



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 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       Optional[int]			,     lowercase	:       Dict			,     lowercase	:       List[str]=3			,     lowercase	:       Dict=32			,     lowercase	:       Union[str, Any]=3			,     lowercase	:       Tuple=10			,     lowercase	:       List[str]=[10, 20, 30, 40]			,     lowercase	:       List[str]=[1, 1, 2, 1]			,     lowercase	:       Dict=True			,     lowercase	:       int=True			,     lowercase	:       Optional[int]="relu"			,     lowercase	:       Optional[int]=3			,     lowercase	:       Union[str, Any]=None			,     )						->					Any:
									"""simple docstring"""







									__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(lowercase   )





			def 		snake_case__			(				self	:       Optional[int]   )						->					Optional[int]:
									"""simple docstring"""







									__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 		snake_case__			(				self	:       str   )						->					Optional[Any]:
									"""simple docstring"""







									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 		snake_case__			(				self	:       int			,     lowercase	:       List[str]			,     lowercase	:       Dict			,     lowercase	:       Tuple   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				TFResNetModel(config=lowercase   )
									__lowercase					=				model(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 		snake_case__			(				self	:       List[str]			,     lowercase	:       List[Any]			,     lowercase	:       List[Any]			,     lowercase	:       List[str]   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.num_labels
									__lowercase					=				TFResNetForImageClassification(lowercase   )
									__lowercase					=				model(lowercase			,     labels=lowercase   )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)   )







			def 		snake_case__			(				self	:       str   )						->					int:
									"""simple docstring"""







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





@require_tf
class 			_lowerCAmelCase   ( _UpperCAmelCase    ,					_UpperCAmelCase    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Union[str, Any]      =			(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
			lowercase__      :  Tuple      =			(
			    {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowercase__      :  List[str]      =			False
			lowercase__      :  List[Any]      =			False
			lowercase__      :  List[str]      =			False
			lowercase__      :  Optional[Any]      =			False
			lowercase__      :  Optional[int]      =			False





			def 		snake_case__			(				self	:       Tuple   )						->					int:
									"""simple docstring"""







									__lowercase					=				TFResNetModelTester(self   )
									__lowercase					=				ConfigTester(self			,     config_class=lowercase			,     has_text_modality=lowercase   )





			def 		snake_case__			(				self	:       int   )						->					Optional[int]:
									"""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 		snake_case__			(				self	:       List[Any]   )						->					Any:
									"""simple docstring"""







									return





			@unittest.skip(reason="""ResNet does not use inputs_embeds"""   )
			def 		snake_case__			(				self	:       Union[str, Any]   )						->					List[Any]:
									"""simple docstring"""







									pass





			@unittest.skip(reason="""ResNet does not support input and output embeddings"""   )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Tuple:
									"""simple docstring"""







									pass





			def 		snake_case__			(				self	:       List[str]   )						->					List[str]:
									"""simple docstring"""







									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()

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

															__lowercase					=				["""pixel_values"""]
															self.assertListEqual(arg_names[:1]			,     lowercase   )





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase   )





			def 		snake_case__			(				self	:       Dict   )						->					Union[str, Any]:
									"""simple docstring"""







									def check_hidden_states_output(lowercase	:       str			,     lowercase	:       List[str]			,     lowercase	:       Any   ):
															__lowercase					=				model_class(lowercase   )
															__lowercase					=				model(**self._prepare_for_class(lowercase			,     lowercase   )   )

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

															__lowercase					=				self.model_tester.num_stages
															self.assertEqual(len(lowercase   )			,     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]			,     )

									__lowercase	,			__lowercase					=				self.model_tester.prepare_config_and_inputs_for_common()
									__lowercase					=				["""basic""", """bottleneck"""]
									for model_class in self.all_model_classes:
															for layer_type in layers_type:
																					__lowercase					=				layer_type
																					__lowercase					=				True
																					check_hidden_states_output(lowercase			,     lowercase			,     lowercase   )

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

																					check_hidden_states_output(lowercase			,     lowercase			,     lowercase   )





			def 		snake_case__			(				self	:       Tuple   )						->					str:
									"""simple docstring"""







