


from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
      from PIL import Image

      from transformers import ConvNextImageProcessor




class  A_ :







 """simple docstring"""





 def __init__(      self       :			Dict							,     _a       :			str							,     _a       :			Tuple=1_3							,     _a       :			Union[str, Any]=3_2							,     _a       :			str=3							,     _a       :			Dict=4							,     _a       :			Dict=[1_0, 2_0, 3_0, 4_0]							,     _a       :			List[Any]=[2, 2, 3, 2]							,     _a       :			Dict=True							,     _a       :			Tuple=True							,     _a       :			Union[str, Any]=3_7							,     _a       :			List[str]="gelu"							,     _a       :			int=1_0							,     _a       :			Optional[int]=0.0_2							,     _a       :			str=3							,     _a       :			Any=None							,     )     ->      Optional[int]:
      UpperCAmelCase    :					Optional[int]			=      parent
      UpperCAmelCase    :					Dict			=      batch_size
      UpperCAmelCase    :					Tuple			=      image_size
      UpperCAmelCase    :					List[Any]			=      num_channels
      UpperCAmelCase    :					Optional[Any]			=      num_stages
      UpperCAmelCase    :					List[Any]			=      hidden_sizes
      UpperCAmelCase    :					Optional[int]			=      depths
      UpperCAmelCase    :					Union[str, Any]			=      is_training
      UpperCAmelCase    :					List[Any]			=      use_labels
      UpperCAmelCase    :					Any			=      intermediate_size
      UpperCAmelCase    :					List[Any]			=      hidden_act
      UpperCAmelCase    :					Optional[int]			=      type_sequence_label_size
      UpperCAmelCase    :					Optional[Any]			=      initializer_range
      UpperCAmelCase    :					List[Any]			=      scope
 def 	_UpperCamelCase     (      self       :			Tuple					)     ->      Optional[int]:
      UpperCAmelCase    :					List[str]			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

      UpperCAmelCase    :					List[Any]			=      self.get_config()

      return config, pixel_values, labels
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      str:
      return ConvNextConfig(
          num_channels=self.num_channels							,     hidden_sizes=self.hidden_sizes							,     depths=self.depths							,     num_stages=self.num_stages							,     hidden_act=self.hidden_act							,     is_decoder=_a							,     initializer_range=self.initializer_range							,     )
 def 	_UpperCamelCase     (      self       :			Any							,     _a       :			int							,     _a       :			Optional[Any]							,     _a       :			Dict					)     ->      str:
      UpperCAmelCase    :					List[str]			=      TFConvNextModel(config=_a					)
      UpperCAmelCase    :					Optional[int]			=      model(_a							,     training=_a					)
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape							,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,     )
 def 	_UpperCamelCase     (      self       :			List[Any]							,     _a       :			Any							,     _a       :			List[str]							,     _a       :			Union[str, Any]					)     ->      Optional[int]:
      UpperCAmelCase    :					Optional[Any]			=      self.type_sequence_label_size
      UpperCAmelCase    :					Optional[int]			=      TFConvNextForImageClassification(_a					)
      UpperCAmelCase    :					List[Any]			=      model(_a							,     labels=_a							,     training=_a					)
      self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.type_sequence_label_size)					)

 def 	_UpperCamelCase     (      self       :			Dict					)     ->      Tuple:
      UpperCAmelCase    :					List[str]			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Any			=      config_and_inputs
      UpperCAmelCase    :					Optional[Any]			=      {"pixel_values": pixel_values}
      return config, inputs_dict




@require_tf
class  A_ (			a_   ,    a_   ,    unittest.TestCase  ):







 """simple docstring"""





 _UpperCamelCase					:			List[Any]     	=       (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
 _UpperCamelCase					:			List[Any]     	=       (
     {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
     if is_tf_available()
     else {}
 )

 _UpperCamelCase					:			Any     	=       False
 _UpperCamelCase					:			Optional[Any]     	=       False
 _UpperCamelCase					:			List[str]     	=       False
 _UpperCamelCase					:			Tuple     	=       False
 _UpperCamelCase					:			List[str]     	=       False
 def 	_UpperCamelCase     (      self       :			int					)     ->      Any:
      UpperCAmelCase    :					Optional[Any]			=      TFConvNextModelTester(self					)
      UpperCAmelCase    :					int			=      ConfigTester(
          self							,     config_class=_a							,     has_text_modality=_a							,     hidden_size=3_7							,     )
 @unittest.skip(reason="ConvNext does not use inputs_embeds"					)
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      int:
      pass
 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"					)					) == 0							,     reason="TF does not support backprop for grouped convolutions on CPU."							,     )
 @slow
 def 	_UpperCamelCase     (      self       :			Optional[int]					)     ->      Tuple:
      super().test_keras_fit()
 @unittest.skip(reason="ConvNext does not support input and output embeddings"					)
 def 	_UpperCamelCase     (      self       :			Any					)     ->      Any:
      pass
 def 	_UpperCamelCase     (      self       :			Optional[int]					)     ->      int:
      UpperCAmelCase	,			UpperCAmelCase    :					int			=      self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCAmelCase    :					Dict			=      ["pixel_values"]
           self.assertListEqual(arg_names[:1]							,     _a					)
 def 	_UpperCamelCase     (      self       :			Optional[Any]					)     ->      Tuple:
      UpperCAmelCase    :					Dict			=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_a					)
 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("GPU"					)					) == 0							,     reason="TF does not support backprop for grouped convolutions on CPU."							,     )
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      Optional[int]:
      super().test_dataset_conversion()
 def 	_UpperCamelCase     (      self       :			int					)     ->      Union[str, Any]:
      def check_hidden_states_output(_a       :			int							,     _a       :			Union[str, Any]							,     _a       :			Union[str, Any]					):
           UpperCAmelCase    :					Union[str, Any]			=      model_class(_a					)

