





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_							=				[
    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_							=				logging.getLogger()





def  __magic_name__  (				)				-> Tuple:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				,       lowercase="eval"				)				-> Any:

							"""simple docstring"""



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


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




class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






		def 						snake_case__				(							self  )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   int  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   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(lowerCAmelCase_,   """argv""",   lowerCAmelCase_  ):
																run_flax_glue.main()
																lowercase_	:   Tuple  				=     get_results(lowerCAmelCase_  )
																self.assertGreaterEqual(result["""eval_accuracy"""],   0.75  )






		@slow
		def 						snake_case__				(							self  )       -> int:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   Any  				=     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()
																lowercase_	:   List[Any]  				=     get_results(lowerCAmelCase_  )
																self.assertLess(result["""eval_perplexity"""],   1_00  )






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






									"""simple docstring"""
									lowercase_	:   Any  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   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(lowerCAmelCase_,   """argv""",   lowerCAmelCase_  ):
																run_summarization_flax.main()
																lowercase_	:   List[Any]  				=     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:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   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(lowerCAmelCase_,   """argv""",   lowerCAmelCase_  ):
																run_mlm_flax.main()
																lowercase_	:   Optional[int]  				=     get_results(lowerCAmelCase_  )
																self.assertLess(result["""eval_perplexity"""],   42  )






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






									"""simple docstring"""
									lowercase_	:   List[str]  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   int  				=     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()
																lowercase_	:   str  				=     get_results(lowerCAmelCase_  )
																self.assertGreaterEqual(result["""eval_accuracy"""],   0.42  )






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






									"""simple docstring"""
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									lowercase_	:   List[str]  				=     7 if get_gpu_count() > 1 else 2

									lowercase_	:   Tuple  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   Tuple  				=     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()
																lowercase_	:   List[str]  				=     get_results(lowerCAmelCase_  )
																self.assertGreaterEqual(result["""eval_accuracy"""],   0.75  )
																self.assertGreaterEqual(result["""eval_f1"""],   0.3  )






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






									"""simple docstring"""
									lowercase_	:   List[str]  				=     self.get_auto_remove_tmp_dir()
									lowercase_	:   Any  				=     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()
																lowercase_	:   Dict  				=     get_results(lowerCAmelCase_  )
																self.assertGreaterEqual(result["""eval_f1"""],   30  )
																self.assertGreaterEqual(result["""eval_exact"""],   30  )







"""simple docstring"""
from __future__ import annotations

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

from sympy import diff






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 10**-10       ):
	A_		:    Tuple						     = a
	while True:
		A_		:    List[str]						     = Decimal(snake_case__       ) - (
		    Decimal(eval(snake_case__       )       ) / Decimal(eval(str(diff(snake_case__       )       )       )       )  # noqa: S307
		)
		# This number dictates the accuracy of the answer
		if abs(eval(snake_case__       )       ) < precision:  # noqa: S307
			return float(snake_case__       )


# 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)}')

