





'''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


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

__UpperCamelCase					    =	{
    "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",
    },
}

__UpperCamelCase					    =	{
    "jukebox": 512,
}




class  _A (					_A		):
		lowercase__:   Any				     =   VOCAB_FILES_NAMES
		lowercase__:   str				     =   PRETRAINED_VOCAB_FILES_MAP
		lowercase__:   Any				     =   PRETRAINED_LYRIC_TOKENS_SIZES
		lowercase__:   str				     =   ["input_ids", "attention_mask"]

		def __init__(   self					:   int  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   int=["v3", "v2", "v2"]  ,      __magic_name__					:   str=5_12  ,      __magic_name__					:   Tuple=5  ,      __magic_name__					:   str="<|endoftext|>"  ,      **__magic_name__					:   Tuple  ,      )					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     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  ,      )
					__snake_case					:      str							      =     version
					__snake_case					:      Union[str, Any]							      =     max_n_lyric_tokens
					__snake_case					:      Union[str, Any]							      =     n_genres

					with open(__lowerCAmelCase  ,      encoding="""utf-8"""			) as vocab_handle:
								__snake_case					:      Dict							      =     json.load(__lowerCAmelCase			)

					with open(__lowerCAmelCase  ,      encoding="""utf-8"""			) as vocab_handle:
								__snake_case					:      int							      =     json.load(__lowerCAmelCase			)

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

					__snake_case					:      Any							      =     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			) == 79:
								__snake_case					:      List[Any]							      =     oov.replace(r"""\-\'"""  ,      r"""\-+\'"""			)

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

		@property
		def 	lowercase__     (   self					:   str			)					->    Tuple:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict			)					->    str:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

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

					__snake_case					:      Tuple							      =     [[self.lyrics_encoder.get(__lowerCAmelCase  ,      0			) for character in list_lyrics[0]], [], []]
					return artists_id, list_genres, lyric_ids

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   Tuple			)					->    Optional[int]:


					"""simple docstring"""

					return list(__lowerCAmelCase			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Tuple  ,      **__magic_name__					:   int			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     self.prepare_for_tokenization(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
					__snake_case					:      List[Any]							      =     self._tokenize(__lowerCAmelCase			)
					return artist, genre, lyrics

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   str  ,      __magic_name__					:   str  ,      __magic_name__					:   str  ,      __magic_name__					:   bool = False			)					->    Tuple[str, str, str, Dict[str, Any]]:


					"""simple docstring"""

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

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

					__snake_case					:      Union[str, Any]							      =     self._run_strip_accents(__lowerCAmelCase			)
					__snake_case					:      Optional[Any]							      =     lyrics.replace("""\\"""  ,      """\n"""			)
					__snake_case					:      List[Any]							      =     self.out_of_vocab.sub(""""""  ,      __lowerCAmelCase			), [], []
					return artists, genres, lyrics

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Tuple			)					->    Union[str, Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      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			)]
					    + ["""."""]
					)
					__snake_case					:      Optional[int]							      =     frozenset(__lowerCAmelCase			)
					__snake_case					:      Optional[Any]							      =     re.compile(r"""_+"""			)
					__snake_case					:      Optional[int]							      =     """""".join([c if c in accepted else """_""" for c in text.lower()]			)
					__snake_case					:      Dict							      =     pattern.sub("""_"""  ,      __lowerCAmelCase			).strip("""_"""			)
					return text

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   List[str]			)					->    str:


					"""simple docstring"""

					return " ".join(__lowerCAmelCase			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   Optional[Union[str, TensorType]] = None  ,      __magic_name__					:   bool = False			)					->    Any:


					"""simple docstring"""

					if not isinstance(__lowerCAmelCase  ,      __lowerCAmelCase			):
								__snake_case					:      Optional[int]							      =     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

								__snake_case					:      Dict							      =     tf.constant
								__snake_case					:      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

								__snake_case					:      Any							      =     torch.tensor
								__snake_case					:      int							      =     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

								__snake_case					:      int							      =     jnp.array
								__snake_case					:      Dict							      =     _is_jax
					else:
								__snake_case					:      int							      =     np.asarray
								__snake_case					:      Optional[Any]							      =     _is_numpy

					# Do the tensor conversion in batch

					try:
								if prepend_batch_axis:
											__snake_case					:      Tuple							      =     [inputs]

								if not is_tensor(__lowerCAmelCase			):
											__snake_case					:      List[Any]							      =     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					:   Optional[Any]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[str]=""  ,      __magic_name__					:   Any="pt"			)					->    BatchEncoding:


					"""simple docstring"""

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

					__snake_case      ,     __snake_case      ,     __snake_case					:      str							      =     self.tokenize(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)
					__snake_case      ,     __snake_case      ,     __snake_case					:      Any							      =     self._convert_token_to_id(__lowerCAmelCase  ,      __lowerCAmelCase  ,      __lowerCAmelCase			)

					__snake_case					:      Optional[Any]							      =     [-INFINITY] * len(full_tokens[-1]			)
					__snake_case					:      Any							      =     [
					    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 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[str] = None			)					->    Tuple[str]:


					"""simple docstring"""

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

					__snake_case					:      str							      =     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			)			)

					__snake_case					:      Tuple							      =     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			)			)

					__snake_case					:      Optional[int]							      =     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 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[str]			)					->    Union[str, Any]:


					"""simple docstring"""

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







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (position - 1) // 2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					Optional[int]      )   ->	None:
    _A      =       []
    _A      =       {}
    _A      =       0

 def __len__(    self					:					str      )   ->	int:
    return self.elements

 def __repr__(    self					:					Optional[int]      )   ->	str:
    return str(self.heap      )

 def     snake_case_      (    self					:					str      )   ->	bool:
    # Check if the priority queue is empty
    return self.elements == 0

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an element with given priority to the queue
    self.heap.append((elem, weight)      )
    _A      =       self.elements
    self.elements += 1
    self._bubble_up(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	T:
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0			,						self.elements - 1      )
    _A  ,    _A      =       self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       _A  ,    _A      =       self.heap[0]
       self._bubble_down(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (upward movement) [to be used internally
    # only]
    _A      =       self.position_map[elem]
    if curr_pos == 0:
       return None
    _A      =       get_parent_position(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (downward movement) [to be used
    # internally only]
    _A      =       self.position_map[elem]
    _A  ,    _A      =       self.heap[curr_pos]
    _A      =       get_child_left_position(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    if child_left_position < self.elements and child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    else:
       return None
    if child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	None:
    # Swap the nodes at the given positions
    _A      =       self.heap[nodea_pos][0]
    _A      =       self.heap[nodea_pos][0]
    _A  ,    _A      =       (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    _A      =       nodea_pos
    _A      =       nodea_pos


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

 def __repr__(    self					:					str      )   ->	str:
    return str(self.connections      )

 def __len__(    self					:					Dict      )   ->	int:
    return self.nodes

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					T      )   ->	None:
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       _A      =       {}
       self.nodes += 1

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _A      =       {node: maxsize for node in graph.connections}
   _A      =       {node: None for node in graph.connections}

   _A      =       MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_snake_case	,   _snake_case				)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   _A      =       priority_queue.extract_min()
   _A      =       0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         _A      =       dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_snake_case	,   dist[neighbour]				)
         _A      =       node

    # running prim's algorithm
   while not priority_queue.is_empty():
      _A      =       priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            _A      =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_snake_case	,   dist[neighbour]				)
            _A      =       node
   return dist, parent



