
from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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 TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor







class 					lowerCamelCase		:





     def __init__(self				:   List[str]    ,   _A				:   Tuple    ,   _A				:   Union[str, Any]=1_3    ,   _A				:   int=3_0    ,   _A				:   Dict=2    ,   _A				:   Dict=3    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[int]=True    ,   _A				:   List[str]=3_2    ,   _A				:   Any=2    ,   _A				:   int=4    ,   _A				:   List[Any]=3_7    ,   _A				:   int="gelu"    ,   _A				:   Dict=0.1    ,   _A				:   Optional[int]=0.1    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Any=0.02    ,   _A				:   int=3    ,   _A				:   str=0.6    ,   _A				:   Optional[int]=None    ,   )						->						str:
         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								= mask_ratio
         snake_case								= scope

         # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
         # (we add 1 for the [CLS] token)
         snake_case								= (image_size // patch_size) ** 2
         snake_case								= int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )





     def 		UpperCAmelCase(self				:   Any       )						->						Optional[Any]:
         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 		UpperCAmelCase(self				:   Tuple       )						->						str:
         return ViTMAEConfig(
             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    ,   decoder_hidden_size=self.hidden_size    ,   decoder_num_hidden_layers=self.num_hidden_layers    ,   decoder_num_attention_heads=self.num_attention_heads    ,   decoder_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=_A    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]    ,   _A				:   str    ,   _A				:   str       )						->						Union[str, Any]:
         snake_case								= TFViTMAEModel(config=_A       )
         snake_case								= model(_A    ,   training=_A       )
         self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)       )





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[int]    ,   _A				:   int    ,   _A				:   str       )						->						Any:
         snake_case								= TFViTMAEForPreTraining(_A       )
         snake_case								= model(_A    ,   training=_A       )
         # expected sequence length = num_patches
         snake_case								= (self.image_size // self.patch_size) ** 2
         snake_case								= self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)       )

         # test greyscale images
         snake_case								= 1
         snake_case								= TFViTMAEForPreTraining(_A       )

         snake_case								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case								= model(_A    ,   training=_A       )
         snake_case								= self.patch_size**2
         self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)       )





     def 		UpperCAmelCase(self				:   int       )						->						Union[str, Any]:
         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 					lowerCamelCase		(  A_							,			A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Union[str, Any]									=		(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
     UpperCAmelCase__    :							Tuple									=		{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

     UpperCAmelCase__    :							List[str]									=		False
     UpperCAmelCase__    :							int									=		False
     UpperCAmelCase__    :							List[Any]									=		False
     UpperCAmelCase__    :							Optional[int]									=		False





     def 		UpperCAmelCase(self				:   str       )						->						List[str]:
         snake_case								= TFViTMAEModelTester(self       )
         snake_case								= ConfigTester(self    ,   config_class=_A    ,   has_text_modality=_A    ,   hidden_size=3_7       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						List[Any]:
         self.config_tester.run_common_tests()





     @unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Tuple:
         pass





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Dict:
         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(_A       )
             self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)       )
             snake_case								= model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_A    ,   tf.keras.layers.Layer       )       )





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						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(_A       )
             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]    ,   _A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						int:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_A       )





     def 		UpperCAmelCase(self				:   Any       )						->						str:
         snake_case								= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Dict:
         # make the mask reproducible
         np.random.seed(2       )

         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= int((config.image_size // config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
             snake_case								= model_class(_A       )
             snake_case								= self._prepare_for_class(_A    ,   _A       )

             snake_case								= model(_A    ,   noise=_A       )

             snake_case								= copy.deepcopy(self._prepare_for_class(_A    ,   _A       )       )
             snake_case								= model(**_A    ,   noise=_A       )
             snake_case								= outputs_dict[0].numpy()
             snake_case								= outputs_keywords[0].numpy()

             self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )    ,   1E-6       )





     def 		UpperCAmelCase(self				:   str       )						->						List[Any]:
         # make the mask reproducible
         np.random.seed(2       )

         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= int((config.image_size // config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         def prepare_numpy_arrays(_A				:   Tuple       ):
             snake_case								= {}
             for k, v in inputs_dict.items():
                 if tf.is_tensor(_A       ):
                     snake_case								= v.numpy()
                 else:
                     snake_case								= np.array(_A       )

             return inputs_np_dict

         for model_class in self.all_model_classes:
             snake_case								= model_class(_A       )

             snake_case								= self._prepare_for_class(_A    ,   _A       )
             snake_case								= prepare_numpy_arrays(_A       )

             snake_case								= model(_A    ,   noise=_A       )
             snake_case								= model(**_A    ,   noise=_A       )
             self.assert_outputs_same(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[Any]    ,   _A				:   List[str]    ,   _A				:   Optional[int]       )						->						Optional[int]:
         # make masks reproducible
         np.random.seed(2       )

         snake_case								= int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         snake_case								= tf.constant(_A       )

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         snake_case								= tf_noise

         super().check_pt_tf_models(_A    ,   _A    ,   _A       )





     def 		UpperCAmelCase(self				:   int       )						->						int:
         # make mask reproducible
         np.random.seed(2       )

