


'''simple docstring'''


import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
     import torch

     from transformers import (
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
         MraModel,
     )
     from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def __init__(							self :       Optional[int]	,_a :       Dict	,_a :       Any=2	,_a :       Dict=8	,_a :       str=True	,_a :       List[Any]=True	,_a :       int=True	,_a :       Union[str, Any]=True	,_a :       List[str]=99	,_a :       Any=16	,_a :       Optional[Any]=5	,_a :       Any=2	,_a :       List[Any]=36	,_a :       Any="gelu"	,_a :       str=0.0	,_a :       List[str]=0.0	,_a :       Union[str, Any]=512	,_a :       Dict=16	,_a :       str=2	,_a :       Any=0.02	,_a :       Union[str, Any]=3	,_a :       List[str]=4	,_a :       Optional[int]=None	,):

        '''simple docstring'''




        A_						:  Optional[Any]          =				parent
        A_						:  Optional[Any]          =				batch_size
        A_						:  Optional[Any]          =				seq_length
        A_						:  List[str]          =				is_training
        A_						:  Optional[int]          =				use_input_mask
        A_						:  List[str]          =				use_token_type_ids
        A_						:  Optional[Any]          =				use_labels
        A_						:  int          =				vocab_size
        A_						:  List[str]          =				hidden_size
        A_						:  Optional[int]          =				num_hidden_layers
        A_						:  Dict          =				num_attention_heads
        A_						:  Optional[int]          =				intermediate_size
        A_						:  int          =				hidden_act
        A_						:  str          =				hidden_dropout_prob
        A_						:  Tuple          =				attention_probs_dropout_prob
        A_						:  Optional[int]          =				max_position_embeddings
        A_						:  Optional[Any]          =				type_vocab_size
        A_						:  Union[str, Any]          =				type_sequence_label_size
        A_						:  Optional[Any]          =				initializer_range
        A_						:  Optional[int]          =				num_labels
        A_						:  Dict          =				num_choices
        A_						:  str          =				scope






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Tuple          =				ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size )

        A_						:  int          =				None
        if self.use_input_mask:
              A_						:  Dict          =				random_attention_mask([self.batch_size, self.seq_length] )

        A_						:  Optional[Any]          =				None
        if self.use_token_type_ids:
              A_						:  Dict          =				ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size )

        A_						:  Dict          =				None
        A_						:  List[str]          =				None
        A_						:  Dict          =				None
        if self.use_labels:
              A_						:  Any          =				ids_tensor([self.batch_size]	,self.type_sequence_label_size )
              A_						:  int          =				ids_tensor([self.batch_size, self.seq_length]	,self.num_labels )
              A_						:  List[Any]          =				ids_tensor([self.batch_size]	,self.num_choices )

        A_						:  Optional[Any]          =				self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return MraConfig(
            vocab_size=self.vocab_size	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,max_position_embeddings=self.max_position_embeddings	,type_vocab_size=self.type_vocab_size	,is_decoder=_a	,initializer_range=self.initializer_range	,)






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.get_config()
        A_						:  Dict          =				300
        return config






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        (
            (
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					
        )						:  Union[str, Any]          =				self.prepare_config_and_inputs()

        A_						:  Optional[int]          =				True
        A_						:  Tuple          =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
        A_						:  Tuple          =				ids_tensor([self.batch_size, self.seq_length]	,vocab_size=2 )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )






  def        _a							(							self :       Tuple	,_a :       str	,_a :       Optional[int]	,_a :       List[Any]	,_a :       Optional[int]	,_a :       str	,_a :       str	,_a :       List[Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				MraModel(config=_a )
        model.to(_a )
        model.eval()
        A_						:  Any          =				model(_a	,attention_mask=_a	,token_type_ids=_a )
        A_						:  List[str]          =				model(_a	,token_type_ids=_a )
        A_						:  str          =				model(_a )
        self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size) )






  def        _a							(							self :       int	,_a :       List[Any]	,_a :       Optional[Any]	,_a :       List[str]	,_a :       Optional[Any]	,_a :       List[str]	,_a :       int	,_a :       Union[str, Any]	,_a :       str	,_a :       List[Any]	,):

        '''simple docstring'''




