

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__												=						[
    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__												=						logging.getLogger()



def   __UpperCAmelCase					(      )     ->	Optional[int]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						argparse.ArgumentParser()
					parser.add_argument('''-f'''      )
					_a      :       List[str]   =						parser.parse_args()
					return args.f



def   __UpperCAmelCase					(      __a :     str						,__a :     Optional[int]="eval"      )     ->	int:

					"""simple docstring"""

					_a      :       Optional[int]   =						os.path.join(__a						,F"""{split}_results.json"""      )
					if os.path.exists(__a      ):
										with open(__a						,'''r'''      ) as f:
															return json.load(__a      )
					raise ValueError(F"""can't find {path}"""      )


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







class 							UpperCAmelCase_						(     __lowercase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       List[str]   =						self.get_auto_remove_tmp_dir()
												_a      :       Union[str, Any]   =						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(_a			,				'''argv'''			,				_a			):
																	run_flax_glue.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_accuracy''']			,				0.75			)


							@slow
							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[Any]   =						self.get_auto_remove_tmp_dir()
												_a      :       Union[str, 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(_a			,				'''argv'''			,				_a			):
																	run_clm_flax.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertLess(result['''eval_perplexity''']			,				1_0_0			)


							@slow
							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						self.get_auto_remove_tmp_dir()
												_a      :       List[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(_a			,				'''argv'''			,				_a			):
																	run_summarization_flax.main()
																	_a      :       Optional[Any]   =						get_results(_a			,				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			)       ->       Dict:
												_a      :       str   =						self.get_auto_remove_tmp_dir()
												_a      :       str   =						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(_a			,				'''argv'''			,				_a			):
																	run_mlm_flax.main()
																	_a      :       List[Any]   =						get_results(_a			)
																	self.assertLess(result['''eval_perplexity''']			,				4_2			)


							@slow
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       List[Any]   =						self.get_auto_remove_tmp_dir()
												_a      :       Tuple   =						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(_a			,				'''argv'''			,				_a			):
																	run_ta_mlm_flax.main()
																	_a      :       Union[str, Any]   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_accuracy''']			,				0.42			)


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

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




							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Tuple   =						self.get_auto_remove_tmp_dir()
												_a      :       Tuple   =						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(_a			,				'''argv'''			,				_a			):
																	run_qa.main()
																	_a      :       Any   =						get_results(_a			)
																	self.assertGreaterEqual(result['''eval_f1''']			,				3_0			)
																	self.assertGreaterEqual(result['''eval_exact''']			,				3_0			)







'''simple docstring'''







from __future__ import annotations

from math import gcd






def       lowercase					(_A ,    _A = 2 ,    _A = 1 ,    _A = 3 ,    ):







       """simple docstring"""







       if num < 2:
              raise ValueError('The input value cannot be less than 2'       )

       # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
       # algorithm struggles to find factors that are divisible by two.
       # As a workaround, we specifically check for two and even inputs.
       #   See: https://math.stackexchange.com/a/2856214/165820
       if num > 2 and num % 2 == 0:
              return 2

       # Pollard's Rho algorithm requires a function that returns pseudorandom
       # values between 0 <= X < ``num``.  It doesn't need to be random in the
       # sense that the output value is cryptographically secure or difficult
       # to calculate, it only needs to be random in the sense that all output
       # values should be equally likely to appear.
       # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
       # However, the success of Pollard's algorithm isn't guaranteed and is
       # determined in part by the initial seed and the chosen random function.
       # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
       # where ``C`` is a value that we can modify between each attempt.
       def rand_fn(_A ,    _A ,    _A       ) -> int:
              return (pow(_A ,    2       ) + step) % modulus

       for _ in range(_A       ):
              # These track the position within the cycle detection logic.
              _lowerCAmelCase     :				Dict       =							seed
              _lowerCAmelCase     :				int       =							seed

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     _lowerCAmelCase     :				str       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Union[str, Any]       =							rand_fn(_A ,    _A ,    _A       )

                     # At some point both the tortoise and the hare will enter a cycle whose
                     # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                     # the tortoise and hare will end up on the same value modulo ``p``.
                     # We can detect when this happens because the position difference between
                     # the tortoise and the hare will share a common divisor with ``num``.
                     _lowerCAmelCase     :				Optional[int]       =							gcd(hare - tortoise ,    _A       )

                     if divisor == 1:
                            # No common divisor yet, just keep searching.
                            continue
                     else:
                            # We found a common divisor!
                            if divisor == num:
                                   # Unfortunately, the divisor is ``num`` itself and is useless.
                                   break
                            else:
                                   # The divisor is a nontrivial factor of ``num``!
                                   return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
              _lowerCAmelCase     :				Tuple       =							hare

              # The new step value for the random function can just be incremented.
              # At first the results will be similar to what the old function would
              # have produced, but the value will quickly diverge after a bit.
              step += 1

       # We haven't found a divisor within the requested number of attempts.
       # We were unlucky or ``num`` itself is actually prime.
       return None


if __name__ == "__main__":
       import argparse

       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument(
           """num""",
           type=int,
           help="""The value to find a divisor of""",
       )
       parser.add_argument(
           """--attempts""",
           type=int,
           default=3,
           help="""The number of attempts before giving up""",
       )
       lowerCAmelCase     :  List[str]											=    parser.parse_args()

       lowerCAmelCase     :  List[str]											=    pollard_rho(args.num, attempts=args.attempts)
       if divisor is None:
              print(F'''{args.num} is probably prime''')
       else:
              lowerCAmelCase     :  Union[str, Any]											=    args.num // divisor
              print(F'''{args.num} = {divisor} * {quotient}''')