									__lowercase					=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowercase   )







			@slow
			def 		snake_case__			(				self	:       Dict   )						->					Dict:
									"""simple docstring"""







									for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__lowercase					=				TFResNetModel.from_pretrained(lowercase   )
															self.assertIsNotNone(lowercase   )






def  UpperCAmelCase__    (    ) ->      str:
						__lowercase					=				Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
						return image







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






			"""simple docstring"""





			@cached_property
			def 		snake_case__			(				self	:       Any   )						->					int:
									"""simple docstring"""







									return (
									    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
									    if is_vision_available()
									    else None
									)







			@slow
			def 		snake_case__			(				self	:       List[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

									__lowercase					=				self.default_image_processor
									__lowercase					=				prepare_img()
									__lowercase					=				image_processor(images=lowercase			,     return_tensors="""tf"""   )

									# forward pass
									__lowercase					=				model(**lowercase   )

									# verify the logits
									__lowercase					=				tf.TensorShape((1, 1_000)   )
									self.assertEqual(outputs.logits.shape			,     lowercase   )

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

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










from __future__ import annotations

from collections.abc import Callable

UpperCamelCase__ =						list[list[float | int]]






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Matrix:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(size + 1   )] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for row in range(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				matrix[row][col]

												__lowercase					=				vector[row][0]

						__lowercase					=				0
						__lowercase					=				0
						while row < size and col < size:
												# pivoting
												__lowercase					=				max((abs(augmented[rowa][col]   ), rowa) for rowa in range(lowercase__						,       lowercase__   )   )[
												    1
												]
												if augmented[pivot_row][col] == 0:
																		col += 1
																		continue
												else:
																		__lowercase	,			__lowercase					=				augmented[pivot_row], augmented[row]

												for rowa in range(row + 1						,       lowercase__   ):
																		__lowercase					=				augmented[rowa][col] / augmented[row][col]
																		__lowercase					=				0
																		for cola in range(col + 1						,       size + 1   ):
																								augmented[rowa][cola] -= augmented[row][cola] * ratio

												row += 1
												col += 1

						# back substitution
						for col in range(1						,       lowercase__   ):
												for row in range(lowercase__   ):
																		__lowercase					=				augmented[row][col] / augmented[col][col]
																		for cola in range(lowercase__						,       size + 1   ):
																								augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
						return [
						    [round(augmented[row][size] / augmented[row][row]						,       10   )] for row in range(lowercase__   )
						]






def  UpperCAmelCase__    (    lowercase__   ) ->      Callable[[int], int]:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[0 for _ in range(lowercase__   )] for _ in range(lowercase__   )]
						__lowercase					=				[[0] for _ in range(lowercase__   )]
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42
						__lowercase					=				42

						for x_val, y_val in enumerate(lowercase__   ):
												for col in range(lowercase__   ):
																		__lowercase					=				(x_val + 1) ** (size - col - 1)
												__lowercase					=				y_val

						__lowercase					=				solve(lowercase__						,       lowercase__   )

						def interpolated_func(lowercase__   ) -> int:
												return sum(
												    round(coeffs[x_val][0]   ) * (var ** (size - x_val - 1))
												    for x_val in range(lowercase__   )   )

						return interpolated_func






def  UpperCAmelCase__    (    lowercase__   ) ->      int:
						return (
						    1
						    - variable
						    + variable**2
						    - variable**3
						    + variable**4
						    - variable**5
						    + variable**6
						    - variable**7
						    + variable**8
						    - variable**9
						    + variable**10
						)






def  UpperCAmelCase__    (    lowercase__ = question_function						,       lowercase__ = 10   ) ->      int:
						__lowercase					=				[func(lowercase__   ) for x_val in range(1						,       order + 1   )]

						__lowercase					=				[
						    interpolate(data_points[:max_coeff]   ) for max_coeff in range(1						,       order + 1   )
						]

						__lowercase					=				0
						__lowercase					=				42
						__lowercase					=				42

						for poly in polynomials:
												__lowercase					=				1
												while func(lowercase__   ) == poly(lowercase__   ):
																		x_val += 1

												ret += poly(lowercase__   )

						return ret


if __name__ == "__main__":
			print(F"""{solution() = }""")





