





'''simple docstring'''




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      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=30      ,       UpperCamelCase_=2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=32      ,       UpperCamelCase_=2      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=10      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=None      ,       )      ->		Optional[int]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   image_size
		snake_case__	       =   patch_size
		snake_case__	       =   num_channels
		snake_case__	       =   is_training
		snake_case__	       =   use_labels
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   scope

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





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

		snake_case__	       =   self.get_config()

		return config, pixel_values, labels





	def   _snake_case   (		self		)      ->		Optional[Any]:
		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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		snake_case__	       =   TFViTModel(config=UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       training=UpperCamelCase_		)
		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.
		snake_case__	       =   self.image_size // 2
		snake_case__	       =   pixel_values[:, :, :image_size, :image_size]
		snake_case__	       =   model(UpperCamelCase_      ,       interpolate_pos_encoding=UpperCamelCase_      ,       training=UpperCamelCase_		)
		snake_case__	       =   (image_size // self.patch_size) ** 2 + 1
		self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, seq_length, self.hidden_size)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   self.type_sequence_label_size
		snake_case__	       =   TFViTForImageClassification(UpperCamelCase_		)
		snake_case__	       =   model(UpperCamelCase_      ,       labels=UpperCamelCase_      ,       training=UpperCamelCase_		)
		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.
		snake_case__	       =   self.image_size // 2
		snake_case__	       =   pixel_values[:, :, :image_size, :image_size]
		snake_case__	       =   model(UpperCamelCase_      ,       interpolate_pos_encoding=UpperCamelCase_      ,       training=UpperCamelCase_		)
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)		)

		# test greyscale images
		snake_case__	       =   1
		snake_case__	       =   TFViTForImageClassification(UpperCamelCase_		)
		snake_case__	       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)		)





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'pixel_values': pixel_values}
		return config, inputs_dict






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

	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   TFViTModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       has_text_modality=UpperCamelCase_      ,       hidden_size=37		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		self.config_tester.run_common_tests()





	@unittest.skip(reason='ViT does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		Any:
		pass





	@unittest.skip(reason='ViT does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		pass





	def   _snake_case   (		self		)      ->		Any:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

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





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

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

			snake_case__	       =   ['pixel_values']
			self.assertListEqual(arg_names[:1]      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
		self.assertIsNotNone(UpperCamelCase_		)





def 						__lowerCamelCase			(	)   ->List[Any]:
	snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







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





	@cached_property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None





	@slow
	def   _snake_case   (		self		)      ->		int:
		snake_case__	       =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

		snake_case__	       =   self.default_image_processor
		snake_case__	       =   prepare_img()
		snake_case__	       =   image_processor(images=UpperCamelCase_      ,       return_tensors='tf'		)

		# forward pass
		snake_case__	       =   model(**UpperCamelCase_		)

		# verify the logits
		snake_case__	       =   tf.TensorShape((1, 1000)		)
		self.assertEqual(outputs.logits.shape      ,       UpperCamelCase_		)

		snake_case__	       =   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]      ,       UpperCamelCase_      ,       atol=1E-4		)










'''simple docstring'''




from __future__ import annotations

import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->int:

	if depth < 0:
		raise ValueError('Depth cannot be less than 0'							)

	if not scores:
		raise ValueError('Scores cannot be empty'							)

	if depth == height:
		return scores[node_index]

	return (
	    max(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	    if is_max
	    else min(
	        minimax(depth + 1					,				node_index * 2					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				minimax(depth + 1					,				node_index * 2 + 1					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)					,				)
	)




def 						__lowerCamelCase			(	)   ->None:
	snake_case__	       =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
	snake_case__	       =   math.log(len(UpperCAmelCase_							)					,				2							)
	print(f'''Optimal value : {minimax(0					,				0					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)}'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



