


'''simple docstring'''

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   snake_case					:





  """simple docstring"""




  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=0.02    ,						UpperCamelCase=3    ,						UpperCamelCase=None    ,						UpperCamelCase=2    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						parent
   lowerCamelCase_  				=						batch_size
   lowerCamelCase_  				=						image_size
   lowerCamelCase_  				=						patch_size
   lowerCamelCase_  				=						num_channels
   lowerCamelCase_  				=						is_training
   lowerCamelCase_  				=						use_labels
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						type_sequence_label_size
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						scope
   lowerCamelCase_  				=						encoder_stride

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

   lowerCamelCase_  				=						self.get_config()

   return config, pixel_values, labels






  def 						snake_case	(					self  ):







   """simple docstring"""
   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=UpperCamelCase    ,						initializer_range=self.initializer_range    ,						encoder_stride=self.encoder_stride    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTModel(config=UpperCamelCase  )
   lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTForMaskedImageModeling(config=UpperCamelCase  )
   lowerCamelCase_  				=						model(UpperCamelCase  )
   self.parent.assertEqual(
       result.reconstruction.shape    ,						(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						TFDeiTForMaskedImageModeling(UpperCamelCase  )

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






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.type_sequence_label_size
   lowerCamelCase_  				=						TFDeiTForImageClassification(UpperCamelCase  )
   lowerCamelCase_  				=						model(UpperCamelCase    ,						labels=UpperCamelCase  )
   self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.type_sequence_label_size)  )

   # test greyscale images
   lowerCamelCase_  				=						1
   lowerCamelCase_  				=						TFDeiTForImageClassification(UpperCamelCase  )

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







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.prepare_config_and_inputs()
   lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						config_and_inputs
   lowerCamelCase_  				=						{"pixel_values": pixel_values}
   return config, inputs_dict





@require_tf
class   snake_case					(					lowercase    ,		lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




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

  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTModelTester(self  )
   lowerCamelCase_  				=						ConfigTester(self    ,						config_class=UpperCamelCase    ,						has_text_modality=UpperCamelCase    ,						hidden_size=37  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   self.config_tester.run_common_tests()






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







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_				,lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_  				=						["pixel_values"]
    self.assertListEqual(arg_names[:1]    ,						UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=False  ):







   """simple docstring"""
   lowerCamelCase_  				=						super()._prepare_for_class(UpperCamelCase    ,						UpperCamelCase    ,						return_labels=UpperCamelCase  )

   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 						snake_case	(					self  ):







   """simple docstring"""
   for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_  				=						TFDeiTModel.from_pretrained(UpperCamelCase  )
    self.assertIsNotNone(UpperCamelCase  )





def __snake_case      (   ):
 lowerCamelCase_  				=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
 return image







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





  """simple docstring"""




  @cached_property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return (
       DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"  )
       if is_vision_available()
       else None
   )







  @slow
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"  )

   lowerCamelCase_  				=						self.default_image_processor
   lowerCamelCase_  				=						prepare_img()
   lowerCamelCase_  				=						image_processor(images=UpperCamelCase    ,						return_tensors="tf"  )

   # forward pass
   lowerCamelCase_  				=						model(**UpperCamelCase  )

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

   lowerCamelCase_  				=						tf.constant([-1.0_266, 0.1_912, -1.2_861]  )

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







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

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

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

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



