


import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       DeiTForImageClassification,
       DeiTForImageClassificationWithTeacher,
       DeiTForMaskedImageModeling,
       DeiTModel,
   )
   from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=30					,   A_=2					,   A_=3					,   A_=True					,   A_=True					,   A_=32					,   A_=5					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=10					,   A_=0.02					,   A_=3					,   A_=None					,   A_=2					,   )  ->       List[str]:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=type_sequence_label_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=scope
         __UpperCamelCase    							=encoder_stride

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






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

         __UpperCamelCase    							=self.get_config()

         return config, pixel_values, labels






    def _a					(			self    )  ->       Optional[Any]:
         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=A_					,   initializer_range=self.initializer_range					,   encoder_stride=self.encoder_stride					,   )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=DeiTModel(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(result.last_hidden_state.shape					,   (self.batch_size, self.seq_length, self.hidden_size)    )






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Optional[int]:
         __UpperCamelCase    							=DeiTForMaskedImageModeling(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_    )
         self.parent.assertEqual(
             result.reconstruction.shape					,   (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

         # test greyscale images
         __UpperCamelCase    							=1
         __UpperCamelCase    							=DeiTForMaskedImageModeling(A_    )
         model.to(A_    )
         model.eval()

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






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       Any:
         __UpperCamelCase    							=self.type_sequence_label_size
         __UpperCamelCase    							=DeiTForImageClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   labels=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.type_sequence_label_size)    )

         # test greyscale images
         __UpperCamelCase    							=1
         __UpperCamelCase    							=DeiTForImageClassification(A_    )
         model.to(A_    )
         model.eval()

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






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs
         __UpperCamelCase    							={'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]      =  (
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__						:		Tuple      =  (
        {
            "feature-extraction": DeiTModel,
            "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__						:		Optional[Any]      =  False
    UpperCAmelCase__						:		str      =  False
    UpperCAmelCase__						:		Dict      =  False






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=DeiTModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   has_text_modality=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Dict:
         self.config_tester.run_common_tests()






    @unittest.skip(reason='DeiT does not use inputs_embeds'    )
    def _a					(			self    )  ->       int:
         pass






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __UpperCamelCase    							=model_class(A_    )
              self.assertIsInstance(model.get_input_embeddings()					,   (nn.Module)    )
              __UpperCamelCase    							=model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(A_					,   nn.Linear    )    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase    							=['pixel_values']
              self.assertListEqual(arg_names[:1]					,   A_    )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*A_    )






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






    def _a					(			self					,   A_					,   A_					,   A_=False    )  ->       str:
         __UpperCamelCase    							=super()._prepare_for_class(A_					,   A_					,   return_labels=A_    )

         if return_labels:
              if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

         return inputs_dict






    def _a					(			self    )  ->       Optional[Any]:
         if not self.model_tester.is_training:
              return

         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         __UpperCamelCase    							=True

         for model_class in self.all_model_classes:
              # DeiTForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(A_    )
                  or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
              ):
                   continue
              __UpperCamelCase    							=model_class(A_    )
              model.to(A_    )
              model.train()
              __UpperCamelCase    							=self._prepare_for_class(A_					,   A_					,   return_labels=A_    )
              __UpperCamelCase    							=model(**A_    ).loss
              loss.backward()






    def _a					(			self    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         __UpperCamelCase    							=False
         __UpperCamelCase    							=True

         for model_class in self.all_model_classes:
              if model_class in get_values(A_    ) or not model_class.supports_gradient_checkpointing:
                   continue
              # DeiTForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   continue
              __UpperCamelCase    							=model_class(A_    )
              model.gradient_checkpointing_enable()
              model.to(A_    )
              model.train()
              __UpperCamelCase    							=self._prepare_for_class(A_					,   A_					,   return_labels=A_    )
              __UpperCamelCase    							=model(**A_    ).loss
              loss.backward()






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase							,      __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs_for_common()

         __UpperCamelCase    							=[
             {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
             {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
             {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
         ]

         for model_class in self.all_model_classes:
              if (
                  model_class
                  not in [
                      *get_values(A_    ),
                      *get_values(A_    ),
                  ]
                  or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
              ):
                   continue

              for problem_type in problem_types:
                   with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'    ):
                        __UpperCamelCase    							=problem_type['title']
                        __UpperCamelCase    							=problem_type['num_labels']

                        __UpperCamelCase    							=model_class(A_    )
                        model.to(A_    )
                        model.train()

                        __UpperCamelCase    							=self._prepare_for_class(A_					,   A_					,   return_labels=A_    )

                        if problem_type["num_labels"] > 1:
                             __UpperCamelCase    							=inputs['labels'].unsqueeze(1    ).repeat(1					,   problem_type['num_labels']    )

                        __UpperCamelCase    							=inputs['labels'].to(problem_type['dtype']    )

                        # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                        # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                        # they have the same size." which is a symptom something in wrong for the regression problem.
                        # See https://github.com/huggingface/transformers/issues/11780
                        with warnings.catch_warnings(record=A_    ) as warning_list:
                             __UpperCamelCase    							=model(**A_    ).loss
                        for w in warning_list:
                             if "Using a target size that is different to the input size" in str(w.message    ):
                                  raise ValueError(
                                      f'Something is going wrong in the regression problem: intercepted {w.message}'    )

                        loss.backward()






    @slow
    def _a					(			self    )  ->       Tuple:
         for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase    							=DeiTModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )

def 						_UpperCAmelCase				(			):
     __UpperCamelCase    							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image






@require_torch
@require_vision
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @cached_property
    def _a					(			self    )  ->       Tuple:
         return (
             DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'    )
             if is_vision_available()
             else None
         )






    @slow
    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'    ).to(
             A_    )

         __UpperCamelCase    							=self.default_image_processor
         __UpperCamelCase    							=prepare_img()
         __UpperCamelCase    							=image_processor(images=A_					,   return_tensors='pt'    ).to(A_    )

         # forward pass
         with torch.no_grad():
              __UpperCamelCase    							=model(**A_    )

         # verify the logits
         __UpperCamelCase    							=torch.Size((1, 1000)    )
         self.assertEqual(outputs.logits.shape					,   A_    )

         __UpperCamelCase    							=torch.tensor([-1.0266, 0.1912, -1.2861]    ).to(A_    )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]					,   A_					,   atol=1E-4    )    )






    @slow
    @require_accelerate
    @require_torch_gpu
    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=DeiTModel.from_pretrained(
             'facebook/deit-base-distilled-patch16-224'					,   torch_dtype=torch.floataa					,   device_map='auto'    )
         __UpperCamelCase    							=self.default_image_processor

         __UpperCamelCase    							=prepare_img()
         __UpperCamelCase    							=image_processor(images=A_					,   return_tensors='pt'    )
         __UpperCamelCase    							=inputs.pixel_values.to(A_    )

         # forward pass to make sure inference works in fp16
         with torch.no_grad():
              __UpperCamelCase    							=model(A_    )






'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






