'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=100					,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_=3					,)			->			Dict:



         '''simple docstring'''



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

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




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



         '''simple docstring'''



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

         UpperCAmelCase__							:				int   				=  None
         if self.use_labels:
            UpperCAmelCase__							:				Optional[int]   				=  ids_tensor([self.batch_size]					,self.type_sequence_label_size							)

         UpperCAmelCase__							:				str   				=  BeitConfig(
             vocab_size=self.vocab_size					,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, labels




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  FlaxBeitModel(config=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  model(lowerCamelCase_							)
         self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  FlaxBeitForMaskedImageModeling(config=lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  model(lowerCamelCase_							)
         self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length - 1, self.vocab_size)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.type_sequence_label_size
         UpperCAmelCase__							:				Tuple   				=  FlaxBeitForImageClassification(config=lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  model(lowerCamelCase_							)
         self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         UpperCAmelCase__							:				int   				=  1
         UpperCAmelCase__							:				Any   				=  FlaxBeitForImageClassification(lowerCamelCase_							)

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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase__
         )				,      (
             UpperCAmelCase__
         )				,      (
             UpperCAmelCase__
         )				,      
         )							:				Union[str, Any]   				=  config_and_inputs
         UpperCAmelCase__							:				Any   				=  {'''pixel_values''': pixel_values}
         return config, inputs_dict




@require_flax
class 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Any      							=   (
          (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
      )




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  FlaxBeitModelTester(self							)
         UpperCAmelCase__							:				Optional[int]   				=  ConfigTester(self					,config_class=lowerCamelCase_					,has_text_modality=lowerCamelCase_					,hidden_size=37							)




      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         self.config_tester.run_common_tests()




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase__							:				int   				=  ['''pixel_values''']
            self.assertListEqual(arg_names[:1]					,lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__							):
               UpperCAmelCase__							:				Union[str, Any]   				=  self._prepare_for_class(lowerCamelCase_					,lowerCamelCase_							)
               UpperCAmelCase__							:				Dict   				=  model_class(lowerCamelCase_							)

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

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

               with self.subTest('''JIT Disabled'''							):
                  with jax.disable_jit():
                     UpperCAmelCase__							:				Optional[Any]   				=  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							)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         for model_class_name in self.all_model_classes:
            UpperCAmelCase__							:				Tuple   				=  model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''							)
            UpperCAmelCase__							:				Optional[Any]   				=  model(np.ones((1, 3, 224, 224)							)							)
            self.assertIsNotNone(lowerCamelCase_							)







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   return image


@require_vision
@require_flax
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      @cached_property
      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''							) if is_vision_available() else None




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  self.default_image_processor
         UpperCAmelCase__							:				Optional[int]   				=  prepare_img()
         UpperCAmelCase__							:				Any   				=  image_processor(images=lowerCamelCase_					,return_tensors='''np'''							).pixel_values

         # prepare bool_masked_pos
         UpperCAmelCase__							:				List[str]   				=  np.ones((1, 196)					,dtype=lowerCamelCase_							)

         # forward pass
         UpperCAmelCase__							:				Optional[int]   				=  model(pixel_values=lowerCamelCase_					,bool_masked_pos=lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  outputs.logits

         # verify the logits
         UpperCAmelCase__							:				Any   				=  (1, 196, 8192)
         self.assertEqual(logits.shape					,lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  np.array(
             [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]							)

         self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]					,lowerCamelCase_					,atol=1e-2							)							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''							)

         UpperCAmelCase__							:				Optional[int]   				=  self.default_image_processor
         UpperCAmelCase__							:				Union[str, Any]   				=  prepare_img()
         UpperCAmelCase__							:				str   				=  image_processor(images=lowerCamelCase_					,return_tensors='''np'''							)

         # forward pass
         UpperCAmelCase__							:				Tuple   				=  model(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  outputs.logits

         # verify the logits
         UpperCAmelCase__							:				List[str]   				=  (1, 1000)
         self.assertEqual(logits.shape					,lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  np.array([-1.2385, -1.0987, -1.0108]							)

         self.assertTrue(np.allclose(logits[0, :3]					,lowerCamelCase_					,atol=1e-4							)							)

         UpperCAmelCase__							:				Tuple   				=  281
         self.assertEqual(logits.argmax(-1							).item()					,lowerCamelCase_							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''							)

         UpperCAmelCase__							:				Optional[int]   				=  self.default_image_processor
         UpperCAmelCase__							:				Any   				=  prepare_img()
         UpperCAmelCase__							:				List[Any]   				=  image_processor(images=lowerCamelCase_					,return_tensors='''np'''							)

         # forward pass
         UpperCAmelCase__							:				Dict   				=  model(**lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  outputs.logits

         # verify the logits
         UpperCAmelCase__							:				Optional[Any]   				=  (1, 21841)
         self.assertEqual(logits.shape					,lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  np.array([1.6881, -0.2787, 0.5901]							)

         self.assertTrue(np.allclose(logits[0, :3]					,lowerCamelCase_					,atol=1e-4							)							)

         UpperCAmelCase__							:				Optional[int]   				=  2396
         self.assertEqual(logits.argmax(-1							).item()					,lowerCamelCase_							)





'''simple docstring'''

def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  (
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   UpperCAmelCase__							:				list   				=  []
   for char_count in range(_A    ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]    )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]    )
   return "".join(_A    )


if __name__ == "__main__":
  print(alternative_string_arrange('AB', 'XYZ'), end=' ')




