






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)
a__       =     logging.getLogger()

def _UpperCAmelCase   (	a						:	Path  ,					a						:	list ):
							snake_case__	  =       """\n""".join(a )
							Path(a ).open("""w""" ).writelines(a )


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

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



class    _lowerCAmelCase			(		lowercase_		):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







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

											snake_case__	  =       str(Path(self.get_auto_remove_tmp_dir()) / """scores.json""")
											snake_case__	  =       """translation_en_to_de""" if model == T5_TINY else """summarization"""
											snake_case__	  =       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(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																		run_generate()
																		assert Path(UpperCamelCase__).exists()
																		# os.remove(Path(output_file_name))



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											self.run_eval_tester(UpperCamelCase__)



				@parameterized.expand([BART_TINY, MBART_TINY])
				@slow
				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Tuple):






											'''simple docstring'''







											self.run_eval_tester(UpperCamelCase__)



				@parameterized.expand([T5_TINY, MBART_TINY])
				@slow
				def 					__magic_name__							(      self    :    Any			,		UpperCamelCase__    :    Any):






											'''simple docstring'''







											snake_case__	  =       Path(self.get_auto_remove_tmp_dir()) / """utest_input.source"""
											snake_case__	  =       input_file_name.parent / """utest_output.txt"""
											assert not output_file_name.exists()

											snake_case__	  =       {
											    """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!""",
											    ],
											}

											snake_case__	  =       Path(self.get_auto_remove_tmp_dir())
											snake_case__	  =       str(tmp_dir / """scores.json""")
											snake_case__	  =       str(tmp_dir / """val.target""")
											_dump_articles(UpperCamelCase__			,		text["""en"""])
											_dump_articles(UpperCamelCase__			,		text["""de"""])
											snake_case__	  =       """translation_en_to_de""" if model == T5_TINY else """summarization"""
											snake_case__	  =       F'''
            run_eval_search.py
            {model}
            {str(UpperCamelCase__)}
            {str(UpperCamelCase__)}
            --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(UpperCamelCase__			,		"""argv"""			,		UpperCamelCase__):
																		with CaptureStdout() as cs:
																									run_search()
																		snake_case__	  =       [""" num_beams | length_penalty""", model, """Best score args"""]
																		snake_case__	  =       ["""Info"""]
																		if "translation" in task:
																									expected_strings.append("""bleu""")
																		else:
																									expected_strings.extend(UpperCamelCase__)
																		for w in expected_strings:
																									assert w in cs.out
																		for w in un_expected_strings:
																									assert w not in cs.out
																		assert Path(UpperCamelCase__).exists()
																		os.remove(Path(UpperCamelCase__))








from math import factorial

a__       =     {str(digit): factorial(digit) for digit in range(1_0)}

def _UpperCAmelCase   (	a						:	int ):
							if not isinstance(a  ,					a ):
														raise TypeError("""Parameter number must be int""" )

							if number < 0:
														raise ValueError("""Parameter number must be greater than or equal to 0""" )

							# Converts number in string to iterate on its digits and adds its factorial.
							return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )

def _UpperCAmelCase   (	a						:	int = 60  ,					a						:	int = 100_0000 ):

							if not isinstance(a  ,					a ) or not isinstance(a  ,					a ):
														raise TypeError("""Parameters chain_length and number_limit must be int""" )

							if chain_length <= 0 or number_limit <= 0:
														raise ValueError(
														    """Parameters chain_length and number_limit must be greater than 0""" )

							# the counter for the chains with the exact desired length
							snake_case__	  =       0
							# the cached sizes of the previous chains
							snake_case__	  =       {}

							for start_chain_element in range(1  ,					a ):
														# The temporary set will contain the elements of the chain
														snake_case__	  =       set()
														snake_case__	  =       0

														# Stop computing the chain when you find a cached size, a repeating item or the
														# length is greater then the desired one.
														snake_case__	  =       start_chain_element
														while (
														    chain_element not in chain_sets_lengths
														    and chain_element not in chain_set
														    and chain_set_length <= chain_length
														):
																					chain_set.add(a )
																					chain_set_length += 1
																					snake_case__	  =       digit_factorial_sum(a )

														if chain_element in chain_sets_lengths:
																					chain_set_length += chain_sets_lengths[chain_element]

														snake_case__	  =       chain_set_length

														# If chain contains the exact amount of elements increase the counter
														if chain_set_length == chain_length:
																					chains_counter += 1

							return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
