



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


__UpperCAmelCase          =       logging.get_logger(__name__)

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

__UpperCAmelCase          =       {
    '''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''',
    },
}

__UpperCAmelCase          =       {
    '''jukebox''': 512,
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Optional[int]        =  VOCAB_FILES_NAMES
       UpperCAmelCase__				:					Dict        =  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__				:					Optional[Any]        =  PRETRAINED_LYRIC_TOKENS_SIZES
       UpperCAmelCase__				:					str        =  ["input_ids", "attention_mask"]
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=["v3", "v2", "v2"],			SCREAMING_SNAKE_CASE_=512,			SCREAMING_SNAKE_CASE_=5,			SCREAMING_SNAKE_CASE_="<|endoftext|>",			**SCREAMING_SNAKE_CASE_,			)     ->		Union[str, Any]:
          UpperCamelCase    :  Union[str, Any]							=	AddedToken(SCREAMING_SNAKE_CASE_,			lstrip=SCREAMING_SNAKE_CASE_,			rstrip=SCREAMING_SNAKE_CASE_       ) if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) else unk_token
          super().__init__(
              unk_token=SCREAMING_SNAKE_CASE_,			n_genres=SCREAMING_SNAKE_CASE_,			version=SCREAMING_SNAKE_CASE_,			max_n_lyric_tokens=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[Any]							=	version
          UpperCamelCase    :  Dict							=	max_n_lyric_tokens
          UpperCamelCase    :  Tuple							=	n_genres

          with open(SCREAMING_SNAKE_CASE_,			encoding='utf-8'       ) as vocab_handle:
             UpperCamelCase    :  Optional[Any]							=	json.load(SCREAMING_SNAKE_CASE_       )

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

          with open(SCREAMING_SNAKE_CASE_,			encoding='utf-8'       ) as vocab_handle:
             UpperCamelCase    :  Union[str, Any]							=	json.load(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	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:
             UpperCamelCase    :  Tuple							=	oov.replace(R'\-\'',			R'\-+\''       )

          UpperCamelCase    :  List[Any]							=	regex.compile(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	{v: k for k, v in self.artists_encoder.items()}
          UpperCamelCase    :  str							=	{v: k for k, v in self.genres_encoder.items()}
          UpperCamelCase    :  int							=	{v: k for k, v in self.lyrics_encoder.items()}
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          return len(self.artists_encoder       ) + len(self.genres_encoder       ) + len(self.lyrics_encoder       )
       def 					snake_case_       (			self       )     ->		Tuple:
          return dict(self.artists_encoder,			self.genres_encoder,			self.lyrics_encoder       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  Tuple							=	[self.artists_encoder.get(SCREAMING_SNAKE_CASE_,			0       ) for artist in list_artists]
          for genres in range(len(SCREAMING_SNAKE_CASE_       )       ):
             UpperCamelCase    :  Dict							=	[self.genres_encoder.get(SCREAMING_SNAKE_CASE_,			0       ) for genre in list_genres[genres]]
             UpperCamelCase    :  Dict							=	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]       ))

          UpperCamelCase    :  Tuple							=	[[self.lyrics_encoder.get(SCREAMING_SNAKE_CASE_,			0       ) for character in list_lyrics[0]], [], []]
          return artists_id, list_genres, lyric_ids
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          return list(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		Tuple:
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Dict							=	self.prepare_for_tokenization(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	self._tokenize(SCREAMING_SNAKE_CASE_       )
          return artist, genre, lyrics
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = False       )     ->		Tuple[str, str, str, Dict[str, Any]]:
          for idx in range(len(self.version       )       ):
             if self.version[idx] == "v3":
                UpperCamelCase    :  Optional[int]							=	artists[idx].lower()
                UpperCamelCase    :  Optional[int]							=	[genres[idx].lower()]
             else:
                UpperCamelCase    :  Dict							=	self._normalize(artists[idx]       ) + '.v2'
                UpperCamelCase    :  Any							=	[
                    self._normalize(SCREAMING_SNAKE_CASE_       ) + '.v2' for genre in genres[idx].split('_'       )
                ]  # split is for the full dictionary with combined genres

