






"""simple docstring"""



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 							__lowercase(							nn.Linear	):






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE = "constant"     ,    _SCREAMING_SNAKE_CASE = 0.0     ,    _SCREAMING_SNAKE_CASE = "topK"     ,    )      ->	Tuple:




											"""simple docstring"""





											super(_SCREAMING_SNAKE_CASE     ,    self).__init__(in_features=_SCREAMING_SNAKE_CASE     ,    out_features=_SCREAMING_SNAKE_CASE     ,    bias=_SCREAMING_SNAKE_CASE)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											lowerCamelCase__					=pruning_method

											if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
																lowerCamelCase__					=mask_scale
																lowerCamelCase__					=mask_init
																lowerCamelCase__					=nn.Parameter(torch.empty(self.weight.size()))
																self.init_mask()






						def 		lowerCAmelCase (       self)      ->	int:




											"""simple docstring"""





											if self.mask_init == "constant":
																init.constant_(self.mask_scores     ,    val=self.mask_scale)
											elif self.mask_init == "uniform":
																init.uniform_(self.mask_scores     ,    a=-self.mask_scale     ,    b=self.mask_scale)
											elif self.mask_init == "kaiming":
																init.kaiming_uniform_(self.mask_scores     ,    a=math.sqrt(5))






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Tuple:




											"""simple docstring"""





											if self.pruning_method == "topK":
																lowerCamelCase__					=TopKBinarizer.apply(self.mask_scores     ,    _SCREAMING_SNAKE_CASE)
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																lowerCamelCase__					='sigmoied' in self.pruning_method
																lowerCamelCase__					=ThresholdBinarizer.apply(self.mask_scores     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)
											elif self.pruning_method == "magnitude":
																lowerCamelCase__					=MagnitudeBinarizer.apply(self.weight     ,    _SCREAMING_SNAKE_CASE)
											elif self.pruning_method == "l0":
																lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=-0.1, 1.1, 2 / 3
																if self.training:
																					lowerCamelCase__					=torch.zeros_like(self.mask_scores).uniform_().clamp(0.0001     ,    0.9999)
																					lowerCamelCase__					=torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
																else:
																					lowerCamelCase__					=torch.sigmoid(self.mask_scores)
																lowerCamelCase__					=s * (r - l) + l
																lowerCamelCase__					=s_bar.clamp(min=0.0     ,    max=1.0)
											# Mask weights with computed mask
											lowerCamelCase__					=mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    self.bias)







"""simple docstring"""



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax




@require_flax
class 							__lowercase:






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.prepare_init_args_and_inputs_for_common()

											lowerCamelCase__					=16
											lowerCamelCase__					=(16, 32)

											lowerCamelCase__					=self.model_class(**_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=model.init(inputs_dict['prng_key']     ,    inputs_dict['sample'])
											jax.lax.stop_gradient(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=model.apply(_SCREAMING_SNAKE_CASE     ,    inputs_dict['sample'])

											if isinstance(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=output.sample

											self.assertIsNotNone(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=inputs_dict['sample'].shape
											self.assertEqual(output.shape     ,    _SCREAMING_SNAKE_CASE     ,    'Input and output shapes do not match')






						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					='kwargs' in inspect.signature(self.model_class.__init__).parameters
											lowerCamelCase__					=len(self.model_class._deprecated_kwargs) > 0

											if has_kwarg_in_model_class and not has_deprecated_kwarg:
																raise ValueError(
																    F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
																    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
																    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
																    ' [<deprecated_argument>]`')

											if not has_kwarg_in_model_class and has_deprecated_kwarg:
																raise ValueError(
																    F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
																    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
																    F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
																    ' from `_deprecated_kwargs = [<deprecated_argument>]`')