






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


__lowerCamelCase	:		str										=   [
    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)

__lowerCamelCase	:		int										=   logging.getLogger()







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    str							       =argparse.ArgumentParser()
 parser.add_argument("""-f"""	)
 snake_case__  :    List[str]							       =parser.parse_args()
 return args.f







def 			A__							(      _a  :					Tuple		,  _a  :					Tuple="eval"	):





 '''simple docstring'''







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


__lowerCamelCase	:		Optional[Any]										=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class  _lowercase   (   UpperCamelCase_						):
      def 						lowercase__					(     self   ):
       snake_case__  :    int							       =self.get_auto_remove_tmp_dir()
       snake_case__  :    Tuple							       =F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_flax_glue.main()
        snake_case__  :    Union[str, Any]							       =get_results(UpperCamelCase__   )
        self.assertGreaterEqual(result["""eval_accuracy"""]				,			0.75   )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =self.get_auto_remove_tmp_dir()
       snake_case__  :    str							       =F"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

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

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_summarization_flax.main()
        snake_case__  :    Union[str, Any]							       =get_results(UpperCamelCase__				,			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 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.get_auto_remove_tmp_dir()
       snake_case__  :    Dict							       =F"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_mlm_flax.main()
        snake_case__  :    List[Any]							       =get_results(UpperCamelCase__   )
        self.assertLess(result["""eval_perplexity"""]				,			4_2   )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =self.get_auto_remove_tmp_dir()
       snake_case__  :    Union[str, Any]							       =F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_ta_mlm_flax.main()
        snake_case__  :    Optional[int]							       =get_results(UpperCamelCase__   )
        self.assertGreaterEqual(result["""eval_accuracy"""]				,			0.42   )
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =7 if get_gpu_count() > 1 else 2

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

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_flax_ner.main()
        snake_case__  :    List[str]							       =get_results(UpperCamelCase__   )
        self.assertGreaterEqual(result["""eval_accuracy"""]				,			0.75   )
        self.assertGreaterEqual(result["""eval_f1"""]				,			0.3   )



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

       with patch.object(UpperCamelCase__				,			"""argv"""				,			UpperCamelCase__   ):
        run_qa.main()
        snake_case__  :    str							       =get_results(UpperCamelCase__   )
        self.assertGreaterEqual(result["""eval_f1"""]				,			3_0   )
        self.assertGreaterEqual(result["""eval_exact"""]				,			3_0   )













from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__lowerCamelCase	:		Any										=   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__lowerCamelCase	:		str										=   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__lowerCamelCase	:		str										=   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 return float((preds == labels).mean()	)







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
                   """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
               }   )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)



      def 						lowercase__					(     self				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       else:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )





