"""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  _snake_case     :






      def __init__(       self      :			Dict							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			str=3							,					UpperCAmelCase      :			List[str]=32							,					UpperCAmelCase      :			List[str]=3							,					UpperCAmelCase      :			Union[str, Any]=10							,					UpperCAmelCase      :			Optional[int]=[8, 16, 32, 64]							,					UpperCAmelCase      :			Tuple=[1, 1, 2, 1]							,					UpperCAmelCase      :			str=True							,					UpperCAmelCase      :			List[str]=True							,					UpperCAmelCase      :			Optional[int]="relu"							,					UpperCAmelCase      :			Optional[int]=3							,					UpperCAmelCase      :			Optional[Any]=None							,					UpperCAmelCase      :			Dict=["stage2", "stage3", "stage4"]							,					UpperCAmelCase      :			List[Any]=[2, 3, 4]							,					UpperCAmelCase      :			int=1							,					):
             __lowerCamelCase					:		Union[str, Any]		   =     parent
             __lowerCamelCase					:		int		   =     batch_size
             __lowerCamelCase					:		Union[str, Any]		   =     image_size
             __lowerCamelCase					:		str		   =     num_channels
             __lowerCamelCase					:		int		   =     embeddings_size
             __lowerCamelCase					:		List[str]		   =     hidden_sizes
             __lowerCamelCase					:		Dict		   =     depths
             __lowerCamelCase					:		Union[str, Any]		   =     is_training
             __lowerCamelCase					:		List[Any]		   =     use_labels
             __lowerCamelCase					:		Tuple		   =     hidden_act
             __lowerCamelCase					:		Optional[int]		   =     num_labels
             __lowerCamelCase					:		Any		   =     scope
             __lowerCamelCase					:		Optional[int]		   =     len(UpperCAmelCase    )
             __lowerCamelCase					:		Dict		   =     out_features
             __lowerCamelCase					:		Union[str, Any]		   =     out_indices
             __lowerCamelCase					:		Optional[int]		   =     num_groups






      def       lowerCamelCase__     (       self      :			str    ):
             __lowerCamelCase					:		Any		   =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

             __lowerCamelCase					:		Tuple		   =     None
             if self.use_labels:
                    __lowerCamelCase					:		Dict		   =     ids_tensor([self.batch_size]							,					self.num_labels    )

             __lowerCamelCase					:		Any		   =     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]							,					UpperCAmelCase      :			Optional[int]							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Optional[int]    ):
             __lowerCamelCase					:		Any		   =     BitModel(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		List[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       lowerCamelCase__     (       self      :			List[str]							,					UpperCAmelCase      :			Dict							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Tuple    ):
             __lowerCamelCase					:		Union[str, Any]		   =     self.num_labels
             __lowerCamelCase					:		str		   =     BitForImageClassification(UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		List[str]		   =     model(UpperCAmelCase							,					labels=UpperCAmelCase    )
             self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )






      def       lowerCamelCase__     (       self      :			int							,					UpperCAmelCase      :			int							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Any    ):
             __lowerCamelCase					:		Optional[int]		   =     BitBackbone(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		List[Any]		   =     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
             __lowerCamelCase					:		str		   =     None
             __lowerCamelCase					:		int		   =     BitBackbone(config=UpperCAmelCase    )
             model.to(UpperCAmelCase    )
             model.eval()
             __lowerCamelCase					:		Union[str, 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       lowerCamelCase__     (       self      :			Any    ):
             __lowerCamelCase					:		str		   =     self.prepare_config_and_inputs()
             __lowerCamelCase   ,  __lowerCamelCase   ,  __lowerCamelCase					:		Union[str, Any]		   =     config_and_inputs
             __lowerCamelCase					:		Optional[Any]		   =     {"pixel_values": pixel_values}
             return config, inputs_dict

