




"""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


A_      =    logging.get_logger(__name__)

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

A_      =    {
    '''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''',
    },
}

A_      =    {
    '''jukebox''': 5_12,
}






class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = VOCAB_FILES_NAMES
 lowercase__			 = PRETRAINED_VOCAB_FILES_MAP
 lowercase__			 = PRETRAINED_LYRIC_TOKENS_SIZES
 lowercase__			 = ["input_ids", "attention_mask"]


 def __init__(    self:		Any,				a_:		Dict,				a_:		List[str],				a_:		int,				a_:		Tuple=["v3", "v2", "v2"],				a_:		Tuple=512,				a_:		Dict=5,				a_:		Dict="<|endoftext|>",				**a_:		Tuple,				):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  AddedToken(a_,				lstrip=a_,				rstrip=a_		) if isinstance(a_,				a_		) else unk_token
   super().__init__(
       unk_token=a_,				n_genres=a_,				version=a_,				max_n_lyric_tokens=a_,				**a_,				)
   _snake_case							:		Tuple								=  version
   _snake_case							:		Tuple								=  max_n_lyric_tokens
   _snake_case							:		Optional[int]								=  n_genres

   with open(a_,				encoding="""utf-8"""		) as vocab_handle:
     _snake_case							:		str								=  json.load(a_		)

   with open(a_,				encoding="""utf-8"""		) as vocab_handle:
     _snake_case							:		str								=  json.load(a_		)

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

   _snake_case							:		Tuple								=  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							:		Dict								=  oov.replace(r"""\-'""",				r"""\-+'"""		)

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


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Any,				a_:		Optional[Any],				a_:		Optional[int],				a_:		Dict		):




   '''simple docstring'''



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

   _snake_case							:		Any								=  [[self.lyrics_encoder.get(a_,				0		) for character in list_lyrics[0]], [], []]
   return artists_id, list_genres, lyric_ids


 def 			UpperCamelCase_       (    self:		Dict,				a_:		Union[str, Any]		):




   '''simple docstring'''



   return list(a_		)


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Any,				a_:		Optional[Any],				a_:		List[Any],				**a_:		int		):




   '''simple docstring'''



   _snake_case		, _snake_case		, _snake_case							:		Dict								=  self.prepare_for_tokenization(a_,				a_,				a_		)
   _snake_case							:		Optional[Any]								=  self._tokenize(a_		)
   return artist, genre, lyrics


 def 			UpperCamelCase_       (    self:		str,				a_:		str,				a_:		str,				a_:		str,				a_:		bool = False		):




   '''simple docstring'''



   for idx in range(len(self.version		)		):
     if self.version[idx] == "v3":
       _snake_case							:		Union[str, Any]								=  artists[idx].lower()
       _snake_case							:		Any								=  [genres[idx].lower()]
     else:
       _snake_case							:		str								=  self._normalize(artists[idx]		) + """.v2"""
       _snake_case							:		List[Any]								=  [
           self._normalize(a_		) + """.v2""" for genre in genres[idx].split("""_"""		)
       ]  # split is for the full dictionary with combined genres

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

   _snake_case							:		List[Any]								=  self._run_strip_accents(a_		)
   _snake_case							:		int								=  lyrics.replace("""\\""",				"""\n"""		)
   _snake_case							:		Optional[int]								=  self.out_of_vocab.sub("""""",				a_		), [], []
   return artists, genres, lyrics


 def 			UpperCamelCase_       (    self:		Tuple,				a_:		List[str]		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Any,				a_:		str		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  (
       [chr(a_		) for i in range(ord("""a"""		),				ord("""z"""		) + 1		)]
       + [chr(a_		) for i in range(ord("""A"""		),				ord("""Z"""		) + 1		)]
       + [chr(a_		) for i in range(ord("""0"""		),				ord("""9"""		) + 1		)]
       + ["""."""]
   )
   _snake_case							:		Union[str, Any]								=  frozenset(a_		)
   _snake_case							:		Optional[Any]								=  re.compile(r"""_+"""		)
   _snake_case							:		List[str]								=  """""".join([c if c in accepted else """_""" for c in text.lower()]		)
   _snake_case							:		Optional[Any]								=  pattern.sub("""_""",				a_		).strip("""_"""		)
   return text


 def 			UpperCamelCase_       (    self:		int,				a_:		List[str]		):




   '''simple docstring'''



   return " ".join(a_		)


 def 			UpperCamelCase_       (    self:		int,				a_:		Union[str, Any],				a_:		Optional[Union[str, TensorType]] = None,				a_:		bool = False		):




   '''simple docstring'''



   if not isinstance(a_,				a_		):
     _snake_case							:		int								=  TensorType(a_		)

   # 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							:		str								=  tf.constant
     _snake_case							:		Dict								=  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							:		str								=  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							:		Any								=  jnp.array
     _snake_case							:		Optional[int]								=  _is_jax
   else:
     _snake_case							:		Optional[Any]								=  np.asarray
     _snake_case							:		Union[str, Any]								=  _is_numpy

