





"""simple docstring"""






import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


__UpperCAmelCase						=			datasets.logging.get_logger(__name__)


__UpperCAmelCase						=			'''\
@InProceedings{moosavi2019minimum,
    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},
    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},
    year = {2019},
    booktitle = {Proceedings of the 57th Annual Meeting of
        the Association for Computational Linguistics (Volume 1: Long Papers)},
    publisher = {Association for Computational Linguistics},
    address = {Florence, Italy},
}

@inproceedings{10.3115/1072399.1072405,
author = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},
title = {A Model-Theoretic Coreference Scoring Scheme},
year = {1995},
isbn = {1558604022},
publisher = {Association for Computational Linguistics},
address = {USA},
url = {https://doi.org/10.3115/1072399.1072405},
doi = {10.3115/1072399.1072405},
booktitle = {Proceedings of the 6th Conference on Message Understanding},
pages = {45–52},
numpages = {8},
location = {Columbia, Maryland},
series = {MUC6 ’95}
}

@INPROCEEDINGS{Bagga98algorithmsfor,
    author = {Amit Bagga and Breck Baldwin},
    title = {Algorithms for Scoring Coreference Chains},
    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},
    year = {1998},
    pages = {563--566}
}

@INPROCEEDINGS{Luo05oncoreference,
    author = {Xiaoqiang Luo},
    title = {On coreference resolution performance metrics},
    booktitle = {In Proc. of HLT/EMNLP},
    year = {2005},
    pages = {25--32},
    publisher = {URL}
}

@inproceedings{moosavi-strube-2016-coreference,
    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",
    author = "Moosavi, Nafise Sadat  and
      Strube, Michael",
    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2016",
    address = "Berlin, Germany",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P16-1060",
    doi = "10.18653/v1/P16-1060",
    pages = "632--642",
}

'''

__UpperCAmelCase						=			'''\
CoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which
implements of the common evaluation metrics including MUC [Vilain et al, 1995],
B-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],
LEA [Moosavi and Strube, 2016] and the averaged CoNLL score
(the average of the F1 values of MUC, B-cubed and CEAFe)
[Denis and Baldridge, 2009a; Pradhan et al., 2011].

This wrapper of CoVal currently only work with CoNLL line format:
The CoNLL format has one word per line with all the annotation for this word in column separated by spaces:
Column	Type	Description
1	Document ID	This is a variation on the document filename
2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
3	Word number
4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.
5	Part-of-Speech
6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.
7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"
8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.
9	Word sense	This is the word sense of the word in Column 3.
10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.
11	Named Entities	These columns identifies the spans representing various named entities.
12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.
N	Coreference	Coreference chain information encoded in a parenthesis structure.
More informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html

Details on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md

CoVal code was written by @ns-moosavi.
Some parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py
The test suite is taken from https://github.com/conll/reference-coreference-scorers/
Mention evaluation and the test suite are added by @andreasvc.
Parsing CoNLL files is developed by Leo Born.
'''

__UpperCAmelCase						=			'''
Calculates coreference evaluation metrics.
Args:
    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.
        Each prediction is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.
        Each reference is a word with its annotations as a string made of columns joined with spaces.
        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)
        See the details on the format in the description of the metric.
    keep_singletons: After extracting all mentions of key or system files,
        mentions whose corresponding coreference chain is of size one,
        are considered as singletons. The default evaluation mode will include
        singletons in evaluations if they are included in the key or the system files.
        By setting \'keep_singletons=False\', all singletons in the key and system files
        will be excluded from the evaluation.
    NP_only: Most of the recent coreference resolvers only resolve NP mentions and
        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.
    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.
        Minimum spans are determined using the MINA algorithm.

Returns:
    \'mentions\': mentions
    \'muc\': MUC metric [Vilain et al, 1995]
    \'bcub\': B-cubed [Bagga and Baldwin, 1998]
    \'ceafe\': CEAFe [Luo et al., 2005]
    \'lea\': LEA [Moosavi and Strube, 2016]
    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)

Examples:

    >>> coval = datasets.load_metric(\'coval\')
    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',
    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',
    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',
    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',
    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',
    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']
    >>> references = [words]
    >>> predictions = [words]
    >>> results = coval.compute(predictions=predictions, references=references)
    >>> print(results) # doctest:+ELLIPSIS
    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}
'''






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			List[str]=False							,			lowerCAmelCase__   :			Dict=False							,			lowerCAmelCase__   :			int=True							,			lowerCAmelCase__   :			List[str]=False							,			lowerCAmelCase__   :			Any="dummy_doc"			)    ->			List[Any]:



