

'''simple docstring'''

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def      __magic_name__							(			A       , A				)      ->		NDArray[floataa]:
	snake_case			,			snake_case							=					np.shape(A				)

	snake_case							=					np.zeros((rows, 1)       , dtype=A				)
	for row in reversed(range(A				)				):
		snake_case							=					0
		for col in range(row + 1       , A				):
			total += coefficients[row, col] * x[col]

		snake_case							=					(vector[row] - total) / coefficients[row, row]

	return x

def      __magic_name__							(			A       , A				)      ->		NDArray[floataa]:
	snake_case			,			snake_case							=					np.shape(A				)
	if rows != columns:
		return np.array(()       , dtype=A				)

	# augmented matrix
	snake_case							=					np.concatenate((coefficients, vector)       , axis=1				)
	snake_case							=					augmented_mat.astype('float64'				)

	# scale the matrix leaving it triangular
	for row in range(rows - 1				):
		snake_case							=					augmented_mat[row, row]
		for col in range(row + 1       , A				):
			snake_case							=					augmented_mat[col, row] / pivot
			augmented_mat[col, :] -= factor * augmented_mat[row, :]

	snake_case							=					retroactive_resolution(
	    augmented_mat[:, 0:columns]       , augmented_mat[:, columns : columns + 1]				)

	return x


if __name__ == "__main__":
						import doctest

						doctest.testmod()








'''simple docstring'''

from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
						from PIL import Image

						from transformers import ConvNextImageProcessor







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=13,						lowercase_=32,						lowercase_=3,						lowercase_=4,						lowercase_=[10, 20, 30, 40],						lowercase_=[2, 2, 3, 2],						lowercase_=True,						lowercase_=True,						lowercase_=37,						lowercase_="gelu",						lowercase_=10,						lowercase_=0.02,						lowercase_=3,						lowercase_=None,						)    ->							Dict:
					snake_case							=					parent
					snake_case							=					batch_size
					snake_case							=					image_size
					snake_case							=					num_channels
					snake_case							=					num_stages
					snake_case							=					hidden_sizes
					snake_case							=					depths
					snake_case							=					is_training
					snake_case							=					use_labels
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					scope

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					snake_case							=					None
					if self.use_labels:
						snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )

					snake_case							=					self.get_config()

					return config, pixel_values, labels

				def 					_lowerCamelCase    (       self )    ->							Any:
					return ConvNextConfig(
					    num_channels=self.num_channels,						hidden_sizes=self.hidden_sizes,						depths=self.depths,						num_stages=self.num_stages,						hidden_act=self.hidden_act,						is_decoder=lowercase_,						initializer_range=self.initializer_range,						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							List[Any]:
					snake_case							=					TFConvNextModel(config=lowercase_ )
					snake_case							=					model(lowercase_,						training=lowercase_ )
					# expected last hidden states: B, C, H // 32, W // 32
					self.parent.assertEqual(
					    result.last_hidden_state.shape,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							Union[str, Any]:
					snake_case							=					self.type_sequence_label_size
					snake_case							=					TFConvNextForImageClassification(lowercase_ )
					snake_case							=					model(lowercase_,						labels=lowercase_,						training=lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.type_sequence_label_size) )






				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					self.prepare_config_and_inputs()
					snake_case			,			snake_case			,			snake_case							=					config_and_inputs
					snake_case							=					{'pixel_values': pixel_values}
					return config, inputs_dict







@require_tf
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				snake_case_							     = (
				    {'''feature-extraction''': TFConvNextModel, '''image-classification''': TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						has_text_modality=lowercase_,						hidden_size=37,						)

				@unittest.skip(reason='ConvNext does not use inputs_embeds' )
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					pass

				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0,						reason='TF does not support backprop for grouped convolutions on CPU.',						)
				@slow
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					super().test_keras_fit()

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

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case							=					['pixel_values']
						self.assertListEqual(arg_names[:1],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('GPU' ) ) == 0,						reason='TF does not support backprop for grouped convolutions on CPU.',						)
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					super().test_dataset_conversion()

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					def check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ ):
						snake_case							=					model_class(lowercase_ )

						snake_case							=					model(**self._prepare_for_class(lowercase_,						lowercase_ ) )
						snake_case							=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						snake_case							=					self.model_tester.num_stages
						self.assertEqual(len(lowercase_ ),						expected_num_stages + 1 )

						# ConvNext'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],						)

					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						snake_case							=					True
						check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ )

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

						check_hidden_states_output(lowercase_,						lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					def check_equivalence(lowercase_,						lowercase_,						lowercase_,						lowercase_={} ):
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ )
						snake_case							=					model(lowercase_,						return_dict=lowercase_,						**lowercase_ ).to_tuple()

						def recursive_check(lowercase_,						lowercase_ ):
							if isinstance(lowercase_,						(List, Tuple) ):
								for tuple_iterable_value, dict_iterable_value in zip(lowercase_,						lowercase_ ):
									recursive_check(lowercase_,						lowercase_ )
							elif tuple_object is None:
								return
							else:
								self.assertTrue(
								    all(tf.equal(lowercase_,						lowercase_ ) ),						msg=(
								        'Tuple and dict output are not equal. Difference:'
								        F''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}'''
								    ),						)

							recursive_check(lowercase_,						lowercase_ )

					for model_class in self.all_model_classes:
						snake_case							=					model_class(lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_ )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						check_equivalence(lowercase_,						lowercase_,						lowercase_,						{'output_hidden_states': True} )

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					TFConvNextModel.from_pretrained('facebook/convnext-tiny-224' )
					self.assertIsNotNone(lowercase_ )

def      __magic_name__							(			)      ->		Optional[int]:
	snake_case							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image







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

				@cached_property
				def 					_lowerCamelCase    (       self )    ->							List[str]:
					return ConvNextImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					TFConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' )

					snake_case							=					self.default_image_processor
					snake_case							=					prepare_img()
					snake_case							=					image_processor(images=lowercase_,						return_tensors='tf' )

					# forward pass
					snake_case							=					model(**lowercase_ )

					# verify the logits
					snake_case							=					tf.TensorShape((1, 1000) )
					self.assertEqual(outputs.logits.shape,						lowercase_ )

					snake_case							=					tf.constant([-0.0_260, -0.4_739, 0.1_911] )

					tf.debugging.assert_near(outputs.logits[0, :3],						lowercase_,						atol=1E-4 )





