





import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=True							,   _lowerCAmelCase="pt"     )						-> int:
 UpperCamelCase	:					Any       	=   {"add_prefix_space": True} if isinstance(_lowerCAmelCase							,   _lowerCAmelCase     ) and not line.startswith(" "     ) else {}
 UpperCamelCase	:					List[str]       	=   padding_side
 return tokenizer(
     [line]							,   max_length=_lowerCAmelCase							,   padding="max_length" if pad_to_max_length else None							,   truncation=_lowerCAmelCase							,   return_tensors=_lowerCAmelCase							,   add_special_tokens=_lowerCAmelCase							,   **_lowerCAmelCase							,   )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=None							,   )						-> str:
 UpperCamelCase	:					List[Any]       	=   input_ids.ne(_lowerCAmelCase     ).any(dim=0     )
 if attention_mask is None:
  return input_ids[:, keep_column_mask]
 else:
  return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_="train"       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=""       ,							):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase	:					str       	=   Path(A_   ).joinpath(type_path + ".source"   )
       UpperCamelCase	:					Tuple       	=   Path(A_   ).joinpath(type_path + ".target"   )
       UpperCamelCase	:					Tuple       	=   self.get_char_lens(self.src_file   )
       UpperCamelCase	:					Optional[Any]       	=   max_source_length
       UpperCamelCase	:					List[str]       	=   max_target_length
       assert min(self.src_lens   ) > 0, F"""found empty line in {self.src_file}"""
       UpperCamelCase	:					Any       	=   tokenizer
       UpperCamelCase	:					Any       	=   prefix
       if n_obs is not None:
        UpperCamelCase	:					Dict       	=   self.src_lens[:n_obs]
       UpperCamelCase	:					List[Any]       	=   src_lang
       UpperCamelCase	:					Optional[int]       	=   tgt_lang





      def __len__( self   ):
       '''simple docstring'''




       return len(self.src_lens   )





      def __getitem__( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   index + 1  # linecache starts at 1
       UpperCamelCase	:					Dict       	=   self.prefix + linecache.getline(str(self.src_file   )       ,							A_   ).rstrip("\n"   )
       UpperCamelCase	:					Dict       	=   linecache.getline(str(self.tgt_file   )       ,							A_   ).rstrip("\n"   )
       assert source_line, F"""empty source line for index {index}"""
       assert tgt_line, F"""empty tgt line for index {index}"""

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer       ,							A_   ):
        source_line += self.tokenizer.eos_token
        tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       UpperCamelCase	:					str       	=   (
           self.tokenizer.question_encoder if isinstance(self.tokenizer       ,							A_   ) else self.tokenizer
       )
       UpperCamelCase	:					Any       	=   self.tokenizer.generator if isinstance(self.tokenizer       ,							A_   ) else self.tokenizer

       UpperCamelCase	:					Dict       	=   encode_line(A_       ,							A_       ,							self.max_source_length       ,							"right"   )
       UpperCamelCase	:					Optional[int]       	=   encode_line(A_       ,							A_       ,							self.max_target_length       ,							"right"   )

       UpperCamelCase	:					List[Any]       	=   source_inputs["input_ids"].squeeze()
       UpperCamelCase	:					Dict       	=   target_inputs["input_ids"].squeeze()
       UpperCamelCase	:					str       	=   source_inputs["attention_mask"].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }





      @staticmethod
      def        __UpperCamelCase( A_   ):
       '''simple docstring'''




       return [len(A_   ) for x in Path(A_   ).open().readlines()]





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   torch.stack([x["input_ids"] for x in batch]   )
       UpperCamelCase	:					Tuple       	=   torch.stack([x["attention_mask"] for x in batch]   )
       UpperCamelCase	:					List[str]       	=   torch.stack([x["decoder_input_ids"] for x in batch]   )
       UpperCamelCase	:					List[Any]       	=   (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer       ,							A_   )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase	:					Optional[int]       	=   (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer       ,							A_   )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase	:					str       	=   trim_batch(A_       ,							A_   )
       UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   trim_batch(A_       ,							A_       ,							attention_mask=A_   )
       UpperCamelCase	:					Optional[int]       	=   {
           "input_ids": source_ids,
           "attention_mask": source_mask,
           "decoder_input_ids": y,
       }
       return batch


