



import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__     = {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__     = {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
UpperCAmelCase__     = {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__     = {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
UpperCAmelCase__     = {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
UpperCAmelCase__     = {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


UpperCAmelCase__     = {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__     = {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
UpperCAmelCase__     = {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase           =  DPRContextEncoderTokenizer







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase           =  DPRQuestionEncoderTokenizer





UpperCAmelCase__     = collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

UpperCAmelCase__     = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


UpperCAmelCase__     = r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "







@add_start_docstrings(A		)
class     __lowerCAmelCase					:



			def __call__(		self     :						List[str]				,	A     :						Any				,	A     :						Optional[str] = None				,	A     :						Optional[str] = None				,	A     :						Union[bool, str] = False				,	A     :						Union[bool, str] = False				,	A     :						Optional[int] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						Optional[bool] = None				,	**A     :						List[Any]				,	)     ->					BatchEncoding:



						"""simple docstring"""






						if titles is None and texts is None:
									return super().__call__(
									    A				,	padding=A				,	truncation=A				,	max_length=A				,	return_tensors=A				,	return_attention_mask=A				,	**A				,	)
						elif titles is None or texts is None:
									_UpperCAmelCase	 =     titles if texts is None else texts
									return super().__call__(
									    A				,	A				,	padding=A				,	truncation=A				,	max_length=A				,	return_tensors=A				,	return_attention_mask=A				,	**A				,	)
						_UpperCAmelCase	 =     titles if not isinstance(A				,	A) else [titles]
						_UpperCAmelCase	 =     texts if not isinstance(A				,	A) else [texts]
						_UpperCAmelCase	 =     len(A)
						_UpperCAmelCase	 =     questions if not isinstance(A				,	A) else [questions] * n_passages
						assert len(A) == len(
						    A), F"There should be as many titles than texts but got {len(A)} titles and {len(A)} texts."
						_UpperCAmelCase	 =     super().__call__(A				,	A				,	padding=A				,	truncation=A)['input_ids']
						_UpperCAmelCase	 =     super().__call__(A				,	add_special_tokens=A				,	padding=A				,	truncation=A)['input_ids']
						_UpperCAmelCase	 =     {
						    'input_ids': [
						        (encoded_question_and_title + encoded_text)[:max_length]
						        if max_length is not None and truncation
						        else encoded_question_and_title + encoded_text
						        for encoded_question_and_title, encoded_text in zip(A				,	A)
						    ]
						}
						if return_attention_mask is not False:
									_UpperCAmelCase	 =     []
									for input_ids in encoded_inputs["input_ids"]:
												attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
									_UpperCAmelCase	 =     attention_mask
						return self.pad(A				,	padding=A				,	max_length=A				,	return_tensors=A)



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						BatchEncoding				,	A     :						DPRReaderOutput				,	A     :						int = 16				,	A     :						int = 64				,	A     :						int = 4				,	)     ->					List[DPRSpanPrediction]:



						"""simple docstring"""






						_UpperCAmelCase	 =     reader_input['input_ids']
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     reader_output[:3]
						_UpperCAmelCase	 =     len(A)
						_UpperCAmelCase	 =     sorted(range(A)				,	reverse=A				,	key=relevance_logits.__getitem__)
						_UpperCAmelCase	 =     []
						for doc_id in sorted_docs:
									_UpperCAmelCase	 =     list(input_ids[doc_id])
									# assuming question & title information is at the beginning of the sequence
									_UpperCAmelCase	 =     sequence_ids.index(self.sep_token_id				,	2) + 1  # second sep id
									if sequence_ids[-1] == self.pad_token_id:
												_UpperCAmelCase	 =     sequence_ids.index(self.pad_token_id)
									else:
												_UpperCAmelCase	 =     len(A)

									_UpperCAmelCase	 =     self._get_best_spans(
									    start_logits=start_logits[doc_id][passage_offset:sequence_len]				,	end_logits=end_logits[doc_id][passage_offset:sequence_len]				,	max_answer_length=A				,	top_spans=A				,	)
									for start_index, end_index in best_spans:
												start_index += passage_offset
												end_index += passage_offset
												nbest_spans_predictions.append(
												    DPRSpanPrediction(
												        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]				,	relevance_score=relevance_logits[doc_id]				,	doc_id=A				,	start_index=A				,	end_index=A				,	text=self.decode(sequence_ids[start_index : end_index + 1])				,	))
									if len(A) >= num_spans:
												break
						return nbest_spans_predictions[:num_spans]



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						List[int]				,	A     :						List[int]				,	A     :						int				,	A     :						int				,	)     ->					List[DPRSpanPrediction]:



						"""simple docstring"""






						_UpperCAmelCase	 =     []
						for start_index, start_score in enumerate(A):
									for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
												scores.append(((start_index, start_index + answer_length), start_score + end_score))
						_UpperCAmelCase	 =     sorted(A				,	key=lambda A: x[1]				,	reverse=A)
						_UpperCAmelCase	 =     []
						for (start_index, end_index), score in scores:
									assert start_index <= end_index, F"Wrong span indices: [{start_index}:{end_index}]"
									_UpperCAmelCase	 =     end_index - start_index + 1
									assert length <= max_answer_length, F"Span is too long: {length} > {max_answer_length}"
									if any(
									    start_index <= prev_start_index <= prev_end_index <= end_index
									    or prev_start_index <= start_index <= end_index <= prev_end_index
									    for (prev_start_index, prev_end_index) in chosen_span_intervals):
												continue
									chosen_span_intervals.append((start_index, end_index))

									if len(A) == top_spans:
												break
						return chosen_span_intervals







@add_end_docstrings(A		)
class     __lowerCAmelCase					(    A       ,		A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  READER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  READER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase           =  ['''input_ids''', '''attention_mask''']
			UpperCamelCase           =  DPRReaderTokenizer





