				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				)
    @slow
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           for model_class_name in self.all_model_classes:
                  lowercase							:					str	     =				model_class_name.from_pretrained("""google/vit-base-patch16-224"""				)
                  lowercase							:					Union[str, Any]	     =				model(np.ones((1, 3, 224, 224)				)				)
                  self.assertIsNotNone(snake_case				)




from bisect import bisect
from itertools import accumulate







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[int]:
       lowercase							:					Dict	     =				sorted(zip(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     key=lambda SCREAMING_SNAKE_CASE__    : x[0] / x[1] ,     reverse=SCREAMING_SNAKE_CASE__    )
       lowercase     ,      lowercase							:					Optional[Any]	     =				[i[0] for i in r], [i[1] for i in r]
       lowercase							:					Any	     =				list(accumulate(SCREAMING_SNAKE_CASE__    )    )
       lowercase							:					int	     =				bisect(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       return (
           0
           if k == 0
           else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
           if k != n
           else sum(vl[:k]    )
       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
