



import mpmath  # for roots of unity
import numpy as np


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase=None     ,lowercase=None):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		list(poly_a or [0])[:]
             UpperCAmelCase_  :	Optional[int]				      =		list(poly_b or [0])[:]

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

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

             # Add 0 to make lengths equal a power of 2
             UpperCAmelCase_  :	str				      =		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
             UpperCAmelCase_  :	List[str]				      =		complex(mpmath.root(x=1     ,n=self.c_max_length     ,k=1))

             # The product
             UpperCAmelCase_  :	int				      =		self.__multiply()



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		[[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]
             #
             UpperCAmelCase_  :	Optional[Any]				      =		self.c_max_length // 2
             while next_ncol > 0:
                   UpperCAmelCase_  :	Dict				      =		[[] for i in range(lowercase)]
                   UpperCAmelCase_  :	Optional[Any]				      =		self.root**next_ncol

                   # First half of next step
                   UpperCAmelCase_  :	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
                   # Second half of next step
                   UpperCAmelCase_  :	Optional[int]				      =		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
                   UpperCAmelCase_  :	List[str]				      =		new_dft
                   UpperCAmelCase_  :	Optional[Any]				      =		next_ncol // 2
             return dft[0]



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.__dft("A")
             UpperCAmelCase_  :	Tuple				      =		self.__dft("B")
             UpperCAmelCase_  :	Optional[int]				      =		[[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
             UpperCAmelCase_  :	Dict				      =		2
             while next_ncol <= self.c_max_length:
                   UpperCAmelCase_  :	str				      =		[[] for i in range(lowercase)]
                   UpperCAmelCase_  :	Tuple				      =		self.root ** (next_ncol // 2)
                   UpperCAmelCase_  :	str				      =		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
                   UpperCAmelCase_  :	Optional[Any]				      =		new_inverse_c
                   next_ncol *= 2
             # Unpack
             UpperCAmelCase_  :	Dict				      =		[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):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		"A = " + " + ".join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]))
             UpperCAmelCase_  :	Any				      =		"B = " + " + ".join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]))
             UpperCAmelCase_  :	Union[str, 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   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=None     ,lowercase=None     ,lowercase=None     ,lowercase="resnet50"     ,lowercase=3     ,lowercase=32     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		parent
             UpperCAmelCase_  :	int				      =		out_indices if out_indices is not None else [4]
             UpperCAmelCase_  :	Any				      =		stage_names
             UpperCAmelCase_  :	List[Any]				      =		out_features
             UpperCAmelCase_  :	List[str]				      =		backbone
             UpperCAmelCase_  :	Union[str, Any]				      =		batch_size
             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	str				      =		num_channels
             UpperCAmelCase_  :	Optional[Any]				      =		use_pretrained_backbone
             UpperCAmelCase_  :	Optional[int]				      =		is_training



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
             UpperCAmelCase_  :	Dict				      =		self.get_config()

             return config, pixel_values



       def  A_   (     self):

             """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  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		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], 14, 14)     ,)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


@require_torch
@require_timm
class   snake_case_ (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				(TimmBackbone,) if is_torch_available() else ()
       _lowerCamelCase			    =				{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		TimmBackboneModelTester(self)
             UpperCAmelCase_  :	Tuple				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase)



       def  A_   (     self):

             """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  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		"resnet18"
             UpperCAmelCase_  :	Tuple				      =		"microsoft/resnet-18"

             UpperCAmelCase_  :	int				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase)
             UpperCAmelCase_  :	Dict				      =		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_  :	Dict				      =		AutoBackbone.from_pretrained(lowercase     ,use_timm_backbone=lowercase     ,out_indices=[1, 2, 3])
             UpperCAmelCase_  :	List[str]				      =		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):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone initialization is managed on the timm side")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone models doesn't have inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("model weights aren't tied in TimmBackbone.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone")
       def  A_   (     self):

             """simple docstring"""





             pass



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

             """simple docstring"""





             pass



       @unittest.skip("TimmBackbone doesn't support output_attentions.")
       def  A_   (     self):

             """simple docstring"""





             pass



       @unittest.skip("Safetensors is not supported by timm.")
       def  A_   (     self):

             """simple docstring"""





             pass



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

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	int				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Dict				      =		True
             UpperCAmelCase_  :	str				      =		self.has_attentions

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

             UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase)
             UpperCAmelCase_  :	Optional[int]				      =		model(**lowercase)
             UpperCAmelCase_  :	Optional[Any]				      =		outputs[0][-1]

             # Encoder-/Decoder-only models
             UpperCAmelCase_  :	List[Any]				      =		outputs.hidden_states[0]
             hidden_states.retain_grad()

             if self.has_attentions:
                   UpperCAmelCase_  :	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):

             """simple docstring"""





             UpperCAmelCase_      ,      UpperCAmelCase_  :	List[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	Any				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		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_  :	Tuple				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	List[Any]				      =		None
                   UpperCAmelCase_  :	Optional[Any]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Tuple				      =		model(**lowercase)

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

                   # Check backbone can be initialized with fresh weights
                   UpperCAmelCase_  :	str				      =		copy.deepcopy(lowercase)
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.eval()
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase)