__lowerCamelCase       :     List[Any]   =     getLogger(__name__)



def 		A_				(     _lowerCAmelCase     )						-> List[Any]:
 return list(itertools.chain.from_iterable(_lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase     )						-> None:
 UpperCamelCase	:					Optional[int]       	=   get_git_info()
 save_json(_lowerCAmelCase							,   os.path.join(_lowerCAmelCase							,   "git_log.json"     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase=4							,   **_lowerCAmelCase     )						-> List[str]:
 with open(_lowerCAmelCase							,   "w"     ) as f:
  json.dump(_lowerCAmelCase							,   _lowerCAmelCase							,   indent=_lowerCAmelCase							,   **_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> Any:
 with open(_lowerCAmelCase     ) as f:
  return json.load(_lowerCAmelCase     )



def 		A_				(     )						-> Any:
 UpperCamelCase	:					List[Any]       	=   git.Repo(search_parent_directories=_lowerCAmelCase     )
 UpperCamelCase	:					int       	=   {
     "repo_id": str(_lowerCAmelCase     ),
     "repo_sha": str(repo.head.object.hexsha     ),
     "repo_branch": str(repo.active_branch     ),
     "hostname": str(socket.gethostname()     ),
 }
 return repo_infos



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List:
 return list(map(_lowerCAmelCase							,   _lowerCAmelCase     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 with open(_lowerCAmelCase							,   "wb"     ) as f:
  return pickle.dump(_lowerCAmelCase							,   _lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> Union[str, Any]:

 def remove_articles(_lowerCAmelCase     ):
  return re.sub(r"\b(a|an|the)\b"							,   " "							,   _lowerCAmelCase     )

 def white_space_fix(_lowerCAmelCase     ):
  return " ".join(text.split()     )

 def remove_punc(_lowerCAmelCase     ):
  UpperCamelCase	:					Any       	=   set(string.punctuation     )
  return "".join(ch for ch in text if ch not in exclude     )

 def lower(_lowerCAmelCase     ):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase     )     )     )     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> List[Any]:
 UpperCamelCase	:					Dict       	=   normalize_answer(_lowerCAmelCase     ).split()
 UpperCamelCase	:					Union[str, Any]       	=   normalize_answer(_lowerCAmelCase     ).split()
 UpperCamelCase	:					str       	=   Counter(_lowerCAmelCase     ) & Counter(_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   sum(common.values()     )
 if num_same == 0:
  return 0
 UpperCamelCase	:					Tuple       	=   1.0 * num_same / len(_lowerCAmelCase     )
 UpperCamelCase	:					Optional[int]       	=   1.0 * num_same / len(_lowerCAmelCase     )
 UpperCamelCase	:					Any       	=   (2 * precision * recall) / (precision + recall)
 return fa



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 return normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> Dict:
 assert len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )
 UpperCamelCase	:					Union[str, Any]       	=   0
 for hypo, pred in zip(_lowerCAmelCase							,   _lowerCAmelCase     ):
  em += exact_match_score(_lowerCAmelCase							,   _lowerCAmelCase     )
 if len(_lowerCAmelCase     ) > 0:
  em /= len(_lowerCAmelCase     )
 return {"em": em}



def 		A_				(     _lowerCAmelCase     )						-> str:
 return model_prefix.startswith("rag"     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Any       	=   {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCamelCase	:					Tuple       	=   "dropout_rate"
 for p in extra_params:
  if getattr(_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     ):
   if not hasattr(_lowerCAmelCase							,   _lowerCAmelCase     ) and not hasattr(_lowerCAmelCase							,   equivalent_param[p]     ):
    logger.info("config doesn't have a `{}` attribute".format(_lowerCAmelCase     )     )
    delattr(_lowerCAmelCase							,   _lowerCAmelCase     )
    continue
   UpperCamelCase	:					Optional[Any]       	=   p if hasattr(_lowerCAmelCase							,   _lowerCAmelCase     ) else equivalent_param[p]
   setattr(_lowerCAmelCase							,   _lowerCAmelCase							,   getattr(_lowerCAmelCase							,   _lowerCAmelCase     )     )
   delattr(_lowerCAmelCase							,   _lowerCAmelCase     )
 return hparams, config





