





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   A			:
			'''simple docstring'''




			def __init__(			self   :    Optional[int]							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    int=3							,	__lowerCAmelCase   :    Optional[int]=32							,	__lowerCAmelCase   :    str=3							,	__lowerCAmelCase   :    Tuple=10							,	__lowerCAmelCase   :    Tuple=[8, 16, 32, 64]							,	__lowerCAmelCase   :    int=[1, 1, 2, 1]							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    Tuple="relu"							,	__lowerCAmelCase   :    Union[str, Any]=3							,	__lowerCAmelCase   :    Optional[int]=None							,	__lowerCAmelCase   :    Any=["stage2", "stage3", "stage4"]							,	__lowerCAmelCase   :    Dict=[2, 3, 4]							,	__lowerCAmelCase   :    Optional[Any]=1							,	)	->	Any:







					"""simple docstring"""






					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(__lowerCAmelCase   )
					A__        =   out_features
					A__        =   out_indices
					A__        =   num_groups






			def    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					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    a_       (			self   :    Dict   )	->	Tuple:







					"""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    a_       (			self   :    Any							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    List[str]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   BitModel(config=__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					A__        =   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    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Union[str, Any]							,	__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    int   )	->	Any:







					"""simple docstring"""






					A__        =   self.num_labels
					A__        =   BitForImageClassification(__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					A__        =   model(__lowerCAmelCase							,	labels=__lowerCAmelCase   )
					self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)   )






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Tuple   )	->	Dict:







					"""simple docstring"""






					A__        =   BitBackbone(config=__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					A__        =   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__        =   None
					A__        =   BitBackbone(config=__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					A__        =   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    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   self.prepare_config_and_inputs()
					A__      ,			A__      ,			A__        =   config_and_inputs
					A__        =   {"""pixel_values""": pixel_values}
					return config, inputs_dict


@require_torch
class   A			(SCREAMING_SNAKE_CASE   ,     SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       Dict							=    (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
			__lowerCamelCase	:       Tuple							=    (
			    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__lowerCamelCase	:       int							=    False
			__lowerCamelCase	:       Optional[Any]							=    False
			__lowerCamelCase	:       Optional[int]							=    False
			__lowerCamelCase	:       List[str]							=    False
			__lowerCamelCase	:       List[str]							=    False






			def    a_       (			self   :    Any   )	->	Any:







					"""simple docstring"""






					A__        =   BitModelTester(self   )
					A__        =   ConfigTester(self							,	config_class=__lowerCAmelCase							,	has_text_modality=__lowerCAmelCase   )






			def    a_       (			self   :    Optional[int]   )	->	Any:







					"""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    a_       (			self   :    str   )	->	Dict:







					"""simple docstring"""






					return






			@unittest.skip(reason="""Bit does not output attentions"""   )
			def    a_       (			self   :    Dict   )	->	str:







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					pass






			def    a_       (			self   :    str   )	->	Optional[int]:







					"""simple docstring"""






					A__      ,			A__        =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							A__        =   model_class(__lowerCAmelCase   )
							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]							,	__lowerCAmelCase   )






			def    a_       (			self   :    Union[str, Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase   )






			def    a_       (			self   :    Optional[Any]   )	->	List[Any]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	List[str]:







					"""simple docstring"""






					A__      ,			A__        =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							A__        =   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    a_       (			self   :    Tuple   )	->	List[str]:







					"""simple docstring"""






					def check_hidden_states_output(__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[Any]							,	__lowerCAmelCase   :    Union[str, Any]   ):
							A__        =   model_class(__lowerCAmelCase   )
							model.to(__lowerCAmelCase   )
							model.eval()

							with torch.no_grad():
									A__        =   model(**self._prepare_for_class(__lowerCAmelCase							,	__lowerCAmelCase   )   )

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

							A__        =   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__      ,			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(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

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

									check_hidden_states_output(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )






			@unittest.skip(reason="""Bit does not use feedforward chunking"""   )
			def    a_       (			self   :    Tuple   )	->	str:







					"""simple docstring"""






					pass






			def    a_       (			self   :    Optional[Any]   )	->	List[str]:







					"""simple docstring"""






					A__        =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase   )







			@slow
			def    a_       (			self   :    Optional[Any]   )	->	List[str]:







					"""simple docstring"""






					for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__        =   BitModel.from_pretrained(__lowerCAmelCase   )
							self.assertIsNotNone(__lowerCAmelCase   )







def    __lowerCamelCase					(							)		->				List[str]:






		"""simple docstring"""

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


@require_torch
@require_vision
class   A			(unittest.TestCase    ):
			'''simple docstring'''




			@cached_property
			def    a_       (			self   :    Union[str, Any]   )	->	Optional[Any]:







					"""simple docstring"""






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







			@slow
			def    a_       (			self   :    Optional[int]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__lowerCAmelCase   )

					A__        =   self.default_image_processor
					A__        =   prepare_img()
					A__        =   image_processor(images=__lowerCAmelCase							,	return_tensors="""pt"""   ).to(__lowerCAmelCase   )

					# forward pass
					with torch.no_grad():
							A__        =   model(**__lowerCAmelCase   )

					# verify the logits
					A__        =   torch.Size((1, 10_00)   )
					self.assertEqual(outputs.logits.shape							,	__lowerCAmelCase   )

					A__        =   torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]   ).to(__lowerCAmelCase   )

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


@require_torch
class   A			(SCREAMING_SNAKE_CASE   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    (BitBackbone,) if is_torch_available() else ()
			__lowerCamelCase	:       str							=    BitConfig

			__lowerCamelCase	:       List[Any]							=    False






			def    a_       (			self   :    List[Any]   )	->	List[Any]:







					"""simple docstring"""






					A__        =   BitModelTester(self   )










from math import ceil







def    __lowerCamelCase					(							__a   :int = 1_0_0_1    )		->				int:






		"""simple docstring"""

		A__        =   1

		for i in range(1    ,     int(ceil(n / 2.0    )    )    ):
				A__        =   2 * i + 1
				A__        =   2 * i
				A__        =   total + 4 * odd**2 - 6 * even

		return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															A    :					List[str] =      int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('''Invalid entry - please enter a number''')



