



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowerCAmelCase__						:	Optional[Any]					=logging.get_logger(__name__)

class   UpperCAmelCase_    :

						'''simple docstring'''




						def __init__(       self ,				_A ,				_A						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   question_encoder
													__SCREAMING_SNAKE_CASE										=   generator
													__SCREAMING_SNAKE_CASE										=   self.question_encoder






						def 			_A      (       self ,				_A						):







													'''simple docstring'''

													if os.path.isfile(_UpperCamelCase						):
																				raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""						)
													os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase						)
													__SCREAMING_SNAKE_CASE										=   os.path.join(_UpperCamelCase ,				'question_encoder_tokenizer'						)
													__SCREAMING_SNAKE_CASE										=   os.path.join(_UpperCamelCase ,				'generator_tokenizer'						)
													self.question_encoder.save_pretrained(_UpperCamelCase						)
													self.generator.save_pretrained(_UpperCamelCase						)






						@classmethod
						def 			_A      (       cls ,				_A ,				**_A						):







													'''simple docstring'''

													from ..auto.tokenization_auto import AutoTokenizer

													__SCREAMING_SNAKE_CASE										=   kwargs.pop('config' ,				_UpperCamelCase						)

													if config is None:
																				__SCREAMING_SNAKE_CASE										=   RagConfig.from_pretrained(_UpperCamelCase						)

													__SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(
													    _UpperCamelCase ,				config=config.question_encoder ,				subfolder='question_encoder_tokenizer'						)
													__SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(
													    _UpperCamelCase ,				config=config.generator ,				subfolder='generator_tokenizer'						)
													return cls(question_encoder=_UpperCamelCase ,				generator=_UpperCamelCase						)






						def __call__(       self ,				*_A ,				**_A						):







													'''simple docstring'''

													return self.current_tokenizer(*_UpperCamelCase ,				**_UpperCamelCase						)






						def 			_A      (       self ,				*_A ,				**_A						):







													'''simple docstring'''

													return self.generator.batch_decode(*_UpperCamelCase ,				**_UpperCamelCase						)






						def 			_A      (       self ,				*_A ,				**_A						):







													'''simple docstring'''

													return self.generator.decode(*_UpperCamelCase ,				**_UpperCamelCase						)






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.question_encoder






						def 			_A      (       self						):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE										=   self.generator






						def 			_A      (       self ,				_A ,				_A = None ,				_A = None ,				_A = None ,				_A = "longest" ,				_A = None ,				_A = True ,				**_A ,				):







													'''simple docstring'''

													warnings.warn(
													    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
													    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
													    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
													    'details' ,				_UpperCamelCase ,				)
													if max_length is None:
																				__SCREAMING_SNAKE_CASE										=   self.current_tokenizer.model_max_length
													__SCREAMING_SNAKE_CASE										=   self(
													    _UpperCamelCase ,				add_special_tokens=_UpperCamelCase ,				return_tensors=_UpperCamelCase ,				max_length=_UpperCamelCase ,				padding=_UpperCamelCase ,				truncation=_UpperCamelCase ,				**_UpperCamelCase ,				)
													if tgt_texts is None:
																				return model_inputs
													# Process tgt_texts
													if max_target_length is None:
																				__SCREAMING_SNAKE_CASE										=   self.current_tokenizer.model_max_length
													__SCREAMING_SNAKE_CASE										=   self(
													    text_target=_UpperCamelCase ,				add_special_tokens=_UpperCamelCase ,				return_tensors=_UpperCamelCase ,				padding=_UpperCamelCase ,				max_length=_UpperCamelCase ,				truncation=_UpperCamelCase ,				**_UpperCamelCase ,				)
													__SCREAMING_SNAKE_CASE										=   labels['input_ids']
													return model_inputs








import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_A  =	"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_A  =	"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_A  =	"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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




				def 	__a		(   self	)							-> str:
							if version.parse(scb.__version__	) < version.parse("1.4.12"	):
										raise ImportWarning(
										    "To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"
										    "You can install it with `pip install \"sacrebleu>=1.4.12\"`."	)
							return datasets.MetricInfo(
							    description=_DESCRIPTION							,							citation=_CITATION							,							homepage="http://www.cs.umd.edu/~snover/tercom/"							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"							,							id="sequence"	),
							            "references": datasets.Sequence(datasets.Value("string"							,							id="sequence"	)							,							id="references"	),
							        }	)							,							codebase_urls=["https://github.com/mjpost/sacreBLEU#ter"]							,							reference_urls=[
							        "https://github.com/jhclark/tercom",
							    ]							,							)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							lowerCAmelCase_         =      len(references[0]	)
							if any(len(_UpperCamelCase	) != references_per_prediction for refs in references	):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"	)
							lowerCAmelCase_         =      [[refs[i] for refs in references] for i in range(_UpperCamelCase	)]

							lowerCAmelCase_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

							return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}



