





import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_MAPPING,
           DataaVecVisionForImageClassification,
           DataaVecVisionForSemanticSegmentation,
           DataaVecVisionModel,
       )
       from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor





class A_   :




   def __init__(self      :List[str]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :str=100       ,  _UpperCamelCase      :List[Any]=13       ,  _UpperCamelCase      :Any=30       ,  _UpperCamelCase      :List[str]=2       ,  _UpperCamelCase      :List[Any]=3       ,  _UpperCamelCase      :Dict=True       ,  _UpperCamelCase      :Optional[Any]=True       ,  _UpperCamelCase      :List[Any]=32       ,  _UpperCamelCase      :int=4       ,  _UpperCamelCase      :Dict=4       ,  _UpperCamelCase      :Optional[Any]=37       ,  _UpperCamelCase      :Optional[Any]="gelu"       ,  _UpperCamelCase      :Union[str, Any]=0.1       ,  _UpperCamelCase      :Tuple=0.1       ,  _UpperCamelCase      :Optional[int]=10       ,  _UpperCamelCase      :int=0.0_2       ,  _UpperCamelCase      :Any=3       ,  _UpperCamelCase      :Optional[int]=None       ,  _UpperCamelCase      :Tuple=[0, 1, 2, 3]       ,  )->   Optional[Any]:
         __A			  =							parent
         __A			  =							100
         __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			  =							out_indices
         __A			  =							num_labels

         # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __A			  =							(image_size // patch_size) ** 2
         __A			  =							num_patches + 1




   def _lowerCAmelCase (self      :str					)->   Union[str, Any]:
         __A			  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

         __A			  =							None
         __A			  =							None
         if self.use_labels:
               __A			  =							ids_tensor([self.batch_size]       ,  self.type_sequence_label_size					)
               __A			  =							ids_tensor([self.batch_size, self.image_size, self.image_size]       ,  self.num_labels					)

         __A			  =							self.get_config()

         return config, pixel_values, labels, pixel_labels




   def _lowerCAmelCase (self      :Union[str, Any]					)->   Union[str, Any]:
         return DataaVecVisionConfig(
             vocab_size=self.vocab_size       ,  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=_UpperCamelCase       ,  initializer_range=self.initializer_range       ,  out_indices=self.out_indices       ,  )




   def _lowerCAmelCase (self      :Optional[int]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :int       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :List[Any]					)->   List[Any]:
         __A			  =							DataaVecVisionModel(config=_UpperCamelCase					)
         model.to(_UpperCamelCase					)
         model.eval()
         __A			  =							model(_UpperCamelCase					)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __A			  =							(self.image_size // self.patch_size) ** 2
         self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, num_patches + 1, self.hidden_size)					)




   def _lowerCAmelCase (self      :Optional[Any]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :List[str]       ,  _UpperCamelCase      :List[str]					)->   int:
         __A			  =							self.type_sequence_label_size
         __A			  =							DataaVecVisionForImageClassification(_UpperCamelCase					)
         model.to(_UpperCamelCase					)
         model.eval()
         __A			  =							model(_UpperCamelCase       ,  labels=_UpperCamelCase					)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)					)




   def _lowerCAmelCase (self      :Optional[Any]       ,  _UpperCamelCase      :List[str]       ,  _UpperCamelCase      :List[Any]       ,  _UpperCamelCase      :Tuple       ,  _UpperCamelCase      :str					)->   int:
         __A			  =							self.num_labels
         __A			  =							DataaVecVisionForSemanticSegmentation(_UpperCamelCase					)
         model.to(_UpperCamelCase					)
         model.eval()
         __A			  =							model(_UpperCamelCase					)
         self.parent.assertEqual(
             result.logits.shape       ,  (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)
         __A			  =							model(_UpperCamelCase       ,  labels=_UpperCamelCase					)
         self.parent.assertEqual(
             result.logits.shape       ,  (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)






   def _lowerCAmelCase (self      :List[Any]					)->   Tuple:
         __A			  =							self.prepare_config_and_inputs()
         __A  ,		__A  ,		__A  ,		__A			  =							config_and_inputs
         __A			  =							{'''pixel_values''': pixel_values}
         return config, inputs_dict





@require_torch
class A_   (     _lowerCamelCase					, _lowerCamelCase					, unittest.TestCase  ):
   lowerCAmelCase__  	=					(
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   lowerCAmelCase__  	=					(
       {
           """feature-extraction""": DataaVecVisionModel,
           """image-classification""": DataaVecVisionForImageClassification,
           """image-segmentation""": DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   lowerCAmelCase__  	=					False
   lowerCAmelCase__  	=					False
   lowerCAmelCase__  	=					False




   def _lowerCAmelCase (self      :List[str]					)->   Tuple:
         __A			  =							DataaVecVisionModelTester(self					)
         __A			  =							ConfigTester(
             self       ,  config_class=_UpperCamelCase       ,  has_text_modality=_UpperCamelCase       ,  hidden_size=37					)




   def _lowerCAmelCase (self      :int					)->   int:
         self.config_tester.run_common_tests()




   def _lowerCAmelCase (self      :int					)->   List[Any]:
         # Data2VecVision does not use inputs_embeds
         pass