           UpperCAmelCase    :					Optional[Any]			=      model(**self._prepare_for_class(_a							,     _a					)					)
           UpperCAmelCase    :					str			=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCAmelCase    :					List[Any]			=      self.model_tester.num_stages
           self.assertEqual(len(_a					)							,     expected_num_stages + 1					)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]					)							,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,     )

      UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCAmelCase    :					List[str]			=      True
           check_hidden_states_output(_a							,     _a							,     _a					)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           UpperCAmelCase    :					int			=      True

           check_hidden_states_output(_a							,     _a							,     _a					)
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Union[str, Any]:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[Any]			=      self.model_tester.prepare_config_and_inputs_for_common()

      def check_equivalence(_a       :			Any							,     _a       :			Any							,     _a       :			Optional[int]							,     _a       :			Union[str, Any]={}					):
           UpperCAmelCase    :					str			=      model(_a							,     return_dict=_a							,     **_a					)
           UpperCAmelCase    :					int			=      model(_a							,     return_dict=_a							,     **_a					).to_tuple()

           def recursive_check(_a       :			Optional[int]							,     _a       :			Tuple					):
                if isinstance(_a							,     (List, Tuple)					):
                     for tuple_iterable_value, dict_iterable_value in zip(_a							,     _a					):
                          recursive_check(_a							,     _a					)
                elif tuple_object is None:
                     return
                else:
                     self.assertTrue(
                         all(tf.equal(_a							,     _a					)					)							,     msg=(
                             "Tuple and dict output are not equal. Difference:"
                             f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object					)					)}'
                         )							,     )

                recursive_check(_a							,     _a					)

      for model_class in self.all_model_classes:
           UpperCAmelCase    :					Union[str, Any]			=      model_class(_a					)

           UpperCAmelCase    :					Union[str, Any]			=      self._prepare_for_class(_a							,     _a					)
           UpperCAmelCase    :					Any			=      self._prepare_for_class(_a							,     _a					)
           check_equivalence(_a							,     _a							,     _a					)

           UpperCAmelCase    :					Union[str, Any]			=      self._prepare_for_class(_a							,     _a							,     return_labels=_a					)
           UpperCAmelCase    :					Any			=      self._prepare_for_class(_a							,     _a							,     return_labels=_a					)
           check_equivalence(_a							,     _a							,     _a					)

           UpperCAmelCase    :					Any			=      self._prepare_for_class(_a							,     _a					)
           UpperCAmelCase    :					List[Any]			=      self._prepare_for_class(_a							,     _a					)
           check_equivalence(_a							,     _a							,     _a							,     {"output_hidden_states": True}					)

           UpperCAmelCase    :					List[str]			=      self._prepare_for_class(_a							,     _a							,     return_labels=_a					)
           UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a							,     return_labels=_a					)
           check_equivalence(_a							,     _a							,     _a							,     {"output_hidden_states": True}					)
 def 	_UpperCamelCase     (      self       :			str					)     ->      List[Any]:
      UpperCAmelCase    :					Tuple			=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_a					)

 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      Union[str, Any]:
      UpperCAmelCase    :					List[Any]			=      TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"					)
      self.assertIsNotNone(_a					)







def 	A (		):
     UpperCAmelCase    :					Tuple			=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
     return image




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







 """simple docstring"""





 @cached_property
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      Dict:
      return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"					) if is_vision_available() else None

 @slow
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Tuple:
      UpperCAmelCase    :					List[str]			=      TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"					)

      UpperCAmelCase    :					Union[str, Any]			=      self.default_image_processor
      UpperCAmelCase    :					Optional[Any]			=      prepare_img()
      UpperCAmelCase    :					List[Any]			=      image_processor(images=_a							,     return_tensors="tf"					)

      # forward pass
      UpperCAmelCase    :					Optional[int]			=      model(**_a					)

      # verify the logits
      UpperCAmelCase    :					int			=      tf.TensorShape((1, 1_0_0_0)					)
      self.assertEqual(outputs.logits.shape							,     _a					)

      UpperCAmelCase    :					Union[str, Any]			=      tf.constant([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]					)

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







import os







def 	A (		):
     UpperCAmelCase    :					Any			=      os.path.dirname(os.path.realpath(A__			)			)
     UpperCAmelCase    :					Optional[int]			=      os.path.join(A__		,		"triangle.txt"			)

     with open(A__			) as f:
          UpperCAmelCase    :					Union[str, Any]			=      f.readlines()

     UpperCAmelCase    :					List[str]			=      [[int(A__			) for y in x.rstrip("\r\n"			).split(" "			)] for x in triangle]

     for i in range(1		,		len(A__			)			):
          for j in range(len(a[i]			)			):
               UpperCAmelCase    :					List[Any]			=      a[i - 1][j] if j != len(a[i - 1]			) else 0
               UpperCAmelCase    :					Optional[int]			=      a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(A__		,		A__			)
     return max(a[-1]			)


if __name__ == "__main__":
      print(solution())