        A_						:  Optional[int]          =				True
        A_						:  Any          =				MraModel(_a )
        model.to(_a )
        model.eval()
        A_						:  List[Any]          =				model(
            _a	,attention_mask=_a	,token_type_ids=_a	,encoder_hidden_states=_a	,encoder_attention_mask=_a	,)
        A_						:  str          =				model(
            _a	,attention_mask=_a	,token_type_ids=_a	,encoder_hidden_states=_a	,)
        A_						:  Union[str, Any]          =				model(_a	,attention_mask=_a	,token_type_ids=_a )
        self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size) )






  def        _a							(							self :       Optional[Any]	,_a :       int	,_a :       Optional[Any]	,_a :       int	,_a :       Tuple	,_a :       Tuple	,_a :       Dict	,_a :       str ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				MraForMaskedLM(config=_a )
        model.to(_a )
        model.eval()
        A_						:  Optional[int]          =				model(_a	,attention_mask=_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size) )






  def        _a							(							self :       Optional[Any]	,_a :       str	,_a :       Optional[Any]	,_a :       Tuple	,_a :       Union[str, Any]	,_a :       int	,_a :       List[Any]	,_a :       str ):

        '''simple docstring'''




        A_						:  str          =				MraForQuestionAnswering(config=_a )
        model.to(_a )
        model.eval()
        A_						:  Any          =				model(
            _a	,attention_mask=_a	,token_type_ids=_a	,start_positions=_a	,end_positions=_a	,)
        self.parent.assertEqual(result.start_logits.shape	,(self.batch_size, self.seq_length) )
        self.parent.assertEqual(result.end_logits.shape	,(self.batch_size, self.seq_length) )






  def        _a							(							self :       str	,_a :       int	,_a :       str	,_a :       Optional[int]	,_a :       Any	,_a :       Optional[Any]	,_a :       str	,_a :       int ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.num_labels
        A_						:  Dict          =				MraForSequenceClassification(_a )
        model.to(_a )
        model.eval()
        A_						:  List[str]          =				model(_a	,attention_mask=_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels) )






  def        _a							(							self :       Any	,_a :       Optional[Any]	,_a :       Optional[int]	,_a :       str	,_a :       Optional[int]	,_a :       Optional[Any]	,_a :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Any          =				self.num_labels
        A_						:  Union[str, Any]          =				MraForTokenClassification(config=_a )
        model.to(_a )
        model.eval()
        A_						:  Any          =				model(_a	,attention_mask=_a	,token_type_ids=_a	,labels=_a )
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels) )






  def        _a							(							self :       Union[str, Any]	,_a :       str	,_a :       List[str]	,_a :       Tuple	,_a :       Optional[Any]	,_a :       str	,_a :       Tuple	,_a :       Tuple ):

        '''simple docstring'''




        A_						:  Any          =				self.num_choices
        A_						:  int          =				MraForMultipleChoice(config=_a )
        model.to(_a )
        model.eval()
        A_						:  Optional[int]          =				input_ids.unsqueeze(1 ).expand(-1	,self.num_choices	,-1 ).contiguous()
        A_						:  Optional[int]          =				token_type_ids.unsqueeze(1 ).expand(-1	,self.num_choices	,-1 ).contiguous()
        A_						:  Any          =				input_mask.unsqueeze(1 ).expand(-1	,self.num_choices	,-1 ).contiguous()
        A_						:  Optional[int]          =				model(
            _a	,attention_mask=_a	,token_type_ids=_a	,labels=_a	,)
        self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_choices) )






  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Any          =				self.prepare_config_and_inputs()
        (
            (
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					(
            A_
        )   ,					
        )						:  Optional[Any]          =				config_and_inputs
        A_						:  Dict          =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict







@require_torch
class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE  , unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							(
      (
          MraModel,
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  a_  					=							False
  a_  					=							False
  a_  					=							False
  a_  					=							False

  a_  					=							()






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        A_						:  List[str]          =				MraModelTester(self )
        A_						:  Optional[int]          =				ConfigTester(self	,config_class=_a	,hidden_size=37 )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        self.config_tester.run_common_tests()






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  List[Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a )






  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Any          =				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              A_						:  List[Any]          =				type
              self.model_tester.create_and_check_model(*_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  str          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_a )






  def        _a							(							self :       List[str] ):

        '''simple docstring'''




        A_						:  int          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_a )






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_a )






  @slow
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              A_						:  Dict          =				MraModel.from_pretrained(_a )
              self.assertIsNotNone(_a )






  @unittest.skip(reason="""MRA does not output attentions""" )
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        return







@require_torch
class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  @slow
  def        _a							(							self :       str ):

        '''simple docstring'''




        A_						:  Any          =				MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
        A_						:  Dict          =				torch.arange(256 ).unsqueeze(0 )

        with torch.no_grad():
              A_						:  Any          =				model(_a )[0]

        A_						:  str          =				torch.Size((1, 256, 768) )
        self.assertEqual(output.shape	,_a )

