
import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class 							UpperCAmelCase	(   nn.Linear					):

					def __init__(			self      :					List[Any] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					bool = True ,      lowerCAmelCase      :					str = "constant" ,      lowerCAmelCase      :					float = 0.0 ,      lowerCAmelCase      :					str = "topK" ,      ):
											super(lowerCAmelCase ,      self					).__init__(in_features=lowerCAmelCase ,      out_features=lowerCAmelCase ,      bias=lowerCAmelCase					)
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											lowercase       :    str											=  pruning_method

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

					def        _lowerCAmelCase	(			self      :					str					):
											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      :					Tuple ,      lowerCAmelCase      :					torch.tensor ,      lowerCAmelCase      :					float					):
											# Get the mask
											if self.pruning_method == "topK":
																	lowercase       :    Tuple											=  TopKBinarizer.apply(self.mask_scores ,      lowerCAmelCase					)
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																	lowercase       :    List[str]											=  '''sigmoied''' in self.pruning_method
																	lowercase       :    Any											=  ThresholdBinarizer.apply(self.mask_scores ,      lowerCAmelCase ,      lowerCAmelCase					)
											elif self.pruning_method == "magnitude":
																	lowercase       :    Dict											=  MagnitudeBinarizer.apply(self.weight ,      lowerCAmelCase					)
											elif self.pruning_method == "l0":
																	lowercase				,				lowercase				,				lowercase       :    Optional[Any]											=  -0.1, 1.1, 2 / 3
																	if self.training:
																							lowercase       :    int											=  torch.zeros_like(self.mask_scores					).uniform_().clamp(0.00_01 ,      0.99_99					)
																							lowercase       :    Dict											=  torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b					)
																	else:
																							lowercase       :    Any											=  torch.sigmoid(self.mask_scores					)
																	lowercase       :    Optional[Any]											=  s * (r - l) + l
																	lowercase       :    List[str]											=  s_bar.clamp(min=0.0 ,      max=1.0					)
											# Mask weights with computed mask
											lowercase       :    str											=  mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(lowerCAmelCase ,      lowerCAmelCase ,      self.bias					)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__  							=							{"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case__  							=							[
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				snake_case__  							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


