


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,   lowerCamelCase,   lowerCamelCase=13,   lowerCamelCase=30,   lowerCamelCase=2,   lowerCamelCase=3,   lowerCamelCase=True,   lowerCamelCase=True,   lowerCamelCase=32,   lowerCamelCase=5,   lowerCamelCase=4,   lowerCamelCase=37,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=10,   lowerCamelCase=0.0_2,   lowerCamelCase=3,   lowerCamelCase=None,   lowerCamelCase=2,   ) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	parent
           _lowercase :							int   	=	batch_size
           _lowercase :							int   	=	image_size
           _lowercase :							str   	=	patch_size
           _lowercase :							int   	=	num_channels
           _lowercase :							Any   	=	is_training
           _lowercase :							Union[str, Any]   	=	use_labels
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							List[str]   	=	num_hidden_layers
           _lowercase :							Optional[Any]   	=	num_attention_heads
           _lowercase :							Optional[int]   	=	intermediate_size
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							str   	=	hidden_dropout_prob
           _lowercase :							Optional[Any]   	=	attention_probs_dropout_prob
           _lowercase :							Tuple   	=	type_sequence_label_size
           _lowercase :							List[str]   	=	initializer_range
           _lowercase :							Any   	=	scope
           _lowercase :							Union[str, Any]   	=	encoder_stride

           # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
           _lowercase :							Union[str, Any]   	=	(image_size // patch_size) ** 2
           _lowercase :							Any   	=	num_patches + 2
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           _lowercase :							str   	=	self.get_config()

           return config, pixel_values, labels
       def     UpperCamelCase  (      self) ->     int:




           """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=lowerCamelCase,   initializer_range=self.initializer_range,   encoder_stride=self.encoder_stride,   )
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Any   	=	DeiTModel(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase)
           self.parent.assertEqual(
               result.reconstruction.shape,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

           # test greyscale images
           _lowercase :							Any   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForMaskedImageModeling(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							Optional[int]   	=	model(lowerCamelCase)
           self.parent.assertEqual(result.reconstruction.shape,   (self.batch_size, 1, self.image_size, self.image_size))
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Any:




           """simple docstring"""

           _lowercase :							str   	=	self.type_sequence_label_size
           _lowercase :							Dict   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))

           # test greyscale images
           _lowercase :							Optional[Any]   	=	1
           _lowercase :							Optional[Any]   	=	DeiTForImageClassification(lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()

           _lowercase :							List[str]   	=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
           _lowercase :							List[Any]   	=	model(lowerCamelCase,   labels=lowerCamelCase)
           self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size))




       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	self.prepare_config_and_inputs()
           (
               (
               _lowercase
           )	,       (
               _lowercase
           )	,       (
               _lowercase
           )	,       
           ) :							List[str]   	=	config_and_inputs
           _lowercase :							List[str]   	=	{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
class _lowerCamelCase(		_a,				_a,				unittest.TestCase     ):
       lowercase_				:			Optional[Any]									=    (
           (
               DeiTModel,
               DeiTForImageClassification,
               DeiTForImageClassificationWithTeacher,
               DeiTForMaskedImageModeling,
           )
           if is_torch_available()
           else ()
       )
       lowercase_				:			Optional[Any]									=    (
           {
               """feature-extraction""": DeiTModel,
               """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       lowercase_				:			Dict									=    False
       lowercase_				:			List[str]									=    False
       lowercase_				:			Union[str, Any]									=    False
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							int   	=	DeiTModelTester(self)
           _lowercase :							Optional[Any]   	=	ConfigTester(self,   config_class=lowerCamelCase,   has_text_modality=lowerCamelCase,   hidden_size=37)
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           self.config_tester.run_common_tests()
       @unittest.skip(reason='DeiT does not use inputs_embeds')
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           pass
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase :							int   	=	model_class(lowerCamelCase)
               self.assertIsInstance(model.get_input_embeddings(),   (nn.Module))
               _lowercase :							Union[str, Any]   	=	model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCamelCase,   nn.Linear))
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase	,       _lowercase :							Any   	=	self.model_tester.prepare_config_and_inputs_for_common()

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

               _lowercase :							str   	=	['pixel_values']
               self.assertListEqual(arg_names[:1],   lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase)
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=False) ->     Any:




