



"""simple docstring"""






import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowerCAmelCase__              =							logging.get_logger(__name__)

lowerCAmelCase__              =							{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

lowerCAmelCase__              =							{
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

lowerCAmelCase__              =							{
    '''jukebox''': 512,
}






class 			__snake_case			(      _lowercase):
  snake_case__							:  List[Any]         =		VOCAB_FILES_NAMES
  snake_case__							:  int         =		PRETRAINED_VOCAB_FILES_MAP
  snake_case__							:  Optional[Any]         =		PRETRAINED_LYRIC_TOKENS_SIZES
  snake_case__							:  Optional[int]         =		["input_ids", "attention_mask"]




  def __init__(					self	:		str		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		Tuple		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Optional[int]=["v3", "v2", "v2"]		,   __lowerCAmelCase	:		Any=5_1_2		,   __lowerCAmelCase	:		Tuple=5		,   __lowerCAmelCase	:		List[str]="<|endoftext|>"		,   **__lowerCAmelCase	:		Any		,   ):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					AddedToken(__lowerCAmelCase		,   lstrip=__lowerCAmelCase		,   rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase		,   __lowerCAmelCase						) else unk_token
         super().__init__(
             unk_token=__lowerCAmelCase		,   n_genres=__lowerCAmelCase		,   version=__lowerCAmelCase		,   max_n_lyric_tokens=__lowerCAmelCase		,   **__lowerCAmelCase		,   )
         _lowerCamelCase		:							List[str]            =					version
         _lowerCamelCase		:							List[str]            =					max_n_lyric_tokens
         _lowerCamelCase		:							Optional[Any]            =					n_genres

         with open(__lowerCAmelCase		,   encoding='''utf-8'''						) as vocab_handle:
                _lowerCamelCase		:							Tuple            =					json.load(__lowerCAmelCase						)

         with open(__lowerCAmelCase		,   encoding='''utf-8'''						) as vocab_handle:
                _lowerCamelCase		:							Any            =					json.load(__lowerCAmelCase						)

         with open(__lowerCAmelCase		,   encoding='''utf-8'''						) as vocab_handle:
                _lowerCamelCase		:							List[Any]            =					json.load(__lowerCAmelCase						)

         _lowerCamelCase		:							List[str]            =					R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder						) == 7_9:
                _lowerCamelCase		:							Optional[Any]            =					oov.replace(R'''\-\''''		,   R'''\-+\''''						)

         _lowerCamelCase		:							Optional[Any]            =					regex.compile(__lowerCAmelCase						)
         _lowerCamelCase		:							Dict            =					{v: k for k, v in self.artists_encoder.items()}
         _lowerCamelCase		:							Tuple            =					{v: k for k, v in self.genres_encoder.items()}
         _lowerCamelCase		:							str            =					{v: k for k, v in self.lyrics_encoder.items()}




  @property
  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         return len(self.artists_encoder						) + len(self.genres_encoder						) + len(self.lyrics_encoder						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         return dict(self.artists_encoder		,   self.genres_encoder		,   self.lyrics_encoder						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Optional[Any]		,   __lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					[self.artists_encoder.get(__lowerCAmelCase		,   0						) for artist in list_artists]
         for genres in range(len(__lowerCAmelCase						)						):
                _lowerCamelCase		:							Union[str, Any]            =					[self.genres_encoder.get(__lowerCAmelCase		,   0						) for genre in list_genres[genres]]
                _lowerCamelCase		:							List[str]            =					list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]						))

         _lowerCamelCase		:							Union[str, Any]            =					[[self.lyrics_encoder.get(__lowerCAmelCase		,   0						) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids




  def 		SCREAMING_SNAKE_CASE							(					self	:		str		,   __lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






         return list(__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		List[str]		,   __lowerCAmelCase	:		Any		,   __lowerCAmelCase	:		Tuple		,   **__lowerCAmelCase	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							int            =					self.prepare_for_tokenization(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					self._tokenize(__lowerCAmelCase						)
         return artist, genre, lyrics




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		bool = False						):






         """simple docstring"""






         for idx in range(len(self.version						)						):
                if self.version[idx] == "v3":
                       _lowerCamelCase		:							Dict            =					artists[idx].lower()
                       _lowerCamelCase		:							List[str]            =					[genres[idx].lower()]
                else:
                       _lowerCamelCase		:							int            =					self._normalize(artists[idx]						) + '''.v2'''
                       _lowerCamelCase		:							List[str]            =					[
                           self._normalize(__lowerCAmelCase						) + '''.v2''' for genre in genres[idx].split('''_'''						)
                       ]  # split is for the full dictionary with combined genres

         if self.version[0] == "v2":
                _lowerCamelCase		:							Tuple            =					regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''						)
                _lowerCamelCase		:							int            =					'''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
                _lowerCamelCase		:							Optional[Any]            =					{vocab[index]: index + 1 for index in range(len(__lowerCAmelCase						)						)}
                _lowerCamelCase		:							List[Any]            =					0
                _lowerCamelCase		:							Any            =					len(__lowerCAmelCase						) + 1
                _lowerCamelCase		:							int            =					self.vocab
                _lowerCamelCase		:							Dict            =					{v: k for k, v in self.vocab.items()}
                _lowerCamelCase		:							Tuple            =					''''''
         else:
                _lowerCamelCase		:							int            =					regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'''						)