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy



@add_start_docstrings(
    'The RoBERTa Model transformer with early exiting (DeeRoBERTa). '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)

														_lowerCAmelCase     :							Optional[Any]    =    RobertaEmbeddings(__a)
														self.init_weights()



@add_start_docstrings(
    'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. '   ,					a   ,					)
class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		RobertaConfig
							lowerCamelCase__   			=		'roberta'







							def __init__(   self,			__a):

														'''simple docstring'''

														super().__init__(__a)
														_lowerCAmelCase     :							Optional[int]    =    config.num_labels
														_lowerCAmelCase     :							Optional[int]    =    config.num_hidden_layers

														_lowerCAmelCase     :							Optional[int]    =    DeeRobertaModel(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    nn.Dropout(config.hidden_dropout_prob)
														_lowerCAmelCase     :							List[str]    =    nn.Linear(config.hidden_size,			self.config.num_labels)







							@add_start_docstrings_to_model_forward(__a)
							def    snake_case__      (   self,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=None,			__a=-1,			__a=False,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.num_layers
														try:
																					_lowerCAmelCase     :							List[Any]    =    self.roberta(
																					    __a,			attention_mask=__a,			token_type_ids=__a,			position_ids=__a,			head_mask=__a,			inputs_embeds=__a,			)

																					_lowerCAmelCase     :							List[Any]    =    outputs[1]

																					_lowerCAmelCase     :							Dict    =    self.dropout(__a)
																					_lowerCAmelCase     :							Dict    =    self.classifier(__a)
																					_lowerCAmelCase     :							Optional[Any]    =    (logits,) + outputs[2:]  # add hidden states and attention if they are here
														except HighwayException as e:
																					_lowerCAmelCase     :							Tuple    =    e.message
																					_lowerCAmelCase     :							Union[str, Any]    =    e.exit_layer
																					_lowerCAmelCase     :							List[Any]    =    outputs[0]

														if not self.training:
																					_lowerCAmelCase     :							int    =    entropy(__a)
																					_lowerCAmelCase     :							List[Any]    =    []
																					_lowerCAmelCase     :							str    =    []
														if labels is not None:
																					if self.num_labels == 1:
																												#  We are doing regression
																												_lowerCAmelCase     :							Optional[Any]    =    MSELoss()
																												_lowerCAmelCase     :							int    =    loss_fct(logits.view(-1),			labels.view(-1))
																					else:
																												_lowerCAmelCase     :							Optional[Any]    =    CrossEntropyLoss()
																												_lowerCAmelCase     :							Optional[Any]    =    loss_fct(logits.view(-1,			self.num_labels),			labels.view(-1))

																					# work with highway exits
																					_lowerCAmelCase     :							Optional[int]    =    []
																					for highway_exit in outputs[-1]:
																												_lowerCAmelCase     :							Any    =    highway_exit[0]
																												if not self.training:
																																			highway_logits_all.append(__a)
																																			highway_entropy.append(highway_exit[2])
																												if self.num_labels == 1:
																																			#  We are doing regression
																																			_lowerCAmelCase     :							List[str]    =    MSELoss()
																																			_lowerCAmelCase     :							List[Any]    =    loss_fct(highway_logits.view(-1),			labels.view(-1))
																												else:
																																			_lowerCAmelCase     :							Dict    =    CrossEntropyLoss()
																																			_lowerCAmelCase     :							Optional[Any]    =    loss_fct(highway_logits.view(-1,			self.num_labels),			labels.view(-1))
																												highway_losses.append(__a)

																					if train_highway:
																												_lowerCAmelCase     :							int    =    (sum(highway_losses[:-1]),) + outputs
																												# exclude the final highway, of course
																					else:
																												_lowerCAmelCase     :							Any    =    (loss,) + outputs
														if not self.training:
																					_lowerCAmelCase     :							Optional[Any]    =    outputs + ((original_entropy, highway_entropy), exit_layer)
																					if output_layer >= 0:
																												_lowerCAmelCase     :							Optional[Any]    =    (
																												    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																												)  # use the highway of the last layer

														return outputs  # (loss), logits, (hidden_states), (attentions), entropy
