
"""simple docstring"""






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase   ):
	UpperCamelCase				=  LEDTokenizer
	UpperCamelCase				=  LEDTokenizerFast
	UpperCamelCase				=  True





	def   __lowerCAmelCase (						self    :str					)							->			List[Any]:







			'''simple docstring'''




			super().setUp()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [
			    '''l''',
			    '''o''',
			    '''w''',
			    '''e''',
			    '''r''',
			    '''s''',
			    '''t''',
			    '''i''',
			    '''d''',
			    '''n''',
			    '''\u0120''',
			    '''\u0120l''',
			    '''\u0120n''',
			    '''\u0120lo''',
			    '''\u0120low''',
			    '''er''',
			    '''\u0120lowest''',
			    '''\u0120newer''',
			    '''\u0120wider''',
			    '''<unk>''',
			]
			SCREAMING_SNAKE_CASE       :     List[str]				= dict(zip(lowerCamelCase_		, range(len(lowerCamelCase_					)					)					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
			SCREAMING_SNAKE_CASE       :     str				= {'''unk_token''': '''<unk>'''}

			SCREAMING_SNAKE_CASE       :     int				= os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''merges_file''']					)
			with open(self.vocab_file		, '''w'''		, encoding='''utf-8'''					) as fp:
					fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)
			with open(self.merges_file		, '''w'''		, encoding='''utf-8'''					) as fp:
					fp.write('''\n'''.join(lowerCamelCase_					)					)





	def   __lowerCAmelCase (						self    :int		, **lowerCamelCase_    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




			kwargs.update(self.special_tokens_map					)
			return self.tokenizer_class.from_pretrained(self.tmpdirname		, **lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[int]		, **lowerCamelCase_    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			kwargs.update(self.special_tokens_map					)
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		, **lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :int					)							->			Optional[int]:







			'''simple docstring'''




			return "lower newer", "lower newer"





	@cached_property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




			return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''					)





	@cached_property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			str:







			'''simple docstring'''




			return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''					)





	@require_torch
	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			SCREAMING_SNAKE_CASE       :     Any				= [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     str				= tokenizer(lowerCamelCase_		, max_length=len(lowerCamelCase_					)		, padding=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIsInstance(lowerCamelCase_		, lowerCamelCase_					)

					self.assertEqual((2, 9)		, batch.input_ids.shape					)
					self.assertEqual((2, 9)		, batch.attention_mask.shape					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= batch.input_ids.tolist()[0]
					self.assertListEqual(lowerCamelCase_		, lowerCamelCase_					)





	@require_torch
	def   __lowerCAmelCase (						self    :Tuple					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Tuple				= tokenizer(lowerCamelCase_		, padding=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIn('''input_ids'''		, lowerCamelCase_					)
					self.assertIn('''attention_mask'''		, lowerCamelCase_					)
					self.assertNotIn('''labels'''		, lowerCamelCase_					)
					self.assertNotIn('''decoder_attention_mask'''		, lowerCamelCase_					)





	@require_torch
	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= [
			    '''Summary of the text.''',
			    '''Another summary.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     int				= tokenizer(text_target=lowerCamelCase_		, max_length=32		, padding='''max_length'''		, return_tensors='''pt'''					)
					self.assertEqual(32		, targets['''input_ids'''].shape[1]					)





	@require_torch
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer(
					    ['''I am a small frog''' * 10_24, '''I am a small frog''']		, padding=lowerCamelCase_		, truncation=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIsInstance(lowerCamelCase_		, lowerCamelCase_					)
					self.assertEqual(batch.input_ids.shape		, (2, 51_22)					)





	@require_torch
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ['''A long paragraph for summarization.''']
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [
			    '''Summary of the text.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer(lowerCamelCase_		, return_tensors='''pt'''					)
					SCREAMING_SNAKE_CASE       :     Dict				= tokenizer(text_target=lowerCamelCase_		, return_tensors='''pt'''					)
					SCREAMING_SNAKE_CASE       :     str				= inputs['''input_ids''']
					SCREAMING_SNAKE_CASE       :     List[str]				= targets['''input_ids''']
					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)





	@require_torch
	def   __lowerCAmelCase (						self    :List[str]					)							->			Any:







			'''simple docstring'''




			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Any				= ['''Summary of the text.''', '''Another summary.''']
					SCREAMING_SNAKE_CASE       :     int				= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer(lowerCamelCase_		, padding=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     str				= [[0] * len(lowerCamelCase_					) for x in encoded_output['''input_ids''']]
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer.pad(lowerCamelCase_					)
					self.assertSequenceEqual(outputs['''global_attention_mask''']		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			pass







	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
							SCREAMING_SNAKE_CASE       :     str				= self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.tokenizer_class.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     List[str]				= '''A, <mask> AllenNLP sentence.'''
							SCREAMING_SNAKE_CASE       :     str				= tokenizer_r.encode_plus(lowerCamelCase_		, add_special_tokens=lowerCamelCase_		, return_token_type_ids=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer_p.encode_plus(lowerCamelCase_		, add_special_tokens=lowerCamelCase_		, return_token_type_ids=lowerCamelCase_					)
							self.assertEqual(sum(tokens_r['''token_type_ids''']					)		, sum(tokens_p['''token_type_ids''']					)					)
							self.assertEqual(
							    sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)		, sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)		, )

							SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
							SCREAMING_SNAKE_CASE       :     Dict				= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)
							self.assertSequenceEqual(tokens_p['''input_ids''']		, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
							self.assertSequenceEqual(tokens_r['''input_ids''']		, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

							self.assertSequenceEqual(
							    lowerCamelCase_		, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
							self.assertSequenceEqual(
							    lowerCamelCase_		, ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(a_     )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





