


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class      UpperCAmelCase_ ( nn.Linear   ):

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A						,    __A = True						,    __A = "constant"						,    __A = 0.0						,    __A = "topK"						,    ):






     """simple docstring"""





     super(__A						,    self			).__init__(in_features=__A						,    out_features=__A						,    bias=__A			)
     assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
     lowerCamelCase			: List[str]   =    pruning_method

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






   def _snake_case   (		self			):






     """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 _snake_case   (		self						,    __A						,    __A			):






     """simple docstring"""





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







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		True






   def __call__(		self						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A = True						,    __A = False						,    __A = None						,    __A = None						,    __A = 0						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = False						,    __A = False						,    __A = True						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     if self.parse_html:
       if html_strings is None:
         raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"			)

       if nodes is not None or xpaths is not None or node_labels is not None:
         raise ValueError(
             "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: Any   =    features["xpaths"]
     else:
       if html_strings is not None:
         raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."			)
       if nodes is None or xpaths is None:
         raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"			)

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
       if isinstance(__A						,    __A			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     lowerCamelCase			: Tuple   =    self.tokenizer(
         text=questions if questions is not None else nodes						,    text_pair=nodes if questions is not None else None						,    xpaths=__A						,    node_labels=__A						,    add_special_tokens=__A						,    padding=__A						,    truncation=__A						,    max_length=__A						,    stride=__A						,    pad_to_multiple_of=__A						,    return_token_type_ids=__A						,    return_attention_mask=__A						,    return_overflowing_tokens=__A						,    return_special_tokens_mask=__A						,    return_offsets_mapping=__A						,    return_length=__A						,    verbose=__A						,    return_tensors=__A						,    **__A						,    )

     return encoded_inputs






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



