




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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True						,  lowerCamelCase="pt" ):
				__magic_name__						:						int										={"""add_prefix_space""": True} if isinstance(lowerCamelCase						,  lowerCamelCase ) and not line.startswith(""" """ ) else {}
				__magic_name__						:						Dict										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=None						,  ):
				__magic_name__						:						Union[str, 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   (      UpperCamelCase__      ):

						def __init__(		self :Tuple     ,			__snake_case :Tuple     ,			__snake_case :Optional[Any]     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Dict="train"     ,			__snake_case :str=None     ,			__snake_case :Union[str, Any]=None     ,			__snake_case :Tuple=None     ,			__snake_case :List[str]=""     ,			):
										'''simple docstring'''

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

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

										return len(self.src_lens			)

						def __getitem__(		self :int     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						str										=index + 1  # linecache starts at 1
										__magic_name__						:						List[Any]										=self.prefix + linecache.getline(str(self.src_file			)     ,			__snake_case			).rstrip("""\n"""			)
										__magic_name__						:						Tuple										=linecache.getline(str(self.tgt_file			)     ,			__snake_case			).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     ,			__snake_case			):
														source_line += self.tokenizer.eos_token
														tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										__magic_name__						:						Dict										=(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer     ,			__snake_case			) else self.tokenizer
										)
										__magic_name__						:						Optional[Any]										=self.tokenizer.generator if isinstance(self.tokenizer     ,			__snake_case			) else self.tokenizer

										__magic_name__						:						Union[str, Any]										=encode_line(__snake_case     ,			__snake_case     ,			self.max_source_length     ,			"""right"""			)
										__magic_name__						:						str										=encode_line(__snake_case     ,			__snake_case     ,			self.max_target_length     ,			"""right"""			)

										__magic_name__						:						Tuple										=source_inputs["""input_ids"""].squeeze()
										__magic_name__						:						Any										=target_inputs["""input_ids"""].squeeze()
										__magic_name__						:						Any										=source_inputs["""attention_mask"""].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}

						@staticmethod
						def      A__    (		__snake_case :List[str]			):
										'''simple docstring'''

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


						def      A__    (		self :Dict     ,			__snake_case :List[str]			):
										'''simple docstring'''

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


UpperCAmelCase_		:					int          =  getLogger(__name__)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return list(itertools.chain.from_iterable(lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=get_git_info()
				save_json(lowerCamelCase						,  os.path.join(lowerCamelCase						,  """git_log.json""" ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=4						,  **lowerCamelCase ):
				with open(lowerCamelCase						,  """w""" ) as f:
								json.dump(lowerCamelCase						,  lowerCamelCase						,  indent=lowerCamelCase						,  **lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				with open(lowerCamelCase ) as f:
								return json.load(lowerCamelCase )




def 		lowerCAmelCase_ (						):
				__magic_name__						:						str										=git.Repo(search_parent_directories=lowerCamelCase )
				__magic_name__						:						Optional[Any]										={
				    """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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return list(map(lowerCamelCase						,  lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				with open(lowerCamelCase						,  """wb""" ) as f:
								return pickle.dump(lowerCamelCase						,  lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				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 ):
								__magic_name__						:						Optional[int]										=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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						str										=normalize_answer(lowerCamelCase ).split()
				__magic_name__						:						Tuple										=normalize_answer(lowerCamelCase ).split()
				__magic_name__						:						Any										=Counter(lowerCamelCase ) & Counter(lowerCamelCase )
				__magic_name__						:						Any										=sum(common.values() )
				if num_same == 0:
								return 0
				__magic_name__						:						List[Any]										=1.0 * num_same / len(lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=1.0 * num_same / len(lowerCamelCase )
				__magic_name__						:						List[str]										=(2 * precision * recall) / (precision + recall)
				return fa




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return normalize_answer(lowerCamelCase ) == normalize_answer(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				assert len(lowerCamelCase ) == len(lowerCamelCase )
				__magic_name__						:						int										=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 		lowerCAmelCase_ (						lowerCamelCase ):
				return model_prefix.startswith("""rag""" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[Any]										={p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				__magic_name__						:						Any										="""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
												__magic_name__						:						str										=p if hasattr(lowerCamelCase						,  lowerCamelCase ) else equivalent_param[p]
												setattr(lowerCamelCase						,  lowerCamelCase						,  getattr(lowerCamelCase						,  lowerCamelCase ) )
												delattr(lowerCamelCase						,  lowerCamelCase )
				return hparams, config










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										if self.task == "multiple-choice":
														__magic_name__						:						str										={0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
														__magic_name__						:						Optional[Any]										={0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										    ]			)