         _lowerCamelCase		:							Any            =					self._run_strip_accents(__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					lyrics.replace('''\\'''		,   '''\n'''						)
         _lowerCamelCase		:							Optional[int]            =					self.out_of_vocab.sub(''''''		,   __lowerCAmelCase						), [], []
         return artists, genres, lyrics




  def 		SCREAMING_SNAKE_CASE							(					self	:		int		,   __lowerCAmelCase	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					unicodedata.normalize('''NFD'''		,   __lowerCAmelCase						)
         _lowerCamelCase		:							Optional[int]            =					[]
         for char in text:
                _lowerCamelCase		:							List[str]            =					unicodedata.category(__lowerCAmelCase						)
                if cat == "Mn":
                       continue
                output.append(__lowerCAmelCase						)
         return "".join(__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple		,   __lowerCAmelCase	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					(
             [chr(__lowerCAmelCase						) for i in range(ord('''a'''						)		,   ord('''z'''						) + 1						)]
             + [chr(__lowerCAmelCase						) for i in range(ord('''A'''						)		,   ord('''Z'''						) + 1						)]
             + [chr(__lowerCAmelCase						) for i in range(ord('''0'''						)		,   ord('''9'''						) + 1						)]
             + ['''.''']
         )
         _lowerCamelCase		:							List[Any]            =					frozenset(__lowerCAmelCase						)
         _lowerCamelCase		:							Any            =					re.compile(R'''_+'''						)
         _lowerCamelCase		:							Optional[int]            =					''''''.join([c if c in accepted else '''_''' for c in text.lower()]						)
         _lowerCamelCase		:							List[str]            =					pattern.sub('''_'''		,   __lowerCAmelCase						).strip('''_'''						)
         return text




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict		,   __lowerCAmelCase	:		List[str]						):






         """simple docstring"""






         return " ".join(__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Optional[Union[str, TensorType]] = None		,   __lowerCAmelCase	:		bool = False						):






         """simple docstring"""






         if not isinstance(__lowerCAmelCase		,   __lowerCAmelCase						):
                _lowerCamelCase		:							str            =					TensorType(__lowerCAmelCase						)

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
                if not is_tf_available():
                       raise ImportError(
                           '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'''						)
                import tensorflow as tf

                _lowerCamelCase		:							Optional[int]            =					tf.constant
                _lowerCamelCase		:							Optional[int]            =					tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
                if not is_torch_available():
                       raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.'''						)
                import torch

                _lowerCamelCase		:							Any            =					torch.tensor
                _lowerCamelCase		:							Tuple            =					torch.is_tensor
         elif tensor_type == TensorType.JAX:
                if not is_flax_available():
                       raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.'''						)
                import jax.numpy as jnp  # noqa: F811

                _lowerCamelCase		:							str            =					jnp.array
                _lowerCamelCase		:							Dict            =					_is_jax
         else:
                _lowerCamelCase		:							Dict            =					np.asarray
                _lowerCamelCase		:							List[str]            =					_is_numpy

         # Do the tensor conversion in batch

         try:
                if prepend_batch_axis:
                       _lowerCamelCase		:							int            =					[inputs]

                if not is_tensor(__lowerCAmelCase						):
                       _lowerCamelCase		:							Dict            =					as_tensor(__lowerCAmelCase						)
         except:  # noqa E722
                raise ValueError(
                    '''Unable to create tensor, you should probably activate truncation and/or padding '''
                    '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'''						)

         return inputs




  def __call__(					self	:		Tuple		,   __lowerCAmelCase	:		Union[str, Any]		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		int=""		,   __lowerCAmelCase	:		Union[str, Any]="pt"						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					[0, 0, 0]
         _lowerCamelCase		:							Optional[Any]            =					[artist] * len(self.version						)
         _lowerCamelCase		:							int            =					[genres] * len(self.version						)

         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							List[Any]            =					self.tokenize(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)
         _lowerCamelCase     ,       _lowerCamelCase     ,       _lowerCamelCase		:							Dict            =					self._convert_token_to_id(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase						)

         _lowerCamelCase		:							Any            =					[-INFINITY] * len(full_tokens[-1]						)
         _lowerCamelCase		:							Optional[int]            =					[
             self.convert_to_tensors(
                 [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]		,   tensor_type=__lowerCAmelCase						)
             for i in range(len(self.version						)						)
         ]
         return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		str		,   __lowerCAmelCase	:		str		,   __lowerCAmelCase	:		Optional[str] = None						):






         """simple docstring"""






         if not os.path.isdir(__lowerCAmelCase						):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                return

