





'''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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=3		,      _UpperCAmelCase=32		,      _UpperCAmelCase=3		,      _UpperCAmelCase=10		,      _UpperCAmelCase=[8, 16, 32, 64]		,      _UpperCAmelCase=[1, 1, 2, 1]		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase="relu"		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=["stage2", "stage3", "stage4"]		,      _UpperCAmelCase=[2, 3, 4]		,      _UpperCAmelCase=1		,      ):

          '''simple docstring'''






          __A		:					int						=			parent
          __A		:					List[str]						=			batch_size
          __A		:					Dict						=			image_size
          __A		:					Dict						=			num_channels
          __A		:					List[str]						=			embeddings_size
          __A		:					Optional[int]						=			hidden_sizes
          __A		:					List[str]						=			depths
          __A		:					List[str]						=			is_training
          __A		:					Union[str, Any]						=			use_labels
          __A		:					Dict						=			hidden_act
          __A		:					Tuple						=			num_labels
          __A		:					int						=			scope
          __A		:					Optional[int]						=			len(_UpperCAmelCase)
          __A		:					Tuple						=			out_features
          __A		:					List[Any]						=			out_indices
          __A		:					Optional[Any]						=			num_groups


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					Tuple						=			None
          if self.use_labels:
               __A		:					Union[str, Any]						=			ids_tensor([self.batch_size]		,      self.num_labels)

          __A		:					Optional[Any]						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''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		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			BitModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					str						=			model(_UpperCAmelCase)
          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		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			self.num_labels
          __A		:					Optional[int]						=			BitForImageClassification(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					List[Any]						=			model(_UpperCAmelCase		,      labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			BitBackbone(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					int						=			model(_UpperCAmelCase)

          # 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
          __A		:					List[Any]						=			None
          __A		:					Optional[int]						=			BitBackbone(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Optional[Any]						=			model(_UpperCAmelCase)

          # 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):

          '''simple docstring'''






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




@require_torch
class 					SCREAMING_SNAKE_CASE       (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 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			BitModelTester(self)
          __A		:					str						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''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):

          '''simple docstring'''






          return


     @unittest.skip(reason='Bit does not output attentions')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='Bit does not use inputs_embeds')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='Bit does not support input and output embeddings')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					str						=			self.model_tester.prepare_config_and_inputs_for_common()

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

               __A		:					str						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          for model_class in self.all_model_classes:
               __A		:					Any						=			model_class(config=_UpperCAmelCase)
               for name, module in model.named_modules():
                    if isinstance(_UpperCAmelCase		,      (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):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					List[str]						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					Union[str, Any]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __A		:					str						=			self.model_tester.num_stages
               self.assertEqual(len(_UpperCAmelCase)		,      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]		,      )

          __A					,__A		:					Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Tuple						=			['preactivation', 'bottleneck']
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    __A		:					Union[str, Any]						=			layer_type
                    __A		:					str						=			True
                    check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

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

                    check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     @unittest.skip(reason='Bit does not use feedforward chunking')
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					int						=			BitModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	Tuple:
     __A		:					int						=			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):

          '''simple docstring'''






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


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_UpperCAmelCase)

          __A		:					str						=			self.default_image_processor
          __A		:					List[str]						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A		:					Union[str, Any]						=			model(**_UpperCAmelCase)

          # verify the logits
          __A		:					Tuple						=			torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)

          __A		:					Any						=			torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(_UpperCAmelCase)

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




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(BitBackbone,) if is_torch_available() else ()
     lowerCAmelCase						     =			BitConfig

     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			BitModelTester(self)






'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()