





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


					def 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






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

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

										__A						:			List[str]								=	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						:			Union[str, Any]								=	BitModel(config=__A)
										model.to(__A)
										model.eval()
										__A						:			Dict								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

										'''simple docstring'''






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


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

										'''simple docstring'''






										__A						:			Union[str, Any]								=	BitBackbone(config=__A)
										model.to(__A)
										model.eval()
										__A						:			List[str]								=	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						:			Optional[int]								=	None
										__A						:			Dict								=	BitBackbone(config=__A)
										model.to(__A)
										model.eval()
										__A						:			Dict								=	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 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






										__A						:			str								=	self.prepare_config_and_inputs()
										__A			,__A			,__A						:			Union[str, Any]								=	config_and_inputs
										__A						:			Optional[int]								=	{'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						:			int								=	BitModelTester(self)
										__A						:			Dict								=	ConfigTester(self						,     config_class=__A						,     has_text_modality=__A)


					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						:			List[Any]								=	self.model_tester.prepare_config_and_inputs_for_common()

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

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


					def 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






										__A						:			Any								=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__A)


					def 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






										__A						:			str								=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__A)


					def 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






										__A			,__A						:			List[str]								=	self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															__A						:			Optional[Any]								=	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 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






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

															with torch.no_grad():
																				__A						:			int								=	model(**self._prepare_for_class(__A						,     __A))

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

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

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

																				check_hidden_states_output(__A						,     __A						,     __A)


					@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						:			Optional[Any]								=	self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__A)


					@slow
					def 							SCREAMING_SNAKE_CASE  (						self):

										'''simple docstring'''






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






def 	_lowerCAmelCase      (  )					->					Union[str, Any]:
					__A						:			Any								=	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(__A)

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

										# forward pass
										with torch.no_grad():
															__A						:			Optional[int]								=	model(**__A)

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

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

										self.assertTrue(torch.allclose(outputs.logits[0, :3]						,     __A						,     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						:			Dict								=	BitModelTester(self)






'''simple docstring'''




import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5			,							int(math.sqrt(__snake_case      ) + 1      )			,							6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 	_lowerCAmelCase      (  __snake_case  :			float = 0.1      )					->					int:
     __A						:			Dict								=	3
     __A						:			int								=	3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1			,							(j + 2) * (j + 2)			,							j + 1      ):
               primes += is_prime(__snake_case      )
          j += 2
     return j


if __name__ == "__main__":
  import doctest

  doctest.testmod()