






from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets





class   snake_case__ (							snake_case_				):
	@slow
	@require_torch
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""      ,       """prajjwal1/bert-tiny"""			)
							__magic_name__      :       Any       						= BertTokenizer.from_pretrained("""bert-base-uncased"""			)

							__magic_name__      :       List[Any]       						= bertabert.config.encoder.vocab_size
							__magic_name__      :       str       						= tokenizer.sep_token_id
							__magic_name__      :       List[Any]       						= tokenizer.cls_token_id
							__magic_name__      :       Union[str, Any]       						= 1_28

							__magic_name__      :       Tuple       						= datasets.load_dataset("""cnn_dailymail"""      ,       """3.0.0"""      ,       split="""train[:1%]"""			)
							__magic_name__      :       Dict       						= datasets.load_dataset("""cnn_dailymail"""      ,       """3.0.0"""      ,       split="""validation[:1%]"""			)

							__magic_name__      :       Dict       						= train_dataset.select(range(32			)			)
							__magic_name__      :       Optional[Any]       						= val_dataset.select(range(16			)			)

							__magic_name__      :       Optional[Any]       						= 4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__			):
													# Tokenizer will automatically set [BOS] <text> [EOS]
													__magic_name__      :       List[str]       						= tokenizer(batch["""article"""]      ,       padding="""max_length"""      ,       truncation=lowerCAmelCase__      ,       max_length=5_12			)
													__magic_name__      :       Union[str, Any]       						= tokenizer(batch["""highlights"""]      ,       padding="""max_length"""      ,       truncation=lowerCAmelCase__      ,       max_length=1_28			)
													__magic_name__      :       Optional[Any]       						= inputs.input_ids
													__magic_name__      :       Optional[int]       						= inputs.attention_mask

													__magic_name__      :       Optional[Any]       						= outputs.input_ids
													__magic_name__      :       List[Any]       						= outputs.input_ids.copy()
													__magic_name__      :       Optional[Any]       						= [
													    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
													]
													__magic_name__      :       Any       						= outputs.attention_mask

													assert all(len(lowerCAmelCase__			) == 5_12 for x in inputs.input_ids			)
													assert all(len(lowerCAmelCase__			) == 1_28 for x in outputs.input_ids			)

													return batch

							def _compute_metrics(lowerCAmelCase__			):
													__magic_name__      :       Union[str, Any]       						= pred.label_ids
													__magic_name__      :       List[str]       						= pred.predictions

													# all unnecessary tokens are removed
													__magic_name__      :       Union[str, Any]       						= tokenizer.batch_decode(lowerCAmelCase__      ,       skip_special_tokens=lowerCAmelCase__			)
													__magic_name__      :       List[Any]       						= tokenizer.batch_decode(lowerCAmelCase__      ,       skip_special_tokens=lowerCAmelCase__			)

													__magic_name__      :       Optional[Any]       						= sum([int(pred_str[i] == label_str[i]			) for i in range(len(lowerCAmelCase__			)			)]			) / len(lowerCAmelCase__			)

													return {"accuracy": accuracy}

							# map train dataset
							__magic_name__      :       List[Any]       						= train_dataset.map(
							    _map_to_encoder_decoder_inputs      ,       batched=lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       remove_columns=["""article""", """highlights"""]      ,       )
							train_dataset.set_format(
							    type="""torch"""      ,       columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,       )

							# same for validation dataset
							__magic_name__      :       int       						= val_dataset.map(
							    _map_to_encoder_decoder_inputs      ,       batched=lowerCAmelCase__      ,       batch_size=lowerCAmelCase__      ,       remove_columns=["""article""", """highlights"""]      ,       )
							val_dataset.set_format(
							    type="""torch"""      ,       columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]      ,       )

							__magic_name__      :       List[str]       						= self.get_auto_remove_tmp_dir()

							__magic_name__      :       List[str]       						= SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__      ,       per_device_train_batch_size=lowerCAmelCase__      ,       per_device_eval_batch_size=lowerCAmelCase__      ,       predict_with_generate=lowerCAmelCase__      ,       evaluation_strategy="""steps"""      ,       do_train=lowerCAmelCase__      ,       do_eval=lowerCAmelCase__      ,       warmup_steps=0      ,       eval_steps=2      ,       logging_steps=2      ,       )

							# instantiate trainer
							__magic_name__      :       Union[str, Any]       						= SeqaSeqTrainer(
							    model=lowerCAmelCase__      ,       args=lowerCAmelCase__      ,       compute_metrics=_compute_metrics      ,       train_dataset=lowerCAmelCase__      ,       eval_dataset=lowerCAmelCase__      ,       tokenizer=lowerCAmelCase__      ,       )

							# start training
							trainer.train()








from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


__magic_name__:					Optional[Any]							=     "\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

__magic_name__:					List[Any]							=     "\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

__magic_name__:					Union[str, Any]							=     "\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,  _KWARGS_DESCRIPTION				)
class 	snake_case__ (							datasets.Metric				):
	def       __magic_name__    (    self			)							->				MetricInfo:
							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"""			),
							        }			)      ,       )







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = 1      ,       lowerCAmelCase__ = 4      ,       )							->				Dict[str, float]:
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=lowerCAmelCase__      ,       hypotheses=lowerCAmelCase__      ,       min_len=lowerCAmelCase__      ,       max_len=lowerCAmelCase__			)
							}
