




'''simple docstring'''






import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



class    A						(					nn.Linear						):




				'''simple docstring'''





				def __init__(self					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase = True					,    _UpperCAmelCase = "constant"					,    _UpperCAmelCase = 0.0					,    _UpperCAmelCase = "topK"					,    )      ->			Union[str, Any]:
							super(lowerCAmelCase__					,    self     ).__init__(in_features=lowerCAmelCase__					,    out_features=lowerCAmelCase__					,    bias=lowerCAmelCase__     )
							assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
							__UpperCamelCase :     Tuple										=  pruning_method

							if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
										__UpperCamelCase :     Tuple										=  mask_scale
										__UpperCamelCase :     Any										=  mask_init
										__UpperCamelCase :     str										=  nn.Parameter(torch.empty(self.weight.size()     )     )
										self.init_mask()
				def 			a_			(self     )      ->			Optional[int]:







							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 			a_			(self					,    _UpperCAmelCase					,    _UpperCAmelCase     )      ->			Union[str, Any]:







							if self.pruning_method == "topK":
										__UpperCamelCase :     Dict										=  TopKBinarizer.apply(self.mask_scores					,    lowerCAmelCase__     )
							elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
										__UpperCamelCase :     List[str]										=  "sigmoied" in self.pruning_method
										__UpperCamelCase :     List[str]										=  ThresholdBinarizer.apply(self.mask_scores					,    lowerCAmelCase__					,    lowerCAmelCase__     )
							elif self.pruning_method == "magnitude":
										__UpperCamelCase :     str										=  MagnitudeBinarizer.apply(self.weight					,    lowerCAmelCase__     )
							elif self.pruning_method == "l0":
										__UpperCamelCase :     List[str]										=  -0.1, 1.1, 2 / 3
										if self.training:
													__UpperCamelCase :     str										=  torch.zeros_like(self.mask_scores     ).uniform_().clamp(0.0_001					,    0.9_999     )
													__UpperCamelCase :     str										=  torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b     )
										else:
													__UpperCamelCase :     Dict										=  torch.sigmoid(self.mask_scores     )
										__UpperCamelCase :     str										=  s * (r - l) + l
										__UpperCamelCase :     Union[str, Any]										=  s_bar.clamp(min=0.0					,    max=1.0     )
							# Mask weights with computed mask
							__UpperCamelCase :     Optional[Any]										=  mask * self.weight
							# Compute output (linear layer) with masked weights
							return nn.functional.linear(lowerCAmelCase__					,    lowerCAmelCase__					,    self.bias     )









"""simple docstring"""
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,
)


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

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

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

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

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_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

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



