
"""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       lowerCAmelCase__				:







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			List[str]=13    ,      _lowerCamelCase			:			Tuple=30    ,      _lowerCamelCase			:			Optional[int]=2    ,      _lowerCamelCase			:			List[Any]=3    ,      _lowerCamelCase			:			List[Any]=True    ,      _lowerCamelCase			:			Dict=True    ,      _lowerCamelCase			:			List[str]=32    ,      _lowerCamelCase			:			Union[str, Any]=2    ,      _lowerCamelCase			:			Optional[int]=4    ,      _lowerCamelCase			:			Optional[int]=37    ,      _lowerCamelCase			:			List[Any]="gelu"    ,      _lowerCamelCase			:			Any=0.1    ,      _lowerCamelCase			:			Dict=0.1    ,      _lowerCamelCase			:			Tuple=10    ,      _lowerCamelCase			:			Dict=0.0_2    ,      _lowerCamelCase			:			List[str]=3    ,      _lowerCamelCase			:			List[str]=None    ,      _lowerCamelCase			:			Optional[int]=2    ,      ):
       _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					     =       scope
       _snake_case					     =       encoder_stride

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







     def      lowercase	(							self			:			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      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=_lowerCamelCase    ,      initializer_range=self.initializer_range    ,      encoder_stride=self.encoder_stride    ,      )







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       TFDeiTModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Tuple    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       TFDeiTForMaskedImageModeling(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       self.parent.assertEqual(
           result.reconstruction.shape    ,      (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

       # test greyscale images
       _snake_case					     =       1
       _snake_case					     =       TFDeiTForMaskedImageModeling(_lowerCamelCase			)

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







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       self.type_sequence_label_size
       _snake_case					     =       TFDeiTForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)

       # test greyscale images
       _snake_case					     =       1
       _snake_case					     =       TFDeiTForImageClassification(_lowerCamelCase			)

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







     def      lowercase	(							self			:			Optional[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	):
     __a						=					(
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     __a						=					(
         {
             """feature-extraction""": TFDeiTModel,
             """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       TFDeiTModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase    ,      hidden_size=37			)







     def      lowercase	(							self			:			str			):
       self.config_tester.run_common_tests()







     @unittest.skip(reason='''DeiT does not use inputs_embeds'''			)
     def      lowercase	(							self			:			List[Any]			):
       pass







     def      lowercase	(							self			:			Union[str, 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(_lowerCamelCase			)
         self.assertIsInstance(model.get_input_embeddings()    ,      (tf.keras.layers.Layer)			)
         _snake_case					     =       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_lowerCamelCase    ,      tf.keras.layers.Dense			)			)







     def      lowercase	(							self			:			int			):
       _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(_lowerCamelCase			)
         _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]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase			)







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			List[str]=False			):
       _snake_case					     =       super()._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase    ,      return_labels=_lowerCamelCase			)

       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			:			Union[str, Any]			):
       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFDeiTModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






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

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







     @cached_property
     def      lowercase	(							self			:			Optional[int]			):
       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

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

       _snake_case					     =       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]    ,      _lowerCamelCase    ,      atol=1e-4			)			)





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


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