   # Do the tensor conversion in batch

   try:
     if prepend_batch_axis:
       _snake_case							:		List[str]								=  [inputs]

     if not is_tensor(a_		):
       _snake_case							:		Tuple								=  as_tensor(a_		)
   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:		Union[str, Any],				a_:		Any,				a_:		Tuple,				a_:		Dict="",				a_:		Dict="pt"		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  [0, 0, 0]
   _snake_case							:		Tuple								=  [artist] * len(self.version		)
   _snake_case							:		List[Any]								=  [genres] * len(self.version		)

   _snake_case		, _snake_case		, _snake_case							:		Dict								=  self.tokenize(a_,				a_,				a_		)
   _snake_case		, _snake_case		, _snake_case							:		Dict								=  self._convert_token_to_id(a_,				a_,				a_		)

   _snake_case							:		Union[str, Any]								=  [-INFINITY] * len(full_tokens[-1]		)
   _snake_case							:		Optional[Any]								=  [
       self.convert_to_tensors(
           [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]],				tensor_type=a_		)
       for i in range(len(self.version		)		)
   ]
   return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}		)


 def 			UpperCamelCase_       (    self:		List[Any],				a_:		str,				a_:		Optional[str] = None		):




   '''simple docstring'''



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

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

   _snake_case							:		Optional[Any]								=  os.path.join(
       a_,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""genres_file"""]		)
   with open(a_,				"""w""",				encoding="""utf-8"""		) as f:
     f.write(json.dumps(self.genres_encoder,				ensure_ascii=a_		)		)

   _snake_case							:		Dict								=  os.path.join(
       a_,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""lyrics_file"""]		)
   with open(a_,				"""w""",				encoding="""utf-8"""		) as f:
     f.write(json.dumps(self.lyrics_encoder,				ensure_ascii=a_		)		)

   return (artists_file, genres_file, lyrics_file)


 def 			UpperCamelCase_       (    self:		Dict,				a_:		Tuple,				a_:		List[str],				a_:		Tuple		):




   '''simple docstring'''



   _snake_case							:		str								=  self.artists_decoder.get(a_		)
   _snake_case							:		Dict								=  [self.genres_decoder.get(a_		) for genre in genres_index]
   _snake_case							:		Dict								=  [self.lyrics_decoder.get(a_		) for character in lyric_index]
   return artist, genres, lyrics








"""simple docstring"""


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    _snake_case							:		List[Any]								=  np.full((len(snake_case__   ), sequence_length, 2)		,       snake_case__   )
  else:
    _snake_case							:		Any								=  np.full((len(snake_case__   ), sequence_length)		,       snake_case__   )

  for i, tensor in enumerate(snake_case__   ):
    if padding_side == "right":
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		Dict								=  tensor[:sequence_length]
      else:
        _snake_case							:		List[Any]								=  tensor[:sequence_length]
    else:
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		str								=  tensor[:sequence_length]
      else:
        _snake_case							:		Tuple								=  tensor[:sequence_length]

  return out_tensor.tolist()


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		str								=  ord(snake_case__   )
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
    return True
  _snake_case							:		Union[str, Any]								=  unicodedata.category(snake_case__   )
  if cat.startswith("""P"""   ):
    return True
  return False






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = True
 lowercase__			 = None
 lowercase__			 = None
 lowercase__			 = -1_00
 lowercase__			 = "pt"


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   import torch

   _snake_case							:		Optional[Any]								=  """label""" if """label""" in features[0].keys() else """labels"""
   _snake_case							:		str								=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   _snake_case							:		Any								=  self.tokenizer.pad(
       a_,				padding=self.padding,				max_length=self.max_length,				pad_to_multiple_of=self.pad_to_multiple_of,				return_tensors="""pt""" if labels is None else None,				)

   if labels is None:
     return batch

   _snake_case							:		Optional[int]								=  torch.tensor(batch["""entity_ids"""]		).shape[1]
   _snake_case							:		Any								=  self.tokenizer.padding_side
   if padding_side == "right":
     _snake_case							:		Optional[int]								=  [
         list(a_		) + [self.label_pad_token_id] * (sequence_length - len(a_		)) for label in labels
     ]
   else:
     _snake_case							:		Union[str, Any]								=  [
         [self.label_pad_token_id] * (sequence_length - len(a_		)) + list(a_		) for label in labels
     ]

   _snake_case							:		List[Any]								=  [feature["""ner_tags"""] for feature in features]
   _snake_case							:		str								=  padding_tensor(a_,				-1,				a_,				a_		)
   _snake_case							:		Any								=  [feature["""original_entity_spans"""] for feature in features]
   _snake_case							:		int								=  padding_tensor(a_,				(-1, -1),				a_,				a_		)
   _snake_case							:		str								=  {k: torch.tensor(a_,				dtype=torch.intaa		) for k, v in batch.items()}

   return batch


