




"""simple docstring"""




import mpmath  # for roots of unity
import numpy as np

class   UpperCAmelCase_    :
		def __init__(						self				,						UpperCamelCase_=None				,						UpperCamelCase_=None       )						->       List[str]:
					# Input as list
					__lowercase			:  str								=       list(poly_a or [0]       )[:]
					__lowercase			:  Tuple								=       list(poly_b or [0]       )[:]

					# Remove leading zero coefficients
					while self.polyA[-1] == 0:
								self.polyA.pop()
					__lowercase			:  str								=       len(self.polyA       )

					while self.polyB[-1] == 0:
								self.polyB.pop()
					__lowercase			:  Optional[Any]								=       len(self.polyB       )

					# Add 0 to make lengths equal a power of 2
					__lowercase			:  Dict								=       int(
					    2 ** np.ceil(np.loga(len(self.polyA       ) + len(self.polyB       ) - 1       )       )       )

					while len(self.polyA       ) < self.c_max_length:
								self.polyA.append(0       )
					while len(self.polyB       ) < self.c_max_length:
								self.polyB.append(0       )
					# A complex root used for the fourier transform
					__lowercase			:  List[str]								=       complex(mpmath.root(x=1				,						n=self.c_max_length				,						k=1       )       )

					# The product
					__lowercase			:  Optional[int]								=       self.__multiply()
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_       )						->       Tuple:
					__lowercase			:  Tuple								=       [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
					# Corner case
					if len(lowercase__       ) <= 1:
								return dft[0]
					#
					__lowercase			:  Optional[int]								=       self.c_max_length // 2
					while next_ncol > 0:
								__lowercase			:  Union[str, Any]								=       [[] for i in range(lowercase__       )]
								__lowercase			:  Optional[Any]								=       self.root**next_ncol

								# First half of next step
								__lowercase			:  Any								=       1
								for j in range(self.c_max_length // (next_ncol * 2)       ):
											for i in range(lowercase__       ):
														new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]       )
											current_root *= root
								# Second half of next step
								__lowercase			:  List[Any]								=       1
								for j in range(self.c_max_length // (next_ncol * 2)       ):
											for i in range(lowercase__       ):
														new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]       )
											current_root *= root
								# Update
								__lowercase			:  Tuple								=       new_dft
								__lowercase			:  Tuple								=       next_ncol // 2
					return dft[0]
		def 	_lowerCamelCase							(						self       )						->       Optional[int]:
					__lowercase			:  Tuple								=       self.__dft('''A'''       )
					__lowercase			:  List[Any]								=       self.__dft('''B'''       )
					__lowercase			:  List[str]								=       [[dft_a[i] * dft_b[i] for i in range(self.c_max_length       )]]
					del dft_a
					del dft_b

					# Corner Case
					if len(inverce_c[0]       ) <= 1:
								return inverce_c[0]
					# Inverse DFT
					__lowercase			:  str								=       2
					while next_ncol <= self.c_max_length:
								__lowercase			:  List[str]								=       [[] for i in range(lowercase__       )]
								__lowercase			:  Optional[int]								=       self.root ** (next_ncol // 2)
								__lowercase			:  Tuple								=       1
								# First half of next step
								for j in range(self.c_max_length // next_ncol       ):
											for i in range(next_ncol // 2       ):
														# Even positions
														new_inverse_c[i].append(
														    (
														        inverce_c[i][j]
														        + inverce_c[i][j + self.c_max_length // next_ncol]
														    )
														    / 2       )
														# Odd positions
														new_inverse_c[i + next_ncol // 2].append(
														    (
														        inverce_c[i][j]
														        - inverce_c[i][j + self.c_max_length // next_ncol]
														    )
														    / (2 * current_root)       )
											current_root *= root
								# Update
								__lowercase			:  List[Any]								=       new_inverse_c
								next_ncol *= 2
					# Unpack
					__lowercase			:  Optional[Any]								=       [round(x[0].real				,						8       ) + round(x[0].imag				,						8       ) * 1J for x in inverce_c]

					# Remove leading 0's
					while inverce_c[-1] == 0:
								inverce_c.pop()
					return inverce_c
		def __str__(						self       )						->       Optional[int]:
					__lowercase			:  Any								=       '''A = ''' + ''' + '''.join(
					    F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]       )       )
					__lowercase			:  int								=       '''B = ''' + ''' + '''.join(
					    F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]       )       )
					__lowercase			:  Optional[Any]								=       '''A*B = ''' + ''' + '''.join(
					    F"""{coef}*x^{i}""" for coef, i in enumerate(self.product       )       )

					return F"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
			import doctest

			doctest.testmod()







