





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							[
    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)

_SCREAMING_SNAKE_CASE	 =							logging.getLogger()


def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      argparse.ArgumentParser()
    parser.add_argument("-f"    )
    _lowerCAmelCase				=      parser.parse_args()
    return args.f


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Optional[int]="eval"    ):



    '''simple docstring'''





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


_SCREAMING_SNAKE_CASE	 =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class    lowerCAmelCase_		(		__magic_name__		):




 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_flax_glue.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertGreaterEqual(result["eval_accuracy"]			,     0.75		)




 @slow
 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_clm_flax.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertLess(result["eval_perplexity"]			,     100		)




 @slow
 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_summarization_flax.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase			,     split="test"		)
         self.assertGreaterEqual(result["test_rouge1"]			,     10		)
         self.assertGreaterEqual(result["test_rouge2"]			,     2		)
         self.assertGreaterEqual(result["test_rougeL"]			,     7		)
         self.assertGreaterEqual(result["test_rougeLsum"]			,     7		)




 @slow
 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_mlm_flax.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertLess(result["eval_perplexity"]			,     42		)




 @slow
 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_ta_mlm_flax.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertGreaterEqual(result["eval_accuracy"]			,     0.42		)




 @slow
 def 							_snake_case   (	self		)    ->							Optional[int]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     _lowerCAmelCase				=      7 if get_gpu_count() > 1 else 2

     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_flax_ner.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertGreaterEqual(result["eval_accuracy"]			,     0.75		)
         self.assertGreaterEqual(result["eval_f1"]			,     0.3		)





 @slow
 def 							_snake_case   (	self		)    ->							Dict:
     _lowerCAmelCase				=      self.get_auto_remove_tmp_dir()
     _lowerCAmelCase				=      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(_lowerCAmelCase			,     "argv"			,     _lowerCAmelCase		):
         run_qa.main()
         _lowerCAmelCase				=      get_results(_lowerCAmelCase		)
         self.assertGreaterEqual(result["eval_f1"]			,     30		)
         self.assertGreaterEqual(result["eval_exact"]			,     30		)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        raise TypeError("Input value must be an 'int' type"    )

    _lowerCAmelCase				=      0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
       import doctest

       doctest.testmod()


