
from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=10       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=None       ,	):


   '''simple docstring'''




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

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowercase__	: List[str]     		=   None
   if self.use_labels:
     lowercase__	: Tuple     		=   ids_tensor([self.batch_size]       ,	self.type_sequence_label_size)

   lowercase__	: Optional[int]     		=   self.get_config()

   return config, pixel_values, labels


 def   lowercase__  (       self):


   '''simple docstring'''




   return 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=SCREAMING_SNAKE_CASE_       ,	initializer_range=self.initializer_range       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Any     		=   TFViTModel(config=SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   model(SCREAMING_SNAKE_CASE_       ,	training=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size))

   # Test with an image with different size than the one specified in config.
   lowercase__	: Optional[Any]     		=   self.image_size // 2
   lowercase__	: Tuple     		=   pixel_values[:, :, :image_size, :image_size]
   lowercase__	: str     		=   model(SCREAMING_SNAKE_CASE_       ,	interpolate_pos_encoding=SCREAMING_SNAKE_CASE_       ,	training=SCREAMING_SNAKE_CASE_)
   lowercase__	: str     		=   (image_size // self.patch_size) ** 2 + 1
   self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, seq_length, self.hidden_size))


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   self.type_sequence_label_size
   lowercase__	: Any     		=   TFViTForImageClassification(SCREAMING_SNAKE_CASE_)
   lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	labels=SCREAMING_SNAKE_CASE_       ,	training=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size))

   # Test with an image with different size than the one specified in config.
   lowercase__	: str     		=   self.image_size // 2
   lowercase__	: Dict     		=   pixel_values[:, :, :image_size, :image_size]
   lowercase__	: Union[str, Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	interpolate_pos_encoding=SCREAMING_SNAKE_CASE_       ,	training=SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size))

   # test greyscale images
   lowercase__	: Any     		=   1
   lowercase__	: Any     		=   TFViTForImageClassification(SCREAMING_SNAKE_CASE_)
   lowercase__	: int     		=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowercase__	: Optional[int]     		=   model(SCREAMING_SNAKE_CASE_)
   self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.type_sequence_label_size))



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.prepare_config_and_inputs()
   lowercase__		,     lowercase__		,     lowercase__	: int     		=   config_and_inputs
   lowercase__	: List[Any]     		=   {"""pixel_values""": pixel_values}
   return config, inputs_dict



@require_tf
class    _snake_case (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Dict	   =      (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 __lowerCAmelCase	:  Dict	   =      (
     {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowerCAmelCase	:  Tuple	   =      False
 __lowerCAmelCase	:  List[str]	   =      False
 __lowerCAmelCase	:  Union[str, Any]	   =      False


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   TFViTModelTester(self)
   lowercase__	: Dict     		=   ConfigTester(self       ,	config_class=SCREAMING_SNAKE_CASE_       ,	has_text_modality=SCREAMING_SNAKE_CASE_       ,	hidden_size=37)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.config_tester.run_common_tests()


 @unittest.skip(reason="""ViT does not use inputs_embeds""")
 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 @unittest.skip(reason="""ViT does not use inputs_embeds""")
 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Any     		=   self.model_tester.prepare_config_and_inputs_for_common()

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Any     		=   self.model_tester.prepare_config_and_inputs_for_common()

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

     lowercase__	: int     		=   ["""pixel_values"""]
     self.assertListEqual(arg_names[:1]       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_)



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   TFViTModel.from_pretrained("""google/vit-base-patch16-224""")
   self.assertIsNotNone(SCREAMING_SNAKE_CASE_)

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


  '''simple docstring'''






  lowercase__	: str     		=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
  return image



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


 @cached_property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""") if is_vision_available() else None



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""")

   lowercase__	: Optional[int]     		=   self.default_image_processor
   lowercase__	: List[str]     		=   prepare_img()
   lowercase__	: Any     		=   image_processor(images=SCREAMING_SNAKE_CASE_       ,	return_tensors="""tf""")

   # forward pass
   lowercase__	: Any     		=   model(**SCREAMING_SNAKE_CASE_)

   # verify the logits
   lowercase__	: Dict     		=   tf.TensorShape((1, 10_00))
   self.assertEqual(outputs.logits.shape       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6])

   tf.debugging.assert_near(outputs.logits[0, :3]       ,	SCREAMING_SNAKE_CASE_       ,	atol=1E-4)








'''simple docstring'''
from __future__ import annotations

lowerCAmelCase					:    Union[str, Any]           = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase					:    List[str]           = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							UpperCamelCase   			=     len(A)

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







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							for i, outer in enumerate(A):
														UpperCamelCase   			=     -1
														for inner in arr[i + 1 :]:
																					if outer < inner:
																												UpperCamelCase   			=     inner
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     len(A)
							UpperCamelCase   			=     []
							UpperCamelCase   			=     [-1] * arr_size

							for index in reversed(range(A)):
														if stack:
																					while stack[-1] <= arr[index]:
																												stack.pop()
																												if not stack:
																																			break
														if stack:
																					UpperCamelCase   			=     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					:    Optional[Any]           = (
						    '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),
						)