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				,     lowercase__				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     lowercase__="resnet50"				,     lowercase__=3				,     lowercase__=3_2				,     lowercase__=3				,     lowercase__=True				,     lowercase__=True				,     ):
							__UpperCAmelCase			:							List[str]	    =	parent
							__UpperCAmelCase			:							Dict	    =	out_indices if out_indices is not None else [4]
							__UpperCAmelCase			:							List[Any]	    =	stage_names
							__UpperCAmelCase			:							int	    =	out_features
							__UpperCAmelCase			:							Union[str, Any]	    =	backbone
							__UpperCAmelCase			:							Optional[Any]	    =	batch_size
							__UpperCAmelCase			:							Optional[Any]	    =	image_size
							__UpperCAmelCase			:							Optional[int]	    =	num_channels
							__UpperCAmelCase			:							List[Any]	    =	use_pretrained_backbone
							__UpperCAmelCase			:							Dict	    =	is_training


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
							__UpperCAmelCase			:							Tuple	    =	self.get_config()

							return config, pixel_values


	def  A(    self):
							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  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Tuple	    =	TimmBackbone(config=lowercase__)
							model.to(lowercase__)
							model.eval()
							with torch.no_grad():
													__UpperCAmelCase			:							Union[str, Any]	    =	model(lowercase__)
							self.parent.assertEqual(
							    result.feature_map[-1].shape				,     (self.batch_size, model.channels[-1], 1_4, 1_4)				,     )


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.prepare_config_and_inputs()
							__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	config_and_inputs
							__UpperCAmelCase			:							List[Any]	    =	{'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
@require_timm
class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Dict										=						(TimmBackbone,) if is_torch_available() else ()
	_lowerCAmelCase     :		str										=						{'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
	_lowerCAmelCase     :		List[str]										=						False
	_lowerCAmelCase     :		str										=						False
	_lowerCAmelCase     :		List[Any]										=						False
	_lowerCAmelCase     :		List[str]										=						False


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	TimmBackboneModelTester(self)
							__UpperCAmelCase			:							int	    =	ConfigTester(self				,     config_class=lowercase__				,     has_text_modality=lowercase__)


	def  A(    self):
							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  A(    self):
							__UpperCAmelCase			:							int	    =	'''resnet18'''
							__UpperCAmelCase			:							List[str]	    =	'''microsoft/resnet-18'''

							__UpperCAmelCase			:							Any	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	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])

							__UpperCAmelCase			:							Union[str, Any]	    =	AutoBackbone.from_pretrained(lowercase__				,     use_timm_backbone=lowercase__				,     out_indices=[1, 2, 3])
							__UpperCAmelCase			:							Any	    =	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  A(    self):
							pass


	@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone initialization is managed on the timm side''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


	@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds''')
	def  A(    self):
							pass


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


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''model weights aren\'t tied in TimmBackbone.''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


	@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone''')
	def  A(    self):
							pass


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


	@unittest.skip('''TimmBackbone doesn\'t support output_attentions.''')
	def  A(    self):
							pass


	@unittest.skip('''Safetensors is not supported by timm.''')
	def  A(    self):
							pass


	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
	def  A(    self):
							pass


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	self.model_tester.prepare_config_and_inputs_for_common()

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

													__UpperCAmelCase			:							Dict	    =	['''pixel_values''']
													self.assertListEqual(arg_names[:1]				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							str	    =	self.model_tester.prepare_config_and_inputs_for_common()
							__UpperCAmelCase			:							Tuple	    =	True
							__UpperCAmelCase			:							Optional[int]	    =	self.has_attentions

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

							__UpperCAmelCase			:							Tuple	    =	self._prepare_for_class(lowercase__				,     lowercase__)
							__UpperCAmelCase			:							Optional[int]	    =	model(**lowercase__)
							__UpperCAmelCase			:							List[str]	    =	outputs[0][-1]

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

							if self.has_attentions:
													__UpperCAmelCase			:							Optional[int]	    =	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  A(    self):
							__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__UpperCAmelCase			:							Optional[Any]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Union[str, Any]	    =	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
													__UpperCAmelCase			:							List[str]	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							str	    =	None
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							Optional[Any]	    =	model(**lowercase__)

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

													# Check backbone can be initialized with fresh weights
													__UpperCAmelCase			:							Tuple	    =	copy.deepcopy(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	False
													__UpperCAmelCase			:							Optional[int]	    =	model_class(lowercase__)
													model.to(lowercase__)
													model.eval()
													__UpperCAmelCase			:							List[Any]	    =	model(**lowercase__)

