


# 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


snake_case							: Any      =   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)

snake_case							: Tuple      =   {'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

snake_case							: int      =   '''zero2'''
snake_case							: List[str]      =   '''zero3'''
snake_case							: Optional[Any]      =   [ZEROa, ZEROa]






def 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       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
							a__    =  parameterized.to_safe_name('_'.join(str(__lowerCAmelCase     ) for x in param.args     )     )
							return F'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
snake_case							: List[str]      =   list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class  snake_case_     (lowerCamelCase_   ):
					@parameterized.expand(__snake_case    ,name_func=__snake_case   )
					def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :List[str]    ,__snake_case     :int   )      ->					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 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :Union[str, Any]    ,__snake_case     :Tuple   )      ->					Tuple:
												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 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Any    ,__snake_case     :str   )      ->					List[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 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Optional[int]   )      ->					Optional[Any]:
												self.run_and_check(
												    stage=__snake_case    ,model=__snake_case    ,distributed=__snake_case    ,fpaa=__snake_case    ,)
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :int   )      ->					Any:
												# 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 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :int = 10    ,__snake_case     :bool = True    ,__snake_case     :bool = True    ,__snake_case     :bool = True    ,)      ->					str:
												a__    =  models[model]

												a__    =  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 				lowerCamelCase__(   self     :Any    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :int = 10    ,__snake_case     :int = 1    ,__snake_case     :bool = True    ,__snake_case     :bool = True    ,)      ->					Tuple:
												a__    =  self.get_auto_remove_tmp_dir('./xxx'    ,after=__snake_case   )
												a__    =  F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(__snake_case   )}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

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

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

												a__    =  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 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Any=False   )      ->					Optional[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)
												a__    =  min(2    ,get_gpu_count()   ) if distributed else 1
												return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()








from sklearn.metrics import fa_score

import datasets


snake_case							: Optional[int]      =   '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''


snake_case							: List[Any]      =   '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.

        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {\'f1\': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results[\'f1\'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric("f1")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results[\'f1\'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")
        >>> print(round(results[\'f1\'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")
        >>> print(round(results[\'f1\'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {\'f1\': array([0.8, 0. , 0. ])}
'''


snake_case							: Union[str, Any]      =   '''
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION   )
class  snake_case_     (datasets.Metric   ):
					def 				lowerCamelCase__(   self     :Any   )      ->					Any:
												return datasets.MetricInfo(
												    description=_DESCRIPTION    ,citation=_CITATION    ,inputs_description=_KWARGS_DESCRIPTION    ,features=datasets.Features(
												        {
												            'predictions': datasets.Sequence(datasets.Value('int32'   )   ),
												            'references': datasets.Sequence(datasets.Value('int32'   )   ),
												        }
												        if self.config_name == 'multilabel'
												        else {
												            'predictions': datasets.Value('int32'   ),
												            'references': datasets.Value('int32'   ),
												        }   )    ,reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html']    ,)



					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str    ,__snake_case     :str    ,__snake_case     :Dict=None    ,__snake_case     :str=1    ,__snake_case     :Optional[int]="binary"    ,__snake_case     :Union[str, Any]=None   )      ->					Tuple:
												a__    =  fa_score(
												    __snake_case    ,__snake_case    ,labels=__snake_case    ,pos_label=__snake_case    ,average=__snake_case    ,sample_weight=__snake_case   )
												return {"f1": float(__snake_case   ) if score.size == 1 else score}