           """simple docstring"""

           _lowercase :							Dict   	=	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     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           if not self.model_tester.is_training:
               return

           _lowercase	,       _lowercase :							str   	=	self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase :							Tuple   	=	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
               _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Optional[Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[str]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()
           if not self.model_tester.is_training:
               return

           _lowercase :							Dict   	=	False
           _lowercase :							Optional[int]   	=	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
               _lowercase :							str   	=	model_class(lowerCamelCase)
               model.gradient_checkpointing_enable()
               model.to(lowerCamelCase)
               model.train()
               _lowercase :							Union[str, Any]   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)
               _lowercase :							List[Any]   	=	model(**lowerCamelCase).loss
               loss.backward()
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase	,       _lowercase :							Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_common()

           _lowercase :							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"]}'''):
                       _lowercase :							List[Any]   	=	problem_type['title']
                       _lowercase :							str   	=	problem_type['num_labels']

                       _lowercase :							Optional[int]   	=	model_class(lowerCamelCase)
                       model.to(lowerCamelCase)
                       model.train()

                       _lowercase :							Tuple   	=	self._prepare_for_class(lowerCamelCase,   lowerCamelCase,   return_labels=lowerCamelCase)

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

                       _lowercase :							Optional[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:
                           _lowercase :							Dict   	=	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     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _lowercase :							Tuple   	=	DeiTModel.from_pretrained(lowerCamelCase)
               self.assertIsNotNone(lowerCamelCase)



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							Optional[Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
    return image






@require_torch
@require_vision
class _lowerCamelCase(		unittest.TestCase     ):
       @cached_property
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           return (
               DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
               if is_vision_available()
               else None
           )
       @slow
       def     UpperCamelCase  (      self) ->     Dict:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
               lowerCamelCase)

           _lowercase :							List[str]   	=	self.default_image_processor
           _lowercase :							List[str]   	=	prepare_img()
           _lowercase :							Tuple   	=	image_processor(images=lowerCamelCase,   return_tensors='pt').to(lowerCamelCase)

           # forward pass
           with torch.no_grad():
               _lowercase :							int   	=	model(**lowerCamelCase)

           # verify the logits
           _lowercase :							Any   	=	torch.Size((1, 10_00))
           self.assertEqual(outputs.logits.shape,   lowerCamelCase)

           _lowercase :							Union[str, Any]   	=	torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]).to(lowerCamelCase)

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




       @slow
       @require_accelerate
       @require_torch_gpu
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           _lowercase :							Tuple   	=	DeiTModel.from_pretrained(
               'facebook/deit-base-distilled-patch16-224',   torch_dtype=torch.floataa,   device_map='auto')
           _lowercase :							Union[str, Any]   	=	self.default_image_processor

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

           # forward pass to make sure inference works in fp16
           with torch.no_grad():
               _lowercase :							Optional[int]   	=	model(lowerCamelCase)






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class _lowerCamelCase(		_a     ):
       lowercase_				:			int									=    ["""image_processor""", """tokenizer"""]
       lowercase_				:			List[str]									=    """CLIPImageProcessor"""
       lowercase_				:			Union[str, Any]									=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")
       def __init__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.',   lowerCamelCase,   )
               _lowercase :							Dict   	=	kwargs.pop('feature_extractor')

           _lowercase :							int   	=	image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
           if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

           super().__init__(lowerCamelCase,   lowerCamelCase)
       def __call__(      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

           if text is not None:
               _lowercase :							Optional[Any]   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if images is not None:
               _lowercase :							List[Any]   	=	self.image_processor(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)

           if text is not None and images is not None:
               _lowercase :							int   	=	image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowerCamelCase),   tensor_type=lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return self.tokenizer.batch_decode(*lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   *lowerCamelCase,   **lowerCamelCase) ->     int:




           """simple docstring"""

           return self.tokenizer.decode(*lowerCamelCase,   **lowerCamelCase)
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           _lowercase :							int   	=	self.tokenizer.model_input_names
           _lowercase :							Dict   	=	self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
       @property
       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.',   lowerCamelCase,   )
           return self.image_processor_class




       @property
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.',   lowerCamelCase,   )
           return self.image_processor


