

import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 						_A     (				snake_case						,    snake_case						,    snake_case				):





  '''simple docstring'''



  @register_to_config
  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = False   ,):

      '''simple docstring'''
      super().__init__()

      snake_case							:		Union[str, Any]        =		nn.Embedding(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[str]        =		nn.Embedding(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		False

      snake_case							:		List[str]        =		nn.Dropout(p=SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		TaConfig(
          vocab_size=SCREAMING_SNAKE_CASE_   ,d_model=SCREAMING_SNAKE_CASE_   ,num_heads=SCREAMING_SNAKE_CASE_   ,d_kv=SCREAMING_SNAKE_CASE_   ,d_ff=SCREAMING_SNAKE_CASE_   ,dropout_rate=SCREAMING_SNAKE_CASE_   ,feed_forward_proj=SCREAMING_SNAKE_CASE_   ,is_decoder=SCREAMING_SNAKE_CASE_   ,is_encoder_decoder=SCREAMING_SNAKE_CASE_   ,)

      snake_case							:		str        =		nn.ModuleList()
      for lyr_num in range(SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Any        =		TaBlock(SCREAMING_SNAKE_CASE_    )
          self.encoders.append(SCREAMING_SNAKE_CASE_    )

      snake_case							:		int        =		TaLayerNorm(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Dict        =		nn.Dropout(p=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''simple docstring'''
      snake_case							:		Dict        =		self.token_embedder(SCREAMING_SNAKE_CASE_    )

      snake_case							:		List[str]        =		encoder_input_tokens.shape[1]
      snake_case							:		str        =		torch.arange(SCREAMING_SNAKE_CASE_   ,device=encoder_input_tokens.device    )
      x += self.position_encoding(SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[int]        =		self.dropout_pre(SCREAMING_SNAKE_CASE_    )

      # inverted the attention mask
      snake_case							:		List[str]        =		encoder_input_tokens.size()
      snake_case							:		List[str]        =		self.get_extended_attention_mask(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      for lyr in self.encoders:
          snake_case							:		Tuple        =		lyr(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )[0]
      snake_case							:		Optional[int]        =		self.layer_norm(SCREAMING_SNAKE_CASE_    )

      return self.dropout_post(SCREAMING_SNAKE_CASE_    ), encoder_inputs_mask







'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





