
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 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3_2			,				SCREAMING_SNAKE_CASE_    :     Any=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=[8, 1_6, 3_2, 6_4]			,				SCREAMING_SNAKE_CASE_    :     str=[1, 1, 2, 1]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[str]="relu"			,				SCREAMING_SNAKE_CASE_    :     Dict=3			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=["stage2", "stage3", "stage4"]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=[2, 3, 4]			,				SCREAMING_SNAKE_CASE_    :     str=1			,				)			->	Union[str, Any]:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   image_size
					lowercase_	    =   num_channels
					lowercase_	    =   embeddings_size
					lowercase_	    =   hidden_sizes
					lowercase_	    =   depths
					lowercase_	    =   is_training
					lowercase_	    =   use_labels
					lowercase_	    =   hidden_act
					lowercase_	    =   num_labels
					lowercase_	    =   scope
					lowercase_	    =   len(_SCREAMING_SNAKE_CASE			)
					lowercase_	    =   out_features
					lowercase_	    =   out_indices
					lowercase_	    =   num_groups
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Union[str, Any]:
					lowercase_	    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

					lowercase_	    =   self.get_config()

					return config, pixel_values, labels
	def 		_lowercase    (			self    :     Tuple			)			->	List[Any]:
					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 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Dict:
					lowercase_	    =   BitModel(config=_SCREAMING_SNAKE_CASE			)
					model.to(_SCREAMING_SNAKE_CASE			)
					model.eval()
					lowercase_	    =   model(_SCREAMING_SNAKE_CASE			)
					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 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   BitForImageClassification(_SCREAMING_SNAKE_CASE			)
					model.to(_SCREAMING_SNAKE_CASE			)
					model.eval()
					lowercase_	    =   model(_SCREAMING_SNAKE_CASE			,				labels=_SCREAMING_SNAKE_CASE			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	Optional[Any]:
					lowercase_	    =   BitBackbone(config=_SCREAMING_SNAKE_CASE			)
					model.to(_SCREAMING_SNAKE_CASE			)
					model.eval()
					lowercase_	    =   model(_SCREAMING_SNAKE_CASE			)

					# 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
					lowercase_	    =   None
					lowercase_	    =   BitBackbone(config=_SCREAMING_SNAKE_CASE			)
					model.to(_SCREAMING_SNAKE_CASE			)
					model.eval()
					lowercase_	    =   model(_SCREAMING_SNAKE_CASE			)

					# 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 		_lowercase    (			self    :     int			)			->	Optional[Any]:
					lowercase_	    =   self.prepare_config_and_inputs()
					lowercase_	    =   config_and_inputs
					lowercase_	    =   {'''pixel_values''': pixel_values}
					return config, inputs_dict

@require_torch
class 					lowercase__(							_a					,		_a					,		unittest.TestCase				):



	"""simple docstring"""




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

	a     :Optional[int]       	=      False
	a     :Optional[Any]       	=      False
	a     :str       	=      False
	a     :Any       	=      False
	a     :str       	=      False
	def 		_lowercase    (			self    :     str			)			->	int:
					lowercase_	    =   BitModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=_SCREAMING_SNAKE_CASE			,				has_text_modality=_SCREAMING_SNAKE_CASE			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Union[str, 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 		_lowercase    (			self    :     int			)			->	Any:
					return
	@unittest.skip(reason='''Bit does not output attentions'''			)
	def 		_lowercase    (			self    :     Dict			)			->	Union[str, Any]:
					pass
	@unittest.skip(reason='''Bit does not use inputs_embeds'''			)
	def 		_lowercase    (			self    :     int			)			->	List[str]:
					pass
	@unittest.skip(reason='''Bit does not support input and output embeddings'''			)
	def 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					pass
	def 		_lowercase    (			self    :     List[str]			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_	    =   ['''pixel_values''']
									self.assertListEqual(arg_names[:1]			,				_SCREAMING_SNAKE_CASE			)
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE			)
	def 		_lowercase    (			self    :     Tuple			)			->	List[str]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE			)
	def 		_lowercase    (			self    :     int			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_	    =   model_class(config=_SCREAMING_SNAKE_CASE			)
									for name, module in model.named_modules():
													if isinstance(_SCREAMING_SNAKE_CASE			,				(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 		_lowercase    (			self    :     int			)			->	Optional[int]:
					def check_hidden_states_output(SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			):
									lowercase_	    =   model_class(_SCREAMING_SNAKE_CASE			)
									model.to(_SCREAMING_SNAKE_CASE			)
									model.eval()

									with torch.no_grad():
													lowercase_	    =   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)			)

