





import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_snake_case  :    Optional[int]						    =  datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
     import pyspark



@dataclass
class 				_UpperCAmelCase    (    datasets.BuilderConfig    ):

  """simple docstring"""


  a_        = None





def   a_    ( lowerCAmelCase_      :			"pyspark.sql.DataFrame",	lowerCAmelCase_      :			List[int],	):
   import pyspark

   def generate_fn():
      __lowerCAmelCase								=	df.select('*',	pyspark.sql.functions.spark_partition_id().alias('part_id'	)	)
      for partition_id in partition_order:
         __lowerCAmelCase								=	df_with_partition_id.select('*'	).where(F"""part_id = {partition_id}"""	).drop('part_id'	)
         __lowerCAmelCase								=	partition_df.collect()
         __lowerCAmelCase								=	0
         for row in rows:
            yield F"""{partition_id}_{row_id}""", row.asDict()
            row_id += 1

   return generate_fn



class 				_UpperCAmelCase    (    _BaseExamplesIterable    ):

  """simple docstring"""


  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		"pyspark.sql.DataFrame"					,  lowerCAmelCase_ :		str=None					,  )							->						Optional[int]:
     __lowerCAmelCase								=	df
     __lowerCAmelCase								=	partition_order or range(self.df.rdd.getNumPartitions()     )
     __lowerCAmelCase								=	_generate_iterable_examples(self.df					,  self.partition_order     )






  def __iter__(				self :		Union[str, Any]     )							->						List[str]:
     yield from self.generate_examples_fn()






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		np.random.Generator     )							->						int:
     __lowerCAmelCase								=	list(range(self.df.rdd.getNumPartitions()     )     )
     generator.shuffle(_lowerCAmelCase     )
     return SparkExamplesIterable(self.df					,  partition_order=_lowerCAmelCase     )






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int     )							->						Tuple:
     __lowerCAmelCase								=	self.split_shard_indices_by_worker(_lowerCAmelCase					,  _lowerCAmelCase     )
     return SparkExamplesIterable(self.df					,  partition_order=_lowerCAmelCase     )






  @property
  def 				lowercase		(				self :		Dict     )							->						Optional[int]:
     return len(self.partition_order     )





class 				_UpperCAmelCase    (    datasets.DatasetBuilder    ):

  """simple docstring"""


  a_        = SparkConfig






  def __init__(				self :		str					,  lowerCAmelCase_ :		"pyspark.sql.DataFrame"					,  lowerCAmelCase_ :		str = None					,  lowerCAmelCase_ :		str = None					,  **lowerCAmelCase_ :		Optional[Any]					,  )							->						Dict:
     import pyspark

     __lowerCAmelCase								=	pyspark.sql.SparkSession.builder.getOrCreate()
     __lowerCAmelCase								=	df
     __lowerCAmelCase								=	working_dir

     super().__init__(
         cache_dir=_lowerCAmelCase					,  config_name=str(self.df.semanticHash()     )					,  **_lowerCAmelCase					,  )






  def 				lowercase		(				self :		Any     )							->						str:
     # Returns the path of the created file.
     def create_cache_and_write_probe(lowerCAmelCase_ :		Optional[Any]     ):
        # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
        # already exist.
        os.makedirs(self._cache_dir					,  exist_ok=_lowerCAmelCase     )
        __lowerCAmelCase								=	os.path.join(self._cache_dir					,  'fs_test' + uuid.uuida().hex     )
        # Opening the file in append mode will create a new file unless it already exists, in which case it will not
        # change the file contents.
        open(_lowerCAmelCase					,  'a'     )
        return [probe_file]

     if self._spark.conf.get('spark.master'					,  ''     ).startswith('local'     ):
        return

     # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
     # accessible to the driver.
     # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
     if self._cache_dir:
        __lowerCAmelCase								=	(
            self._spark.sparkContext.parallelize(range(1     )					,  1     ).mapPartitions(_lowerCAmelCase     ).collect()
        )
        if os.path.isfile(probe[0]     ):
           return

     raise ValueError(
         'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'     )






  def 				lowercase		(				self :		Any     )							->						int:
     return datasets.DatasetInfo(features=self.config.features     )






