




'''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  __lowercase    (	unittest.TestCase			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=13   ,				UpperCamelCase=30   ,				UpperCamelCase=2   ,				UpperCamelCase=3   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=32   ,				UpperCamelCase=5   ,				UpperCamelCase=4   ,				UpperCamelCase=37   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=10   ,				UpperCamelCase=0.02   ,				)					->     Any:
     __a								=       parent
     __a								=       batch_size
     __a								=       image_size
     __a								=       patch_size
     __a								=       num_channels
     __a								=       is_training
     __a								=       use_labels
     __a								=       hidden_size
     __a								=       num_hidden_layers
     __a								=       num_attention_heads
     __a								=       intermediate_size
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       type_sequence_label_size
     __a								=       initializer_range

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







   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     __a								=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

     __a								=       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=lowerCAmelCase__   ,				initializer_range=self.initializer_range   ,				)

     return config, pixel_values







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Tuple:
     __a								=       FlaxViTModel(config=lowerCAmelCase__   )
     __a								=       model(lowerCAmelCase__   )
     # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
     __a								=       (self.image_size, self.image_size)
     __a								=       (self.patch_size, self.patch_size)
     __a								=       (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   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Union[str, Any]:
     __a								=       self.type_sequence_label_size
     __a								=       FlaxViTForImageClassification(config=lowerCAmelCase__   )
     __a								=       model(lowerCAmelCase__   )
     self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)   )

     # test greyscale images
     __a								=       1
     __a								=       FlaxViTForImageClassification(lowerCAmelCase__   )

     __a								=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
     __a								=       model(lowerCAmelCase__   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       self.prepare_config_and_inputs()
     (
         __a      
     )								=       config_and_inputs
     __a								=       {'pixel_values': pixel_values}
     return config, inputs_dict





@require_flax
class  __lowercase    (	__SCREAMING_SNAKE_CASE							,				unittest.TestCase			):
   _a    						=   (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       FlaxViTModelTester(self   )
     __a								=       ConfigTester(self   ,				config_class=lowerCAmelCase__   ,				has_text_modality=lowerCAmelCase__   ,				hidden_size=37   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     self.config_tester.run_common_tests()







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase__   )







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__   )







   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       self.model_tester.prepare_config_and_inputs_for_common()

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

       __a								=       ['pixel_values']
       self.assertListEqual(arg_names[:1]   ,				lowerCAmelCase__   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__   ):
         __a								=       self._prepare_for_class(lowerCAmelCase__   ,				lowerCAmelCase__   )
         __a								=       model_class(lowerCAmelCase__   )

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

         with self.subTest('JIT Enabled'   ):
           __a								=       model_jitted(**lowerCAmelCase__   ).to_tuple()

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







   @slow
   def 		UpperCamelCase__      (						self   )					->     Dict:




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







from __future__ import annotations






def 						a__			(				A__,      A__ = None,      A__ = None				):
			if start is None:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					0

			if end is None:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					len(A__				) - 1

			if start >= end:
						return

			SCREAMING_SNAKE_CASE_		:     Tuple					=					(start + end) // 2

			slowsort(A__,      A__,      A__				)
			slowsort(A__,      mid + 1,      A__				)

			if sequence[end] < sequence[mid]:
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					sequence[mid], sequence[end]

			slowsort(A__,      A__,      end - 1				)


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

				testmod()