   @require_torch_multi_gpu
   @unittest.skip(
       reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''					)
   def _lowerCAmelCase (self      :List[str]					)->   Optional[Any]:
         pass




   def _lowerCAmelCase (self      :Union[str, Any]					)->   Any:
         __A  ,		__A			  =							self.model_tester.prepare_config_and_inputs_for_common()

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




   def _lowerCAmelCase (self      :Tuple					)->   Tuple:
         __A  ,		__A			  =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               __A			  =							model_class(_UpperCamelCase					)
               __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]       ,  _UpperCamelCase					)




   def _lowerCAmelCase (self      :Tuple					)->   Tuple:
         __A			  =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCamelCase					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   int:
         __A			  =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_segmentation(*_UpperCamelCase					)




   def _lowerCAmelCase (self      :Union[str, Any]					)->   Dict:
         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:
               if model_class in [*get_values(_UpperCamelCase					)]:
                     continue

               __A			  =							model_class(_UpperCamelCase					)
               model.to(_UpperCamelCase					)
               model.train()
               __A			  =							self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase       ,  return_labels=_UpperCamelCase					)
               __A			  =							model(**_UpperCamelCase					).loss
               loss.backward()




   def _lowerCAmelCase (self      :Tuple					)->   Dict:
         __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(_UpperCamelCase					)] or not model_class.supports_gradient_checkpointing:
                     continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                     __A  ,		__A  ,		__A  ,		__A			  =							inputs_dict['''pixel_values'''].shape
                     __A			  =							torch.zeros(
                         [self.model_tester.batch_size, height, width]       ,  device=_UpperCamelCase					).long()
               __A			  =							model_class(_UpperCamelCase					)
               model.gradient_checkpointing_enable()
               model.to(_UpperCamelCase					)
               model.train()
               __A			  =							self._prepare_for_class(_UpperCamelCase       ,  _UpperCamelCase       ,  return_labels=_UpperCamelCase					)
               __A			  =							model(**_UpperCamelCase					).loss
               loss.backward()




   def _lowerCAmelCase (self      :int					)->   Union[str, Any]:
         __A  ,		__A			  =							self.model_tester.prepare_config_and_inputs_for_common()

         __A			  =							_config_zero_init(_UpperCamelCase					)
         for model_class in self.all_model_classes:
               __A			  =							model_class(config=_UpperCamelCase					)
               for name, param in model.named_parameters():
                     # we skip lambda parameters as these require special initial values
                     # determined by config.layer_scale_init_value
                     if "lambda" in name:
                           continue
                     if param.requires_grad:
                           self.assertIn(
                               ((param.data.mean() * 1e9).round() / 1e9).item()       ,  [0.0, 1.0]       ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""       ,  )




   def _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :int       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :List[str]=2e-4       ,  _UpperCamelCase      :Dict="outputs"       ,  _UpperCamelCase      :List[str]=None					)->   Tuple:
         # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
         super().check_pt_tf_outputs(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)




   def _lowerCAmelCase (self      :int					)->   str:
         __A			  =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase					)






   @slow
   def _lowerCAmelCase (self      :Optional[int]					)->   Dict:
         for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A			  =							DataaVecVisionModel.from_pretrained(_UpperCamelCase					)
               self.assertIsNotNone(_UpperCamelCase					)





def _a							(      )      ->		str:







      '''simple docstring'''






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







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




   @cached_property
   def _lowerCAmelCase (self      :Dict					)->   Tuple:
         return (
             BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''					) if is_vision_available() else None
         )






   @slow
   def _lowerCAmelCase (self      :Optional[int]					)->   Optional[Any]:
         __A			  =							DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''					).to(
             _UpperCamelCase					)

         __A			  =							self.default_image_processor
         __A			  =							prepare_img()
         __A			  =							image_processor(images=_UpperCamelCase       ,  return_tensors='''pt'''					).to(_UpperCamelCase					)

         # forward pass
         with torch.no_grad():
               __A			  =							model(**_UpperCamelCase					)
         __A			  =							outputs.logits

         # verify the logits
         __A			  =							torch.Size((1, 1000)					)
         self.assertEqual(logits.shape       ,  _UpperCamelCase					)

         __A			  =							torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]					).to(_UpperCamelCase					)

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

         __A			  =							[model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
         self.assertEqual(logits[0].topk(2					).indices.cpu().tolist()       ,  _UpperCamelCase					)












def _a							(      lowerCamelCase:  list					)      ->		list:







      '''simple docstring'''






      __A			  =							len(lowerCamelCase					)
      for cycle_start in range(0   ,							array_len - 1					):
            __A			  =							array[cycle_start]

            __A			  =							cycle_start
            for i in range(cycle_start + 1   ,							lowerCamelCase					):
                  if array[i] < item:
                        pos += 1

            if pos == cycle_start:
                  continue

            while item == array[pos]:
                  pos += 1

            __A  ,		__A			  =							item, array[pos]
            while pos != cycle_start:
                  __A			  =							cycle_start
                  for i in range(cycle_start + 1   ,							lowerCamelCase					):
                        if array[i] < item:
                              pos += 1

                  while item == array[pos]:
                        pos += 1

                  __A  ,		__A			  =							item, array[pos]

      return array


if __name__ == "__main__":
       assert cycle_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert cycle_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]





