


'''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 	UpperCamelCase__:





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

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     embeddings_size
										UpperCAmelCase					    =     hidden_sizes
										UpperCAmelCase					    =     depths
										UpperCAmelCase					    =     is_training
										UpperCAmelCase					    =     use_labels
										UpperCAmelCase					    =     hidden_act
										UpperCAmelCase					    =     num_labels
										UpperCAmelCase					    =     scope
										UpperCAmelCase					    =     len(lowerCAmelCase				)
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     out_indices
										UpperCAmelCase					    =     num_groups





				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

										UpperCAmelCase					    =     self.get_config()

										return config, pixel_values, labels





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

										"""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      :			str				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			List[Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     BitModel(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     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      :			Tuple				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Tuple				)->					Dict:

										"""simple docstring"""




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





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			Optional[int]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     BitBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     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
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     BitBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										UpperCAmelCase					    =     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      :			Dict				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     config_and_inputs
										UpperCAmelCase					    =     {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				__magic_name__    :				str  		=	(
				    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__magic_name__    :				Dict  		=	False
				__magic_name__    :				Optional[Any]  		=	False
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				List[Any]  		=	False
				__magic_name__    :				List[str]  		=	False





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

										"""simple docstring"""




										UpperCAmelCase					    =     BitModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)





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

										"""simple docstring"""




										return





				@unittest.skip(reason='''Bit does not output attentions'''				)
				def 					a__(		self      :			Tuple				)->					Dict:

										"""simple docstring"""




										pass





				@unittest.skip(reason='''Bit does not use inputs_embeds'''				)
				def 					a__(		self      :			Union[str, Any]				)->					List[Any]:

										"""simple docstring"""




										pass





				@unittest.skip(reason='''Bit does not support input and output embeddings'''				)
				def 					a__(		self      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										pass





				def 					a__(		self      :			Dict				)->					int:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase					    =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,   lowerCAmelCase				)





				def 					a__(		self      :			Dict				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase				)





				def 					a__(		self      :			Tuple				)->					Any:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     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      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[Any]				):
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()

																with torch.no_grad():
																						UpperCAmelCase					    =     model(**self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)				)

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

																UpperCAmelCase					    =     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]				,   )

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

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

																						check_hidden_states_output(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)





				@unittest.skip(reason='''Bit does not use feedforward chunking'''				)
				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




										pass





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase				)





				@slow
				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




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







def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
						return image






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





				@cached_property
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




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





				@slow
				def 					a__(		self      :			List[Any]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(lowerCAmelCase				)

										UpperCAmelCase					    =     self.default_image_processor
										UpperCAmelCase					    =     prepare_img()
										UpperCAmelCase					    =     image_processor(images=lowerCAmelCase				,   return_tensors='''pt'''				).to(lowerCAmelCase				)

										# forward pass
										with torch.no_grad():
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

										# verify the logits
										UpperCAmelCase					    =     torch.Size((1, 1000)				)
										self.assertEqual(outputs.logits.shape				,   lowerCAmelCase				)

										UpperCAmelCase					    =     torch.tensor([[-0.6526, -0.5263, -1.4398]]				).to(lowerCAmelCase				)

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







@require_torch
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				str  		=	(BitBackbone,) if is_torch_available() else ()
				__magic_name__    :				List[Any]  		=	BitConfig

				__magic_name__    :				Optional[Any]  		=	False





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

										"""simple docstring"""




										UpperCAmelCase					    =     BitModelTester(self				)









'''simple docstring'''



from __future__ import annotations

from typing import Any






class 	UpperCamelCase__:




				def __init__(		self      :			Any				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			float = 0				)->					None:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     row, column
										UpperCAmelCase					    =     [[default_value for c in range(lowerCAmelCase				)] for r in range(lowerCAmelCase				)]




				def __str__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

										# Make string identifier
										UpperCAmelCase					    =     0
										for row_vector in self.array:
																for obj in row_vector:
																						UpperCAmelCase					    =     max(lowerCAmelCase				,   len(str(lowerCAmelCase				)				)				)
										UpperCAmelCase					    =     F"""%{max_element_length}s"""

										# Make string and return
										def single_line(lowerCAmelCase      :			list[float]				) -> str:
																nonlocal string_format_identifier
																UpperCAmelCase					    =     '''['''
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
																line += "]"
																return line

										s += "\n".join(single_line(lowerCAmelCase				) for row_vector in self.array				)
										return s




				def __repr__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										return str(self				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			tuple[int, int]				)->					bool:

										"""simple docstring"""




										if not (isinstance(lowerCAmelCase				,   (list, tuple)				) and len(lowerCAmelCase				) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True




				def __getitem__(		self      :			int				,   lowerCAmelCase      :			tuple[int, int]				)->					Any:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										return self.array[loc[0]][loc[1]]




				def __setitem__(		self      :			List[str]				,   lowerCAmelCase      :			tuple[int, int]				,   lowerCAmelCase      :			float				)->					None:

										"""simple docstring"""




										assert self.validate_indicies(lowerCAmelCase				)
										UpperCAmelCase					    =     value




				def __add__(		self      :			int				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == another.row and self.column == another.column

										# Add
										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c] + another[r, c]
										return result




				def __neg__(		self      :			Dict				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     -self[r, c]
										return result




				def __sub__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Matrix				)->					Matrix:

										"""simple docstring"""




										return self + (-another)




				def __mul__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			int | float | Matrix				)->					Matrix:

										"""simple docstring"""




										if isinstance(lowerCAmelCase				,   (int, float)				):  # Scalar multiplication
																UpperCAmelCase					    =     Matrix(self.row				,   self.column				)
																for r in range(self.row				):
																						for c in range(self.column				):
																												UpperCAmelCase					    =     self[r, c] * another
																return result
										elif isinstance(lowerCAmelCase				,   lowerCAmelCase				):  # Matrix multiplication
																assert self.column == another.row
																UpperCAmelCase					    =     Matrix(self.row				,   another.column				)
																for r in range(self.row				):
																						for c in range(another.column				):
																												for i in range(self.column				):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																UpperCAmelCase					    =     F"""Unsupported type given for another ({type(lowerCAmelCase				)})"""
																raise TypeError(lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				)->					Matrix:

										"""simple docstring"""




										UpperCAmelCase					    =     Matrix(self.column				,   self.row				)
										for r in range(self.row				):
																for c in range(self.column				):
																						UpperCAmelCase					    =     self[r, c]
										return result





				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			Matrix				,   lowerCAmelCase      :			Matrix				)->					Any:

										"""simple docstring"""




										assert isinstance(lowerCAmelCase				,   lowerCAmelCase				) and isinstance(lowerCAmelCase				,   lowerCAmelCase				)
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										UpperCAmelCase					    =     v.transpose()
										UpperCAmelCase					    =     (v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							UpperCAmelCase					    =     Matrix(3				,       3				,       0					)
							for i in range(3					):
													UpperCAmelCase					    =     1
							print(f"""a^(-1) is {ainv}"""					)
							# u, v
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 2, -3
							UpperCAmelCase					    =     Matrix(3				,       1				,       0					)
							UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     4, -2, 5
							print(f"""u is {u}"""					)
							print(f"""v is {v}"""					)
							print(f"""uv^T is {u * v.transpose()}"""					)
							# Sherman Morrison
							print(f"""(a + uv^T)^(-1) is {ainv.sherman_morrison(A				,       A					)}"""					)


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							import doctest

							doctest.testmod()




	testa()





