



"""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


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

_lowerCAmelCase    :List[str]     =					{
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

_lowerCAmelCase    :Optional[int]     =					{
    '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',
    },
}

_lowerCAmelCase    :Tuple     =					{
    'jukebox': 512,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_LYRIC_TOKENS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A       ,	A       ,	A=["v3", "v2", "v2"]       ,	A=5_1_2       ,	A=5       ,	A="<|endoftext|>"       ,	**A       ,	)							->					Optional[Any]:
         _UpperCAmelCase							:  List[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       ,	)
         _UpperCAmelCase							:  Optional[Any]											=    version
         _UpperCAmelCase							:  int											=    max_n_lyric_tokens
         _UpperCAmelCase							:  Optional[Any]											=    n_genres

         with open(A       ,	encoding='''utf-8'''		) as vocab_handle:
             _UpperCAmelCase							:  int											=    json.load(A		)

         with open(A       ,	encoding='''utf-8'''		) as vocab_handle:
             _UpperCAmelCase							:  List[Any]											=    json.load(A		)

         with open(A       ,	encoding='''utf-8'''		) as vocab_handle:
             _UpperCAmelCase							:  Tuple											=    json.load(A		)

         _UpperCAmelCase							:  List[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		) == 7_9:
             _UpperCAmelCase							:  Tuple											=    oov.replace(r'''\-\''''       ,	r'''\-+\''''		)

         _UpperCAmelCase							:  int											=    regex.compile(A		)
         _UpperCAmelCase							:  List[str]											=    {v: k for k, v in self.artists_encoder.items()}
         _UpperCAmelCase							:  Dict											=    {v: k for k, v in self.genres_encoder.items()}
         _UpperCAmelCase							:  Union[str, Any]											=    {v: k for k, v in self.lyrics_encoder.items()}





     @property
     def  __lowerCAmelCase	(					self		)							->					Tuple:
         return len(self.artists_encoder		) + len(self.genres_encoder		) + len(self.lyrics_encoder		)





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         return dict(self.artists_encoder       ,	self.genres_encoder       ,	self.lyrics_encoder		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[Any]											=    [self.artists_encoder.get(A       ,	0		) for artist in list_artists]
         for genres in range(len(A		)		):
             _UpperCAmelCase							:  Optional[Any]											=    [self.genres_encoder.get(A       ,	0		) for genre in list_genres[genres]]
             _UpperCAmelCase							:  Tuple											=    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]		))

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





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:
         return list(A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	**A		)							->					Optional[Any]:
         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  Tuple											=    self.prepare_for_tokenization(A       ,	A       ,	A		)
         _UpperCAmelCase							:  Union[str, Any]											=    self._tokenize(A		)
         return artist, genre, lyrics





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A = False		)							->					Tuple[str, str, str, Dict[str, Any]]:
         for idx in range(len(self.version		)		):
             if self.version[idx] == "v3":
                 _UpperCAmelCase							:  str											=    artists[idx].lower()
                 _UpperCAmelCase							:  Optional[Any]											=    [genres[idx].lower()]
             else:
                 _UpperCAmelCase							:  Optional[int]											=    self._normalize(artists[idx]		) + '''.v2'''
                 _UpperCAmelCase							:  Dict											=    [
                     self._normalize(A		) + '''.v2''' for genre in genres[idx].split('''_'''		)
                 ]  # split is for the full dictionary with combined genres

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

         _UpperCAmelCase							:  Dict											=    self._run_strip_accents(A		)
         _UpperCAmelCase							:  List[Any]											=    lyrics.replace('''\\'''       ,	'''\n'''		)
         _UpperCAmelCase							:  int											=    self.out_of_vocab.sub(''''''       ,	A		), [], []
         return artists, genres, lyrics





     def  __lowerCAmelCase	(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    unicodedata.normalize('''NFD'''       ,	A		)
         _UpperCAmelCase							:  int											=    []
         for char in text:
             _UpperCAmelCase							:  str											=    unicodedata.category(A		)
             if cat == "Mn":
                 continue
             output.append(A		)
         return "".join(A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					str:
         _UpperCAmelCase							:  Any											=    (
             [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		)]
             + ['''.''']
         )
         _UpperCAmelCase							:  Union[str, Any]											=    frozenset(A		)
         _UpperCAmelCase							:  List[Any]											=    re.compile(r'''_+'''		)
         _UpperCAmelCase							:  List[Any]											=    ''''''.join([c if c in accepted else '''_''' for c in text.lower()]		)
         _UpperCAmelCase							:  List[Any]											=    pattern.sub('''_'''       ,	A		).strip('''_'''		)
         return text





     def  __lowerCAmelCase	(					self       ,	A		)							->					str:
         return " ".join(A		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					Any:
         # Convert to TensorType
         if not isinstance(A       ,	A		):
             _UpperCAmelCase							:  Optional[Any]											=    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

             _UpperCAmelCase							:  Union[str, Any]											=    tf.constant
             _UpperCAmelCase							:  Union[str, 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							:  Dict											=    torch.tensor
             _UpperCAmelCase							:  Optional[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

