




"""simple docstring"""







from __future__ import annotations

from scipy.special import comb  # type: ignore

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_)				->   Optional[Any]:
       UpperCamelCase              =	list_of_points
       # Degree determines the flexibility of the curve.
       # Degree = 1 will produce a straight line.
       UpperCamelCase              =	len(lowerCamelCase_) - 1







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   list[float]:
       assert 0 <= t <= 1, "Time t must be between 0 and 1."
       UpperCamelCase              =	[]
       for i in range(len(self.list_of_points)):
           # basis function for each i
           output_values.append(
               comb(self.degree       ,					lowerCamelCase_) * ((1 - t) ** (self.degree - i)) * (t**i))
       # the basis must sum up to 1 for it to produce a valid Bezier curve.
       assert round(sum(lowerCamelCase_)       ,					5) == 1
       return output_values







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   tuple[float, float]:
       assert 0 <= t <= 1, "Time t must be between 0 and 1."

       UpperCamelCase              =	self.basis_function(lowerCamelCase_)
       UpperCamelCase              =	0.0
       UpperCamelCase              =	0.0
       for i in range(len(self.list_of_points)):
           # For all points, sum up the product of i-th basis function and i-th point.
           x += basis_function[i] * self.list_of_points[i][0]
           y += basis_function[i] * self.list_of_points[i][1]
       return (x, y)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_ = 0.01)				->   List[Any]:
       from matplotlib import pyplot as plt  # type: ignore

       UpperCamelCase              =	[]  # x coordinates of points to plot
       UpperCamelCase              =	[]  # y coordinates of points to plot

       UpperCamelCase              =	0.0
       while t <= 1:
           UpperCamelCase              =	self.bezier_curve_function(lowerCamelCase_)
           to_plot_x.append(value[0])
           to_plot_y.append(value[1])
           t += step_size

       UpperCamelCase              =	[i[0] for i in self.list_of_points]
       UpperCamelCase              =	[i[1] for i in self.list_of_points]

       plt.plot(
           lowerCamelCase_       ,					lowerCamelCase_       ,					color='''blue'''       ,					label='''Curve of Degree ''' + str(self.degree)       ,					)
       plt.scatter(lowerCamelCase_       ,					lowerCamelCase_       ,					color='''red'''       ,					label='''Control Points''')
       plt.legend()
       plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
  BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
  BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3



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       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_="resnet50"       ,  lowercase_=3       ,  lowercase_=32       ,  lowercase_=3       ,  lowercase_=True       ,  lowercase_=True       ,  )  -> Union[str, Any]:
											a__      =parent
											a__      =out_indices if out_indices is not None else [4]
											a__      =stage_names
											a__      =out_features
											a__      =backbone
											a__      =batch_size
											a__      =image_size
											a__      =num_channels
											a__      =use_pretrained_backbone
											a__      =is_training





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
											a__      =self.get_config()

											return config, pixel_values





				def 						__UpperCamelCase    (				self)  -> Tuple:
											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       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =TimmBackbone(config=lowercase_)
											model.to(lowercase_)
											model.eval()
											with torch.no_grad():
																		a__      =model(lowercase_)
											self.parent.assertEqual(
											    result.feature_map[-1].shape       ,  (self.batch_size, model.channels[-1], 14, 14)       ,  )





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =self.prepare_config_and_inputs()
											a__    ,     a__      =config_and_inputs
											a__      ={'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
@require_timm
class       lowercase_					(lowercase__      ,					lowercase__      ,					lowercase__      ,					unittest.TestCase   ):
				snake_case	    =(TimmBackbone,) if is_torch_available() else ()
				snake_case	    ={'feature-extraction': TimmBackbone} if is_torch_available() else {}
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False
				snake_case	    =False





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =TimmBackboneModelTester(self)
											a__      =ConfigTester(self       ,  config_class=lowercase_       ,  has_text_modality=lowercase_)





				def 						__UpperCamelCase    (				self)  -> Dict:
											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)  -> str:
											a__      ='resnet18'
											a__      ='microsoft/resnet-18'

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_)
											a__      =AutoBackbone.from_pretrained(lowercase_)

											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])

											a__      =AutoBackbone.from_pretrained(lowercase_       ,  use_timm_backbone=lowercase_       ,  out_indices=[1, 2, 3])
											a__      =AutoBackbone.from_pretrained(lowercase_       ,  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)  -> int:
											pass





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





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





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





				@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds')
				def 						__UpperCamelCase    (				self)  -> List[str]:
											pass





				@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





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





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





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





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





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





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





				@unittest.skip('TimmBackbone doesn\'t support output_attentions.')
				def 						__UpperCamelCase    (				self)  -> str:
											pass





				@unittest.skip('Safetensors is not supported by timm.')
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											pass





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





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

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

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





				def 						__UpperCamelCase    (				self)  -> Any:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()
											a__      =True
											a__      =self.has_attentions

											# no need to test all models as different heads yield the same functionality
											a__      =self.all_model_classes[0]
											a__      =model_class(lowercase_)
											model.to(lowercase_)

											a__      =self._prepare_for_class(lowercase_       ,  lowercase_)
											a__      =model(**lowercase_)
											a__      =outputs[0][-1]

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

											if self.has_attentions:
																		a__      =outputs.attentions[0]
																		attentions.retain_grad()

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

											self.assertIsNotNone(hidden_states.grad)

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





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__    ,     a__      =self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

																		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
																		a__      =copy.deepcopy(lowercase_)
																		a__      =None
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

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

																		# Check backbone can be initialized with fresh weights
																		a__      =copy.deepcopy(lowercase_)
																		a__      =False
																		a__      =model_class(lowercase_)
																		model.to(lowercase_)
																		model.eval()
																		a__      =model(**lowercase_)

