




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


__a      :       Union[str, 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)

__a      :       Any				  =   logging.getLogger()



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Tuple="eval"    )						->      List[Any]:





    """simple docstring"""

    __A         =	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}"    )


__a      :       List[Any]				  =   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class       __lowercase      (	__a  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertGreaterEqual(result["""eval_accuracy"""] ,	0.75				)




     @slow
     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertLess(result["""eval_perplexity"""] ,	100				)




     @slow
     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	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 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertLess(result["""eval_perplexity"""] ,	42				)




     @slow
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertGreaterEqual(result["""eval_accuracy"""] ,	0.42				)




     @slow
     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	7 if get_gpu_count() > 1 else 2

         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertGreaterEqual(result["""eval_accuracy"""] ,	0.75				)
             self.assertGreaterEqual(result["""eval_f1"""] ,	0.3				)





     @slow
     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         __A         =	self.get_auto_remove_tmp_dir()
         __A         =	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()
             __A         =	get_results(A__				)
             self.assertGreaterEqual(result["""eval_f1"""] ,	30				)
             self.assertGreaterEqual(result["""eval_exact"""] ,	30				)












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}""")
  # Find root of polynomial
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




