



'''simple docstring'''






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 							a__:







    def __init__(   self				:						Tuple		,    __snake_case				:						int		,    __snake_case				:						Tuple=13		,    __snake_case				:						List[str]=30		,    __snake_case				:						Tuple=2		,    __snake_case				:						Any=3		,    __snake_case				:						Dict=True		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						int=32		,    __snake_case				:						List[str]=5		,    __snake_case				:						Any=4		,    __snake_case				:						Optional[int]=37		,    __snake_case				:						List[Any]="gelu"		,    __snake_case				:						int=0.1		,    __snake_case				:						Union[str, Any]=0.1		,    __snake_case				:						Optional[Any]=10		,    __snake_case				:						Dict=0.02		,    __snake_case				:						Tuple=3		,    __snake_case				:						Union[str, Any]=None		,    __snake_case				:						List[Any]=2		,    ):
      a		:      List[Any]       =  parent
      a		:      Optional[int]       =  batch_size
      a		:      Any       =  image_size
      a		:      Optional[Any]       =  patch_size
      a		:      Optional[Any]       =  num_channels
      a		:      Tuple       =  is_training
      a		:      Optional[Any]       =  use_labels
      a		:      Union[str, Any]       =  hidden_size
      a		:      Tuple       =  num_hidden_layers
      a		:      Union[str, Any]       =  num_attention_heads
      a		:      Union[str, Any]       =  intermediate_size
      a		:      Any       =  hidden_act
      a		:      Tuple       =  hidden_dropout_prob
      a		:      Dict       =  attention_probs_dropout_prob
      a		:      Any       =  type_sequence_label_size
      a		:      Tuple       =  initializer_range
      a		:      List[Any]       =  scope
      a		:      Optional[int]       =  encoder_stride

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







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Union[str, Any]       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

      a		:      Dict       =  self.get_config()

      return config, pixel_values, labels







    def     lowercase_ (   self				:						str			):
      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				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						int		,    __snake_case				:						List[str]			):
      a		:      Optional[int]       =  DeiTModel(config=__lowerCamelCase			)
      model.to(__lowerCamelCase			)
      model.eval()
      a		:      List[Any]       =  model(__lowerCamelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Dict		,    __snake_case				:						Any			):
      a		:      List[Any]       =  DeiTForMaskedImageModeling(config=__lowerCamelCase			)
      model.to(__lowerCamelCase			)
      model.eval()
      a		:      Optional[int]       =  model(__lowerCamelCase			)
      self.parent.assertEqual(
          result.reconstruction.shape		,    (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

      # test greyscale images
      a		:      List[str]       =  1
      a		:      Optional[int]       =  DeiTForMaskedImageModeling(__lowerCamelCase			)
      model.to(__lowerCamelCase			)
      model.eval()

      a		:      Dict       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      a		:      Optional[int]       =  model(__lowerCamelCase			)
      self.parent.assertEqual(result.reconstruction.shape		,    (self.batch_size, 1, self.image_size, self.image_size)			)







    def     lowercase_ (   self				:						Any		,    __snake_case				:						str		,    __snake_case				:						List[str]		,    __snake_case				:						int			):
      a		:      str       =  self.type_sequence_label_size
      a		:      List[str]       =  DeiTForImageClassification(__lowerCamelCase			)
      model.to(__lowerCamelCase			)
      model.eval()
      a		:      Tuple       =  model(__lowerCamelCase		,    labels=__lowerCamelCase			)
      self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)			)

      # test greyscale images
      a		:      Any       =  1
      a		:      str       =  DeiTForImageClassification(__lowerCamelCase			)
      model.to(__lowerCamelCase			)
      model.eval()

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







    def     lowercase_ (   self				:						Any			):
      a		:      Dict       =  self.prepare_config_and_inputs()
      (
          a 
      )		:      Tuple       =  config_and_inputs
      a		:      int       =  {"pixel_values": pixel_values}
      return config, inputs_dict

@require_torch
class 							a__(      _A	,				_A	,				unittest.TestCase			):
    _a				=					(
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    _a				=					(
        {
            """feature-extraction""": DeiTModel,
            """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    _a				=					False
    _a				=					False
    _a				=					False







    def     lowercase_ (   self				:						int			):
      a		:      str       =  DeiTModelTester(self			)
      a		:      Optional[int]       =  ConfigTester(self		,    config_class=__lowerCamelCase		,    has_text_modality=__lowerCamelCase		,    hidden_size=37			)







    def     lowercase_ (   self				:						Dict			):
      self.config_tester.run_common_tests()







    @unittest.skip(reason='DeiT does not use inputs_embeds'			)
    def     lowercase_ (   self				:						Any			):
      pass







    def     lowercase_ (   self				:						Any			):
      a		:      Dict       =  self.model_tester.prepare_config_and_inputs_for_common()

