




"""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      UpperCAmelCase_  :




    def __init__(   self						:	Dict     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[str]=13     ,			snake_case_						:	List[Any]=30     ,			snake_case_						:	Dict=2     ,			snake_case_						:	int=3     ,			snake_case_						:	Dict=True     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=32     ,			snake_case_						:	Optional[Any]=5     ,			snake_case_						:	List[Any]=4     ,			snake_case_						:	int=37     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Tuple=10     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Dict=3     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=2     ,			)				->							Any:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    is_training
     A__	=    use_labels
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    scope
     A__	=    encoder_stride

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




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     A__	=    None
     if self.use_labels:
      A__	=    ids_tensor([self.batch_size]     ,			self.type_sequence_label_size				)

     A__	=    self.get_config()

     return config, pixel_values, labels




    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     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=snake_case_     ,			initializer_range=self.initializer_range     ,			encoder_stride=self.encoder_stride     ,			)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    DeiTModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	int     ,			snake_case_						:	Dict     ,			snake_case_						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    DeiTForMaskedImageModeling(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     self.parent.assertEqual(
         result.reconstruction.shape     ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

     # test greyscale images
     A__	=    1
     A__	=    DeiTForMaskedImageModeling(snake_case_				)
     model.to(snake_case_				)
     model.eval()

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




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.type_sequence_label_size
     A__	=    DeiTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)				)

     # test greyscale images
     A__	=    1
     A__	=    DeiTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()

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




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     (
         (
         A__
     ),				(
         A__
     ),				(
         A__
     ),				
     )	=    config_and_inputs
     A__	=    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {
            '''feature-extraction''': DeiTModel,
            '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    DeiTModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			has_text_modality=snake_case_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	Any				)				->							Optional[int]:




     '''simple docstring'''

     self.config_tester.run_common_tests()




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




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    model_class(snake_case_				)
      A__	=    inspect.signature(model.forward				)
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      A__	=    [*signature.parameters.keys()]

      A__	=    ["pixel_values"]
      self.assertListEqual(arg_names[:1]     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	int     ,			snake_case_						:	str=False				)				->							Dict:




     '''simple docstring'''

     A__	=    super()._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

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

     return inputs_dict




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     if not self.model_tester.is_training:
      return

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     A__	=    True

     for model_class in self.all_model_classes:
      # DeiTForImageClassificationWithTeacher supports inference-only
      if (
          model_class in get_values(snake_case_				)
          or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
      ):
       continue
      A__	=    model_class(snake_case_				)
      model.to(snake_case_				)
      model.train()
      A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)
      A__	=    model(**snake_case_				).loss
      loss.backward()




    def 							__magic_name__							(   self						:	Any				)				->							str:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
      return

     A__	=    False
     A__	=    True

     for model_class in self.all_model_classes:
      if model_class in get_values(snake_case_				) or not model_class.supports_gradient_checkpointing:
       continue
      # DeiTForImageClassificationWithTeacher supports inference-only
      if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
       continue
      A__	=    model_class(snake_case_				)
      model.gradient_checkpointing_enable()
      model.to(snake_case_				)
      model.train()
      A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)
      A__	=    model(**snake_case_				).loss
      loss.backward()




    def 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     A__	=    [
         {"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(snake_case_				),
              *get_values(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"]}"""				):
        A__	=    problem_type["title"]
        A__	=    problem_type["num_labels"]

        A__	=    model_class(snake_case_				)
        model.to(snake_case_				)
        model.train()

        A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

        if problem_type["num_labels"] > 1:
         A__	=    inputs["labels"].unsqueeze(1				).repeat(1     ,			problem_type["num_labels"]				)

        A__	=    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=snake_case_				) as warning_list:
         A__	=    model(**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 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    DeiTModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)







def   _SCREAMING_SNAKE_CASE       (							)						->		Tuple:
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image






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




    @cached_property
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[int]:




     '''simple docstring'''

     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
         if is_vision_available()
         else None
     )




    @slow
    def 							__magic_name__							(   self						:	List[str]				)				->							str:




     '''simple docstring'''

     A__	=    DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				).to(
         snake_case_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				).to(snake_case_				)

     # forward pass
     with torch.no_grad():
      A__	=    model(**snake_case_				)

     # verify the logits
     A__	=    torch.Size((1, 1_000)				)
     self.assertEqual(outputs.logits.shape     ,			snake_case_				)

     A__	=    torch.tensor([-1.0266, 0.1912, -1.2861]				).to(snake_case_				)

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




    @slow
    @require_accelerate
    @require_torch_gpu
    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    DeiTModel.from_pretrained(
         "facebook/deit-base-distilled-patch16-224"     ,			torch_dtype=torch.floataa     ,			device_map="auto"				)
     A__	=    self.default_image_processor

     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				)
     A__	=    inputs.pixel_values.to(snake_case_				)

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











"""simple docstring"""



import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate







def   _SCREAMING_SNAKE_CASE       (							lowercase_						)						->		int:
 return x + 2






class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	Any				)				->							Any:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)

     A__	=    "x = y"
     A__	=    {"y": 5}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 5, "y": 5}				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    "y = add_two(x)"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)

     # Won't work without the tool
     with CaptureStdout() as out:
      A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result is None
     assert "tried to execute add_two" in out.out




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    "x = 3"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3}				)




    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




    def 							__magic_name__							(   self						:	int				)				->							str:




     '''simple docstring'''

     A__	=    "x = 3\ny = 5"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 5}				)




    def 							__magic_name__							(   self						:	Any				)				->							List[Any]:




     '''simple docstring'''

     A__	=    "text = f'This is x: {x}.'"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == "This is x: 3."
     self.assertDictEqual(snake_case_     ,			{"x": 3, "text": "This is x: 3."}				)




    def 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    "if x <= 3:\n    y = 2\nelse:\n    y = 5"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 2}				)

     A__	=    {"x": 8}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     # evaluate returns the value of the last assignment.
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 8, "y": 5}				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     self.assertListEqual(snake_case_     ,			[3, 5]				)
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)




    def 							__magic_name__							(   self						:	Tuple				)				->							int:




     '''simple docstring'''

     A__	=    "y = x"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{}     ,			state=snake_case_				)
     assert result == 3
     self.assertDictEqual(snake_case_     ,			{"x": 3, "y": 3}				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "test_list = [x, add_two(x)]\ntest_list[1]"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_list": [3, 5]}				)

     A__	=    "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
     A__	=    {"x": 3}
     A__	=    evaluate(snake_case_     ,			{"add_two": add_two}     ,			state=snake_case_				)
     assert result == 5
     self.assertDictEqual(snake_case_     ,			{"x": 3, "test_dict": {"x": 3, "y": 5}}				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple:




     '''simple docstring'''

     A__	=    "x = 0\nfor i in range(3):\n    x = i"
     A__	=    {}
     A__	=    evaluate(snake_case_     ,			{"range": range}     ,			state=snake_case_				)
     assert result == 2
     self.assertDictEqual(snake_case_     ,			{"x": 2, "i": 2}				)





