



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case     :List[str]			       ='\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

__snake_case     :Union[str, Any]			       ='\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

__snake_case     :Optional[Any]			       ='\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'



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


				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											return datasets.MetricInfo(
											    description=_DESCRIPTION						, citation=_CITATION						, inputs_description=_KWARGS_DESCRIPTION						, features=datasets.Features(
											        {
											            'predictions': datasets.Sequence(datasets.Value('string'						, id='token'       )						, id='sequence'       ),
											            'references': datasets.Sequence(
											                datasets.Sequence(datasets.Value('string'						, id='token'       )						, id='sequence'       )						, id='references'       ),
											        }       )						, codebase_urls=['https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py']						, reference_urls=[
											        'https://en.wikipedia.org/wiki/BLEU',
											        'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
											    ]						, )


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  str						, __UpperCamelCase      :  Any=4						, __UpperCamelCase      :  Tuple=False       )		->     str:
											A      						=    compute_bleu(
											    reference_corpus=__UpperCamelCase						, translation_corpus=__UpperCamelCase						, max_order=__UpperCamelCase						, smooth=__UpperCamelCase       )
											((A)						,   (A)						,   (A)						,   (A)						,   (A)						,   (A))      						=    score
											return {
											    "bleu": bleu,
											    "precisions": precisions,
											    "brevity_penalty": bp,
											    "length_ratio": ratio,
											    "translation_length": translation_length,
											    "reference_length": reference_length,
											}





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="longformer"



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[List[int], int] = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   int = 1					,							snake_case__	:   int = 0					,							snake_case__	:   int = 2					,							snake_case__	:   int = 3_05_22					,							snake_case__	:   int = 7_68					,							snake_case__	:   int = 12					,							snake_case__	:   int = 12					,							snake_case__	:   int = 30_72					,							snake_case__	:   str = "gelu"					,							snake_case__	:   float = 0.1					,							snake_case__	:   float = 0.1					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   float = 0.02					,							snake_case__	:   float = 1e-12					,							snake_case__	:   bool = False					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							attention_window
								SCREAMING_SNAKE_CASE_					      =							sep_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							type_vocab_size
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							layer_norm_eps
								SCREAMING_SNAKE_CASE_					      =							onnx_export



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Dict					,							snake_case__	:   "PretrainedConfig"					,							snake_case__	:   str = "default"					,							snake_case__	:   "List[PatchingSpec]" = None  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							True



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if self.task == "multiple-choice":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('global_attention_mask', dynamic_axis),
								    ]  )



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().outputs
								if self.task == "default":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
								return outputs



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return 1e-4



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return max(super().default_onnx_opset					,							14  )






						def 	__a					(			self	:   List[str]					,							snake_case__	:   "PreTrainedTokenizerBase"					,							snake_case__	:   int = -1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[TensorType] = None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().generate_dummy_inputs(
								    preprocessor=snake_case__					,							batch_size=snake_case__					,							seq_length=snake_case__					,							is_pair=snake_case__					,							framework=snake_case__  )
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(inputs['input_ids']  )
								# make every second token global
								SCREAMING_SNAKE_CASE_					      =							1

								return inputs