






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   _lowerCamelCase						(    )   ->       Dict:
    _UpperCAmelCase					:   str      	=					argparse.ArgumentParser()
    parser.add_argument('''-f'''			)
    _UpperCAmelCase					:   int      	=					parser.parse_args()
    return args.f

def   _lowerCamelCase						(    __A     :				str		,		__A     :				int="eval"			)   ->       Union[str, Any]:
    _UpperCAmelCase					:   List[str]      	=					os.path.join(UpperCAmelCase_		,		f'''{split}_results.json'''			)
    if os.path.exists(UpperCAmelCase_			):
        with open(UpperCAmelCase_		,		'''r'''			) as f:
            return json.load(UpperCAmelCase_			)
    raise ValueError(f'''can\'t find {path}'''			)


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


class 				A_     (    __lowercase      ):






 '''simple docstring'''




 def 			snake_case__					(						self)			->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   Union[str, Any]      	=					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(__A				,	'''argv'''				,	__A):
         run_flax_glue.main()
         _UpperCAmelCase					:   List[Any]      	=					get_results(__A)
         self.assertGreaterEqual(result['''eval_accuracy''']				,	0.75)


 @slow
 def 			snake_case__					(						self)			->		List[str]:

     """simple docstring"""



     _UpperCAmelCase					:   Optional[Any]      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   List[Any]      	=					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(__A				,	'''argv'''				,	__A):
         run_clm_flax.main()
         _UpperCAmelCase					:   Tuple      	=					get_results(__A)
         self.assertLess(result['''eval_perplexity''']				,	100)


 @slow
 def 			snake_case__					(						self)			->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   Dict      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   str      	=					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(__A				,	'''argv'''				,	__A):
         run_summarization_flax.main()
         _UpperCAmelCase					:   str      	=					get_results(__A				,	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:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   Any      	=					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(__A				,	'''argv'''				,	__A):
         run_mlm_flax.main()
         _UpperCAmelCase					:   List[str]      	=					get_results(__A)
         self.assertLess(result['''eval_perplexity''']				,	42)


 @slow
 def 			snake_case__					(						self)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   Tuple      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   Optional[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(__A				,	'''argv'''				,	__A):
         run_ta_mlm_flax.main()
         _UpperCAmelCase					:   List[str]      	=					get_results(__A)
         self.assertGreaterEqual(result['''eval_accuracy''']				,	0.42)


 @slow
 def 			snake_case__					(						self)			->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					7 if get_gpu_count() > 1 else 2

     _UpperCAmelCase					:   Optional[int]      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   int      	=					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(__A				,	'''argv'''				,	__A):
         run_flax_ner.main()
         _UpperCAmelCase					:   str      	=					get_results(__A)
         self.assertGreaterEqual(result['''eval_accuracy''']				,	0.75)
         self.assertGreaterEqual(result['''eval_f1''']				,	0.3)


 @slow
 def 			snake_case__					(						self)			->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   List[str]      	=					self.get_auto_remove_tmp_dir()
     _UpperCAmelCase					:   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(__A				,	'''argv'''				,	__A):
         run_qa.main()
         _UpperCAmelCase					:   Union[str, Any]      	=					get_results(__A)
         self.assertGreaterEqual(result['''eval_f1''']				,	30)
         self.assertGreaterEqual(result['''eval_exact''']				,	30)






from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
