


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    :

				"""simple docstring"""







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

				def      _UpperCAmelCase					(						self   )	->				int:
					A								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

					A								=				self.get_config()

					return config, pixel_values, labels

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

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Dict:
					A								=				BitBackbone(config=A_   )
					model.to(A_   )
					model.eval()
					A								=				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
					A								=				None
					A								=				BitBackbone(config=A_   )
					model.to(A_   )
					model.eval()
					A								=				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      _UpperCAmelCase					(						self   )	->				Any:
					A								=				self.prepare_config_and_inputs()
					A					,			A					,			A								=				config_and_inputs
					A								=				{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class       _UpperCamelCase    (					_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		unittest.TestCase				):

				"""simple docstring"""







				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      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				BitModelTester(self   )
					A								=				ConfigTester(self				,   config_class=A_				,   has_text_modality=A_   )

				def      _UpperCAmelCase					(						self   )	->				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      _UpperCAmelCase					(						self   )	->				Dict:
					return

				@unittest.skip(reason="""Bit does not output attentions"""   )
				def      _UpperCAmelCase					(						self   )	->				Any:
					pass

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

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

				def      _UpperCAmelCase					(						self   )	->				List[Any]:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

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

						A								=				["""pixel_values"""]
						self.assertListEqual(arg_names[:1]				,   A_   )

				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A_   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*A_   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						A								=				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      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					def check_hidden_states_output(a__				,   a__				,   a__   ):
						A								=				model_class(A_   )
						model.to(A_   )
						model.eval()

						with torch.no_grad():
							A								=				model(**self._prepare_for_class(A_				,   A_   )   )

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

						A								=				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]				,   )

					A					,			A								=				self.model_tester.prepare_config_and_inputs_for_common()
					A								=				["""preactivation""", """bottleneck"""]
					for model_class in self.all_model_classes:
						for layer_type in layers_type:
							A								=				layer_type
							A								=				True
							check_hidden_states_output(A_				,   A_				,   A_   )

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

							check_hidden_states_output(A_				,   A_				,   A_   )

				@unittest.skip(reason="""Bit does not use feedforward chunking"""   )
				def      _UpperCAmelCase					(						self   )	->				Dict:
					pass

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A_   )

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





def   _lowerCAmelCase				(   ) ->   Tuple:





	"""simple docstring"""







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






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

				"""simple docstring"""







				@cached_property
				def      _UpperCAmelCase					(						self   )	->				Optional[Any]:
					return (
					    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
					)

				@slow
				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(A_   )

					A								=				self.default_image_processor
					A								=				prepare_img()
					A								=				image_processor(images=A_				,   return_tensors="""pt"""   ).to(A_   )

					# forward pass
					with torch.no_grad():
						A								=				model(**A_   )

					# verify the logits
					A								=				torch.Size((1, 1000)   )
					self.assertEqual(outputs.logits.shape				,   A_   )

					A								=				torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]   ).to(A_   )

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






@require_torch
class       _UpperCamelCase    (					_SCREAMING_SNAKE_CASE    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= (BitBackbone,) if is_torch_available() else ()
				lowerCAmelCase 		= BitConfig

				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				BitModelTester(self   )



def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   bool:





	"""simple docstring"""







	return str(UpperCamelCase__						) == str(UpperCamelCase__						)[::-1]





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	return int(UpperCamelCase__						) + int(str(UpperCamelCase__						)[::-1]						)





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 1_00_00						) ->   int:





	"""simple docstring"""







	A								=				[]
	for num in range(1							,					UpperCamelCase__						):
		A								=				0
		A								=				num
		while iterations < 50:
			A								=				sum_reverse(UpperCamelCase__						)
			iterations += 1
			if is_palindrome(UpperCamelCase__						):
				break
		else:
			lychrel_nums.append(UpperCamelCase__						)
	return len(UpperCamelCase__						)


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



