





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_				= logging.get_logger(__name__)

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

lowerCAmelCase_				= {
    '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_				= {
    'jukebox': 5_1_2,
}

class 					A     (_SCREAMING_SNAKE_CASE					):
      _SCREAMING_SNAKE_CASE       =				VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE       =				PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE       =				PRETRAINED_LYRIC_TOKENS_SIZES
      _SCREAMING_SNAKE_CASE       =				["""input_ids""", """attention_mask"""]



      def __init__(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_=["v3", "v2", "v2"]  ,	lowercase_=512  ,	lowercase_=5  ,	lowercase_="<|endoftext|>"  ,	**lowercase_  ,	)   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    AddedToken(__snake_case  ,	lstrip=__snake_case  ,	rstrip=__snake_case       ) if isinstance(__snake_case  ,	__snake_case       ) else unk_token
             super().__init__(
                 unk_token=__snake_case  ,	n_genres=__snake_case  ,	version=__snake_case  ,	max_n_lyric_tokens=__snake_case  ,	**__snake_case  ,	)
             _snake_case   :				Union[str, Any]  					=    version
             _snake_case   :				Dict  					=    max_n_lyric_tokens
             _snake_case   :				List[Any]  					=    n_genres

             with open(__snake_case  ,	encoding='''utf-8'''       ) as vocab_handle:
                    _snake_case   :				List[str]  					=    json.load(__snake_case       )

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

             with open(__snake_case  ,	encoding='''utf-8'''       ) as vocab_handle:
                    _snake_case   :				Optional[int]  					=    json.load(__snake_case       )

             _snake_case   :				str  					=    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   :				Union[str, Any]  					=    oov.replace(R'''\-\''''  ,	R'''\-+\''''       )

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



      @property
      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

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



      def  __a			(       self       )   -> Any:



             '''simple docstring'''

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



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_       )   -> int:



             '''simple docstring'''

             _snake_case   :				str  					=    [self.artists_encoder.get(__snake_case  ,	0       ) for artist in list_artists]
             for genres in range(len(__snake_case       )       ):
                    _snake_case   :				Any  					=    [self.genres_encoder.get(__snake_case  ,	0       ) for genre in list_genres[genres]]
                    _snake_case   :				Dict  					=    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]       ))

             _snake_case   :				Dict  					=    [[self.lyrics_encoder.get(__snake_case  ,	0       ) for character in list_lyrics[0]], [], []]
             return artists_id, list_genres, lyric_ids



      def  __a			(       self  ,	lowercase_       )   -> str:



             '''simple docstring'''

             return list(__snake_case       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	**lowercase_       )   -> Any:



             '''simple docstring'''

             _snake_case					,     _snake_case					,     _snake_case   :				Dict  					=    self.prepare_for_tokenization(__snake_case  ,	__snake_case  ,	__snake_case       )
             _snake_case   :				Optional[int]  					=    self._tokenize(__snake_case       )
             return artist, genre, lyrics



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_ = False       )   -> Dict:



             '''simple docstring'''

             for idx in range(len(self.version       )       ):
                    if self.version[idx] == "v3":
                           _snake_case   :				Dict  					=    artists[idx].lower()
                           _snake_case   :				Optional[Any]  					=    [genres[idx].lower()]
                    else:
                           _snake_case   :				Optional[int]  					=    self._normalize(artists[idx]       ) + '''.v2'''
                           _snake_case   :				Dict  					=    [
                               self._normalize(__snake_case       ) + '''.v2''' for genre in genres[idx].split('''_'''       )
                           ]  # split is for the full dictionary with combined genres

