


'''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   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=2     ,	)							->	List[str]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      str														=						batch_size
         __lowerCamelCase  :      List[Any]														=						image_size
         __lowerCamelCase  :      Any														=						patch_size
         __lowerCamelCase  :      Tuple														=						num_channels
         __lowerCamelCase  :      Dict														=						is_training
         __lowerCamelCase  :      List[str]														=						use_labels
         __lowerCamelCase  :      Optional[Any]														=						hidden_size
         __lowerCamelCase  :      int														=						num_hidden_layers
         __lowerCamelCase  :      int														=						num_attention_heads
         __lowerCamelCase  :      str														=						intermediate_size
         __lowerCamelCase  :      Optional[int]														=						hidden_act
         __lowerCamelCase  :      List[str]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[Any]														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range
         __lowerCamelCase  :      Union[str, Any]														=						scope
         __lowerCamelCase  :      Union[str, Any]														=						encoder_stride

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







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __lowerCamelCase  :      List[Any]														=						None
         if self.use_labels:
              __lowerCamelCase  :      Optional[int]														=						ids_tensor([self.batch_size]     ,	self.type_sequence_label_size      )

         __lowerCamelCase  :      Dict														=						self.get_config()

         return config, pixel_values, labels







    def  lowercase_							(				self      )							->	Tuple:
         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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	encoder_stride=self.encoder_stride     ,	)







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Union[str, Any]														=						TFDeiTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[str]														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase  :      Tuple														=						TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(
             result.reconstruction.shape     ,	(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      Any														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.reconstruction.shape     ,	(self.batch_size, 1, self.image_size, self.image_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         __lowerCamelCase  :      Dict														=						self.type_sequence_label_size
         __lowerCamelCase  :      Optional[Any]														=						TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						model(SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      Tuple														=						1
         __lowerCamelCase  :      List[Any]														=						TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_     ,	labels=SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Union[str, Any]														=						self.prepare_config_and_inputs()
         __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						config_and_inputs
         __lowerCamelCase  :      Union[str, Any]														=						{'pixel_values': pixel_values}
         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     (
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase    :   List[str]					=     (
        {
            'feature-extraction': TFDeiTModel,
            'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

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







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      Any														=						TFDeiTModelTester(self      )
         __lowerCamelCase  :      Any														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







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







    @unittest.skip(reason='DeiT does not use inputs_embeds'      )
    def  lowercase_							(				self      )							->	Union[str, Any]:
         pass







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self.model_tester.prepare_config_and_inputs_for_common()

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







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Union[str, Any]														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						inspect.signature(model.call      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowerCamelCase  :      Tuple														=						[*signature.parameters.keys()]

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Tuple														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False      )							->	Dict:
         __lowerCamelCase  :      Optional[int]														=						super()._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	return_labels=SCREAMING_SNAKE_CASE_      )

         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  lowercase_							(				self      )							->	Dict:
         for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Tuple														=						TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







def      UpperCAmelCase__   (      )	->						Optional[int]:
     __lowerCamelCase  :      int														=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
     return image





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





    """simple docstring"""






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







    @slow
    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'      )

         __lowerCamelCase  :      Union[str, Any]														=						self.default_image_processor
         __lowerCamelCase  :      Dict														=						prepare_img()
         __lowerCamelCase  :      Dict														=						image_processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'      )

         # forward pass
         __lowerCamelCase  :      Dict														=						model(**SCREAMING_SNAKE_CASE_      )

         # verify the logits
         __lowerCamelCase  :      Union[str, Any]														=						tf.TensorShape((1, 10_00)      )
         self.assertEqual(outputs.logits.shape     ,	SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[str]														=						tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]      )

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













def  __magic_name__  (				lowercase = 100				)				-> int:

							"""simple docstring"""



							lowercase_	:   Dict  				=     (n * (n + 1) // 2) ** 2
							lowercase_	:   List[str]  				=     n * (n + 1) * (2 * n + 1) // 6
							return sum_cubes - sum_squares


if __name__ == "__main__":
							print(F'''{solution() = }''')