



# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_   :Dict        =      Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_   :Optional[int]        =      {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

a_   :Tuple        =      "zero2"
a_   :int        =      "zero3"
a_   :Union[str, Any]        =      [ZEROa, ZEROa]
def        lowercase_							(A : List[Any]		,					A : str		,					A : int      ):
       # customize the test name generator function as we want both params to appear in the sub-test
       # name, as by default it shows only the first param
       snake_case__							:	Dict   	=	parameterized.to_safe_name('_'.join(str(A      ) for x in param.args      )      )
       return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_   :Any        =      list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @parameterized.expand(_snake_case, name_func=_snake_case					)
     def    lowercase_   (       self  :  List[str], _snake_case  :  int, _snake_case  :  Dict					)   ->Optional[Any]:
            self.run_and_check(
                stage=_snake_case, model=_snake_case, distributed=_snake_case, fpaa=_snake_case, )






     @require_torch_multi_gpu
     @parameterized.expand(_snake_case, name_func=_snake_case					)
     def    lowercase_   (       self  :  List[Any], _snake_case  :  Any, _snake_case  :  Union[str, Any]					)   ->Optional[Any]:
            self.run_and_check(
                stage=_snake_case, model=_snake_case, distributed=_snake_case, fpaa=_snake_case, )






     @parameterized.expand(_snake_case, name_func=_snake_case					)
     def    lowercase_   (       self  :  List[Any], _snake_case  :  List[Any], _snake_case  :  List[str]					)   ->List[str]:
            self.run_and_check(
                stage=_snake_case, model=_snake_case, distributed=_snake_case, fpaa=_snake_case, )






     @require_torch_multi_gpu
     @parameterized.expand(_snake_case, name_func=_snake_case					)
     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any], _snake_case  :  Optional[Any]					)   ->int:
            self.run_and_check(
                stage=_snake_case, model=_snake_case, distributed=_snake_case, fpaa=_snake_case, )






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Optional[Any]					)   ->Optional[int]:
            # XXX: run_asr is premature and doesn't save any results
            # so all we check for now is that the process didn't fail
            pass






     def    lowercase_   (       self  :  List[str], _snake_case  :  str, _snake_case  :  str, _snake_case  :  int = 1_0, _snake_case  :  bool = True, _snake_case  :  bool = True, _snake_case  :  bool = True, )   ->str:
            snake_case__							:	str   	=	models[model]

            snake_case__							:	Tuple   	=	self.run_trainer(
                stage=_snake_case, model_name=_snake_case, eval_steps=_snake_case, num_train_epochs=1, distributed=_snake_case, fpaa=_snake_case, )

            self.do_checks(_snake_case					)

            return output_dir






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  str, _snake_case  :  str, _snake_case  :  int = 1_0, _snake_case  :  int = 1, _snake_case  :  bool = True, _snake_case  :  bool = True, )   ->Optional[int]:
            snake_case__							:	Any   	=	self.get_auto_remove_tmp_dir('./xxx', after=_snake_case					)
            snake_case__							:	Dict   	=	F'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_snake_case					)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

            if fpaa:
                   args.extend(['--fp16']					)

            # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
            # hence the separate config files
            snake_case__							:	Dict   	=	F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
            snake_case__							:	Union[str, Any]   	=	[F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
            snake_case__							:	List[str]   	=	self.get_launcher(_snake_case					)

            snake_case__							:	Optional[int]   	=	launcher + script + args + ds_args
            # keep for quick debug
            # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
            execute_subprocess_async(_snake_case, env=self.get_env()					)

            return output_dir






     def    lowercase_   (       self  :  Any, _snake_case  :  Tuple=False					)   ->Any:
            # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
            # - it won't be able to handle that
            # 2. for now testing with just 2 gpus max (since some quality tests may give different
            # results with mode gpus because we use very little data)
            snake_case__							:	Optional[int]   	=	min(2, get_gpu_count()					) if distributed else 1
            return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()











from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


a_   :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"

a_   :List[str]        =      "\\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"

a_   :List[str]        =      "\\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     ):






     """simple docstring"""


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