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







    def     lowercase_ (   self				:						Any			):
      a		:      Optional[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a		:      Union[str, Any]       =  model_class(__lowerCamelCase			)
        a		:      Optional[int]       =  inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a		:      Any       =  [*signature.parameters.keys()]

        a		:      Any       =  ["pixel_values"]
        self.assertListEqual(arg_names[:1]		,    __lowerCamelCase			)







    def     lowercase_ (   self				:						int			):
      a		:      Any       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCamelCase			)







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase			)







    def     lowercase_ (   self				:						int			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						int		,    __snake_case				:						Optional[Any]		,    __snake_case				:						List[Any]=False			):
      a		:      Union[str, Any]       =  super()._prepare_for_class(__lowerCamelCase		,    __lowerCamelCase		,    return_labels=__lowerCamelCase			)

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

      return inputs_dict







    def     lowercase_ (   self				:						Optional[int]			):
      if not self.model_tester.is_training:
        return

      a		:      Optional[int]       =  self.model_tester.prepare_config_and_inputs_for_common()
      a		:      Dict       =  True

      for model_class in self.all_model_classes:
        # DeiTForImageClassificationWithTeacher supports inference-only
        if (
            model_class in get_values(__lowerCamelCase			)
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
          continue
        a		:      Union[str, Any]       =  model_class(__lowerCamelCase			)
        model.to(__lowerCamelCase			)
        model.train()
        a		:      Union[str, Any]       =  self._prepare_for_class(__lowerCamelCase		,    __lowerCamelCase		,    return_labels=__lowerCamelCase			)
        a		:      Dict       =  model(**__lowerCamelCase			).loss
        loss.backward()







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      Optional[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
        return

      a		:      Tuple       =  False
      a		:      Any       =  True

      for model_class in self.all_model_classes:
        if model_class in get_values(__lowerCamelCase			) or not model_class.supports_gradient_checkpointing:
          continue
        # DeiTForImageClassificationWithTeacher supports inference-only
        if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
          continue
        a		:      List[str]       =  model_class(__lowerCamelCase			)
        model.gradient_checkpointing_enable()
        model.to(__lowerCamelCase			)
        model.train()
        a		:      Dict       =  self._prepare_for_class(__lowerCamelCase		,    __lowerCamelCase		,    return_labels=__lowerCamelCase			)
        a		:      Tuple       =  model(**__lowerCamelCase			).loss
        loss.backward()







    def     lowercase_ (   self				:						str			):
      a		:      Optional[Any]       =  self.model_tester.prepare_config_and_inputs_for_common()

      a		:      int       =  [
          {"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(__lowerCamelCase			),
                *get_values(__lowerCamelCase			),
            ]
            or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
        ):
          continue

        for problem_type in problem_types:
          with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""			):
            a		:      Tuple       =  problem_type["title"]
            a		:      Optional[Any]       =  problem_type["num_labels"]

            a		:      List[str]       =  model_class(__lowerCamelCase			)
            model.to(__lowerCamelCase			)
            model.train()

            a		:      List[Any]       =  self._prepare_for_class(__lowerCamelCase		,    __lowerCamelCase		,    return_labels=__lowerCamelCase			)

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

            a		:      int       =  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=__lowerCamelCase			) as warning_list:
              a		:      Optional[Any]       =  model(**__lowerCamelCase			).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     lowercase_ (   self				:						List[Any]			):
      for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:      Optional[Any]       =  DeiTModel.from_pretrained(__lowerCamelCase			)
        self.assertIsNotNone(__lowerCamelCase			)
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image

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







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







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

      a		:      List[Any]       =  self.default_image_processor
      a		:      List[Any]       =  prepare_img()
      a		:      Optional[Any]       =  image_processor(images=__lowerCamelCase		,    return_tensors='pt'			).to(__lowerCamelCase			)

      # forward pass
      with torch.no_grad():
        a		:      List[str]       =  model(**__lowerCamelCase			)

      # verify the logits
      a		:      str       =  torch.Size((1, 10_00)			)
      self.assertEqual(outputs.logits.shape		,    __lowerCamelCase			)

      a		:      List[str]       =  torch.tensor([-1.0266, 0.1912, -1.2861]			).to(__lowerCamelCase			)

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







    @slow
    @require_accelerate
    @require_torch_gpu
    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      str       =  DeiTModel.from_pretrained(
          'facebook/deit-base-distilled-patch16-224'		,    torch_dtype=torch.floataa		,    device_map='auto'			)
      a		:      Dict       =  self.default_image_processor

      a		:      Optional[int]       =  prepare_img()
      a		:      Union[str, Any]       =  image_processor(images=__lowerCamelCase		,    return_tensors='pt'			)
      a		:      Union[str, Any]       =  inputs.pixel_values.to(__lowerCamelCase			)

      # forward pass to make sure inference works in fp16
      with torch.no_grad():
        a		:      List[str]       =  model(__lowerCamelCase			)




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


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