


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
 import torch

 from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class _lowerCamelCase:
       def __init__(   self, lowerCamelCase, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase="resnet50", lowerCamelCase=3, lowerCamelCase=32, lowerCamelCase=3, lowerCamelCase=True, lowerCamelCase=True, )			->  Tuple:




           """simple docstring"""

           _lowercase			:				Optional[Any] 						=						parent
           _lowercase			:				List[Any] 						=						out_indices if out_indices is not None else [4]
           _lowercase			:				Optional[int] 						=						stage_names
           _lowercase			:				Any 						=						out_features
           _lowercase			:				int 						=						backbone
           _lowercase			:				Optional[int] 						=						batch_size
           _lowercase			:				Dict 						=						image_size
           _lowercase			:				List[Any] 						=						num_channels
           _lowercase			:				Union[str, Any] 						=						use_pretrained_backbone
           _lowercase			:				Optional[int] 						=						is_training
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
           _lowercase			:				List[str] 						=						self.get_config()

           return config, pixel_values
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return TimmBackboneConfig(
               image_size=self.image_size, num_channels=self.num_channels, out_features=self.out_features, out_indices=self.out_indices, stage_names=self.stage_names, use_pretrained_backbone=self.use_pretrained_backbone, backbone=self.backbone, )
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase)			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						TimmBackbone(config=lowerCamelCase)
           model.to(lowerCamelCase)
           model.eval()
           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(lowerCamelCase)
           self.parent.assertEqual(
               result.feature_map[-1].shape, (self.batch_size, model.channels[-1], 14, 14), )




       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				List[str] 						=						self.prepare_config_and_inputs()
           _lowercase						,							_lowercase			:				List[Any] 						=						config_and_inputs
           _lowercase			:				Optional[Any] 						=						{'pixel_values': pixel_values}
           return config, inputs_dict






@require_torch
@require_timm
class _lowerCamelCase(		_a,					_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Dict       					=     (TimmBackbone,) if is_torch_available() else ()
       lowercase_					:      Optional[Any]       					=     {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       lowercase_					:      Union[str, Any]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      Optional[Any]       					=     False
       lowercase_					:      Any       					=     False
       def      UpperCamelCase	(   self)			->  Tuple:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						TimmBackboneModelTester(self)
           _lowercase			:				str 						=						ConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase)
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           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)			->  List[str]:




           """simple docstring"""

           _lowercase			:				int 						=						'resnet18'
           _lowercase			:				Any 						=						'microsoft/resnet-18'

           _lowercase			:				List[str] 						=						AutoBackbone.from_pretrained(lowerCamelCase, use_timm_backbone=lowerCamelCase)
           _lowercase			:				int 						=						AutoBackbone.from_pretrained(lowerCamelCase)

           self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
           self.assertEqual(len(timm_model.stage_names), len(transformers_model.stage_names))
           self.assertEqual(timm_model.channels, transformers_model.channels)
           # Out indices are set to the last layer by default. For timm models, we don't know
           # the number of layers in advance, so we set it to (-1,), whereas for transformers
           # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
           self.assertEqual(timm_model.out_indices, (-1,))
           self.assertEqual(transformers_model.out_indices, [len(timm_model.stage_names) - 1])

           _lowercase			:				List[Any] 						=						AutoBackbone.from_pretrained(lowerCamelCase, use_timm_backbone=lowerCamelCase, out_indices=[1, 2, 3])
           _lowercase			:				int 						=						AutoBackbone.from_pretrained(lowerCamelCase, out_indices=[1, 2, 3])

           self.assertEqual(timm_model.out_indices, transformers_model.out_indices)
           self.assertEqual(len(timm_model.out_features), len(transformers_model.out_features))
           self.assertEqual(timm_model.channels, transformers_model.channels)
       @unittest.skip('TimmBackbone doesn\'t support feed forward chunking')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone initialization is managed on the timm side')
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('model weights aren\'t tied in TimmBackbone.')
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           pass
       @unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone')
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip('TimmBackbone doesn\'t support output_attentions.')
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           pass
       @unittest.skip('Safetensors is not supported by timm.')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           pass
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)
               _lowercase			:				Dict 						=						inspect.signature(model.forward)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _lowercase			:				List[Any] 						=						[*signature.parameters.keys()]

               _lowercase			:				Dict 						=						['pixel_values']
               self.assertListEqual(arg_names[:1], lowerCamelCase)
       def      UpperCamelCase	(   self)			->  List[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()
           _lowercase			:				str 						=						True
           _lowercase			:				List[Any] 						=						self.has_attentions

           # no need to test all models as different heads yield the same functionality
           _lowercase			:				str 						=						self.all_model_classes[0]
           _lowercase			:				Optional[Any] 						=						model_class(lowerCamelCase)
           model.to(lowerCamelCase)

           _lowercase			:				Union[str, Any] 						=						self._prepare_for_class(lowerCamelCase, lowerCamelCase)
           _lowercase			:				List[str] 						=						model(**lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						outputs[0][-1]

           # Encoder-/Decoder-only models
           _lowercase			:				str 						=						outputs.hidden_states[0]
           hidden_states.retain_grad()

           if self.has_attentions:
               _lowercase			:				List[Any] 						=						outputs.attentions[0]
               attentions.retain_grad()

           output.flatten()[0].backward(retain_graph=lowerCamelCase)

           self.assertIsNotNone(hidden_states.grad)

           if self.has_attentions:
               self.assertIsNotNone(attentions.grad)




       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Tuple 						=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               _lowercase			:				Union[str, Any] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				int 						=						model(**lowerCamelCase)

               self.assertEqual(len(result.feature_maps), len(config.out_indices))
               self.assertEqual(len(model.channels), len(config.out_indices))

               # Check output of last stage is taken if out_features=None, out_indices=None
               _lowercase			:				Dict 						=						copy.deepcopy(lowerCamelCase)
               _lowercase			:				Optional[Any] 						=						None
               _lowercase			:				List[str] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				List[str] 						=						model(**lowerCamelCase)

               self.assertEqual(len(result.feature_maps), 1)
               self.assertEqual(len(model.channels), 1)

               # Check backbone can be initialized with fresh weights
               _lowercase			:				Dict 						=						copy.deepcopy(lowerCamelCase)
               _lowercase			:				List[str] 						=						False
               _lowercase			:				List[Any] 						=						model_class(lowerCamelCase)
               model.to(lowerCamelCase)
               model.eval()
               _lowercase			:				Dict 						=						model(**lowerCamelCase)





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





