





"""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 	lowerCAmelCase					:






       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    ,  ):
              _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     ):
              _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     ):
              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__     ):
              _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__     ):
              _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__     ):
              _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     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              (
                  (
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,							(
                  _UpperCAmelCase							
              )    ,														
              )     =     config_and_inputs
              _UpperCAmelCase     =     {'pixel_values': pixel_values}
              return config, inputs_dict



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

       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     DeiTModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  has_text_modality=a__    ,  hidden_size=37     )






       def 							__A (   self     ):
              self.config_tester.run_common_tests()






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






       def 							__A (   self     ):
              _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     ):
              _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     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*a__     )






       def 							__A (   self     ):
              _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     ):
              _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     ):
              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     ):
              _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     ):
              _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     ):
              for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _UpperCAmelCase     =     DeiTModel.from_pretrained(a__     )
                     self.assertIsNotNone(a__     )


def       __lowerCamelCase    (       )		->       Optional[int]:






       """simple docstring"""




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



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






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






       @slow
       def 							__A (   self     ):
              _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, 10_00)     )
              self.assertEqual(outputs.logits.shape    ,  a__     )

              _UpperCAmelCase     =     torch.tensor([-1.0_266, 0.1_912, -1.2_861]     ).to(a__     )

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






       @slow
       @require_accelerate
       @require_torch_gpu
       def 							__A (   self     ):
              _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"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError('the value of both inputs must be positive'							)

       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"
       _UpperCAmelCase     =     str(bin(SCREAMING_SNAKE_CASE							)							)[2:]  # remove the leading "0b"

       _UpperCAmelCase     =     max(len(SCREAMING_SNAKE_CASE							),len(SCREAMING_SNAKE_CASE							)							)

       return "0b" + "".join(
           str(int(char_a == '1' and char_b == '1'							)							)
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE							),b_binary.zfill(SCREAMING_SNAKE_CASE							)							)							)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






