






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					:		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)

__UpperCamelCase					:		Tuple	  =						logging.getLogger()
def      a_ (      )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     argparse.ArgumentParser()
				parser.add_argument('-f'  )
				snake_case__									=     parser.parse_args()
				return args.f
def      a_ (      _A      ,   _A="eval"  )			->			str:







				"""simple docstring"""
				snake_case__									=     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}'''  )


__UpperCamelCase					:		Optional[Any]	  =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class   __SCREAMING_SNAKE_CASE(	a_	):
							def lowerCAmelCase_ (     self:      Dict						)		->		List[Any]:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_flax_glue.main()
															snake_case__									=     get_results(UpperCamelCase						)
															self.assertGreaterEqual(result['eval_accuracy']       ,	0.75						)
							@slow
							def lowerCAmelCase_ (     self:      int						)		->		List[Any]:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_clm_flax.main()
															snake_case__									=     get_results(UpperCamelCase						)
															self.assertLess(result['eval_perplexity']       ,	1_00						)
							@slow
							def lowerCAmelCase_ (     self:      List[str]						)		->		int:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_summarization_flax.main()
															snake_case__									=     get_results(UpperCamelCase       ,	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:      Dict						)		->		Dict:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_mlm_flax.main()
															snake_case__									=     get_results(UpperCamelCase						)
															self.assertLess(result['eval_perplexity']       ,	42						)
							@slow
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_ta_mlm_flax.main()
															snake_case__									=     get_results(UpperCamelCase						)
															self.assertGreaterEqual(result['eval_accuracy']       ,	0.42						)
							@slow
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											snake_case__									=     7 if get_gpu_count() > 1 else 2

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


							@slow
							def lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											snake_case__									=     self.get_auto_remove_tmp_dir()
											snake_case__									=     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(UpperCamelCase       ,	'argv'       ,	UpperCamelCase						):
															run_qa.main()
															snake_case__									=     get_results(UpperCamelCase						)
															self.assertGreaterEqual(result['eval_f1']       ,	30						)
															self.assertGreaterEqual(result['eval_exact']       ,	30						)












__UpperCamelCase					:		List[str]	  =						256
# Modulus to hash a string
__UpperCamelCase					:		int	  =						1000003
def      a_ (      _A      ,   _A  )			->			bool:







				"""simple docstring"""
				snake_case__									=     len(_A  )
				snake_case__									=     len(_A  )
				if p_len > t_len:
								return False

				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     1

				# Calculating the hash of pattern and substring of text
				for i in range(_A  ):
								snake_case__									=     (ord(pattern[i]  ) + p_hash * alphabet_size) % modulus
								snake_case__									=     (ord(text[i]  ) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								snake_case__									=     (modulus_power * alphabet_size) % modulus

				for i in range(0      ,   t_len - p_len + 1  ):
								if text_hash == p_hash and text[i : i + p_len] == pattern:
												return True
								if i == t_len - p_len:
												continue
								# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
								snake_case__									=     (
								    (text_hash - ord(text[i]  ) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]  )
								) % modulus
				return False
def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     'abc1abc12'
				snake_case__									=     'alskfjaldsabc1abc1abc12k23adsfabcabc'
				snake_case__									=     'alskfjaldsk23adsfabcabc'
				assert rabin_karp(_A      ,   _A  ) and not rabin_karp(_A      ,   _A  )

				# Test 2)
				snake_case__									=     'ABABX'
				snake_case__									=     'ABABZABABYABABX'
				assert rabin_karp(_A      ,   _A  )

				# Test 3)
				snake_case__									=     'AAAB'
				snake_case__									=     'ABAAAAAB'
				assert rabin_karp(_A      ,   _A  )

				# Test 4)
				snake_case__									=     'abcdabcy'
				snake_case__									=     'abcxabcdabxabcdabcdabcy'
				assert rabin_karp(_A      ,   _A  )

				# Test 5)
				snake_case__									=     'Lü'
				snake_case__									=     'Lüsai'
				assert rabin_karp(_A      ,   _A  )
				snake_case__									=     'Lue'
				assert not rabin_karp(_A      ,   _A  )
				print('Success.'  )


if __name__ == "__main__":
							test_rabin_karp()