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		datasets.download.download_manager.DownloadManager     )							->						str:
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN     )]






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		Optional[int]     )							->						str:
     import pyspark

     def get_arrow_batch_size(lowerCAmelCase_ :		Optional[Any]     ):
        for batch in it:
           yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}     )

     __lowerCAmelCase								=	self.df.count()
     __lowerCAmelCase								=	df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
     # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
     __lowerCAmelCase								=	(
         self.df.limit(_lowerCAmelCase     )
         .repartition(1     )
         .mapInArrow(_lowerCAmelCase					,  'batch_bytes: long'     )
         .agg(pyspark.sql.functions.sum('batch_bytes'     ).alias('sample_bytes'     )     )
         .collect()[0]
         .sample_bytes
         / sample_num_rows
     )
     __lowerCAmelCase								=	approx_bytes_per_row * df_num_rows
     if approx_total_size > max_shard_size:
        # Make sure there is at least one row per partition.
        __lowerCAmelCase								=	min(_lowerCAmelCase					,  int(approx_total_size / max_shard_size     )     )
        __lowerCAmelCase								=	self.df.repartition(_lowerCAmelCase     )






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		int					,  )							->						List[str]:
     import pyspark

     __lowerCAmelCase								=	ParquetWriter if file_format == 'parquet' else ArrowWriter
     __lowerCAmelCase								=	os.path.join(self._working_dir					,  os.path.basename(_lowerCAmelCase     )     ) if self._working_dir else fpath
     __lowerCAmelCase								=	file_format == 'parquet'

     # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
     # pickling the SparkContext.
     __lowerCAmelCase								=	self.config.features
     __lowerCAmelCase								=	self._writer_batch_size
     __lowerCAmelCase								=	self._fs.storage_options

     def write_arrow(lowerCAmelCase_ :		Tuple     ):
        # Within the same SparkContext, no two task attempts will share the same attempt ID.
        __lowerCAmelCase								=	pyspark.TaskContext().taskAttemptId()
        __lowerCAmelCase								=	next(_lowerCAmelCase					,  _lowerCAmelCase     )
        if first_batch is None:
           # Some partitions might not receive any data.
           return pa.RecordBatch.from_arrays(
               [[task_id], [0], [0]]					,  names=['task_id', 'num_examples', 'num_bytes']					,  )
        __lowerCAmelCase								=	0
        __lowerCAmelCase								=	writer_class(
            features=_lowerCAmelCase					,  path=working_fpath.replace('SSSSS'					,  f"""{shard_id:05d}"""     ).replace('TTTTT'					,  f"""{task_id:05d}"""     )					,  writer_batch_size=_lowerCAmelCase					,  storage_options=_lowerCAmelCase					,  embed_local_files=_lowerCAmelCase					,  )
        __lowerCAmelCase								=	pa.Table.from_batches([first_batch]     )
        writer.write_table(_lowerCAmelCase     )
        for batch in it:
           if max_shard_size is not None and writer._num_bytes >= max_shard_size:
              __lowerCAmelCase	,	__lowerCAmelCase								=	writer.finalize()
              writer.close()
              yield pa.RecordBatch.from_arrays(
                  [[task_id], [num_examples], [num_bytes]]					,  names=['task_id', 'num_examples', 'num_bytes']					,  )
              shard_id += 1
              __lowerCAmelCase								=	writer_class(
                  features=writer._features					,  path=working_fpath.replace('SSSSS'					,  f"""{shard_id:05d}"""     ).replace('TTTTT'					,  f"""{task_id:05d}"""     )					,  writer_batch_size=_lowerCAmelCase					,  storage_options=_lowerCAmelCase					,  embed_local_files=_lowerCAmelCase					,  )
           __lowerCAmelCase								=	pa.Table.from_batches([batch]     )
           writer.write_table(_lowerCAmelCase     )

