
'''simple docstring'''






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  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       int,__A      :       Union[str, Any]=3,__A      :       Optional[Any]=3_2,__A      :       int=3,__A      :       Optional[int]=1_0,__A      :       str=[8, 1_6, 3_2, 6_4],__A      :       Optional[Any]=[1, 1, 2, 1],__A      :       Any=True,__A      :       str=True,__A      :       Union[str, Any]="relu",__A      :       List[Any]=3,__A      :       Union[str, Any]=None,__A      :       Union[str, Any]=["stage2", "stage3", "stage4"],__A      :       Tuple=[2, 3, 4],__A      :       Any=1,):
          _lowerCamelCase				:   Dict								=   parent
          _lowerCamelCase				:   Union[str, Any]								=   batch_size
          _lowerCamelCase				:   List[str]								=   image_size
          _lowerCamelCase				:   List[str]								=   num_channels
          _lowerCamelCase				:   Optional[Any]								=   embeddings_size
          _lowerCamelCase				:   str								=   hidden_sizes
          _lowerCamelCase				:   str								=   depths
          _lowerCamelCase				:   List[str]								=   is_training
          _lowerCamelCase				:   Dict								=   use_labels
          _lowerCamelCase				:   int								=   hidden_act
          _lowerCamelCase				:   Dict								=   num_labels
          _lowerCamelCase				:   str								=   scope
          _lowerCamelCase				:   Tuple								=   len(__A     )
          _lowerCamelCase				:   Optional[int]								=   out_features
          _lowerCamelCase				:   Any								=   out_indices
          _lowerCamelCase				:   Union[str, Any]								=   num_groups



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

          _lowerCamelCase				:   Tuple								=   None
          if self.use_labels:
                _lowerCamelCase				:   List[str]								=   ids_tensor([self.batch_size],self.num_labels     )

          _lowerCamelCase				:   Tuple								=   self.get_config()

          return config, pixel_values, labels



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          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   lowerCamelCase_  (	self      :       List[Any],__A      :       str,__A      :       List[str],__A      :       Tuple     ):
          _lowerCamelCase				:   Dict								=   BitModel(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Tuple								=   model(__A     )
          self.parent.assertEqual(
              result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),)



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[Any],__A      :       Dict,__A      :       Union[str, Any]     ):
          _lowerCamelCase				:   int								=   self.num_labels
          _lowerCamelCase				:   Any								=   BitForImageClassification(__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Dict								=   model(__A,labels=__A     )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[str],__A      :       Union[str, Any],__A      :       Optional[Any]     ):
          _lowerCamelCase				:   str								=   BitBackbone(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Optional[Any]								=   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
          _lowerCamelCase				:   Optional[int]								=   None
          _lowerCamelCase				:   Dict								=   BitBackbone(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Tuple								=   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   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase				:   int								=   config_and_inputs
          _lowerCamelCase				:   str								=   {"pixel_values": pixel_values}
          return config, inputs_dict




@require_torch
class  UpperCAmelCase__						(       A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowerCAmelCase_				    =		(
        {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False
    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   str								=   BitModelTester(self     )
          _lowerCamelCase				:   int								=   ConfigTester(self,config_class=__A,has_text_modality=__A     )



    def   lowerCamelCase_  (	self      :       Tuple     ):
          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   lowerCamelCase_  (	self      :       int     ):
          return



    @unittest.skip(reason="Bit does not output attentions"     )
    def   lowerCamelCase_  (	self      :       List[Any]     ):
          pass



    @unittest.skip(reason="Bit does not use inputs_embeds"     )
    def   lowerCamelCase_  (	self      :       Tuple     ):
          pass



    @unittest.skip(reason="Bit does not support input and output embeddings"     )
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          pass



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs_for_common()

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

                _lowerCamelCase				:   str								=   ["pixel_values"]
                self.assertListEqual(arg_names[:1],__A     )



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Tuple								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   str								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*__A     )



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   int								=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                _lowerCamelCase				:   Any								=   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   lowerCamelCase_  (	self      :       Dict     ):
          def check_hidden_states_output(__A      :       Dict,__A      :       Union[str, Any],__A      :       Optional[Any]     ):
                _lowerCamelCase				:   str								=   model_class(__A     )
                model.to(__A     )
                model.eval()

                with torch.no_grad():
                      _lowerCamelCase				:   str								=   model(**self._prepare_for_class(__A,__A     )     )

                _lowerCamelCase				:   List[str]								=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _lowerCamelCase				:   Union[str, Any]								=   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],)

          _lowerCamelCase				:   str								=   self.model_tester.prepare_config_and_inputs_for_common()
          _lowerCamelCase				:   str								=   ["preactivation", "bottleneck"]
          for model_class in self.all_model_classes:
                for layer_type in layers_type:
                      _lowerCamelCase				:   Tuple								=   layer_type
                      _lowerCamelCase				:   Tuple								=   True
                      check_hidden_states_output(__A,__A,__A     )

                      # check that output_hidden_states also work using config
                      del inputs_dict["output_hidden_states"]
                      _lowerCamelCase				:   Union[str, Any]								=   True

                      check_hidden_states_output(__A,__A,__A     )



    @unittest.skip(reason="Bit does not use feedforward chunking"     )
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          pass



    def   lowerCamelCase_  (	self      :       Tuple     ):
          _lowerCamelCase				:   Any								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__A     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase				:   int								=   BitModel.from_pretrained(__A     )
                self.assertIsNotNone(__A     )







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
      return image




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



    @cached_property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ) if is_vision_available() else None
          )



    @slow
    def   lowerCamelCase_  (	self      :       Dict     ):
          _lowerCamelCase				:   Dict								=   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(__A     )

          _lowerCamelCase				:   int								=   self.default_image_processor
          _lowerCamelCase				:   List[Any]								=   prepare_img()
          _lowerCamelCase				:   int								=   image_processor(images=__A,return_tensors="pt"     ).to(__A     )

          # forward pass
          with torch.no_grad():
                _lowerCamelCase				:   List[Any]								=   model(**__A     )

          # verify the logits
          _lowerCamelCase				:   Dict								=   torch.Size((1, 1_0_0_0)     )
          self.assertEqual(outputs.logits.shape,__A     )

          _lowerCamelCase				:   Optional[int]								=   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  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(BitBackbone,) if is_torch_available() else ()
    lowerCAmelCase_				    =		BitConfig

    lowerCAmelCase_				    =		False



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Tuple								=   BitModelTester(self     )

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()