





"""simple docstring"""





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 			snake_case		:

      def __init__(			self   :List[Any]						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Dict=1_0_0						,							_lowerCamelCase   :Any=1_3						,							_lowerCamelCase   :Optional[int]=3_0						,							_lowerCamelCase   :Union[str, Any]=2						,							_lowerCamelCase   :Tuple=3						,							_lowerCamelCase   :int=True						,							_lowerCamelCase   :Optional[Any]=True						,							_lowerCamelCase   :int=3_2						,							_lowerCamelCase   :List[Any]=4						,							_lowerCamelCase   :Dict=4						,							_lowerCamelCase   :List[Any]=3_7						,							_lowerCamelCase   :Optional[Any]="gelu"						,							_lowerCamelCase   :List[str]=0.1						,							_lowerCamelCase   :Tuple=0.1						,							_lowerCamelCase   :List[str]=1_0						,							_lowerCamelCase   :List[Any]=0.0_2						,							_lowerCamelCase   :Optional[int]=3						,							_lowerCamelCase   :Tuple=None						,							_lowerCamelCase   :Union[str, Any]=[0, 1, 2, 3]						,							):
       __SCREAMING_SNAKE_CASE :			Any												=       parent
       __SCREAMING_SNAKE_CASE :			Dict												=       1_0_0
       __SCREAMING_SNAKE_CASE :			str												=       batch_size
       __SCREAMING_SNAKE_CASE :			List[Any]												=       image_size
       __SCREAMING_SNAKE_CASE :			Dict												=       patch_size
       __SCREAMING_SNAKE_CASE :			Tuple												=       num_channels
       __SCREAMING_SNAKE_CASE :			Dict												=       is_training
       __SCREAMING_SNAKE_CASE :			Tuple												=       use_labels
       __SCREAMING_SNAKE_CASE :			List[str]												=       hidden_size
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       num_hidden_layers
       __SCREAMING_SNAKE_CASE :			Dict												=       num_attention_heads
       __SCREAMING_SNAKE_CASE :			Dict												=       intermediate_size
       __SCREAMING_SNAKE_CASE :			Any												=       hidden_act
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       hidden_dropout_prob
       __SCREAMING_SNAKE_CASE :			List[Any]												=       attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE :			List[Any]												=       type_sequence_label_size
       __SCREAMING_SNAKE_CASE :			Any												=       initializer_range
       __SCREAMING_SNAKE_CASE :			Dict												=       scope
       __SCREAMING_SNAKE_CASE :			Tuple												=       out_indices
       __SCREAMING_SNAKE_CASE :			int												=       num_labels

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       __SCREAMING_SNAKE_CASE :			Any												=       None
       __SCREAMING_SNAKE_CASE :			Tuple												=       None
       if self.use_labels:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       ids_tensor([self.batch_size]						,							self.type_sequence_label_size						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       ids_tensor([self.batch_size, self.image_size, self.image_size]						,							self.num_labels						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.get_config()

       return config, pixel_values, labels, pixel_labels

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       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=_lowerCamelCase						,							initializer_range=self.initializer_range						,							out_indices=self.out_indices						,							)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       DataaVecVisionModel(config=_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(_lowerCamelCase						)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       __SCREAMING_SNAKE_CASE :			Tuple												=       (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 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Any												=       self.type_sequence_label_size
       __SCREAMING_SNAKE_CASE :			List[Any]												=       DataaVecVisionForImageClassification(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Dict						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.num_labels
       __SCREAMING_SNAKE_CASE :			Tuple												=       DataaVecVisionForSemanticSegmentation(_lowerCamelCase						)
       model.to(_lowerCamelCase						)
       model.eval()
       __SCREAMING_SNAKE_CASE :			Any												=       model(_lowerCamelCase						)
       self.parent.assertEqual(
           result.logits.shape						,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(
           result.logits.shape						,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.prepare_config_and_inputs()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       config_and_inputs
       __SCREAMING_SNAKE_CASE :			Any												=       {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     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 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Any												=       DataaVecVisionModelTester(self						)
       __SCREAMING_SNAKE_CASE :			int												=       ConfigTester(
           self						,							config_class=_lowerCamelCase						,							has_text_modality=_lowerCamelCase						,							hidden_size=3_7						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       self.config_tester.run_common_tests()

      def 						SCREAMING_SNAKE_CASE_			(			self   :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 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       pass