        A_						:  List[Any]          =				torch.tensor(
            [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )

        self.assertTrue(torch.allclose(output[:, :3, :3]	,_a	,atol=1e-4 ) )






  @slow
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Optional[int]          =				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
        A_						:  Union[str, Any]          =				torch.arange(256 ).unsqueeze(0 )

        with torch.no_grad():
              A_						:  Tuple          =				model(_a )[0]

        A_						:  str          =				50265

        A_						:  Dict          =				torch.Size((1, 256, vocab_size) )
        self.assertEqual(output.shape	,_a )

        A_						:  Tuple          =				torch.tensor(
            [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )

        self.assertTrue(torch.allclose(output[:, :3, :3]	,_a	,atol=1e-4 ) )






  @slow
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        A_						:  str          =				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
        A_						:  Optional[int]          =				torch.arange(4096 ).unsqueeze(0 )

        with torch.no_grad():
              A_						:  List[Any]          =				model(_a )[0]

        A_						:  List[str]          =				50265

        A_						:  Union[str, Any]          =				torch.Size((1, 4096, vocab_size) )
        self.assertEqual(output.shape	,_a )

        A_						:  Optional[int]          =				torch.tensor(
            [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )

        self.assertTrue(torch.allclose(output[:, :3, :3]	,_a	,atol=1e-4 ) )




'''simple docstring'''


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  :      Any							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      int							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      str=True							,						lowerCamelCase  :      Optional[Any]="pt"):
      A_						:  Optional[int]          =				{"""add_prefix_space""": True} if isinstance(lowerCamelCase							,						lowerCamelCase) and not line.startswith(""" """) else {}
      A_						:  Optional[int]          =				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  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]=None							,						):
      A_						:  Dict          =				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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







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

        '''simple docstring'''




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






  def __len__(							self :       Tuple ):

        '''simple docstring'''




        return len(self.src_lens )






  def __getitem__(							self :       List[str]	,_a :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				index + 1  # linecache starts at 1
        A_						:  Union[str, Any]          =				self.prefix + linecache.getline(str(self.src_file )	,_a ).rstrip("""\n""" )
        A_						:  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
        A_						:  List[str]          =				(
            self.tokenizer.question_encoder if isinstance(self.tokenizer	,_a ) else self.tokenizer
        )
        A_						:  Any          =				self.tokenizer.generator if isinstance(self.tokenizer	,_a ) else self.tokenizer

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

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






  @staticmethod
  def        _a							(							_a :       int ):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int]	,_a :       Dict ):

        '''simple docstring'''




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







__magic_name__			=      getLogger(__name__)




def      lowerCamelCase					(      lowerCamelCase  :      List[List]):
      return list(itertools.chain.from_iterable(lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  Union[str, Any]          =				get_git_info()
      save_json(lowerCamelCase							,						os.path.join(lowerCamelCase							,						"""git_log.json"""))




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[str]=4							,						**lowerCamelCase  :      List[str]):
      with open(lowerCamelCase							,						"""w""") as f:
            json.dump(lowerCamelCase							,						lowerCamelCase							,						indent=lowerCamelCase							,						**lowerCamelCase)




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




def      lowerCamelCase					(      ):
      A_						:  List[str]          =				git.Repo(search_parent_directories=lowerCamelCase)
      A_						:  Union[str, 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  :      Callable							,						lowerCamelCase  :      Iterable):
      return list(map(lowerCamelCase							,						lowerCamelCase))




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Union[str, Any]):
      with open(lowerCamelCase							,						"""wb""") as f:
            return pickle.dump(lowerCamelCase							,						lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      List[str]):

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

      def white_space_fix(lowerCamelCase  :      List[Any]):
            return " ".join(text.split())

      def remove_punc(lowerCamelCase  :      Union[str, Any]):
            A_						:  Optional[int]          =				set(string.punctuation)
            return "".join(ch for ch in text if ch not in exclude)

      def lower(lowerCamelCase  :      List[str]):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase))))




def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      int):
      A_						:  Tuple          =				normalize_answer(lowerCamelCase).split()
      A_						:  Dict          =				normalize_answer(lowerCamelCase).split()
      A_						:  int          =				Counter(lowerCamelCase) & Counter(lowerCamelCase)
      A_						:  Any          =				sum(common.values())
      if num_same == 0:
            return 0
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				1.0 * num_same / len(lowerCamelCase)
      A_						:  Any          =				(2 * precision * recall) / (precision + recall)
      return fa




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




def      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      List[str]):
      assert len(lowerCamelCase) == len(lowerCamelCase)
      A_						:  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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      return model_prefix.startswith("""rag""")






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