




from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
				from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=10						,		_SCREAMING_SNAKE_CASE=[10, 20, 30, 40]						,		_SCREAMING_SNAKE_CASE=[1, 1, 2, 1]						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE=True						,		_SCREAMING_SNAKE_CASE="relu"						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=None						,		):
								_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(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_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 				UpperCAmelCase (    self			):
								return ResNetConfig(
								    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						,		image_size=self.image_size						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			TFResNetModel(config=_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE			)
								# 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 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.num_labels
								_UpperCAmelCase      =			TFResNetForImageClassification(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			model(_SCREAMING_SNAKE_CASE						,		labels=_SCREAMING_SNAKE_CASE			)
								self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)			)






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




@require_tf
class       _A				(		__lowercase       ,     __lowercase       ,     unittest.TestCase     ):
		__a					    =       (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		__a					    =       (
		    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		__a					    =       False
		__a					    =       False
		__a					    =       False
		__a					    =       False
		__a					    =       False






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			TFResNetModelTester(self			)
								_UpperCAmelCase      =			ConfigTester(self						,		config_class=_SCREAMING_SNAKE_CASE						,		has_text_modality=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    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 				UpperCAmelCase (    self			):
								return






		@unittest.skip(reason="""ResNet does not use inputs_embeds"""			)
		def 				UpperCAmelCase (    self			):
								pass






		@unittest.skip(reason="""ResNet does not support input and output embeddings"""			)
		def 				UpperCAmelCase (    self			):
								pass






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase    ,					_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs_for_common()

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

														_UpperCAmelCase      =			["""pixel_values"""]
														self.assertListEqual(arg_names[:1]						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								def check_hidden_states_output(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			model_class(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)			)

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

														_UpperCAmelCase      =			self.model_tester.num_stages
														self.assertEqual(len(_SCREAMING_SNAKE_CASE			)						,		expected_num_stages + 1			)

														# ResNet'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      =			["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				_UpperCAmelCase      =			layer_type
																				_UpperCAmelCase      =			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"]
																				_UpperCAmelCase      =			True

																				check_hidden_states_output(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE			)






		@slow
		def 				UpperCAmelCase (    self			):
								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase      =			TFResNetModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
														self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)




def        _SCREAMING_SNAKE_CASE      (		)						->				List[Any]:
						_UpperCAmelCase      =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
						return image




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






		@cached_property
		def 				UpperCAmelCase (    self			):
								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
								    if is_vision_available()
								    else None
								)






		@slow
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

								_UpperCAmelCase      =			self.default_image_processor
								_UpperCAmelCase      =			prepare_img()
								_UpperCAmelCase      =			image_processor(images=_SCREAMING_SNAKE_CASE						,		return_tensors="""tf"""			)

								# forward pass
								_UpperCAmelCase      =			model(**_SCREAMING_SNAKE_CASE			)

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

								_UpperCAmelCase      =			tf.constant([-11.1069, -9.7877, -8.3777]			)

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





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """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(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												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     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			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()