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       self.model_tester.prepare_config_and_inputs_for_common()

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

        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       ['''pixel_values''']
        self.assertListEqual(arg_names[:1]						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			int												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_segmentation(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       if not self.model_tester.is_training:
        return

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True

       for model_class in self.all_model_classes:
        if model_class in [*get_values(_lowerCamelCase						)]:
         continue

        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model_class(_lowerCamelCase						)
        model.to(_lowerCamelCase						)
        model.train()
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						,							return_labels=_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			List[Any]												=       model(**_lowerCamelCase						).loss
        loss.backward()

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
        return

       __SCREAMING_SNAKE_CASE :			List[Any]												=       False
       __SCREAMING_SNAKE_CASE :			str												=       True

       for model_class in self.all_model_classes:
        if model_class in [*get_values(_lowerCamelCase						)] 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":
         __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Optional[int]												=       inputs_dict['''pixel_values'''].shape
         __SCREAMING_SNAKE_CASE :			Dict												=       torch.zeros(
             [self.model_tester.batch_size, height, width]						,							device=_lowerCamelCase						).long()
        __SCREAMING_SNAKE_CASE :			int												=       model_class(_lowerCamelCase						)
        model.gradient_checkpointing_enable()
        model.to(_lowerCamelCase						)
        model.train()
        __SCREAMING_SNAKE_CASE :			Any												=       self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						,							return_labels=_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       model(**_lowerCamelCase						).loss
        loss.backward()

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Tuple												=       _config_zero_init(_lowerCamelCase						)
       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			str												=       model_class(config=_lowerCamelCase						)
        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 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[int]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :List[Any]=2e-4						,							_lowerCamelCase   :str="outputs"						,							_lowerCamelCase   :str=None						):
       # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
       super().check_pt_tf_outputs(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			str												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase						)



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __SCREAMING_SNAKE_CASE :			int												=       DataaVecVisionModel.from_pretrained(_lowerCamelCase						)
        self.assertIsNotNone(_lowerCamelCase						)







def 		lowerCAmelCase_	(  ):

 '''simple docstring'''






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






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

      @cached_property
      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       return (
           BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						) if is_vision_available() else None
       )



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						).to(
           _lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       self.default_image_processor
       __SCREAMING_SNAKE_CASE :			List[Any]												=       prepare_img()
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       image_processor(images=_lowerCamelCase						,							return_tensors='''pt'''						).to(_lowerCamelCase						)

       # forward pass
       with torch.no_grad():
        __SCREAMING_SNAKE_CASE :			int												=       model(**_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       outputs.logits

       # verify the logits
       __SCREAMING_SNAKE_CASE :			List[Any]												=       torch.Size((1, 1_0_0_0)						)
       self.assertEqual(logits.shape						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]						).to(_lowerCamelCase						)

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

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








"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       len(lowercase_		)
 for i in range(length - 1		):
  __SCREAMING_SNAKE_CASE :			str												=       False
  for j in range(length - 1 - i		):
   if collection[j] > collection[j + 1]:
    __SCREAMING_SNAKE_CASE :			Any												=       True
    __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[str]												=       collection[j + 1], collection[j]
  if not swapped:
   break  # Stop iteration if the collection is sorted.
 return collection


if __name__ == "__main__":
    import doctest
    import time

    doctest.testmod()

    _lowerCamelCase       =	input('''Enter numbers separated by a comma:''').strip()
    _lowerCamelCase       =	[int(item) for item in user_input.split(''',''')]
    _lowerCamelCase       =	time.process_time()
    print(*bubble_sort(unsorted), sep=''',''')
    print(f'Processing time: {(time.process_time() - start)%1e9 + 7}')

