


"""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


snake_case__						:   Dict			 = logging.get_logger(__name__)

snake_case__						:   Union[str, Any]			 = {
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

snake_case__						:   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''',
    },
}

snake_case__						:   Tuple			 = {
    '''jukebox''': 512,
}



class 						snake_case_(				a__	):
     __UpperCamelCase     =    VOCAB_FILES_NAMES
     __UpperCamelCase     =    PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase     =    PRETRAINED_LYRIC_TOKENS_SIZES
     __UpperCamelCase     =    ['''input_ids''', '''attention_mask''']






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[str]=["v3", "v2", "v2"]    , UpperCamelCase_     :    Any=5_1_2    , UpperCamelCase_     :    Optional[int]=5    , UpperCamelCase_     :    Optional[int]="<|endoftext|>"    , **UpperCamelCase_     :    Union[str, Any]    , ):
           lowerCAmelCase    :		List[str]				=      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_    , )
           lowerCAmelCase    :		Union[str, Any]				=      version
           lowerCAmelCase    :		str				=      max_n_lyric_tokens
           lowerCAmelCase    :		str				=      n_genres

           with open(UpperCamelCase_    , encoding='''utf-8''' ) as vocab_handle:
                 lowerCAmelCase    :		Dict				=      json.load(UpperCamelCase_ )

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

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

           lowerCAmelCase    :		Optional[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:
                 lowerCAmelCase    :		Dict				=      oov.replace(r'''\-\''''    , r'''\-+\'''' )

           lowerCAmelCase    :		Any				=      regex.compile(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      {v: k for k, v in self.artists_encoder.items()}
           lowerCAmelCase    :		Tuple				=      {v: k for k, v in self.genres_encoder.items()}
           lowerCAmelCase    :		List[str]				=      {v: k for k, v in self.lyrics_encoder.items()}






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






     def    lowerCamelCase__   (		self     :    List[Any] ):
           return dict(self.artists_encoder    , self.genres_encoder    , self.lyrics_encoder )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Union[str, Any] ):
           lowerCAmelCase    :		Dict				=      [self.artists_encoder.get(UpperCamelCase_    , 0 ) for artist in list_artists]
           for genres in range(len(UpperCamelCase_ ) ):
                 lowerCAmelCase    :		List[Any]				=      [self.genres_encoder.get(UpperCamelCase_    , 0 ) for genre in list_genres[genres]]
                 lowerCAmelCase    :		Optional[Any]				=      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

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






     def    lowerCamelCase__   (		self     :    str    , UpperCamelCase_     :    Tuple ):
           return list(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    Any    , **UpperCamelCase_     :    int ):
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.prepare_for_tokenization(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      self._tokenize(UpperCamelCase_ )
           return artist, genre, lyrics






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    str    , UpperCamelCase_     :    bool = False ):
           for idx in range(len(self.version ) ):
                 if self.version[idx] == "v3":
                       lowerCAmelCase    :		Optional[int]				=      artists[idx].lower()
                       lowerCAmelCase    :		Union[str, Any]				=      [genres[idx].lower()]
                 else:
                       lowerCAmelCase    :		Dict				=      self._normalize(artists[idx] ) + '''.v2'''
                       lowerCAmelCase    :		int				=      [
                           self._normalize(UpperCamelCase_ ) + '''.v2''' for genre in genres[idx].split('''_''' )
                       ]  # split is for the full dictionary with combined genres

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

           lowerCAmelCase    :		int				=      self._run_strip_accents(UpperCamelCase_ )
           lowerCAmelCase    :		Any				=      lyrics.replace('''\\'''    , '''\n''' )
           lowerCAmelCase    :		str				=      self.out_of_vocab.sub(''''''    , UpperCamelCase_ ), [], []
           return artists, genres, lyrics