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			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Dict=True							,     _UpperCAmelCase       :     Tuple="pt"					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {'add_prefix_space': True} if isinstance(_UpperCAmelCase							,     _UpperCAmelCase					) and not line.startswith(' '					) else {}
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Dict=None							,     )       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     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					(    A		):



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



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.source')
						_UpperCAmelCase	 =     Path(A).joinpath(type_path + '.target')
						_UpperCAmelCase	 =     self.get_char_lens(self.src_file)
						_UpperCAmelCase	 =     max_source_length
						_UpperCAmelCase	 =     max_target_length
						assert min(self.src_lens) > 0, F"found empty line in {self.src_file}"
						_UpperCAmelCase	 =     tokenizer
						_UpperCAmelCase	 =     prefix
						if n_obs is not None:
									_UpperCAmelCase	 =     self.src_lens[:n_obs]
						_UpperCAmelCase	 =     src_lang
						_UpperCAmelCase	 =     tgt_lang



			def __len__(		self     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						return len(self.src_lens)



			def __getitem__(		self     :						Any				,	A     :						Dict)     ->					Dict[str, torch.Tensor]:



						"""simple docstring"""






						_UpperCAmelCase	 =     index + 1  # linecache starts at 1
						_UpperCAmelCase	 =     self.prefix + linecache.getline(str(self.src_file)				,	A).rstrip('\n')
						_UpperCAmelCase	 =     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	 =     (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer				,	A) else self.tokenizer
						)
						_UpperCAmelCase	 =     self.tokenizer.generator if isinstance(self.tokenizer				,	A) else self.tokenizer

						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_source_length				,	'right')
						_UpperCAmelCase	 =     encode_line(A				,	A				,	self.max_target_length				,	'right')

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



			@staticmethod
			def 					_lowerCamelCase (		A     :						str)     ->					Tuple:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						int				,	A     :						int)     ->					Dict[str, torch.Tensor]:



						"""simple docstring"""






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





UpperCAmelCase__     = getLogger(__name__)





def 							A			(  _UpperCAmelCase       :     List[List]					)       ->				Union[str, Any]:







			'''simple docstring'''
			return list(itertools.chain.from_iterable(_UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     str					)       ->				None:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_git_info()
			save_json(_UpperCAmelCase							,     os.path.join(_UpperCAmelCase							,     'git_log.json'					)					)





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Optional[int]=4							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Dict:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'w'					) as f:
						json.dump(_UpperCAmelCase							,     _UpperCAmelCase							,     indent=_UpperCAmelCase							,     **_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase					) as f:
						return json.load(_UpperCAmelCase					)





def 							A			(  )       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     git.Repo(search_parent_directories=_UpperCAmelCase					)
			_UpperCAmelCase	 =     {
			    '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 							A			(  _UpperCAmelCase       :     Callable							,     _UpperCAmelCase       :     Iterable					)       ->				List:







			'''simple docstring'''
			return list(map(_UpperCAmelCase							,     _UpperCAmelCase					)					)





def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Union[str, Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'wb'					) as f:
						return pickle.dump(_UpperCAmelCase							,     _UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     int					)       ->				str:







			'''simple docstring'''

			def remove_articles(_UpperCAmelCase       :     Optional[int]					):
						return re.sub(R'\b(a|an|the)\b'							,     ' '							,     _UpperCAmelCase					)

			def white_space_fix(_UpperCAmelCase       :     Optional[int]					):
						return " ".join(text.split()					)

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

			def lower(_UpperCAmelCase       :     str					):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(_UpperCAmelCase					)					)					)					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     normalize_answer(_UpperCAmelCase					).split()
			_UpperCAmelCase	 =     Counter(_UpperCAmelCase					) & Counter(_UpperCAmelCase					)
			_UpperCAmelCase	 =     sum(common.values()					)
			if num_same == 0:
						return 0
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     1.0 * num_same / len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     (2 * precision * recall) / (precision + recall)
			return fa





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			return normalize_answer(_UpperCAmelCase					) == normalize_answer(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     List[str]					)       ->				Dict:







			'''simple docstring'''
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)
			_UpperCAmelCase	 =     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 							A			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				int:







			'''simple docstring'''
			return model_prefix.startswith('rag'					)





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     {p: p for p in extra_params}
			# T5 models don't have `dropout` param, they have `dropout_rate` instead
			_UpperCAmelCase	 =     '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
									_UpperCAmelCase	 =     p if hasattr(_UpperCAmelCase							,     _UpperCAmelCase					) else equivalent_param[p]
									setattr(_UpperCAmelCase							,     _UpperCAmelCase							,     getattr(_UpperCAmelCase							,     _UpperCAmelCase					)					)
									delattr(_UpperCAmelCase							,     _UpperCAmelCase					)
			return hparams, config