        if writer._num_bytes > 0:
           __lowerCAmelCase	,	__lowerCAmelCase								=	writer.finalize()
           writer.close()
           yield pa.RecordBatch.from_arrays(
               [[task_id], [num_examples], [num_bytes]]					,  names=['task_id', 'num_examples', 'num_bytes']					,  )

        if working_fpath != fpath:
           for file in os.listdir(os.path.dirname(_lowerCAmelCase     )     ):
              __lowerCAmelCase								=	os.path.join(os.path.dirname(_lowerCAmelCase     )					,  os.path.basename(_lowerCAmelCase     )     )
              shutil.move(_lowerCAmelCase					,  _lowerCAmelCase     )

     __lowerCAmelCase								=	(
         self.df.mapInArrow(_lowerCAmelCase					,  'task_id: long, num_examples: long, num_bytes: long'     )
         .groupBy('task_id'     )
         .agg(
             pyspark.sql.functions.sum('num_examples'     ).alias('total_num_examples'     )					,  pyspark.sql.functions.sum('num_bytes'     ).alias('total_num_bytes'     )					,  pyspark.sql.functions.count('num_bytes'     ).alias('num_shards'     )					,  pyspark.sql.functions.collect_list('num_examples'     ).alias('shard_lengths'     )					,  )
         .collect()
     )
     for row in stats:
        yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		"datasets.SplitGenerator"					,  lowerCAmelCase_ :		str = "arrow"					,  lowerCAmelCase_ :		Optional[Union[str, int]] = None					,  lowerCAmelCase_ :		Optional[int] = None					,  **lowerCAmelCase_ :		List[Any]					,  )							->						Union[str, Any]:
     self._validate_cache_dir()

     __lowerCAmelCase								=	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE     )
     self._repartition_df_if_needed(_lowerCAmelCase     )
     __lowerCAmelCase								=	not is_remote_filesystem(self._fs     )
     __lowerCAmelCase								=	os.path.join if is_local else posixpath.join

     __lowerCAmelCase								=	'-TTTTT-SSSSS-of-NNNNN'
     __lowerCAmelCase								=	f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
     __lowerCAmelCase								=	path_join(self._output_dir					,  _lowerCAmelCase     )

     __lowerCAmelCase								=	0
     __lowerCAmelCase								=	0
     __lowerCAmelCase								=	0
     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	[]

     for task_id, content in self._prepare_split_single(_lowerCAmelCase					,  _lowerCAmelCase					,  _lowerCAmelCase     ):
        (
            (
            __lowerCAmelCase 
        )	,	(
            __lowerCAmelCase 
        )	,	(
            __lowerCAmelCase 
        )	,	(
            __lowerCAmelCase 
        )	,	 
        )								=	content
        if num_bytes > 0:
           total_num_examples += num_examples
           total_num_bytes += num_bytes
           total_shards += num_shards
           task_id_and_num_shards.append((task_id, num_shards)     )
           all_shard_lengths.extend(_lowerCAmelCase     )

     __lowerCAmelCase								=	total_num_examples
     __lowerCAmelCase								=	total_num_bytes

     # should rename everything at the end
     logger.debug(f"""Renaming {total_shards} shards."""     )
     if total_shards > 1:
        __lowerCAmelCase								=	all_shard_lengths

        # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
        # pickling error due to pickling the SparkContext.
        __lowerCAmelCase								=	self._fs

        # use the -SSSSS-of-NNNNN pattern
        def _rename_shard(
            lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  ):
           rename(
               _lowerCAmelCase					,  fpath.replace('SSSSS'					,  f"""{shard_id:05d}"""     ).replace('TTTTT'					,  f"""{task_id:05d}"""     )					,  fpath.replace('TTTTT-SSSSS'					,  f"""{global_shard_id:05d}"""     ).replace('NNNNN'					,  f"""{total_shards:05d}"""     )					,  )

