





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
      from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image
class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       Optional[int]    ,  a    :       Optional[int]    ,  a    :       List[str]=3    ,  a    :       List[str]=32    ,  a    :       Optional[Any]=3    ,  a    :       List[Any]=10    ,  a    :       Union[str, Any]=[8, 16, 32, 64]    ,  a    :       List[str]=[1, 1, 2, 1]    ,  a    :       List[str]=True    ,  a    :       Optional[int]=True    ,  a    :       Any="relu"    ,  a    :       Union[str, Any]=3    ,  a    :       int=None    ,  a    :       str=["stage2", "stage3", "stage4"]    ,  a    :       List[Any]=[2, 3, 4]    ,  a    :       Union[str, Any]=1    ,  )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						image_size
     lowercase__			    =						num_channels
     lowercase__			    =						embeddings_size
     lowercase__			    =						hidden_sizes
     lowercase__			    =						depths
     lowercase__			    =						is_training
     lowercase__			    =						use_labels
     lowercase__			    =						hidden_act
     lowercase__			    =						num_labels
     lowercase__			    =						scope
     lowercase__			    =						len(a )
     lowercase__			    =						out_features
     lowercase__			    =						out_indices
     lowercase__			    =						num_groups


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Optional[int]:





     """simple docstring"""







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

     lowercase__			    =						None
     if self.use_labels:
       lowercase__			    =						ids_tensor([self.batch_size]    ,  self.num_labels )

     lowercase__			    =						self.get_config()

     return config, pixel_values, labels


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Optional[int]:





     """simple docstring"""







     return BitConfig(
         num_channels=self.num_channels    ,  embeddings_size=self.embeddings_size    ,  hidden_sizes=self.hidden_sizes    ,  depths=self.depths    ,  hidden_act=self.hidden_act    ,  num_labels=self.num_labels    ,  out_features=self.out_features    ,  out_indices=self.out_indices    ,  num_groups=self.num_groups    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       Any    ,  a    :       Dict    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						BitModel(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )
     self.parent.assertEqual(
         result.last_hidden_state.shape    ,  (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       List[str]    ,  a    :       Optional[int]    ,  a    :       List[str] )->     List[str]:





     """simple docstring"""







     lowercase__			    =						self.num_labels
     lowercase__			    =						BitForImageClassification(a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a    ,  labels=a )
     self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.num_labels) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       List[Any]    ,  a    :       List[Any]    ,  a    :       List[str] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						BitBackbone(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,  len(config.out_features ) )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,  [self.batch_size, self.hidden_sizes[1], 4, 4] )

     # verify channels
     self.parent.assertEqual(len(model.channels )    ,  len(config.out_features ) )
     self.parent.assertListEqual(model.channels    ,  config.hidden_sizes[1:] )

     # verify backbone works with out_features=None
     lowercase__			    =						None
     lowercase__			    =						BitBackbone(config=a )
     model.to(a )
     model.eval()
     lowercase__			    =						model(a )

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps )    ,  1 )
     self.parent.assertListEqual(list(result.feature_maps[0].shape )    ,  [self.batch_size, self.hidden_sizes[-1], 1, 1] )

     # verify channels
     self.parent.assertEqual(len(model.channels )    ,  1 )
     self.parent.assertListEqual(model.channels    ,  [config.hidden_sizes[-1]] )


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





     """simple docstring"""







     lowercase__			    =						self.prepare_config_and_inputs()
     lowercase__    ,					lowercase__    ,					lowercase__			    =						config_and_inputs
     lowercase__			    =						{'pixel_values': pixel_values}
     return config, inputs_dict






@require_torch
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Union[str, Any]				      = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   _UpperCamelCase					:	Dict				      = (
       {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
       if is_torch_available()
       else {}
   )

   _UpperCamelCase					:	Dict				      = False
   _UpperCamelCase					:	Union[str, Any]				      = False
   _UpperCamelCase					:	Optional[Any]				      = False
   _UpperCamelCase					:	Union[str, Any]				      = False
   _UpperCamelCase					:	Any				      = False


