




from __future__ import annotations

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


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

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import (
       TFDeiTForImageClassification,
       TFDeiTForImageClassificationWithTeacher,
       TFDeiTForMaskedImageModeling,
       TFDeiTModel,
   )
   from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Tuple=13				,				_lowerCAmelCase  :		Dict=30				,				_lowerCAmelCase  :		Tuple=2				,				_lowerCAmelCase  :		Dict=3				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		int=32				,				_lowerCAmelCase  :		List[Any]=2				,				_lowerCAmelCase  :		Tuple=4				,				_lowerCAmelCase  :		Optional[int]=37				,				_lowerCAmelCase  :		List[Any]="gelu"				,				_lowerCAmelCase  :		Optional[Any]=0.1				,				_lowerCAmelCase  :		Any=0.1				,				_lowerCAmelCase  :		Optional[Any]=10				,				_lowerCAmelCase  :		Dict=0.02				,				_lowerCAmelCase  :		Optional[Any]=3				,				_lowerCAmelCase  :		Optional[Any]=None				,				_lowerCAmelCase  :		List[Any]=2				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			image_size
       SCREAMING_SNAKE_CASE_							=			patch_size
       SCREAMING_SNAKE_CASE_							=			num_channels
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			intermediate_size
       SCREAMING_SNAKE_CASE_							=			hidden_act
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			scope
       SCREAMING_SNAKE_CASE_							=			encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE_							=			(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE_							=			num_patches + 2



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.type_sequence_label_size						)

       SCREAMING_SNAKE_CASE_							=			self.get_config()

       return config, pixel_values, labels



 def       lowerCAmelCase_  (							self  :		Tuple						):
       return DeiTConfig(
           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				,				encoder_stride=self.encoder_stride				,				)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTModel(config=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTForMaskedImageModeling(config=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(
           result.reconstruction.shape				,				(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       SCREAMING_SNAKE_CASE_							=			1
       SCREAMING_SNAKE_CASE_							=			TFDeiTForMaskedImageModeling(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase						)
       self.parent.assertEqual(result.reconstruction.shape				,				(self.batch_size, 1, self.image_size, self.image_size)						)



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassification(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       SCREAMING_SNAKE_CASE_							=			1
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassification(_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       SCREAMING_SNAKE_CASE_							=			model(_lowerCAmelCase				,				labels=_lowerCAmelCase						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)						)




 def       lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{'pixel_values': pixel_values}
       return config, inputs_dict





@require_tf
class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE       ,   _SCREAMING_SNAKE_CASE       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 lowercase_				    =    (
     {
         "feature-extraction": TFDeiTModel,
         "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False
 lowercase_				    =    False



 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=_lowerCAmelCase				,				has_text_modality=_lowerCAmelCase				,				hidden_size=37						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='DeiT does not use inputs_embeds'						)
 def       lowerCAmelCase_  (							self  :		Any						):
       pass



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_							=			model_class(_lowerCAmelCase						)
             self.assertIsInstance(model.get_input_embeddings()				,				(tf.keras.layers.Layer)						)
             SCREAMING_SNAKE_CASE_							=			model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_lowerCAmelCase				,				tf.keras.layers.Dense						)						)



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_							=			['pixel_values']
             self.assertListEqual(arg_names[:1]				,				_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Optional[Any]=False						):
       SCREAMING_SNAKE_CASE_							=			super()._prepare_for_class(_lowerCAmelCase				,				_lowerCAmelCase				,				return_labels=_lowerCAmelCase						)

       if return_labels:
             if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call						).parameters:
                   del inputs_dict["labels"]

       return inputs_dict




 @slow
 def       lowerCAmelCase_  (							self  :		Union[str, Any]						):
       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_							=			TFDeiTModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)


def 					UpperCAmelCase_							(      )					-> str:
      SCREAMING_SNAKE_CASE_							=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





@require_tf
@require_vision
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @cached_property
 def       lowerCAmelCase_  (							self  :		List[str]						):
       return (
           DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'						)
           if is_vision_available()
           else None
       )




 @slow
 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'						)

       SCREAMING_SNAKE_CASE_							=			self.default_image_processor
       SCREAMING_SNAKE_CASE_							=			prepare_img()
       SCREAMING_SNAKE_CASE_							=			image_processor(images=_lowerCAmelCase				,				return_tensors='tf'						)

       # forward pass
       SCREAMING_SNAKE_CASE_							=			model(**_lowerCAmelCase						)

       # verify the logits
       SCREAMING_SNAKE_CASE_							=			tf.TensorShape((1, 1_000)						)
       self.assertEqual(outputs.logits.shape				,				_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			tf.constant([-1.0266, 0.1912, -1.2861]						)

       self.assertTrue(np.allclose(outputs.logits[0, :3]				,				_lowerCAmelCase				,				atol=1E-4						)						)