        __lowerCAmelCase								=	[]
        __lowerCAmelCase								=	0
        for i in range(len(_lowerCAmelCase     )     ):
           __lowerCAmelCase	,	__lowerCAmelCase								=	task_id_and_num_shards[i]
           for shard_id in range(_lowerCAmelCase     ):
              args.append([task_id, shard_id, global_shard_id]     )
              global_shard_id += 1
        self._spark.sparkContext.parallelize(_lowerCAmelCase					,  len(_lowerCAmelCase     )     ).map(lambda lowerCAmelCase_     : _rename_shard(*_lowerCAmelCase     )     ).collect()
     else:
        # don't use any pattern
        __lowerCAmelCase								=	0
        __lowerCAmelCase								=	task_id_and_num_shards[0][0]
        self._rename(
            fpath.replace('SSSSS'					,  f"""{shard_id:05d}"""     ).replace('TTTTT'					,  f"""{task_id:05d}"""     )					,  fpath.replace(_lowerCAmelCase					,  ''     )					,  )






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		"datasets.SplitGenerator"					,  )							->						int:
     return SparkExamplesIterable(self.df     )







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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Optional[Any]=True			,			__UpperCAmelCase						:	Any="pt"							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			{'add_prefix_space': True} if isinstance(__UpperCAmelCase			,			__UpperCAmelCase							) and not line.startswith(' '							) else {}
      SCREAMING_SNAKE_CASE_							=			padding_side
      return tokenizer(
          [line]			,			max_length=__UpperCAmelCase			,			padding='max_length' if pad_to_max_length else None			,			truncation=__UpperCAmelCase			,			return_tensors=__UpperCAmelCase			,			add_special_tokens=__UpperCAmelCase			,			**__UpperCAmelCase			,			)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Dict=None			,			)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE_							=			input_ids.ne(__UpperCAmelCase							).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]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Union[str, Any]="train"				,				_lowerCAmelCase  :		Optional[Any]=None				,				_lowerCAmelCase  :		List[str]=None				,				_lowerCAmelCase  :		Optional[int]=None				,				_lowerCAmelCase  :		Optional[int]=""				,				):
       super().__init__()
       SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						).joinpath(type_path + '.source'						)
       SCREAMING_SNAKE_CASE_							=			Path(_lowerCAmelCase						).joinpath(type_path + '.target'						)
       SCREAMING_SNAKE_CASE_							=			self.get_char_lens(self.src_file						)
       SCREAMING_SNAKE_CASE_							=			max_source_length
       SCREAMING_SNAKE_CASE_							=			max_target_length
       assert min(self.src_lens						) > 0, F"found empty line in {self.src_file}"
       SCREAMING_SNAKE_CASE_							=			tokenizer
       SCREAMING_SNAKE_CASE_							=			prefix
       if n_obs is not None:
             SCREAMING_SNAKE_CASE_							=			self.src_lens[:n_obs]
       SCREAMING_SNAKE_CASE_							=			src_lang
       SCREAMING_SNAKE_CASE_							=			tgt_lang



 def __len__(							self  :		Tuple						):
       return len(self.src_lens						)



 def __getitem__(							self  :		List[str]				,				_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			index + 1  # linecache starts at 1
       SCREAMING_SNAKE_CASE_							=			self.prefix + linecache.getline(str(self.src_file						)				,				_lowerCAmelCase						).rstrip('\n'						)
       SCREAMING_SNAKE_CASE_							=			linecache.getline(str(self.tgt_file						)				,				_lowerCAmelCase						).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				,				_lowerCAmelCase						):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       SCREAMING_SNAKE_CASE_							=			(
           self.tokenizer.question_encoder if isinstance(self.tokenizer				,				_lowerCAmelCase						) else self.tokenizer
       )
       SCREAMING_SNAKE_CASE_							=			self.tokenizer.generator if isinstance(self.tokenizer				,				_lowerCAmelCase						) else self.tokenizer

