
"""simple docstring"""


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase								=		logging.getLogger()






def   lowerCAmelCase_  (						snake_case_						:     Optional[int]				,       snake_case_						:     Dict     )  ->Optional[Any]:
			lowerCamelCase__      :	Any						="\n".join(snake_case_     )
			Path(snake_case_     ).open('w'     ).writelines(snake_case_     )


lowerCAmelCase								=		"""patrickvonplaten/t5-tiny-random"""
lowerCAmelCase								=		"""sshleifer/bart-tiny-random"""
lowerCAmelCase								=		"""sshleifer/tiny-mbart"""

lowerCAmelCase								=		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class        A_      (	A__  ):



							"""simple docstring"""






							def  UpperCAmelCase__				(    self    :Union[str, Any]	,				lowerCamelCase_    :List[str]			):





										"""simple docstring"""

										lowerCamelCase__      :	Dict						=Path(self.get_auto_remove_tmp_dir()			) / "utest_input.source"
										lowerCamelCase__      :	Any						=input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()
										lowerCamelCase__      :	Optional[Any]						=[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
										_dump_articles(a_	,				a_			)

										lowerCamelCase__      :	str						=str(Path(self.get_auto_remove_tmp_dir()			) / 'scores.json'			)
										lowerCamelCase__      :	Tuple						="translation_en_to_de" if model == T5_TINY else "summarization"
										lowerCamelCase__      :	int						=f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

										with patch.object(a_	,				'argv'	,				a_			):
													run_generate()
													assert Path(a_			).exists()
													# os.remove(Path(output_file_name))





							def  UpperCAmelCase__				(    self    :List[Any]			):





										"""simple docstring"""

										self.run_eval_tester(a_			)





							@parameterized.expand([BART_TINY, MBART_TINY]			)
							@slow
							def  UpperCAmelCase__				(    self    :List[Any]	,				lowerCamelCase_    :str			):





										"""simple docstring"""

										self.run_eval_tester(a_			)





							@parameterized.expand([T5_TINY, MBART_TINY]			)
							@slow
							def  UpperCAmelCase__				(    self    :List[Any]	,				lowerCamelCase_    :Optional[Any]			):





										"""simple docstring"""

										lowerCamelCase__      :	Any						=Path(self.get_auto_remove_tmp_dir()			) / "utest_input.source"
										lowerCamelCase__      :	Union[str, Any]						=input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()

										lowerCamelCase__      :	int						={
										    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
										    "de": [
										        "Maschinelles Lernen ist großartig, oder?",
										        "Ich esse gerne Bananen",
										        "Morgen ist wieder ein toller Tag!",
										    ],
										}

										lowerCamelCase__      :	List[Any]						=Path(self.get_auto_remove_tmp_dir()			)
										lowerCamelCase__      :	Dict						=str(tmp_dir / 'scores.json'			)
										lowerCamelCase__      :	List[str]						=str(tmp_dir / 'val.target'			)
										_dump_articles(a_	,				text['en']			)
										_dump_articles(a_	,				text['de']			)
										lowerCamelCase__      :	Any						="translation_en_to_de" if model == T5_TINY else "summarization"
										lowerCamelCase__      :	int						=f"""
            run_eval_search.py
            {model}
            {str(a_			)}
            {str(a_			)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
										testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']			)

										with patch.object(a_	,				'argv'	,				a_			):
													with CaptureStdout() as cs:
																run_search()
													lowerCamelCase__      :	Dict						=[" num_beams | length_penalty", model, "Best score args"]
													lowerCamelCase__      :	int						=["Info"]
													if "translation" in task:
																expected_strings.append('bleu'			)
													else:
																expected_strings.extend(a_			)
													for w in expected_strings:
																assert w in cs.out
													for w in un_expected_strings:
																assert w not in cs.out
													assert Path(a_			).exists()
													os.remove(Path(a_			)			)



'''simple docstring'''
lowerCAmelCase   : List[str]             =				2_5_6
# Modulus to hash a string
lowerCAmelCase   : Tuple             =				1_0_0_0_0_0_3




def 	_A  (       A							,A							)  ->				bool:
	lowercase						:		List[Any]   					=		len(A							)
	lowercase						:		List[Any]   					=		len(A							)
	if p_len > t_len:
		return False

	lowercase						:		List[str]   					=		0
	lowercase						:		Dict   					=		0
	lowercase						:		Any   					=		1

	# Calculating the hash of pattern and substring of text
	for i in range(A							):
		lowercase						:		Optional[int]   					=		(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
		lowercase						:		Any   					=		(ord(text[i]							) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		lowercase						:		List[str]   					=		(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
		lowercase						:		Any   					=		(
		    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]							)
		) % modulus
	return False




def 	_A  (       )  ->				None:
	lowercase						:		Dict   					=		"abc1abc12"
	lowercase						:		Union[str, Any]   					=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
	lowercase						:		Any   					=		"alskfjaldsk23adsfabcabc"
	assert rabin_karp(A							,A							) and not rabin_karp(A							,A							)

	# Test 2)
	lowercase						:		str   					=		"ABABX"
	lowercase						:		Union[str, Any]   					=		"ABABZABABYABABX"
	assert rabin_karp(A							,A							)

	# Test 3)
	lowercase						:		str   					=		"AAAB"
	lowercase						:		List[str]   					=		"ABAAAAAB"
	assert rabin_karp(A							,A							)

	# Test 4)
	lowercase						:		List[str]   					=		"abcdabcy"
	lowercase						:		str   					=		"abcxabcdabxabcdabcdabcy"
	assert rabin_karp(A							,A							)

	# Test 5)
	lowercase						:		int   					=		"Lü"
	lowercase						:		Optional[Any]   					=		"Lüsai"
	assert rabin_karp(A							,A							)
	lowercase						:		Tuple   					=		"Lue"
	assert not rabin_karp(A							,A							)
	print("Success."							)


if __name__ == "__main__":
					test_rabin_karp()


