
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:

  """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_=5	,      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_=0.02	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=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 	UpperCAmelCase_ (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 	UpperCAmelCase_ (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=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      encoder_stride=self.encoder_stride	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    DeiTModel(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)
       self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    DeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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
       UpperCamelCase__				=    1
       UpperCamelCase__				=    DeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

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







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.type_sequence_label_size
       UpperCamelCase__				=    DeiTForImageClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()
       UpperCamelCase__				=    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
       UpperCamelCase__				=    1
       UpperCamelCase__				=    DeiTForImageClassification(SCREAMING_SNAKE_CASE_						)
       model.to(SCREAMING_SNAKE_CASE_						)
       model.eval()

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







  def 	UpperCAmelCase_ (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      __A(    __lowerCamelCase    ,				__lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      (
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE__         =      (
      {
          """feature-extraction""": DeiTModel,
          """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False
  SCREAMING_SNAKE_CASE__         =      False







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    DeiTModelTester(self						)
       UpperCamelCase__				=    ConfigTester(self	,      config_class=SCREAMING_SNAKE_CASE_	,      has_text_modality=SCREAMING_SNAKE_CASE_	,      hidden_size=37						)







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







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__		,	UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            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]	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False						):
       UpperCamelCase__				=    super()._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      return_labels=SCREAMING_SNAKE_CASE_						)

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

       return inputs_dict







  def 	UpperCAmelCase_ (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(SCREAMING_SNAKE_CASE_						)
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                 continue
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.to(SCREAMING_SNAKE_CASE_						)
            model.train()
            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      return_labels=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						).loss
            loss.backward()







  def 	UpperCAmelCase_ (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(SCREAMING_SNAKE_CASE_						) or not model_class.supports_gradient_checkpointing:
                 continue
            # DeiTForImageClassificationWithTeacher supports inference-only
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                 continue
            UpperCamelCase__				=    model_class(SCREAMING_SNAKE_CASE_						)
            model.gradient_checkpointing_enable()
            model.to(SCREAMING_SNAKE_CASE_						)
            model.train()
            UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      return_labels=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						).loss
            loss.backward()







  def 	UpperCAmelCase_ (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(SCREAMING_SNAKE_CASE_						),
                    *get_values(SCREAMING_SNAKE_CASE_						),
                ]
                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(SCREAMING_SNAKE_CASE_						)
                      model.to(SCREAMING_SNAKE_CASE_						)
                      model.train()

                      UpperCamelCase__				=    self._prepare_for_class(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      return_labels=SCREAMING_SNAKE_CASE_						)

                      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=SCREAMING_SNAKE_CASE_						) as warning_list:
                           UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						).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 	UpperCAmelCase_ (self						):
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__				=    DeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
     return image




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

  """simple docstring"""







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







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""						).to(
           SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.default_image_processor
       UpperCamelCase__				=    prepare_img()
       UpperCamelCase__				=    image_processor(images=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						).to(SCREAMING_SNAKE_CASE_						)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__				=    model(**SCREAMING_SNAKE_CASE_						)

       # verify the logits
       UpperCamelCase__				=    torch.Size((1, 10_00)						)
       self.assertEqual(outputs.logits.shape	,      SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    torch.tensor([-1.0266, 0.1912, -1.2861]						).to(SCREAMING_SNAKE_CASE_						)

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







  @slow
  @require_accelerate
  @require_torch_gpu
  def 	UpperCAmelCase_ (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=SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""						)
       UpperCamelCase__				=    inputs.pixel_values.to(SCREAMING_SNAKE_CASE_						)

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







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor






def 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




     return (data["data"], data["target"])






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