			'''simple docstring'''
			a__  :	str		 =			{doc: key_lines}
			a__  :	Tuple		 =			{doc: sys_lines}

			a__  :	Dict		 =			{}

			a__  :	str		 =			0
			a__  :	Optional[Any]		 =			0
			a__  :	Dict		 =			0
			a__  :	Tuple		 =			0
			a__  :	Union[str, Any]		 =			0
			a__  :	Dict		 =			0

			a__ ,	a__  :	Tuple		 =			reader.get_doc_mentions(lowerCAmelCase__							,			key_doc_lines[doc]							,			lowerCAmelCase__			)
			key_singletons_num += singletons_num

			if NP_only or min_span:
						a__  :	List[Any]		 =			reader.set_annotated_parse_trees(lowerCAmelCase__							,			key_doc_lines[doc]							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			a__ ,	a__  :	Optional[int]		 =			reader.get_doc_mentions(lowerCAmelCase__							,			sys_doc_lines[doc]							,			lowerCAmelCase__			)
			sys_singletons_num += singletons_num

			if NP_only or min_span:
						a__  :	Dict		 =			reader.set_annotated_parse_trees(lowerCAmelCase__							,			key_doc_lines[doc]							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			if remove_nested:
						a__ ,	a__  :	List[str]		 =			reader.remove_nested_coref_mentions(lowerCAmelCase__							,			lowerCAmelCase__			)
						key_nested_coref_num += nested_mentions
						key_removed_nested_clusters += removed_clusters

						a__ ,	a__  :	str		 =			reader.remove_nested_coref_mentions(lowerCAmelCase__							,			lowerCAmelCase__			)
						sys_nested_coref_num += nested_mentions
						sys_removed_nested_clusters += removed_clusters

			a__  :	Union[str, Any]		 =			reader.get_mention_assignments(lowerCAmelCase__							,			lowerCAmelCase__			)
			a__  :	Dict		 =			reader.get_mention_assignments(lowerCAmelCase__							,			lowerCAmelCase__			)

			a__  :	Optional[int]		 =			(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

			if remove_nested:
						logger.info(
						    "Number of removed nested coreferring mentions in the key "
						    F"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"			)
						logger.info(
						    "Number of resulting singleton clusters in the key "
						    F"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"			)

			if not keep_singletons:
						logger.info(
						    F"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
						    "files, respectively"			)

			return doc_coref_infos






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Union[str, Any]			)    ->			List[str]:



			'''simple docstring'''
			a__  :	Union[str, Any]		 =			get_coref_infos(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			a__  :	Optional[Any]		 =			{}
			a__  :	str		 =			0
			a__  :	List[Any]		 =			0

			for name, metric in metrics:
						a__ ,	a__ ,	a__  :	Optional[Any]		 =			evaluator.evaluate_documents(lowerCAmelCase__							,			lowerCAmelCase__							,			beta=1			)
						if name in ["muc", "bcub", "ceafe"]:
									conll += fa
									conll_subparts_num += 1
						output_scores.update({F"{name}/recall": recall, F"{name}/precision": precision, F"{name}/f1": fa}			)

						logger.info(
						    name.ljust(1_0			)							,			F"Recall: {recall * 1_0_0:.2f}"							,			F" Precision: {precision * 1_0_0:.2f}"							,			F" F1: {fa * 1_0_0:.2f}"							,			)

			if conll_subparts_num == 3:
						a__  :	List[Any]		 =			(conll / 3) * 1_0_0
						logger.info(F"CoNLL score: {conll:.2f}"			)
						output_scores.update({"conll_score": conll}			)

			return output_scores






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			Optional[Any]:



			'''simple docstring'''
			a__  :	Optional[Any]		 =			False
			for line in key_lines:
						if not line.startswith("#"			):
									if len(line.split()			) > 6:
												a__  :	Optional[Any]		 =			line.split()[5]
												if not parse_col == "-":
															a__  :	Tuple		 =			True
															break
												else:
															break
			return has_gold_parse





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION  )
class 					__UpperCAmelCase							(						datasets.Metric  ):
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							return datasets.MetricInfo(
							    description=_DESCRIPTION	, citation=_CITATION	, inputs_description=_KWARGS_DESCRIPTION	, features=datasets.Features(
							        {
							            "predictions": datasets.Sequence(datasets.Value("string"    )    ),
							            "references": datasets.Sequence(datasets.Value("string"    )    ),
							        }    )	, codebase_urls=["https://github.com/ns-moosavi/coval"]	, reference_urls=[
							        "https://github.com/ns-moosavi/coval",
							        "https://www.aclweb.org/anthology/P16-1060",
							        "http://www.conll.cemantix.org/2012/data.html",
							    ]	, )






				def       UpperCAmelCase      (		self    :			Any	, a_    :			Union[str, Any]	, a_    :			str	, a_    :			str=True	, a_    :			Optional[Any]=False	, a_    :			Tuple=False	, a_    :			str=False    )	->	Any:



