




"""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							:		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							:		Dict								=  self.tokenize(a_,				a_,				a_		)
   _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"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_      =    {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    ['''MaskFormerFeatureExtractor''']
     A_      =    ['''MaskFormerImageProcessor''']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_      =    [
         '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''MaskFormerForInstanceSegmentation''',
         '''MaskFormerModel''',
         '''MaskFormerPreTrainedModel''',
     ]
     A_      =    [
         '''MaskFormerSwinBackbone''',
         '''MaskFormerSwinModel''',
         '''MaskFormerSwinPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A_      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)