@require_torch
class  _snake_case     (					a__				, a__				, unittest.TestCase		):
      snake_case__			  =			(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      snake_case__			  =			(
          {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      snake_case__			  =			False
      snake_case__			  =			False
      snake_case__			  =			False
      snake_case__			  =			False
      snake_case__			  =			False






      def       lowerCamelCase__     (       self      :			Tuple    ):
             __lowerCamelCase					:		Optional[Any]		   =     BitModelTester(self    )
             __lowerCamelCase					:		str		   =     ConfigTester(self							,					config_class=UpperCAmelCase							,					has_text_modality=UpperCAmelCase    )






      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      :			Tuple    ):
             return






      @unittest.skip(reason="Bit does not output attentions"    )
      def       lowerCamelCase__     (       self      :			int    ):
             pass






      @unittest.skip(reason="Bit does not use inputs_embeds"    )
      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             pass






      @unittest.skip(reason="Bit does not support input and output embeddings"    )
      def       lowerCamelCase__     (       self      :			List[str]    ):
             pass






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     self.model_tester.prepare_config_and_inputs_for_common()

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

                    __lowerCamelCase					:		Optional[Any]		   =     ["pixel_values"]
                    self.assertListEqual(arg_names[:1]							,					UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		Optional[int]		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		str		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_backbone(*UpperCAmelCase    )






      def       lowerCamelCase__     (       self      :			List[str]    ):
             __lowerCamelCase   ,  __lowerCamelCase					:		Any		   =     self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    __lowerCamelCase					:		Optional[int]		   =     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       lowerCamelCase__     (       self      :			List[str]    ):




             def check_hidden_states_output(UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			str    ):
                    __lowerCamelCase					:		int		   =     model_class(UpperCAmelCase    )
                    model.to(UpperCAmelCase    )
                    model.eval()

                    with torch.no_grad():
                           __lowerCamelCase					:		List[Any]		   =     model(**self._prepare_for_class(UpperCAmelCase							,					UpperCAmelCase    )    )

                    __lowerCamelCase					:		int		   =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                    __lowerCamelCase					:		int		   =     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]							,					)

             __lowerCamelCase   ,  __lowerCamelCase					:		Optional[Any]		   =     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					:		Optional[int]		   =     layer_type
                           __lowerCamelCase					:		Optional[int]		   =     True
                           check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )

                           # check that output_hidden_states also work using config
                           del inputs_dict["output_hidden_states"]
                           __lowerCamelCase					:		int		   =     True

                           check_hidden_states_output(UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase    )






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






      def       lowerCamelCase__     (       self      :			List[str]    ):
             __lowerCamelCase					:		int		   =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase    )







      @slow
      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):




             for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __lowerCamelCase					:		Any		   =     BitModel.from_pretrained(UpperCAmelCase    )
                    self.assertIsNotNone(UpperCAmelCase    )


def    lowercase_   (   ) ->      int:



       '''simple docstring'''

       __lowerCamelCase					:		List[str]		   =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
       return image
@require_torch
@require_vision
class  _snake_case     (					unittest.TestCase		):






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







      @slow
      def       lowerCamelCase__     (       self      :			Optional[Any]    ):
             __lowerCamelCase					:		str		   =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(UpperCAmelCase    )

             __lowerCamelCase					:		List[Any]		   =     self.default_image_processor
             __lowerCamelCase					:		List[Any]		   =     prepare_img()
             __lowerCamelCase					:		List[str]		   =     image_processor(images=UpperCAmelCase							,					return_tensors="pt"    ).to(UpperCAmelCase    )

             # forward pass
             with torch.no_grad():
                    __lowerCamelCase					:		int		   =     model(**UpperCAmelCase    )

             # verify the logits
             __lowerCamelCase					:		Dict		   =     torch.Size((1, 1000)    )
             self.assertEqual(outputs.logits.shape							,					UpperCAmelCase    )

             __lowerCamelCase					:		Union[str, Any]		   =     torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]    ).to(UpperCAmelCase    )

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

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

      snake_case__			  =			False






      def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
             __lowerCamelCase					:		Union[str, Any]		   =     BitModelTester(self    )
"""simple docstring"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


if __name__ == "__main__":
				print(F"""{solution() = }""")