
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


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

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

lowercase__	:List[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",
    },
}

lowercase__	:Union[str, Any]										=					{
    "jukebox": 512,
}







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =VOCAB_FILES_NAMES
							lowercase_ : Any       =PRETRAINED_VOCAB_FILES_MAP
							lowercase_ : Tuple       =PRETRAINED_LYRIC_TOKENS_SIZES
							lowercase_ : Tuple       =['''input_ids''', '''attention_mask''']




							def __init__(       self     ,A__     ,A__     ,A__     ,A__=["v3", "v2", "v2"]     ,A__=5_1_2     ,A__=5     ,A__="<|endoftext|>"     ,**A__     ,):
												lowercase					=		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__     ,)
												lowercase					=		version
												lowercase					=		max_n_lyric_tokens
												lowercase					=		n_genres

												with open(A__     ,encoding='''utf-8''') as vocab_handle:
																	lowercase					=		json.load(A__)

												with open(A__     ,encoding='''utf-8''') as vocab_handle:
																	lowercase					=		json.load(A__)

												with open(A__     ,encoding='''utf-8''') as vocab_handle:
																	lowercase					=		json.load(A__)

												lowercase					=		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:
																	lowercase					=		oov.replace(r'''\-\''''     ,r'''\-+\'''')

												lowercase					=		regex.compile(A__)
												lowercase					=		{v: k for k, v in self.artists_encoder.items()}
												lowercase					=		{v: k for k, v in self.genres_encoder.items()}
												lowercase					=		{v: k for k, v in self.lyrics_encoder.items()}




							@property
							def 				A__   (       self):
												return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)




							def 				A__   (       self):
												return dict(self.artists_encoder     ,self.genres_encoder     ,self.lyrics_encoder)




							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		[self.artists_encoder.get(A__     ,0) for artist in list_artists]
												for genres in range(len(A__)):
																	lowercase					=		[self.genres_encoder.get(A__     ,0) for genre in list_genres[genres]]
																	lowercase					=		list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))

												lowercase					=		[[self.lyrics_encoder.get(A__     ,0) for character in list_lyrics[0]], [], []]
												return artists_id, list_genres, lyric_ids




							def 				A__   (       self     ,A__):
												return list(A__)




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												lowercase			,		lowercase			,		lowercase					=		self.prepare_for_tokenization(A__     ,A__     ,A__)
												lowercase					=		self._tokenize(A__)
												return artist, genre, lyrics




							def 				A__   (       self     ,A__     ,A__     ,A__     ,A__ = False):
												for idx in range(len(self.version)):
																	if self.version[idx] == "v3":
																						lowercase					=		artists[idx].lower()
																						lowercase					=		[genres[idx].lower()]
																	else:
																						lowercase					=		self._normalize(artists[idx]) + '''.v2'''
																						lowercase					=		[
																						    self._normalize(A__) + '''.v2''' for genre in genres[idx].split('''_''')
																						]  # split is for the full dictionary with combined genres

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

												lowercase					=		self._run_strip_accents(A__)
												lowercase					=		lyrics.replace('''\\'''     ,'''\n''')
												lowercase					=		self.out_of_vocab.sub(''''''     ,A__), [], []
												return artists, genres, lyrics




							def 				A__   (       self     ,A__):
												lowercase					=		unicodedata.normalize('''NFD'''     ,A__)
												lowercase					=		[]
												for char in text:
																	lowercase					=		unicodedata.category(A__)
																	if cat == "Mn":
																						continue
																	output.append(A__)
												return "".join(A__)




							def 				A__   (       self     ,A__):
												lowercase					=		(
												    [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)]
												    + ['''.''']
												)
												lowercase					=		frozenset(A__)
												lowercase					=		re.compile(r'''_+''')
												lowercase					=		''''''.join([c if c in accepted else '''_''' for c in text.lower()])
												lowercase					=		pattern.sub('''_'''     ,A__).strip('''_''')
												return text




							def 				A__   (       self     ,A__):
												return " ".join(A__)




							def 				A__   (       self     ,A__     ,A__ = None     ,A__ = False):
												# Convert to TensorType
												if not isinstance(A__     ,A__):
																	lowercase					=		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

																	lowercase					=		tf.constant
																	lowercase					=		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

																	lowercase					=		torch.tensor
																	lowercase					=		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

																	lowercase					=		jnp.array
																	lowercase					=		_is_jax
												else:
																	lowercase					=		np.asarray
																	lowercase					=		_is_numpy

												# Do the tensor conversion in batch

												try:
																	if prepend_batch_axis:
																						lowercase					=		[inputs]

																	if not is_tensor(A__):
																						lowercase					=		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     ,A__     ,A__     ,A__=""     ,A__="pt"):
												lowercase					=		[0, 0, 0]
												lowercase					=		[artist] * len(self.version)
												lowercase					=		[genres] * len(self.version)

												lowercase			,		lowercase			,		lowercase					=		self.tokenize(A__     ,A__     ,A__)
												lowercase			,		lowercase			,		lowercase					=		self._convert_token_to_id(A__     ,A__     ,A__)

												lowercase					=		[-INFINITY] * len(full_tokens[-1])
												lowercase					=		[
												    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 				A__   (       self     ,A__     ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return

												lowercase					=		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__))

												lowercase					=		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__))

												lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		self.artists_decoder.get(A__)
												lowercase					=		[self.genres_decoder.get(A__) for genre in genres_index]
												lowercase					=		[self.lyrics_decoder.get(A__) for character in lyric_index]
												return artist, genres, lyrics






from typing import TYPE_CHECKING

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


lowercase__	:Tuple										=					{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Union[str, Any]										=					[
							    "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BioGptForCausalLM",
							    "BioGptForTokenClassification",
							    "BioGptForSequenceClassification",
							    "BioGptModel",
							    "BioGptPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase__	:Any										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