         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= {
             module_member
             for model_class in self.all_model_classes
             for module in (import_module(model_class.__module__       ),)
             for module_member_name in dir(_A       )
             if module_member_name.endswith("MainLayer"       )
             # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
             and module_member_name[: -len("MainLayer"       )] == model_class.__name__[: -len("Model"       )]
             for module_member in (getattr(_A    ,   _A       ),)
             if isinstance(_A    ,   _A       )
             and tf.keras.layers.Layer in module_member.__bases__
             and getattr(_A    ,   "_keras_serializable"    ,   _A       )
         }

         snake_case								= int((config.image_size // config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
         snake_case								= tf.convert_to_tensor(_A       )
         inputs_dict.update({"noise": noise}       )

         for main_layer_class in tf_main_layer_classes:
             snake_case								= main_layer_class(_A       )

             snake_case								= {
                 name: tf.keras.Input(tensor.shape[1:]    ,   dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
             }

             snake_case								= tf.keras.Model(_A    ,   outputs=main_layer(_A       )       )
             snake_case								= model(_A       )

             with tempfile.TemporaryDirectory() as tmpdirname:
                 snake_case								= os.path.join(_A    ,   "keras_model.h5"       )
                 model.save(_A       )
                 snake_case								= tf.keras.models.load_model(
                     _A    ,   custom_objects={main_layer_class.__name__: main_layer_class}       )
                 assert isinstance(_A    ,   tf.keras.Model       )
                 snake_case								= model(_A       )
                 self.assert_outputs_same(_A    ,   _A       )





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						Optional[int]:
         # make mask reproducible
         np.random.seed(2       )

         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= int((config.image_size // config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
             snake_case								= model_class(_A       )
             snake_case								= self._prepare_for_class(_A    ,   _A       )
             snake_case								= model(_A    ,   noise=_A       )

             if model_class.__name__ == "TFViTMAEModel":
                 snake_case								= outputs.last_hidden_state.numpy()
                 snake_case								= 0
             else:
                 snake_case								= outputs.logits.numpy()
                 snake_case								= 0

             with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(_A    ,   saved_model=_A       )
                 snake_case								= model_class.from_pretrained(_A       )
                 snake_case								= model(_A    ,   noise=_A       )

                 if model_class.__name__ == "TFViTMAEModel":
                     snake_case								= after_outputs["last_hidden_state"].numpy()
                     snake_case								= 0
                 else:
                     snake_case								= after_outputs["logits"].numpy()
                     snake_case								= 0

                 snake_case								= np.amax(np.abs(out_a - out_a       )       )
                 self.assertLessEqual(_A    ,   1E-5       )





     def 		UpperCAmelCase(self				:   int       )						->						int:
         # make mask reproducible
         np.random.seed(2       )

         snake_case					,							snake_case								= self.model_tester.prepare_config_and_inputs_for_common()

         snake_case								= int((config.image_size // config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

         for model_class in self.all_model_classes:
             snake_case								= model_class(_A       )
             snake_case								= self._prepare_for_class(_A    ,   _A       )

             snake_case								= model(_A    ,   noise=_A       )
             snake_case								= model.get_config()
             # make sure that returned config is jsonifiable, which is required by keras
             json.dumps(_A       )
             snake_case								= model_class.from_config(model.get_config()       )
             # make sure it also accepts a normal config
             snake_case								= model_class.from_config(model.config       )
             snake_case								= new_model(_A       )  # Build model
             new_model.set_weights(model.get_weights()       )
             snake_case								= new_model(_A    ,   noise=_A       )

             self.assert_outputs_same(_A    ,   _A       )





     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
     def 		UpperCAmelCase(self				:   Dict       )						->						Any:
         pass





     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"       )
     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         pass





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						List[Any]:
         snake_case								= TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"       )
         self.assertIsNotNone(_A       )



def 						lowercase_      (       )							->							Optional[Any]:
    """simple docstring"""

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







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





     @cached_property
     def 		UpperCAmelCase(self				:   Any       )						->						Union[str, Any]:
         return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None





     @slow
     def 		UpperCAmelCase(self				:   List[Any]       )						->						List[Any]:
         # make random mask reproducible across the PT and TF model
         np.random.seed(2       )

         snake_case								= TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       )

         snake_case								= self.default_image_processor
         snake_case								= prepare_img()
         snake_case								= image_processor(images=_A    ,   return_tensors="tf"       )

         # prepare a noise vector that will be also used for testing the TF model
         # (this way we can ensure that the PT and TF models operate on the same inputs)
         snake_case								= ViTMAEConfig()
         snake_case								= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
         snake_case								= np.random.uniform(size=(1, num_patches)       )

         # forward pass
         snake_case								= model(**_A    ,   noise=_A       )

         # verify the logits
         snake_case								= tf.convert_to_tensor([1, 1_9_6, 7_6_8]       )
         self.assertEqual(outputs.logits.shape    ,   _A       )

         snake_case								= tf.convert_to_tensor(
             [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]       )

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



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