							'''simple docstring'''



							a__  :	int		 =			[
							    ("mentions", evaluator.mentions),
							    ("muc", evaluator.muc),
							    ("bcub", evaluator.b_cubed),
							    ("ceafe", evaluator.ceafe),
							    ("lea", evaluator.lea),
							]

							if min_span:
										a__  :	Any		 =			util.check_gold_parse_annotation(a_    )
										if not has_gold_parse:
													raise NotImplementedError("References should have gold parse annotation to use 'min_span'."    )
													# util.parse_key_file(key_file)
													# key_file = key_file + ".parsed"

							a__  :	List[str]		 =			evaluate(
							    key_lines=a_	, sys_lines=a_	, metrics=a_	, NP_only=a_	, remove_nested=a_	, keep_singletons=a_	, min_span=a_	, )

							return score






"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

__UpperCAmelCase						=			logging.get_logger(__name__)


__UpperCAmelCase						=			{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCAmelCase						=			{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

__UpperCAmelCase						=			{'''facebook/blenderbot-3B''': 128}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowercase__ (					)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			(
			    list(range(ord("!"			)							,			ord("~"			) + 1			)			) + list(range(ord("¡"			)							,			ord("¬"			) + 1			)			) + list(range(ord("®"			)							,			ord("ÿ"			) + 1			)			)
			)
			a__  :	Any		 =			bs[:]
			a__  :	int		 =			0
			for b in range(2**8			):
						if b not in bs:
									bs.append(lowerCAmelCase__			)
									cs.append(2**8 + n			)
									n += 1
			a__  :	Tuple		 =			[chr(lowerCAmelCase__			) for n in cs]
			return dict(zip(lowerCAmelCase__							,			lowerCAmelCase__			)			)






def      lowercase__ (					lowerCAmelCase__   :			List[str]			)    ->			Any:



			'''simple docstring'''
			a__  :	Tuple		 =			set()
			a__  :	Union[str, Any]		 =			word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						a__  :	Tuple		 =			char
			return pairs





class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	int				     =				VOCAB_FILES_NAMES
				__lowerCamelCase				:	List[Any]				     =				PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase				:	Optional[int]				     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase				:	List[str]				     =				["input_ids", "attention_mask"]
				def __init__(		self    :			str	, a_    :			Optional[Any]	, a_    :			Union[str, Any]	, a_    :			int="replace"	, a_    :			Optional[Any]="<s>"	, a_    :			Optional[Any]="</s>"	, a_    :			Any="</s>"	, a_    :			Dict="<s>"	, a_    :			Optional[int]="<unk>"	, a_    :			Optional[int]="<pad>"	, a_    :			Any="<mask>"	, a_    :			Optional[Any]=False	, **a_    :			str	, )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[int]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else bos_token
							a__  :	Any		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else eos_token
							a__  :	List[str]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else sep_token
							a__  :	str		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else cls_token
							a__  :	Optional[Any]		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else unk_token
							a__  :	Tuple		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							a__  :	Dict		 =			AddedToken(a_	, lstrip=a_	, rstrip=a_    ) if isinstance(a_	, a_    ) else mask_token

							super().__init__(
							    errors=a_	, bos_token=a_	, eos_token=a_	, unk_token=a_	, sep_token=a_	, cls_token=a_	, pad_token=a_	, mask_token=a_	, add_prefix_space=a_	, **a_	, )

							with open(a_	, encoding="utf-8"    ) as vocab_handle:
										a__  :	List[Any]		 =			json.load(a_    )
							a__  :	str		 =			{v: k for k, v in self.encoder.items()}
							a__  :	int		 =			errors  # how to handle errors in decoding
							a__  :	int		 =			bytes_to_unicode()
							a__  :	List[Any]		 =			{v: k for k, v in self.byte_encoder.items()}
							with open(a_	, encoding="utf-8"    ) as merges_handle:
										a__  :	Dict		 =			merges_handle.read().split("\n"    )[1:-1]
							a__  :	Dict		 =			[tuple(merge.split()    ) for merge in bpe_merges]
							a__  :	str		 =			dict(zip(a_	, range(len(a_    )    )    )    )
							a__  :	Tuple		 =			{}
							a__  :	Optional[int]		 =			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							a__  :	List[str]		 =			re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )
				@property
				# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
				def       UpperCAmelCase      (		self    :			Any    )	->	Optional[int]:



							'''simple docstring'''



							return len(self.encoder    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							return dict(self.encoder	, **self.added_tokens_encoder    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			str    )	->	int:



							'''simple docstring'''



							if token in self.cache:
										return self.cache[token]
							a__  :	List[str]		 =			tuple(a_    )
							a__  :	Dict		 =			get_pairs(a_    )

							if not pairs:
										return token

							while True:
										a__  :	Dict		 =			min(a_	, key=lambda a_    : self.bpe_ranks.get(a_	, float("inf"    )    )    )
										if bigram not in self.bpe_ranks:
													break
										a__ ,	a__  :	List[Any]		 =			bigram
										a__  :	int		 =			[]
										a__  :	List[Any]		 =			0
										while i < len(a_    ):
													try:
																a__  :	List[str]		 =			word.index(a_	, a_    )
													except ValueError:
																new_word.extend(word[i:]    )
																break
													else:
																new_word.extend(word[i:j]    )
																a__  :	Dict		 =			j

													if word[i] == first and i < len(a_    ) - 1 and word[i + 1] == second:
																new_word.append(first + second    )
																i += 2
													else:
																new_word.append(word[i]    )
																i += 1
										a__  :	List[Any]		 =			tuple(a_    )
										a__  :	str		 =			new_word
										if len(a_    ) == 1:
													break
										else:
													a__  :	Optional[int]		 =			get_pairs(a_    )
							a__  :	Union[str, Any]		 =			" ".join(a_    )
							a__  :	str		 =			word
							return word
				def       UpperCAmelCase      (		self    :			Optional[int]	, a_    :			Union[str, Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	List[Any]		 =			[]
							for token in re.findall(self.pat	, a_    ):
										a__  :	Dict		 =			"".join(
										    self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a_    ).split(" "    )    )
							return bpe_tokens
				def       UpperCAmelCase      (		self    :			Union[str, Any]	, a_    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.encoder.get(a_	, self.encoder.get(self.unk_token    )    )
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			Optional[Any]    )	->	Optional[Any]:



							'''simple docstring'''



							return self.decoder.get(a_    )
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	str		 =			"".join(a_    )
							a__  :	List[str]		 =			bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"	, errors=self.errors    )
							return text
				def       UpperCAmelCase      (		self    :			Any	, a_    :			str	, a_    :			Optional[str] = None    )	->	Tuple[str]:



							'''simple docstring'''



							if not os.path.isdir(a_    ):
										logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
										return
							a__  :	Dict		 =			os.path.join(
							    a_	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
							a__  :	List[str]		 =			os.path.join(
							    a_	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

							with open(a_	, "w"	, encoding="utf-8"    ) as f:
										f.write(json.dumps(self.encoder	, indent=2	, sort_keys=a_	, ensure_ascii=a_    ) + "\n"    )

							a__  :	Dict		 =			0
							with open(a_	, "w"	, encoding="utf-8"    ) as writer:
										writer.write("#version: 0.2\n"    )
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	, key=lambda a_    : kv[1]    ):
													if index != token_index:
																logger.warning(
																    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																    " Please check that the tokenizer is not corrupted!"    )
																a__  :	Optional[Any]		 =			token_index
													writer.write(" ".join(a_    ) + "\n"    )
													index += 1

							return vocab_file, merge_file
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None	, a_    :			bool = False    )	->	List[int]:



							'''simple docstring'''



							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=a_	, token_ids_a=a_	, already_has_special_tokens=a_    )

							if token_ids_a is None:
										return [1] + ([0] * len(a_    )) + [1]
							return [1] + ([0] * len(a_    )) + [1, 1] + ([0] * len(a_    )) + [1]
				def       UpperCAmelCase      (		self    :			Optional[Any]	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	List[int]:



							'''simple docstring'''



							a__  :	Dict		 =			[self.sep_token_id]
							a__  :	Tuple		 =			[self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep    ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
				def       UpperCAmelCase      (		self    :			Any	, a_    :			Optional[Any]	, a_    :			Union[str, Any]=False	, **a_    :			Dict    )	->	Dict:



							'''simple docstring'''



							a__  :	int		 =			kwargs.pop("add_prefix_space"	, self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(a_    ) > 0 and not text[0].isspace()):
										a__  :	int		 =			" " + text
							return (text, kwargs)
				def       UpperCAmelCase      (		self    :			str	, a_    :			List[int]	, a_    :			Optional[List[int]] = None    )	->	Optional[int]:



							'''simple docstring'''



							return token_ids_a + [self.eos_token_id]






				def       UpperCAmelCase      (		self    :			str	, a_    :			"Conversation"    )	->	List[int]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			[]
							for is_user, text in conversation.iter_texts():
										if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(" " + text    )
										else:
													# Generated responses should contain them already.
													inputs.append(a_    )

							a__  :	Dict		 =			"  ".join(a_    )
							a__  :	List[str]		 =			self.encode(a_    )
							if len(a_    ) > self.model_max_length:
										a__  :	Union[str, Any]		 =			input_ids[-self.model_max_length :]
										logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."    )
							return input_ids