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		Any				=      unicodedata.normalize('''NFD'''    , UpperCamelCase_ )
           lowerCAmelCase    :		str				=      []
           for char in text:
                 lowerCAmelCase    :		List[str]				=      unicodedata.category(UpperCamelCase_ )
                 if cat == "Mn":
                       continue
                 output.append(UpperCamelCase_ )
           return "".join(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    str ):
           lowerCAmelCase    :		Dict				=      (
               [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 )]
               + ['''.''']
           )
           lowerCAmelCase    :		Optional[Any]				=      frozenset(UpperCamelCase_ )
           lowerCAmelCase    :		Union[str, Any]				=      re.compile(r'''_+''' )
           lowerCAmelCase    :		Dict				=      ''''''.join([c if c in accepted else '''_''' for c in text.lower()] )
           lowerCAmelCase    :		Any				=      pattern.sub('''_'''    , UpperCamelCase_ ).strip('''_''' )
           return text






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    List[str] ):
           return " ".join(UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , UpperCamelCase_     :    bool = False ):
           # Convert to TensorType
           if not isinstance(UpperCamelCase_    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Any				=      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

                 lowerCAmelCase    :		List[str]				=      tf.constant
                 lowerCAmelCase    :		str				=      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

                 lowerCAmelCase    :		str				=      torch.tensor
                 lowerCAmelCase    :		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

                 lowerCAmelCase    :		List[Any]				=      jnp.array
                 lowerCAmelCase    :		Optional[Any]				=      _is_jax
           else:
                 lowerCAmelCase    :		Any				=      np.asarray
                 lowerCAmelCase    :		Union[str, Any]				=      _is_numpy

           # Do the tensor conversion in batch

           try:
                 if prepend_batch_axis:
                       lowerCAmelCase    :		Optional[Any]				=      [inputs]

                 if not is_tensor(UpperCamelCase_ ):
                       lowerCAmelCase    :		str				=      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     :    Tuple    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]=""    , UpperCamelCase_     :    Optional[Any]="pt" ):
           lowerCAmelCase    :		Optional[Any]				=      [0, 0, 0]
           lowerCAmelCase    :		List[Any]				=      [artist] * len(self.version )
           lowerCAmelCase    :		Dict				=      [genres] * len(self.version )

           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      self.tokenize(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )
           lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		str				=      self._convert_token_to_id(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )

           lowerCAmelCase    :		int				=      [-INFINITY] * len(full_tokens[-1] )
           lowerCAmelCase    :		int				=      [
               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    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[str] = None ):
           if not os.path.isdir(UpperCamelCase_ ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                 return

           lowerCAmelCase    :		Dict				=      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_ ) )

           lowerCAmelCase    :		int				=      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_ ) )

           lowerCAmelCase    :		Tuple				=      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    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		Any				=      self.artists_decoder.get(UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      [self.genres_decoder.get(UpperCamelCase_ ) for genre in genres_index]
           lowerCAmelCase    :		Tuple				=      [self.lyrics_decoder.get(UpperCamelCase_ ) for character in lyric_index]
           return artist, genres, lyrics










"""simple docstring"""



import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
    import jax
    import jax.numpy as jnp
    from flax.jax_utils import replicate
    from flax.training.common_utils import shard

@slow
@require_flax
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()







    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2'							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'stabilityai/stable-diffusion-2'
      lowercase    ,     lowercase							    =       FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCamelCase							,		subfolder='scheduler'       )
      lowercase    ,     lowercase							    =       FlaxStableDiffusionPipeline.from_pretrained(
          _lowerCamelCase							,		scheduler=_lowerCamelCase							,		revision='bf16'							,		dtype=jnp.bfloataa							,		)
      lowercase							    =       scheduler_params

      lowercase							    =       'A painting of a squirrel eating a burger'
      lowercase							    =       jax.device_count()
      lowercase							    =       num_samples * [prompt]
      lowercase							    =       sd_pipe.prepare_inputs(_lowerCamelCase       )

      lowercase							    =       replicate(_lowerCamelCase       )
      lowercase							    =       shard(_lowerCamelCase       )

      lowercase							    =       jax.random.PRNGKey(0       )
      lowercase							    =       jax.random.split(_lowerCamelCase							,		jax.device_count()       )

      lowercase							    =       sd_pipe(_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		num_inference_steps=2_5							,		jit=_lowerCamelCase       )[0]
      assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

      lowercase							    =       images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]       )
      lowercase							    =       images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      lowercase							    =       jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      lowercase							    =       jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]       )
      print(F'output_slice: {output_slice}'       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2