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





     """simple docstring"""







     lowercase__			    =						BitModelTester(self )
     lowercase__			    =						ConfigTester(self    ,  config_class=a    ,  has_text_modality=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     List[str]:





     """simple docstring"""







     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[str]:





     """simple docstring"""







     return


   @unittest.skip(reason='Bit does not output attentions' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[Any]:





     """simple docstring"""







     pass


   @unittest.skip(reason='Bit does not use inputs_embeds' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     pass


   @unittest.skip(reason='Bit does not support input and output embeddings' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Optional[Any]:





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

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

       lowercase__			    =						['pixel_values']
       self.assertListEqual(arg_names[:1]    ,  a )


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





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     str:





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*a )


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





     """simple docstring"""







     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       lowercase__			    =						model_class(config=a )
       for name, module in model.named_modules():
         if isinstance(a    ,  (nn.BatchNormad, nn.GroupNorm) ):
           self.assertTrue(
               torch.all(module.weight == 1 )    ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,  )
           self.assertTrue(
               torch.all(module.bias == 0 )    ,  msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     def check_hidden_states_output(a    :       int    ,  a    :       Optional[Any]    ,  a    :       Dict ):
       lowercase__			    =						model_class(a )
       model.to(a )
       model.eval()

       with torch.no_grad():
         lowercase__			    =						model(**self._prepare_for_class(a    ,  a ) )

       lowercase__			    =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       lowercase__			    =						self.model_tester.num_stages
       self.assertEqual(len(a )    ,  expected_num_stages + 1 )

       # Bit's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:] )    ,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,  )

     lowercase__    ,					lowercase__			    =						self.model_tester.prepare_config_and_inputs_for_common()
     lowercase__			    =						['preactivation', 'bottleneck']
     for model_class in self.all_model_classes:
       for layer_type in layers_type:
         lowercase__			    =						layer_type
         lowercase__			    =						True
         check_hidden_states_output(a    ,  a    ,  a )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         lowercase__			    =						True

         check_hidden_states_output(a    ,  a    ,  a )


   @unittest.skip(reason='Bit does not use feedforward chunking' )
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     int:





     """simple docstring"""







     pass


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





     """simple docstring"""







     lowercase__			    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*a )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[Any]:





     """simple docstring"""







     for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       lowercase__			    =						BitModel.from_pretrained(a )
       self.assertIsNotNone(a )






def     __UpperCamelCase						()       ->     Dict:
  lowercase__			    =						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
  return image
@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   @cached_property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Dict:





     """simple docstring"""







     return (
         BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
     )


   @slow
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Dict:





     """simple docstring"""







     lowercase__			    =						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(a )

     lowercase__			    =						self.default_image_processor
     lowercase__			    =						prepare_img()
     lowercase__			    =						image_processor(images=a    ,  return_tensors='pt' ).to(a )

     # forward pass
     with torch.no_grad():
       lowercase__			    =						model(**a )

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

     lowercase__			    =						torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(a )

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






@require_torch
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	List[str]				      = (BitBackbone,) if is_torch_available() else ()
   _UpperCamelCase					:	Any				      = BitConfig

   _UpperCamelCase					:	Optional[int]				      = False


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





     """simple docstring"""







     lowercase__			    =						BitModelTester(self )









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  stooge(_SCREAMING_SNAKE_CASE	,		0	,		len(_SCREAMING_SNAKE_CASE	) - 1	)
  return arr




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    lowercase__    ,					lowercase__			    =						arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    lowercase__			    =						(int)((h - i + 1) / 3	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)

    # Recursively sort last 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		i + t	,		(_SCREAMING_SNAKE_CASE)	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)


if __name__ == "__main__":
      lowercase_					     =	input("""Enter numbers separated by a comma:\n""").strip()
      lowercase_					     =	[int(item) for item in user_input.split(""",""")]
      print(stooge_sort(unsorted))