       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_source_length				,				'right'						)
       SCREAMING_SNAKE_CASE_							=			encode_line(_lowerCAmelCase				,				_lowerCAmelCase				,				self.max_target_length				,				'right'						)

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



 @staticmethod
 def       lowerCAmelCase_  (							_lowerCAmelCase  :		Optional[int]						):
       return [len(_lowerCAmelCase						) for x in Path(_lowerCAmelCase						).open().readlines()]




 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['input_ids'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['attention_mask'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			torch.stack([x['decoder_input_ids'] for x in batch]						)
       SCREAMING_SNAKE_CASE_							=			(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer				,				_lowerCAmelCase						)
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE_							=			(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer				,				_lowerCAmelCase						)
           else self.tokenizer.pad_token_id
       )
       SCREAMING_SNAKE_CASE_							=			trim_batch(_lowerCAmelCase				,				_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_							=			trim_batch(_lowerCAmelCase				,				_lowerCAmelCase				,				attention_mask=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_							=			{
           'input_ids': source_ids,
           'attention_mask': source_mask,
           'decoder_input_ids': y,
       }
       return batch


lowerCamelCase__ :   List[str]					=							getLogger(__name__)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[List]							)					-> Tuple:
      return list(itertools.chain.from_iterable(__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> None:
      SCREAMING_SNAKE_CASE_							=			get_git_info()
      save_json(__UpperCAmelCase			,			os.path.join(__UpperCAmelCase			,			'git_log.json'							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Tuple			,			__UpperCAmelCase						:	Optional[int]=4			,			**__UpperCAmelCase						:	Tuple							)					-> str:
      with open(__UpperCAmelCase			,			'w'							) as f:
            json.dump(__UpperCAmelCase			,			__UpperCAmelCase			,			indent=__UpperCAmelCase			,			**__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]							)					-> int:
      with open(__UpperCAmelCase							) as f:
            return json.load(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      )					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			git.Repo(search_parent_directories=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			{
          'repo_id': str(__UpperCAmelCase							),
          'repo_sha': str(repo.head.object.hexsha							),
          'repo_branch': str(repo.active_branch							),
          'hostname': str(socket.gethostname()							),
      }
      return repo_infos

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Callable			,			__UpperCAmelCase						:	Iterable							)					-> List:
      return list(map(__UpperCAmelCase			,			__UpperCAmelCase							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Dict							)					-> Dict:
      with open(__UpperCAmelCase			,			'wb'							) as f:
            return pickle.dump(__UpperCAmelCase			,			__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[Any]							)					-> Any:

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

      def white_space_fix(__UpperCAmelCase						:	List[str]							):
            return " ".join(text.split()							)

      def remove_punc(__UpperCAmelCase						:	List[Any]							):
            SCREAMING_SNAKE_CASE_							=			set(string.punctuation							)
            return "".join(ch for ch in text if ch not in exclude							)

      def lower(__UpperCAmelCase						:	Union[str, Any]							):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(__UpperCAmelCase							)							)							)							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Any							)					-> List[str]:
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			normalize_answer(__UpperCAmelCase							).split()
      SCREAMING_SNAKE_CASE_							=			Counter(__UpperCAmelCase							) & Counter(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			sum(common.values()							)
      if num_same == 0:
            return 0
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			1.0 * num_same / len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			(2 * precision * recall) / (precision + recall)
      return fa

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Optional[int]							)					-> Any:
      return normalize_answer(__UpperCAmelCase							) == normalize_answer(__UpperCAmelCase							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	List[str]			,			__UpperCAmelCase						:	List[str]							)					-> Dict:
      assert len(__UpperCAmelCase							) == len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			0
      for hypo, pred in zip(__UpperCAmelCase			,			__UpperCAmelCase							):
            em += exact_match_score(__UpperCAmelCase			,			__UpperCAmelCase							)
      if len(__UpperCAmelCase							) > 0:
            em /= len(__UpperCAmelCase							)
      return {"em": em}

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]							)					-> Dict:
      return model_prefix.startswith('rag'							)





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