



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        lowerCAmelCase__					:


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  Tuple=13						, __UpperCamelCase      :  Union[str, Any]=30						, __UpperCamelCase      :  List[str]=2						, __UpperCamelCase      :  str=3						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[int]=32						, __UpperCamelCase      :  Optional[Any]=2						, __UpperCamelCase      :  Optional[Any]=4						, __UpperCamelCase      :  Any=37						, __UpperCamelCase      :  Dict="gelu"						, __UpperCamelCase      :  str=0.1						, __UpperCamelCase      :  Dict=0.1						, __UpperCamelCase      :  List[Any]=10						, __UpperCamelCase      :  str=0.0_2						, __UpperCamelCase      :  str=3						, __UpperCamelCase      :  str=None						, )		->     Union[str, 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
											A      						=    scope

											# 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[int]       )		->     Dict:
											A      						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

											A      						=    self.get_config()

											return config, pixel_values, labels


				def __UpperCamelCase						(							self      :  Any       )		->     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 __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  int						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											A      						=    TFViTModel(config=__UpperCamelCase       )
											A      						=    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.
											A      						=    self.image_size // 2
											A      						=    pixel_values[:, :, :image_size, :image_size]
											A      						=    model(__UpperCamelCase						, interpolate_pos_encoding=__UpperCamelCase						, training=__UpperCamelCase       )
											A      						=    (image_size // self.patch_size) ** 2 + 1
											self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, seq_length, self.hidden_size)       )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[str]       )		->     Optional[int]:
											A      						=    self.type_sequence_label_size
											A      						=    TFViTForImageClassification(__UpperCamelCase       )
											A      						=    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.
											A      						=    self.image_size // 2
											A      						=    pixel_values[:, :, :image_size, :image_size]
											A      						=    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
											A      						=    1
											A      						=    TFViTForImageClassification(__UpperCamelCase       )
											A      						=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											A      						=    model(__UpperCamelCase       )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.type_sequence_label_size)       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     int:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A      						=    config_and_inputs
											A      						=    {'pixel_values': pixel_values}
											return config, inputs_dict





@require_tf
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
				A_   :     List[Any]								=     (
				    {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				A_   :     List[str]								=     False
				A_   :     Optional[int]								=     False
				A_   :     List[str]								=     False


				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											A      						=    TFViTModelTester(self       )
											A      						=    ConfigTester(self						, config_class=__UpperCamelCase						, has_text_modality=__UpperCamelCase						, hidden_size=37       )


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


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


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


				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

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


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[int]:
											A						,   A      						=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		A      						=    model_class(__UpperCamelCase       )
																		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]						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Optional[Any]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  List[str]       )		->     str:
											A      						=    TFViTModel.from_pretrained('google/vit-base-patch16-224'       )
											self.assertIsNotNone(__UpperCamelCase       )






def  lowerCamelCase_	(      )       ->							str:

							'''simple docstring'''






							A      						=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
							return image



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


				@cached_property
				def __UpperCamelCase						(							self      :  List[str]       )		->     Union[str, Any]:
											return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'       ) if is_vision_available() else None


				@slow
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     List[str]:
											A      						=    TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'       )

											A      						=    self.default_image_processor
											A      						=    prepare_img()
											A      						=    image_processor(images=__UpperCamelCase						, return_tensors='tf'       )

											# forward pass
											A      						=    model(**__UpperCamelCase       )

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

											A      						=    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       )




from collections.abc import Sequence






def  lowerCamelCase_	(      lowerCAmelCase__	:	Sequence[int] | None = None				)       ->							int:

							'''simple docstring'''






							if nums is None or not nums:
														raise ValueError('Input sequence should not be empty'				)

							A      						=    nums[0]
							for i in range(1			,	len(lowerCAmelCase__				)				):
														A      						=    nums[i]
														A      						=    max(lowerCAmelCase__			,	ans + num			,	lowerCAmelCase__				)

							return ans


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				# Try on a sample input from the user
				__snake_case     :str			       =int(input('Enter number of elements : ').strip())
				__snake_case     :Optional[int]			       =list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
				print(max_subsequence_sum(array))