


import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCAmelCase_   :				Any						      = [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCAmelCase_   :				List[str]						      = logging.getLogger()







def  SCREAMING_SNAKE_CASE_		(    )			->	Any:

       """simple docstring"""

       a_      :	List[str]							=						argparse.ArgumentParser()
       parser.add_argument('-f'       )
       a_      :	List[Any]							=						parser.parse_args()
       return args.f







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    int="eval"       )			->	Dict:

       """simple docstring"""

       a_      :	Dict							=						os.path.join(__A						,					F"""{split}_results.json"""       )
       if os.path.exists(__A       ):
              with open(__A						,					'r'       ) as f:
                     return json.load(__A       )
       raise ValueError(F"""can't find {path}"""       )


UpperCAmelCase_   :				Dict						      = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Optional[int]:
             a_      :	List[str]							=						self.get_auto_remove_tmp_dir()
             a_      :	List[str]							=						F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_flax_glue.main()
                    a_      :	str							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertGreaterEqual(result['eval_accuracy']  ,							0.75  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[Any]:
             a_      :	List[str]							=						self.get_auto_remove_tmp_dir()
             a_      :	Optional[int]							=						F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_clm_flax.main()
                    a_      :	Union[str, Any]							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertLess(result['eval_perplexity']  ,							1_0_0  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[Any]:
             a_      :	Any							=						self.get_auto_remove_tmp_dir()
             a_      :	Optional[Any]							=						F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_summarization_flax.main()
                    a_      :	Any							=						get_results(SCREAMING_SNAKE_CASE__  ,							split='test'  )
                    self.assertGreaterEqual(result['test_rouge1']  ,							1_0  )
                    self.assertGreaterEqual(result['test_rouge2']  ,							2  )
                    self.assertGreaterEqual(result['test_rougeL']  ,							7  )
                    self.assertGreaterEqual(result['test_rougeLsum']  ,							7  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[int]:
             a_      :	str							=						self.get_auto_remove_tmp_dir()
             a_      :	Any							=						F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_mlm_flax.main()
                    a_      :	Dict							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertLess(result['eval_perplexity']  ,							4_2  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    int:
             a_      :	Dict							=						self.get_auto_remove_tmp_dir()
             a_      :	Dict							=						F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_ta_mlm_flax.main()
                    a_      :	Tuple							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertGreaterEqual(result['eval_accuracy']  ,							0.42  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    str:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_      :	List[str]							=						7 if get_gpu_count() > 1 else 2

             a_      :	Any							=						self.get_auto_remove_tmp_dir()
             a_      :	int							=						F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_flax_ner.main()
                    a_      :	int							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertGreaterEqual(result['eval_accuracy']  ,							0.75  )
                    self.assertGreaterEqual(result['eval_f1']  ,							0.3  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Any:
             a_      :	Optional[Any]							=						self.get_auto_remove_tmp_dir()
             a_      :	int							=						F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

             with patch.object(SCREAMING_SNAKE_CASE__  ,							'argv'  ,							SCREAMING_SNAKE_CASE__  ):
                    run_qa.main()
                    a_      :	Tuple							=						get_results(SCREAMING_SNAKE_CASE__  )
                    self.assertGreaterEqual(result['eval_f1']  ,							3_0  )
                    self.assertGreaterEqual(result['eval_exact']  ,							3_0  )






from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_   :				List[Any]						      = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


UpperCAmelCase_   :				Optional[Any]						      = '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'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.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> 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])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


UpperCAmelCase_   :				Optional[Any]						      = '\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             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  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : List[str]=1  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="binary"  ,							SCREAMING_SNAKE_CASE__       : Dict=None  )	->    Any:
             a_      :	List[Any]							=						fa_score(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							labels=SCREAMING_SNAKE_CASE__  ,							pos_label=SCREAMING_SNAKE_CASE__  ,							average=SCREAMING_SNAKE_CASE__  ,							sample_weight=SCREAMING_SNAKE_CASE__  )
             return {"f1": float(SCREAMING_SNAKE_CASE__  ) if score.size == 1 else score}