             _UpperCAmelCase							:  Optional[Any]											=    jnp.array
             _UpperCAmelCase							:  List[Any]											=    _is_jax
         else:
             _UpperCAmelCase							:  Tuple											=    np.asarray
             _UpperCAmelCase							:  List[str]											=    _is_numpy

         # Do the tensor conversion in batch

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

             if not is_tensor(A		):
                 _UpperCAmelCase							:  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       ,	A       ,	A       ,	A=""       ,	A="pt"		)							->					BatchEncoding:
         _UpperCAmelCase							:  List[Any]											=    [0, 0, 0]
         _UpperCAmelCase							:  Dict											=    [artist] * len(self.version		)
         _UpperCAmelCase							:  Any											=    [genres] * len(self.version		)

         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    self.tokenize(A       ,	A       ,	A		)
         _UpperCAmelCase							,				_UpperCAmelCase							,				_UpperCAmelCase							:  int											=    self._convert_token_to_id(A       ,	A       ,	A		)

         _UpperCAmelCase							:  List[str]											=    [-INFINITY] * len(full_tokens[-1]		)
         _UpperCAmelCase							:  List[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  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return

         _UpperCAmelCase							:  int											=    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		)		)

         _UpperCAmelCase							:  Dict											=    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		)		)

         _UpperCAmelCase							:  List[Any]											=    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  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A		)							->					str:
         _UpperCAmelCase							:  List[Any]											=    self.artists_decoder.get(A		)
         _UpperCAmelCase							:  Optional[Any]											=    [self.genres_decoder.get(A		) for genre in genres_index]
         _UpperCAmelCase							:  str											=    [self.lyrics_decoder.get(A		) for character in lyric_index]
         return artist, genres, lyrics







"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM
@require_torch
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Dict											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[str]											=    -1

         _UpperCAmelCase							:  List[str]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[str]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  List[Any]											=    tokenizer.decode(greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  str											=    TextStreamer(A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  List[str]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  List[Any]											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    -1

         _UpperCAmelCase							:  Union[str, Any]											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  str											=    tokenizer.decode(greedy_ids[0]		)

         _UpperCAmelCase							:  Union[str, Any]											=    TextIteratorStreamer(A		)
         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Any											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()
         _UpperCAmelCase							:  Any											=    ''''''
         for new_text in streamer:
             streamer_text += new_text

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  str											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Any											=    -1

         _UpperCAmelCase							:  Dict											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  Dict											=    model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A		)
         _UpperCAmelCase							:  Dict											=    greedy_ids[:, input_ids.shape[1] :]
         _UpperCAmelCase							:  List[str]											=    tokenizer.decode(new_greedy_ids[0]		)

         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Any											=    TextStreamer(A       ,	skip_prompt=A		)
             model.generate(A       ,	max_new_tokens=1_0       ,	do_sample=A       ,	streamer=A		)
         # The greedy text should be printed to stdout, except for the final "\n" in the streamer
         _UpperCAmelCase							:  Union[str, Any]											=    cs.out[:-1]

         self.assertEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
         # with actual models -- the dummy models' tokenizers are not aligned with their models, and
         # `skip_special_tokens=True` has no effect on them
         _UpperCAmelCase							:  int											=    AutoTokenizer.from_pretrained('''distilgpt2'''		)
         _UpperCAmelCase							:  Union[str, Any]											=    AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(A		)
         _UpperCAmelCase							:  Tuple											=    -1

         _UpperCAmelCase							:  int											=    torch.ones((1, 5)       ,	device=A		).long() * model.config.bos_token_id
         with CaptureStdout() as cs:
             _UpperCAmelCase							:  Optional[Any]											=    TextStreamer(A       ,	skip_special_tokens=A		)
             model.generate(A       ,	max_new_tokens=1       ,	do_sample=A       ,	streamer=A		)

         # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
         # re-tokenized, must only contain one token
         _UpperCAmelCase							:  Tuple											=    cs.out[:-1]  # Remove the final "\n"
         _UpperCAmelCase							:  int											=    tokenizer(A       ,	return_tensors='''pt'''		)
         self.assertEqual(streamer_text_tokenized.input_ids.shape       ,	(1, 1)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
         _UpperCAmelCase							:  Any											=    AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(A		)
         _UpperCAmelCase							:  Dict											=    -1

         _UpperCAmelCase							:  str											=    ids_tensor((1, 5)       ,	vocab_size=model.config.vocab_size		).to(A		)
         _UpperCAmelCase							:  List[Any]											=    TextIteratorStreamer(A       ,	timeout=0.001		)
         _UpperCAmelCase							:  Union[str, Any]											=    {'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
         _UpperCAmelCase							:  Optional[Any]											=    Thread(target=model.generate       ,	kwargs=A		)
         thread.start()

         # The streamer will timeout after 0.001 seconds, so an exception will be raised
         with self.assertRaises(A		):
             _UpperCAmelCase							:  Optional[Any]											=    ''''''
             for new_text in streamer:
                 streamer_text += new_text