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

             _snake_case   :				Optional[int]  					=    self._run_strip_accents(__snake_case       )
             _snake_case   :				Union[str, Any]  					=    lyrics.replace('''\\'''  ,	'''\n'''       )
             _snake_case   :				List[str]  					=    self.out_of_vocab.sub(''''''  ,	__snake_case       ), [], []
             return artists, genres, lyrics



      def  __a			(       self  ,	lowercase_       )   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				Dict  					=    unicodedata.normalize('''NFD'''  ,	__snake_case       )
             _snake_case   :				Any  					=    []
             for char in text:
                    _snake_case   :				List[Any]  					=    unicodedata.category(__snake_case       )
                    if cat == "Mn":
                           continue
                    output.append(__snake_case       )
             return "".join(__snake_case       )



      def  __a			(       self  ,	lowercase_       )   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    (
                 [chr(__snake_case       ) for i in range(ord('''a'''       )  ,	ord('''z'''       ) + 1       )]
                 + [chr(__snake_case       ) for i in range(ord('''A'''       )  ,	ord('''Z'''       ) + 1       )]
                 + [chr(__snake_case       ) for i in range(ord('''0'''       )  ,	ord('''9'''       ) + 1       )]
                 + ['''.''']
             )
             _snake_case   :				Optional[Any]  					=    frozenset(__snake_case       )
             _snake_case   :				Optional[Any]  					=    re.compile(R'''_+'''       )
             _snake_case   :				Optional[int]  					=    ''''''.join([c if c in accepted else '''_''' for c in text.lower()]       )
             _snake_case   :				Union[str, Any]  					=    pattern.sub('''_'''  ,	__snake_case       ).strip('''_'''       )
             return text



      def  __a			(       self  ,	lowercase_       )   -> int:



             '''simple docstring'''

             return " ".join(__snake_case       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_ = None  ,	lowercase_ = False       )   -> int:



             '''simple docstring'''

             if not isinstance(__snake_case  ,	__snake_case       ):
                    _snake_case   :				List[Any]  					=    TensorType(__snake_case       )

             # 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   :				int  					=    tf.constant
                    _snake_case   :				Optional[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

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

             # Do the tensor conversion in batch

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

                    if not is_tensor(__snake_case       ):
                           _snake_case   :				str  					=    as_tensor(__snake_case       )
             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  ,	lowercase_  ,	lowercase_  ,	lowercase_=""  ,	lowercase_="pt"       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				Tuple  					=    [0, 0, 0]
             _snake_case   :				Optional[int]  					=    [artist] * len(self.version       )
             _snake_case   :				Optional[Any]  					=    [genres] * len(self.version       )

             _snake_case					,     _snake_case					,     _snake_case   :				List[Any]  					=    self.tokenize(__snake_case  ,	__snake_case  ,	__snake_case       )
             _snake_case					,     _snake_case					,     _snake_case   :				List[Any]  					=    self._convert_token_to_id(__snake_case  ,	__snake_case  ,	__snake_case       )

             _snake_case   :				int  					=    [-INFINITY] * len(full_tokens[-1]       )
             _snake_case   :				Union[str, Any]  					=    [
                 self.convert_to_tensors(
                     [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]  ,	tensor_type=__snake_case       )
                 for i in range(len(self.version       )       )
             ]
             return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_ = None       )   -> List[Any]:



             '''simple docstring'''

             if not os.path.isdir(__snake_case       ):
                    logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
                    return

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

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

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

             return (artists_file, genres_file, lyrics_file)



      def  __a			(       self  ,	lowercase_  ,	lowercase_  ,	lowercase_       )   -> Optional[int]:



             '''simple docstring'''

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













import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


a__      :     Optional[int]         =		False





class UpperCAmelCase_				( unittest.TestCase ):
  pass





@slow
@require_torch_gpu
class UpperCAmelCase_				( unittest.TestCase ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
       pipe.to(__snake_case )
       pipe.set_progress_bar_config(disable=__snake_case )

       A_          =       load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg''' )
       A_          =       torch.manual_seed(0 )
       A_          =       pipe(
           image=__snake_case ,generator=__snake_case ,guidance_scale=7.5 ,num_inference_steps=5_0 ,output_type='''numpy''' ,).images

       A_          =       image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       A_          =       np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



