



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 	lowerCamelCase_    :






      '''simple docstring'''







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







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[Any]:
         A							:					Optional[Any]              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         A							:					Optional[int]              =						None
         if self.use_labels:
            A							:					Union[str, Any]              =						ids_tensor([self.batch_size]	,	self.num_labels )

         A							:					Tuple              =						self.get_config()

         return config, pixel_values, labels







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







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			str )      ->				List[Any]:
         A							:					Dict              =						BitBackbone(config=__lowerCamelCase )
         model.to(__lowerCamelCase )
         model.eval()
         A							:					Optional[Any]              =						model(__lowerCamelCase )

         # 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							:					Optional[Any]              =						None
         A							:					Optional[int]              =						BitBackbone(config=__lowerCamelCase )
         model.to(__lowerCamelCase )
         model.eval()
         A							:					Any              =						model(__lowerCamelCase )

         # 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 )      ->				Dict:
         A							:					List[str]              =						self.prepare_config_and_inputs()
         A							:					Tuple              =						config_and_inputs
         A							:					Tuple              =						{"pixel_values": pixel_values}
         return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






      '''simple docstring'''
      a__   =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      a__   =  (
          {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      a__   =  False
      a__   =  False
      a__   =  False
      a__   =  False
      a__   =  False







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
         A							:					Any              =						BitModelTester(self )
         A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[Any]:
         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      :			List[str] )      ->				Union[str, Any]:
         return







      @unittest.skip(reason="Bit does not output attentions" )
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
         pass







      @unittest.skip(reason="Bit does not use inputs_embeds" )
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
         pass







      @unittest.skip(reason="Bit does not support input and output embeddings" )
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[str]:
         pass







      def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
         A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()

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

            A							:					List[str]              =						["pixel_values"]
            self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
         A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowerCamelCase )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
         A							:					int              =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*__lowerCamelCase )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
         A							:					List[str]              =						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            A							:					Optional[int]              =						model_class(config=__lowerCamelCase )
            for name, module in model.named_modules():
               if isinstance(__lowerCamelCase	,	(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      :			Union[str, Any] )      ->				Optional[Any]:
         def check_hidden_states_output(__lowerCamelCase      :			str	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Union[str, Any] ):
            A							:					Dict              =						model_class(__lowerCamelCase )
            model.to(__lowerCamelCase )
            model.eval()

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

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

            A							:					List[Any]              =						self.model_tester.num_stages
            self.assertEqual(len(__lowerCamelCase )	,	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							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs_for_common()
         A							:					Dict              =						["preactivation", "bottleneck"]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               A							:					Dict              =						layer_type
               A							:					Union[str, Any]              =						True
               check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

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

               check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







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







      def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
         A							:					Union[str, Any]              =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







      @slow
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
         for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A							:					Optional[Any]              =						BitModel.from_pretrained(__lowerCamelCase )
            self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
   A							:					Tuple              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
   return image

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






      '''simple docstring'''







      @cached_property
      def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Union[str, Any]:
         return (
             BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
         )







      @slow
      def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
         A							:					Union[str, Any]              =						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__lowerCamelCase )

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

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

         # verify the logits
         A							:					str              =						torch.Size((1, 10_00) )
         self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

         A							:					Optional[Any]              =						torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(__lowerCamelCase )

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







@require_torch
class 	lowerCamelCase_    ( _A						,unittest.TestCase						):






      '''simple docstring'''
      a__   =  (BitBackbone,) if is_torch_available() else ()
      a__   =  BitConfig

      a__   =  False







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
         A							:					Union[str, Any]              =						BitModelTester(self )




from math import sqrt




def 	UpperCAmelCase   (						_lowerCamelCase = 100_0000    ):
			A							:					int              =						0
			A							:					int              =						0
			A							:					int

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2 ,    2 * max_cuboid_size + 1    ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
												num_cuboids += (
												    min(_lowerCamelCase ,    sum_shortest_sides // 2    )
												    - max(1 ,    sum_shortest_sides - max_cuboid_size    )
												    + 1
												)

			return max_cuboid_size


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