





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__:    List[str]  	=	logging.get_logger(__name__)

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

A__:    Optional[Any]  	=	{
    '''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__:    Optional[Any]  	=	{
    '''jukebox''': 512,
}




class        _a    (    UpperCamelCase__):

      """simple docstring"""


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






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


          UpperCamelCase__:	str					=     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	,    )
          UpperCamelCase__:	str					=     version
          UpperCamelCase__:	List[Any]					=     max_n_lyric_tokens
          UpperCamelCase__:	List[str]					=     n_genres

          with open(__lowerCamelCase	,    encoding="utf-8"					) as vocab_handle:
              UpperCamelCase__:	Optional[int]					=     json.load(__lowerCamelCase					)

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

          with open(__lowerCamelCase	,    encoding="utf-8"					) as vocab_handle:
              UpperCamelCase__:	List[Any]					=     json.load(__lowerCamelCase					)

          UpperCamelCase__:	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:
              UpperCamelCase__:	List[str]					=     oov.replace(R"\-'"	,    R"\-+'"					)

          UpperCamelCase__:	str					=     regex.compile(__lowerCamelCase					)
          UpperCamelCase__:	List[str]					=     {v: k for k, v in self.artists_encoder.items()}
          UpperCamelCase__:	int					=     {v: k for k, v in self.genres_encoder.items()}
          UpperCamelCase__:	str					=     {v: k for k, v in self.lyrics_encoder.items()}






      @property
      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			str	,    __lowerCamelCase:			Optional[int]	,    __lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     [self.artists_encoder.get(__lowerCamelCase	,    0					) for artist in list_artists]
          for genres in range(len(__lowerCamelCase					)					):
              UpperCamelCase__:	str					=     [self.genres_encoder.get(__lowerCamelCase	,    0					) for genre in list_genres[genres]]
              UpperCamelCase__:	str					=     list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]					))

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






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			Tuple					):
          '''simple docstring'''


          return list(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			List[Any]	,    __lowerCamelCase:			List[str]	,    **__lowerCamelCase:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Union[str, Any]					=     self.prepare_for_tokenization(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)
          UpperCamelCase__:	List[Any]					=     self._tokenize(__lowerCamelCase					)
          return artist, genre, lyrics






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			str	,    __lowerCamelCase:			str	,    __lowerCamelCase:			str	,    __lowerCamelCase:			bool = False					):
          '''simple docstring'''


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

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

          UpperCamelCase__:	List[Any]					=     self._run_strip_accents(__lowerCamelCase					)
          UpperCamelCase__:	Optional[Any]					=     lyrics.replace("\\"	,    "\n"					)
          UpperCamelCase__:	Dict					=     self.out_of_vocab.sub(""	,    __lowerCamelCase					), [], []
          return artists, genres, lyrics






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			Tuple					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			str					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, 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					)]
              + ["."]
          )
          UpperCamelCase__:	int					=     frozenset(__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     re.compile(R"_+"					)
          UpperCamelCase__:	List[str]					=     "".join([c if c in accepted else "_" for c in text.lower()]					)
          UpperCamelCase__:	Any					=     pattern.sub("_"	,    __lowerCamelCase					).strip("_"					)
          return text






      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			List[str]					):
          '''simple docstring'''


          return " ".join(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			Optional[Union[str, TensorType]] = None	,    __lowerCamelCase:			bool = False					):
          '''simple docstring'''


          if not isinstance(__lowerCamelCase	,    __lowerCamelCase					):
              UpperCamelCase__:	Any					=     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

              UpperCamelCase__:	str					=     tf.constant
              UpperCamelCase__:	List[Any]					=     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

              UpperCamelCase__:	Optional[Any]					=     torch.tensor
              UpperCamelCase__:	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

              UpperCamelCase__:	Union[str, Any]					=     jnp.array
              UpperCamelCase__:	Dict					=     _is_jax
          else:
              UpperCamelCase__:	List[str]					=     np.asarray
              UpperCamelCase__:	int					=     _is_numpy

          # Do the tensor conversion in batch

          try:
              if prepend_batch_axis:
                  UpperCamelCase__:	Any					=     [inputs]

              if not is_tensor(__lowerCamelCase					):
                  UpperCamelCase__:	Optional[int]					=     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:			int	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			Tuple=""	,    __lowerCamelCase:			Optional[int]="pt"					):
          '''simple docstring'''


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

          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Optional[int]					=     self.tokenize(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)
          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Tuple					=     self._convert_token_to_id(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)

          UpperCamelCase__:	int					=     [-INFINITY] * len(full_tokens[-1]					)
          UpperCamelCase__:	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 			UpperCAmelCase_       (		self:			List[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

          UpperCamelCase__:	List[Any]					=     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					)					)

          UpperCamelCase__:	Optional[Any]					=     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					)					)

          UpperCamelCase__:	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 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Tuple					):
          '''simple docstring'''


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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__:    List[Any]  	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''OPTForCausalLM''',
       '''OPTModel''',
       '''OPTPreTrainedModel''',
       '''OPTForSequenceClassification''',
       '''OPTForQuestionAnswering''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]  	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    str  	=	[
       '''FlaxOPTForCausalLM''',
       '''FlaxOPTModel''',
       '''FlaxOPTPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   A__:    Optional[Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

