




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     :

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

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  List[Any]		       =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

								lowerCAmelCase__				:  Dict		       =     None
								if self.use_labels:
										lowerCAmelCase__				:  Optional[Any]		       =     ids_tensor([self.batch_size]      ,				self.num_labels		)

								lowerCAmelCase__				:  Union[str, Any]		       =     self.get_config()

								return config, pixel_values, labels

						def 	__magic_name__(   self		):
								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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Union[str, Any]		       =     BitModel(config=__UpperCAmelCase		)
								model.to(__UpperCAmelCase		)
								model.eval()
								lowerCAmelCase__				:  Dict		       =     model(__UpperCAmelCase		)
								self.parent.assertEqual(
								    result.last_hidden_state.shape      ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)      ,				)

						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Dict		       =     self.num_labels
								lowerCAmelCase__				:  Dict		       =     BitForImageClassification(__UpperCAmelCase		)
								model.to(__UpperCAmelCase		)
								model.eval()
								lowerCAmelCase__				:  List[Any]		       =     model(__UpperCAmelCase      ,				labels=__UpperCAmelCase		)
								self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)		)

						def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
								lowerCAmelCase__				:  Dict		       =     BitBackbone(config=__UpperCAmelCase		)
								model.to(__UpperCAmelCase		)
								model.eval()
								lowerCAmelCase__				:  List[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
								lowerCAmelCase__				:  List[Any]		       =     None
								lowerCAmelCase__				:  Union[str, Any]		       =     BitBackbone(config=__UpperCAmelCase		)
								model.to(__UpperCAmelCase		)
								model.eval()
								lowerCAmelCase__				:  str		       =     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 	__magic_name__(   self		):
								lowerCAmelCase__				:  Dict		       =     self.prepare_config_and_inputs()
								lowerCAmelCase__				:  Any		       =     config_and_inputs
								lowerCAmelCase__				:  Dict		       =     {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  _lowerCAmelCase     (  __A    ,			__A    ,			unittest.TestCase	):
						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 	__magic_name__(   self		):
								lowerCAmelCase__				:  Dict		       =     BitModelTester(self		)
								lowerCAmelCase__				:  List[Any]		       =     ConfigTester(self      ,				config_class=__UpperCAmelCase      ,				has_text_modality=__UpperCAmelCase		)

						def 	__magic_name__(   self		):
								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 	__magic_name__(   self		):
								return

						@unittest.skip(reason='''Bit does not output attentions'''		)
						def 	__magic_name__(   self		):
								pass

						@unittest.skip(reason='''Bit does not use inputs_embeds'''		)
						def 	__magic_name__(   self		):
								pass

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

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										lowerCAmelCase__				:  Optional[int]		       =     model_class(__UpperCAmelCase		)
										lowerCAmelCase__				:  Any		       =     inspect.signature(model.forward		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowerCAmelCase__				:  Tuple		       =     [*signature.parameters.keys()]

										lowerCAmelCase__				:  Optional[int]		       =     ['''pixel_values''']
										self.assertListEqual(arg_names[:1]      ,				__UpperCAmelCase		)

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__UpperCAmelCase		)

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_backbone(*__UpperCAmelCase		)

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Optional[Any]		       =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										lowerCAmelCase__				:  Optional[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 	__magic_name__(   self		):

								def check_hidden_states_output(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
										lowerCAmelCase__				:  Dict		       =     model_class(__UpperCAmelCase		)
										model.to(__UpperCAmelCase		)
										model.eval()

										with torch.no_grad():
												lowerCAmelCase__				:  Tuple		       =     model(**self._prepare_for_class(__UpperCAmelCase      ,				__UpperCAmelCase		)		)

										lowerCAmelCase__				:  List[Any]		       =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										lowerCAmelCase__				:  Optional[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]      ,				)

								lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__				:  str		       =     ['''preactivation''', '''bottleneck''']
								for model_class in self.all_model_classes:
										for layer_type in layers_type:
												lowerCAmelCase__				:  Union[str, Any]		       =     layer_type
												lowerCAmelCase__				:  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"]
												lowerCAmelCase__				:  str		       =     True

												check_hidden_states_output(__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		)

						@unittest.skip(reason='''Bit does not use feedforward chunking'''		)
						def 	__magic_name__(   self		):
								pass

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Any		       =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase		)







						@slow
						def 	__magic_name__(   self		):

								for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__				:  Union[str, Any]		       =     BitModel.from_pretrained(__UpperCAmelCase		)
										self.assertIsNotNone(__UpperCAmelCase		)



def      __lowerCAmelCase			(					)     ->	Any:
		lowerCAmelCase__				:  Dict		       =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
		return image






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

						@cached_property
						def 	__magic_name__(   self		):
								return (
								    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		) if is_vision_available() else None
								)







						@slow
						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Tuple		       =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__UpperCAmelCase		)

								lowerCAmelCase__				:  Union[str, Any]		       =     self.default_image_processor
								lowerCAmelCase__				:  int		       =     prepare_img()
								lowerCAmelCase__				:  Union[str, Any]		       =     image_processor(images=__UpperCAmelCase      ,				return_tensors='''pt'''		).to(__UpperCAmelCase		)

								# forward pass
								with torch.no_grad():
										lowerCAmelCase__				:  Tuple		       =     model(**__UpperCAmelCase		)

								# verify the logits
								lowerCAmelCase__				:  Optional[Any]		       =     torch.Size((1, 1000)		)
								self.assertEqual(outputs.logits.shape      ,				__UpperCAmelCase		)

								lowerCAmelCase__				:  List[Any]		       =     torch.tensor([[-0.6526, -0.5263, -1.4398]]		).to(__UpperCAmelCase		)

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






@require_torch
class  _lowerCAmelCase     (  __A    ,			unittest.TestCase	):
						A__		     =		(BitBackbone,) if is_torch_available() else ()
						A__		     =		BitConfig

						A__		     =		False

						def 	__magic_name__(   self		):
								lowerCAmelCase__				:  Tuple		       =     BitModelTester(self		)







import math



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * power_factor



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	float:
  if (
      not isinstance(UpperCamelCase	,      (int, float)      )
      or power_factor < -1
      or power_factor > 1
  ):
    raise ValueError('''power_factor must be a valid float value between -1 and 1.'''      )
  return apparent_power * math.sqrt(1 - power_factor**2      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

