

"""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


SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE_					:	Dict							  =						{
    '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',
    },
}

SCREAMING_SNAKE_CASE_					:	List[str]							  =						{
    'jukebox': 5_1_2,
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



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

    def __init__(     self: str    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Dict    ,						UpperCamelCase: Optional[Any]=["v3", "v2", "v2"]    ,						UpperCamelCase: int=5_12    ,						UpperCamelCase: Tuple=5    ,						UpperCamelCase: Tuple="<|endoftext|>"    ,						**UpperCamelCase: List[Any]    ,						):






         """simple docstring"""


         A__										=  AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase       ) if isinstance(UpperCamelCase    ,						UpperCamelCase       ) else unk_token
         super().__init__(
             unk_token=UpperCamelCase    ,						n_genres=UpperCamelCase    ,						version=UpperCamelCase    ,						max_n_lyric_tokens=UpperCamelCase    ,						**UpperCamelCase    ,						)
         A__										=  version
         A__										=  max_n_lyric_tokens
         A__										=  n_genres

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

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

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

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

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

    @property
    def 				UpperCamelCase	(     self: List[Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


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

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

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         return list(UpperCamelCase       )

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Any    ,						**UpperCamelCase: Optional[Any]       ):






         """simple docstring"""


         A__			,				A__			,				A__										=  self.prepare_for_tokenization(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase       )
         A__										=  self._tokenize(UpperCamelCase       )
         return artist, genre, lyrics

    def 				UpperCamelCase	(     self: Tuple    ,						UpperCamelCase: str    ,						UpperCamelCase: str    ,						UpperCamelCase: str    ,						UpperCamelCase: bool = False       ):






         """simple docstring"""


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

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

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

    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Any    ,						UpperCamelCase: str       ):






         """simple docstring"""


         A__										=  (
             [chr(UpperCamelCase       ) for i in range(ord("""a"""       )    ,						ord("""z"""       ) + 1       )]
             + [chr(UpperCamelCase       ) for i in range(ord("""A"""       )    ,						ord("""Z"""       ) + 1       )]
             + [chr(UpperCamelCase       ) for i in range(ord("""0"""       )    ,						ord("""9"""       ) + 1       )]
             + ["""."""]
         )
         A__										=  frozenset(UpperCamelCase       )
         A__										=  re.compile(r"""_+"""       )
         A__										=  """""".join([c if c in accepted else """_""" for c in text.lower()]       )
         A__										=  pattern.sub("""_"""    ,						UpperCamelCase       ).strip("""_"""       )
         return text

    def 				UpperCamelCase	(     self: Optional[int]    ,						UpperCamelCase: List[str]       ):






         """simple docstring"""


         return " ".join(UpperCamelCase       )

    def 				UpperCamelCase	(     self: str    ,						UpperCamelCase: List[str]    ,						UpperCamelCase: Optional[Union[str, TensorType]] = None    ,						UpperCamelCase: bool = False       ):






         """simple docstring"""


         if not isinstance(UpperCamelCase    ,						UpperCamelCase       ):
              A__										=  TensorType(UpperCamelCase       )

         # 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

              A__										=  tf.constant
              A__										=  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

              A__										=  torch.tensor
              A__										=  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

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

         # Do the tensor conversion in batch

         try:
              if prepend_batch_axis:
                   A__										=  [inputs]

              if not is_tensor(UpperCamelCase       ):
                   A__										=  as_tensor(UpperCamelCase       )
         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: Any    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: Optional[int]    ,						UpperCamelCase: Optional[Any]=""    ,						UpperCamelCase: List[str]="pt"       ):






         """simple docstring"""


         A__										=  [0, 0, 0]
         A__										=  [artist] * len(self.version       )
         A__										=  [genres] * len(self.version       )

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

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

    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[str] = None       ):






         """simple docstring"""


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

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

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

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

         return (artists_file, genres_file, lyrics_file)





    def 				UpperCamelCase	(     self: int    ,						UpperCamelCase: Tuple    ,						UpperCamelCase: List[Any]    ,						UpperCamelCase: List[str]       ):






         """simple docstring"""


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









"""simple docstring"""



import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
 import torch



@is_pipeline_test
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    UpperCAmelCase						 =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    UpperCAmelCase						 =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: str    ,						UpperCamelCase: Optional[Any]    ,						UpperCamelCase: Any       ):






         """simple docstring"""


         A__										=  TextaTextGenerationPipeline(model=UpperCamelCase    ,						tokenizer=UpperCamelCase       )
         return generator, ["Something to write", "Something else"]

    def 				UpperCamelCase	(     self: List[Any]    ,						UpperCamelCase: Any    ,						UpperCamelCase: Dict       ):






         """simple docstring"""


         A__										=  generator("""Something there"""       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": ANY(UpperCamelCase       )}]       )
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""       )       )

         A__										=  generator(["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         A__										=  generator(
             ["""This is great !""", """Something else"""]    ,						num_return_sequences=2    ,						batch_size=2    ,						do_sample=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
                 [{"""generated_text""": ANY(UpperCamelCase       )}, {"""generated_text""": ANY(UpperCamelCase       )}],
             ]    ,						)

         with self.assertRaises(UpperCamelCase       ):
              generator(4       )

    @require_torch
    def 				UpperCamelCase	(     self: Optional[Any]       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""pt"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )

         A__										=  3
         A__										=  generator(
             """Something there"""    ,						num_return_sequences=UpperCamelCase    ,						num_beams=UpperCamelCase    ,						)
         A__										=  [
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
             {"""generated_text""": """"""},
         ]
         self.assertEqual(UpperCamelCase    ,						UpperCamelCase       )

         A__										=  generator("""This is a test"""    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						return_tensors=UpperCamelCase       )
         self.assertEqual(
             UpperCamelCase    ,						[
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
                 {"""generated_token_ids""": ANY(torch.Tensor       )},
             ]    ,						)
         A__										=  generator.model.config.eos_token_id
         A__										=  """<pad>"""
         A__										=  generator(
             ["""This is a test""", """This is a second test"""]    ,						do_sample=UpperCamelCase    ,						num_return_sequences=2    ,						batch_size=2    ,						return_tensors=UpperCamelCase    ,						)
         self.assertEqual(
             UpperCamelCase    ,						[
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
                 [
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                     {"""generated_token_ids""": ANY(torch.Tensor       )},
                 ],
             ]    ,						)





    @require_tf
    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  pipeline("""text2text-generation"""    ,						model="""patrickvonplaten/t5-tiny-random"""    ,						framework="""tf"""       )
         # do_sample=False necessary for reproducibility
         A__										=  generator("""Something there"""    ,						do_sample=UpperCamelCase       )
         self.assertEqual(UpperCamelCase    ,						[{"""generated_text""": """"""}]       )