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

									lowercase_	    =   self.model_tester.num_stages
									self.assertEqual(len(_SCREAMING_SNAKE_CASE			)			,				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]			,				)

					lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()
					lowercase_	    =   ['''preactivation''', '''bottleneck''']
					for model_class in self.all_model_classes:
									for layer_type in layers_type:
													lowercase_	    =   layer_type
													lowercase_	    =   True
													check_hidden_states_output(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)

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

													check_hidden_states_output(_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE			)
	@unittest.skip(reason='''Bit does not use feedforward chunking'''			)
	def 		_lowercase    (			self    :     Dict			)			->	List[str]:
					pass
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[int]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE			)




	@slow
	def 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   BitModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)




def 							a		(				):


				'''simple docstring'''
				lowercase_	    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
				return image
@require_torch
@require_vision
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




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




	@slow
	def 		_lowercase    (			self    :     str			)			->	List[Any]:
					lowercase_	    =   BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(_SCREAMING_SNAKE_CASE			)

					lowercase_	    =   self.default_image_processor
					lowercase_	    =   prepare_img()
					lowercase_	    =   image_processor(images=_SCREAMING_SNAKE_CASE			,				return_tensors='''pt'''			).to(_SCREAMING_SNAKE_CASE			)

					# forward pass
					with torch.no_grad():
									lowercase_	    =   model(**_SCREAMING_SNAKE_CASE			)

					# verify the logits
					lowercase_	    =   torch.Size((1, 1_0_0_0)			)
					self.assertEqual(outputs.logits.shape			,				_SCREAMING_SNAKE_CASE			)

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

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

@require_torch
class 					lowercase__(							_a					,		unittest.TestCase				):



	"""simple docstring"""




	a     :List[Any]       	=      (BitBackbone,) if is_torch_available() else ()
	a     :int       	=      BitConfig

	a     :List[str]       	=      False
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[int]:
					lowercase_	    =   BitModelTester(self			)










import unittest

import numpy as np







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase = None					,		):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     np.shape(_lowercase )
     UpperCAmelCase_     :				Tuple  =     np.shape(_lowercase )

     if shape_a[0] != shape_b[0]:
          UpperCAmelCase_     :				Tuple  =     (
              '''Expected the same number of rows for A and B. '''
              f'''Instead found A of size {shape_a} and B of size {shape_b}'''
          )
          raise ValueError(_lowercase )

     if shape_b[1] != shape_c[1]:
          UpperCAmelCase_     :				List[Any]  =     (
              '''Expected the same number of columns for B and C. '''
              f'''Instead found B of size {shape_b} and C of size {shape_c}'''
          )
          raise ValueError(_lowercase )

     UpperCAmelCase_     :				Dict  =     pseudo_inv
     if a_inv is None:
          try:
               UpperCAmelCase_     :				Any  =     np.linalg.inv(_lowercase )
          except np.linalg.LinAlgError:
               raise ValueError(
                   '''Input matrix A is not invertible. Cannot compute Schur complement.''' )

     return mat_c - mat_b.T @ a_inv @ mat_b

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Any  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				List[str]  =     np.array([[2, 1], [6, 3]]    )

         UpperCAmelCase_     :				Tuple  =     schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     np.block([[a, b], [b.T, c]]    )

         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				List[Any]  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Any  =     np.linalg.det(_SCREAMING_SNAKE_CASE    )

         self.assertAlmostEqual(_SCREAMING_SNAKE_CASE  ,det_a * det_s    )





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				str  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				Optional[int]  =     np.array([[2, 1], [6, 3]]    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						None:
         UpperCAmelCase_     :				Optional[int]  =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
         UpperCAmelCase_     :				Optional[Any]  =     np.array([[0, 3], [3, 0], [2, 3]]    )
         UpperCAmelCase_     :				int  =     np.array([[2, 1, 3], [6, 3, 5]]    )

         with self.assertRaises(_SCREAMING_SNAKE_CASE    ):
              schur_complement(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     unittest.main()