






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    ,		_UpperCamelCase    ,		_UpperCamelCase=3    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_0    ,		_UpperCamelCase=[8, 1_6, 3_2, 6_4]    ,		_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    ,		)     ->							Any:
							UpperCAmelCase_   :				Optional[int]						=						parent
							UpperCAmelCase_   :				Dict						=						batch_size
							UpperCAmelCase_   :				Optional[Any]						=						image_size
							UpperCAmelCase_   :				Tuple						=						num_channels
							UpperCAmelCase_   :				Optional[Any]						=						embeddings_size
							UpperCAmelCase_   :				Optional[Any]						=						hidden_sizes
							UpperCAmelCase_   :				Tuple						=						depths
							UpperCAmelCase_   :				Tuple						=						is_training
							UpperCAmelCase_   :				Union[str, Any]						=						use_labels
							UpperCAmelCase_   :				str						=						hidden_act
							UpperCAmelCase_   :				List[str]						=						num_labels
							UpperCAmelCase_   :				Optional[int]						=						scope
							UpperCAmelCase_   :				Any						=						len(_UpperCamelCase       )
							UpperCAmelCase_   :				List[str]						=						out_features
							UpperCAmelCase_   :				List[Any]						=						out_indices
							UpperCAmelCase_   :				Optional[Any]						=						num_groups



		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							UpperCAmelCase_   :				List[str]						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							UpperCAmelCase_   :				Any						=						None
							if self.use_labels:
												UpperCAmelCase_   :				str						=						ids_tensor([self.batch_size]    ,		self.num_labels       )

							UpperCAmelCase_   :				Tuple						=						self.get_config()

							return config, pixel_values, labels



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							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    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[Any]						=						BitModel(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				int						=						model(_UpperCamelCase       )
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				Tuple						=						self.num_labels
							UpperCAmelCase_   :				Optional[int]						=						BitForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Union[str, Any]						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						BitBackbone(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				str						=						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
							UpperCAmelCase_   :				Optional[int]						=						None
							UpperCAmelCase_   :				Optional[int]						=						BitBackbone(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				int						=						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  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				List[Any]						=						self.prepare_config_and_inputs()
							UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						config_and_inputs
							UpperCAmelCase_   :				Union[str, Any]						=						{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class   lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Any							=  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		_snake_case   :   List[Any]							=  (
		    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_snake_case   :   str							=  False
		_snake_case   :   Optional[int]							=  False
		_snake_case   :   Tuple							=  False
		_snake_case   :   Any							=  False
		_snake_case   :   int							=  False



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				List[str]						=						BitModelTester(self       )
							UpperCAmelCase_   :				Union[str, Any]						=						ConfigTester(self    ,		config_class=_UpperCamelCase    ,		has_text_modality=_UpperCamelCase       )



		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       )     ->							Tuple:
							pass



		@unittest.skip(reason='Bit does not use inputs_embeds'       )
		def  __UpperCAmelCase      (							self       )     ->							int:
							pass



		@unittest.skip(reason='Bit does not support input and output embeddings'       )
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							pass



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Union[str, Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase_   :				List[Any]						=						['pixel_values']
												self.assertListEqual(arg_names[:1]    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Tuple						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				Union[str, Any]						=						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  __UpperCAmelCase      (							self       )     ->							str:
							def check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				Optional[Any]						=						model_class(_UpperCamelCase       )
												model.to(_UpperCamelCase       )
												model.eval()

												with torch.no_grad():
																	UpperCAmelCase_   :				Union[str, Any]						=						model(**self._prepare_for_class(_UpperCamelCase    ,		_UpperCamelCase       )       )

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

												UpperCAmelCase_   :				Union[str, Any]						=						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]    ,		)

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

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

																	check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		@unittest.skip(reason='Bit does not use feedforward chunking'       )
		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				List[str]						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				int						=						BitModel.from_pretrained(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				List[str]						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image



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







		'''simple docstring'''



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



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				int						=						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						self.default_image_processor
							UpperCAmelCase_   :				List[Any]						=						prepare_img()
							UpperCAmelCase_   :				List[str]						=						image_processor(images=_UpperCamelCase    ,		return_tensors='pt'       ).to(_UpperCamelCase       )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_   :				int						=						model(**_UpperCamelCase       )

							# verify the logits
							UpperCAmelCase_   :				List[Any]						=						torch.Size((1, 1_0_0_0)       )
							self.assertEqual(outputs.logits.shape    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]       ).to(_UpperCamelCase       )

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





@require_torch
class   lowerCamelCase				(_snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   List[Any]							=  (BitBackbone,) if is_torch_available() else ()
		_snake_case   :   Dict							=  BitConfig

		_snake_case   :   Dict							=  False



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				str						=						BitModelTester(self       )














def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Tuple						=						[1]

					UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						0, 0, 0
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
					UpperCAmelCase_   :				Tuple						=						ugly_nums[ia] * 3
					UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 5

					for _ in range(1					,	__snake_case  ):
										UpperCAmelCase_   :				Tuple						=						min(__snake_case					,	__snake_case					,	__snake_case  )
										ugly_nums.append(__snake_case  )
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Union[str, Any]						=						ugly_nums[ia] * 2
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				Any						=						ugly_nums[ia] * 3
										if next_num == next_a:
															ia += 1
															UpperCAmelCase_   :				List[str]						=						ugly_nums[ia] * 5
					return ugly_nums[-1]


if __name__ == "__main__":
		from doctest import testmod

		testmod(verbose=True)
		print(F'{ugly_numbers(200) = }')