         _lowerCamelCase		:							Optional[int]            =					os.path.join(
             __lowerCAmelCase		,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file''']						)
         with open(__lowerCAmelCase		,   '''w'''		,   encoding='''utf-8'''						) as f:
                f.write(json.dumps(self.artists_encoder		,   ensure_ascii=__lowerCAmelCase						)						)

         _lowerCamelCase		:							Dict            =					os.path.join(
             __lowerCAmelCase		,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file''']						)
         with open(__lowerCAmelCase		,   '''w'''		,   encoding='''utf-8'''						) as f:
                f.write(json.dumps(self.genres_encoder		,   ensure_ascii=__lowerCAmelCase						)						)

         _lowerCamelCase		:							Optional[Any]            =					os.path.join(
             __lowerCAmelCase		,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file''']						)
         with open(__lowerCAmelCase		,   '''w'''		,   encoding='''utf-8'''						) as f:
                f.write(json.dumps(self.lyrics_encoder		,   ensure_ascii=__lowerCAmelCase						)						)

         return (artists_file, genres_file, lyrics_file)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]		,   __lowerCAmelCase	:		int		,   __lowerCAmelCase	:		Dict		,   __lowerCAmelCase	:		Dict						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					self.artists_decoder.get(__lowerCAmelCase						)
         _lowerCamelCase		:							Optional[Any]            =					[self.genres_decoder.get(__lowerCAmelCase						) for genre in genres_index]
         _lowerCamelCase		:							Optional[int]            =					[self.lyrics_decoder.get(__lowerCAmelCase						) for character in lyric_index]
         return artist, genres, lyrics




"""simple docstring"""
from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	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.
SCREAMING_SNAKE_CASE_					     =	0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  len([g for position, g in enumerate(_lowerCAmelCase						) if g == main_target[position]]						)
						return (item, float(_lowerCAmelCase						))

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  random.randint(0 ,    len(_lowerCAmelCase						) - 1						)
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						__lowerCAmelCase     =  parent_a[:random_slice] + parent_a[random_slice:]
						return (child_a, child_a)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  list(_lowerCAmelCase						)
						if random.uniform(0 ,    1						) < MUTATION_PROBABILITY:
												__lowerCAmelCase     =  random.choice(_lowerCAmelCase						)
						return "".join(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    ):
						__lowerCAmelCase     =  []
						# Generate more children proportionally to the fitness score.
						__lowerCAmelCase     =  int(parent_a[1] * 100						) + 1
						__lowerCAmelCase     =  10 if child_n >= 10 else child_n
						for _ in range(_lowerCAmelCase						):
												__lowerCAmelCase     =  population_score[random.randint(0 ,    _lowerCAmelCase						)][0]

												__lowerCAmelCase   ,     __lowerCAmelCase     =  crossover(parent_a[0] ,    _lowerCAmelCase						)
												# Append new string to the population list.
												pop.append(mutate(_lowerCAmelCase ,    _lowerCAmelCase						)						)
												pop.append(mutate(_lowerCAmelCase ,    _lowerCAmelCase						)						)
						return pop

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = True						):

						# Verify if N_POPULATION is bigger than N_SELECTED
						if N_POPULATION < N_SELECTED:
												__lowerCAmelCase     =  f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
												raise ValueError(_lowerCAmelCase						)
						# Verify that the target contains no genes besides the ones inside genes variable.
						__lowerCAmelCase     =  sorted({c for c in target if c not in genes}						)
						if not_in_genes_list:
												__lowerCAmelCase     =  f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
												raise ValueError(_lowerCAmelCase						)

						# Generate random starting population.
						__lowerCAmelCase     =  []
						for _ in range(_lowerCAmelCase						):
												population.append("""""".join([random.choice(_lowerCAmelCase						) for i in range(len(_lowerCAmelCase						)						)]						)						)

						# Just some logs to know what the algorithms is doing.
						__lowerCAmelCase   ,     __lowerCAmelCase     =  0, 0

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

												# 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.
												__lowerCAmelCase     =  [evaluate(_lowerCAmelCase ,    _lowerCAmelCase						) for item in population]

												# Check if there is a matching evolution.
												__lowerCAmelCase     =  sorted(_lowerCAmelCase ,    key=lambda _lowerCAmelCase						: x[1] ,    reverse=_lowerCAmelCase						)
												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.
												__lowerCAmelCase     =  population[: int(N_POPULATION / 3						)]
												population.clear()
												population.extend(_lowerCAmelCase						)
												# Normalize population score to be between 0 and 1.
												__lowerCAmelCase     =  [
												    (item, score / len(_lowerCAmelCase						)) for item, score in population_score
												]

												# This is selection
												for i in range(_lowerCAmelCase						):
																		population.extend(select(population_score[int(_lowerCAmelCase						)] ,    _lowerCAmelCase ,    _lowerCAmelCase						)						)
																		# 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(_lowerCAmelCase						) > N_POPULATION:
																								break


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