






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 		UpperCAmelCase_						(	UpperCAmelCase_					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   List[str]						=					None
						A     :   Tuple						=					BloomTokenizerFast
						A     :   List[str]						=					BloomTokenizerFast
						A     :   Union[str, Any]						=					True
						A     :   Optional[int]						=					False
						A     :   Dict						=					'tokenizer_file'
						A     :   List[str]						=					{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								super().setUp()
								snake_case_				:							int						 =       BloomTokenizerFast.from_pretrained("bigscience/tokenizer"				)
								tokenizer.save_pretrained(self.tmpdirname				)






						def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Any:
								kwargs.update(self.special_tokens_map				)
								return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**_lowercase				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								snake_case_				:							Optional[int]						 =       self.get_rust_tokenizer()

								snake_case_				:							Any						 =       ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
								snake_case_				:							Dict						 =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

								snake_case_				:							List[Any]						 =       tokenizer.batch_encode_plus(_lowercase				)['input_ids']
								self.assertListEqual(_lowercase		,				_lowercase				)

								snake_case_				:							Optional[Any]						 =       tokenizer.batch_decode(_lowercase				)
								self.assertListEqual(_lowercase		,				_lowercase				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=6				) ->				Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
												snake_case_				:							int						 =       self.rust_tokenizer_class.from_pretrained(_lowercase		,				**_lowercase				)
												# tokenizer_r.pad_token = None # Hotfixing padding = None
												# Simple input
												snake_case_				:							Dict						 =       'This is a simple input'
												snake_case_				:							List[str]						 =       ['This is a simple input 1', 'This is a simple input 2']
												snake_case_				:							List[Any]						 =       ('This is a simple input', 'This is a pair')
												snake_case_				:							List[str]						 =       [
												    ('This is a simple input 1', 'This is a simple input 2'),
												    ('This is a simple pair 1', 'This is a simple pair 2'),
												]

												# Simple input tests
												try:
														tokenizer_r.encode(_lowercase		,				max_length=_lowercase				)
														tokenizer_r.encode_plus(_lowercase		,				max_length=_lowercase				)

														tokenizer_r.batch_encode_plus(_lowercase		,				max_length=_lowercase				)
														tokenizer_r.encode(_lowercase		,				max_length=_lowercase				)
														tokenizer_r.batch_encode_plus(_lowercase		,				max_length=_lowercase				)
												except ValueError:
														self.fail("Bloom Tokenizer should be able to deal with padding"				)

												snake_case_				:							List[Any]						 =       None  # Hotfixing padding = None
												self.assertRaises(_lowercase		,				tokenizer_r.encode		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"				)

												# Simple input
												self.assertRaises(_lowercase		,				tokenizer_r.encode_plus		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"				)

												# Simple input
												self.assertRaises(
												    _lowercase		,				tokenizer_r.batch_encode_plus		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"		,				)

												# Pair input
												self.assertRaises(_lowercase		,				tokenizer_r.encode		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"				)

												# Pair input
												self.assertRaises(_lowercase		,				tokenizer_r.encode_plus		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"				)

												# Pair input
												self.assertRaises(
												    _lowercase		,				tokenizer_r.batch_encode_plus		,				_lowercase		,				max_length=_lowercase		,				padding="max_length"		,				)






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							List[str]						 =       self.get_rust_tokenizer()
								snake_case_				:							Dict						 =       load_dataset("xnli"		,				"all_languages"		,				split="test"		,				streaming=_lowercase				)

								snake_case_				:							Optional[Any]						 =       next(iter(_lowercase				)				)['premise']  # pick up one data
								snake_case_				:							Any						 =       list(sample_data.values()				)

								snake_case_				:							int						 =       list(map(tokenizer.encode		,				_lowercase				)				)
								snake_case_				:							Optional[Any]						 =       [tokenizer.decode(_lowercase		,				clean_up_tokenization_spaces=_lowercase				) for x in output_tokens]
								self.assertListEqual(_lowercase		,				_lowercase				)






						def       _lowerCAmelCase       (	self				) ->				int:
								# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
								# any sequence length constraints. This test of the parent class will fail since it relies on the
								# maximum sequence length of the positoonal embeddings.
								self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,				1				)
								self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,				1				)








from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowercase  :							Optional[Any]			  =    2_00
# 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.
lowercase  :							Optional[Any]			  =    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.
lowercase  :							Union[str, Any]			  =    0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 10_00))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Union[str, Any]						 =       len([g for position, g in enumerate(_a    ) if g == main_target[position]]    )
		return (item, float(_a    ))

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str    ):
		snake_case_				:							Tuple						 =       random.randint(0  ,      len(_a    ) - 1    )
		snake_case_				:							Any						 =       parent_a[:random_slice] + parent_a[random_slice:]
		snake_case_				:							Optional[Any]						 =       parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]    ):
		snake_case_				:							str						 =       list(_a    )
		if random.uniform(0  ,      1    ) < MUTATION_PROBABILITY:
				snake_case_				:							Optional[Any]						 =       random.choice(_a    )
		return "".join(_a    )

def      lowerCAmelCase__	(    _a       :			tuple[str, float]  ,      _a       :			list[tuple[str, float]]  ,      _a       :			list[str]  ,      ):
		snake_case_				:							Tuple						 =       []
		# Generate more children proportionally to the fitness score.
		snake_case_				:							Optional[int]						 =       int(parent_a[1] * 1_00    ) + 1
		snake_case_				:							Tuple						 =       10 if child_n >= 10 else child_n
		for _ in range(_a    ):
				snake_case_				:							int						 =       population_score[random.randint(0  ,      _a    )][0]

				snake_case_ ,       snake_case_				:							Any						 =       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      lowerCAmelCase__	(    _a       :			str  ,      _a       :			list[str]  ,      _a       :			bool = True    ):

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				snake_case_				:							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.
		snake_case_				:							Optional[Any]						 =       sorted({c for c in target if c not in genes}    )
		if not_in_genes_list:
				snake_case_				:							str						 =       F'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
				raise ValueError(_a    )

		# Generate random starting population.
		snake_case_				:							Optional[int]						 =       []
		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.
		snake_case_ ,       snake_case_				:							Dict						 =       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.
				snake_case_				:							Optional[int]						 =       [evaluate(_a  ,      _a    ) for item in population]

				# Check if there is a matching evolution.
				snake_case_				:							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.
				snake_case_				:							Union[str, Any]						 =       population[: int(N_POPULATION / 3    )]
				population.clear()
				population.extend(_a    )
				# Normalize population score to be between 0 and 1.
				snake_case_				:							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__":
							lowercase  :							Dict			  =    (
							    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
							)
							lowercase  :							Optional[int]			  =    list(
							    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
							    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
							)
							lowercase  ,lowercase  ,lowercase  :							Optional[Any]			  =    basic(target_str, genes_list)
							print(
							    F"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
							)
