


'''simple docstring'''



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


_lowercase      :      Optional[Any]            =					"""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

_lowercase      :      int            =					"""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

_lowercase      :      Dict            =					"""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,    _KWARGS_DESCRIPTION		)
class 	UpperCamelCase__(       datasets.Metric		):




				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										return datasets.MetricInfo(
										    description=_DESCRIPTION				,   citation=_CITATION				,   inputs_description=_KWARGS_DESCRIPTION				,   features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''string'''				,   id='''sequence'''				),
										            '''references''': datasets.Value('''string'''				,   id='''sequence'''				),
										        }				)				,   codebase_urls=['''https://github.com/google-research/google-research/tree/master/rouge''']				,   reference_urls=[
										        '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
										        '''https://github.com/google-research/google-research/tree/master/rouge''',
										    ]				,   )





				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			Optional[Any]=False				)->					int:

										"""simple docstring"""




										if rouge_types is None:
																UpperCAmelCase					    =     ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

										UpperCAmelCase					    =     rouge_scorer.RougeScorer(rouge_types=lowerCAmelCase				,   use_stemmer=lowerCAmelCase				)
										if use_aggregator:
																UpperCAmelCase					    =     scoring.BootstrapAggregator()
										else:
																UpperCAmelCase					    =     []

										for ref, pred in zip(lowerCAmelCase				,   lowerCAmelCase				):
																UpperCAmelCase					    =     scorer.score(lowerCAmelCase				,   lowerCAmelCase				)
																if use_aggregator:
																						aggregator.add_scores(lowerCAmelCase				)
																else:
																						scores.append(lowerCAmelCase				)

										if use_aggregator:
																UpperCAmelCase					    =     aggregator.aggregate()
										else:
																UpperCAmelCase					    =     {}
																for key in scores[0]:
																						UpperCAmelCase					    =     [score[key] for score in scores]

										return result









'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_lowercase      :      str            =					logging.get_logger(__name__)

_lowercase      :      Any            =					{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Tuple            =					{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_lowercase      :      Union[str, Any]            =					{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Dict  		=	VOCAB_FILES_NAMES
				__magic_name__    :				str  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				List[Any]  		=	["input_ids", "attention_mask"]
				__magic_name__    :				List[Any]  		=	GPTaTokenizer




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			List[Any]=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Tuple="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Union[str, Any]="<|endoftext|>"				,   lowerCAmelCase      :			Optional[int]=False				,   **lowerCAmelCase      :			Tuple				,   )->					int:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     kwargs.pop('''add_bos_token'''				,   lowerCAmelCase				)

										UpperCAmelCase					    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get('''add_prefix_space'''				,   lowerCAmelCase				) != add_prefix_space:
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   pre_tok_state.pop('''type'''				)				)
																UpperCAmelCase					    =     add_prefix_space
																UpperCAmelCase					    =     pre_tok_class(**lowerCAmelCase				)

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Union[str, Any]				,   **lowerCAmelCase      :			Dict				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			List[str]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			Tuple				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			str				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				) + [self.eos_token_id]				)

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