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

          UpperCamelCase    :  List[str]							=	self._run_strip_accents(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	lyrics.replace('\\',			'\n'       )
          UpperCamelCase    :  int							=	self.out_of_vocab.sub('',			SCREAMING_SNAKE_CASE_       ), [], []
          return artists, genres, lyrics
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Union[str, Any]							=	unicodedata.normalize('NFD',			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  int							=	[]
          for char in text:
             UpperCamelCase    :  str							=	unicodedata.category(SCREAMING_SNAKE_CASE_       )
             if cat == "Mn":
                continue
             output.append(SCREAMING_SNAKE_CASE_       )
          return "".join(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          UpperCamelCase    :  str							=	(
              [chr(SCREAMING_SNAKE_CASE_       ) for i in range(ord('a'       ),			ord('z'       ) + 1       )]
              + [chr(SCREAMING_SNAKE_CASE_       ) for i in range(ord('A'       ),			ord('Z'       ) + 1       )]
              + [chr(SCREAMING_SNAKE_CASE_       ) for i in range(ord('0'       ),			ord('9'       ) + 1       )]
              + ['.']
          )
          UpperCamelCase    :  Any							=	frozenset(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	re.compile(R'_+'       )
          UpperCamelCase    :  Union[str, Any]							=	''.join([c if c in accepted else '_' for c in text.lower()]       )
          UpperCamelCase    :  Dict							=	pattern.sub('_',			SCREAMING_SNAKE_CASE_       ).strip('_'       )
          return text
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          return " ".join(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False       )     ->		List[Any]:
          # Convert to TensorType
          if not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[Any]							=	TensorType(SCREAMING_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

             UpperCamelCase    :  Optional[int]							=	tf.constant
             UpperCamelCase    :  Optional[int]							=	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    :  int							=	torch.tensor
             UpperCamelCase    :  List[Any]							=	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[int]							=	jnp.array
             UpperCamelCase    :  int							=	_is_jax
          else:
             UpperCamelCase    :  Any							=	np.asarray
             UpperCamelCase    :  List[Any]							=	_is_numpy

          # Do the tensor conversion in batch

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

             if not is_tensor(SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  str							=	as_tensor(SCREAMING_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,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_="",			SCREAMING_SNAKE_CASE_="pt"       )     ->		BatchEncoding:
          UpperCamelCase    :  str							=	[0, 0, 0]
          UpperCamelCase    :  Optional[Any]							=	[artist] * len(self.version       )
          UpperCamelCase    :  List[Any]							=	[genres] * len(self.version       )

          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[str]							=	self.tokenize(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[Any]							=	self._convert_token_to_id(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	[-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=SCREAMING_SNAKE_CASE_       )
              for i in range(len(self.version       )       )
          ]
          return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None       )     ->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_       ):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
             return

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

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

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

          return (artists_file, genres_file, lyrics_file)






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Optional[Any]:
          UpperCamelCase    :  Union[str, Any]							=	self.artists_decoder.get(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Tuple							=	[self.genres_decoder.get(SCREAMING_SNAKE_CASE_       ) for genre in genres_index]
          UpperCamelCase    :  Any							=	[self.lyrics_decoder.get(SCREAMING_SNAKE_CASE_       ) for character in lyric_index]
          return artist, genres, lyrics








import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    import PIL.Image

    from .features import FeatureType


__UpperCAmelCase          =       None
__UpperCAmelCase          =       '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__UpperCAmelCase          =       [
    np.dtype('''|b1'''),
    np.dtype('''|u1'''),
    np.dtype('''<u2'''),
    np.dtype('''>u2'''),
    np.dtype('''<i2'''),
    np.dtype('''>i2'''),
    np.dtype('''<u4'''),
    np.dtype('''>u4'''),
    np.dtype('''<i4'''),
    np.dtype('''>i4'''),
    np.dtype('''<f4'''),
    np.dtype('''>f4'''),
    np.dtype('''<f8'''),
    np.dtype('''>f8'''),
]



@dataclass
class 				lowerCAmelCase_		:
       UpperCAmelCase__				:					bool        =  True
       UpperCAmelCase__				:					Optional[str]        =  None
       # Automatically constructed
       UpperCAmelCase__				:					ClassVar[str]        =  "PIL.Image.Image"
       UpperCAmelCase__				:					ClassVar[Any]        =  pa.struct({"bytes": pa.binary(), "path": pa.string()}							)
       UpperCAmelCase__				:					str        =  field(default="Image"       ,    init=a__       ,    repr=a__							)
       def __call__(			self       )     ->		Any:
          return self.pa_type
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		dict:
          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('To support encoding images, please install \'Pillow\'.'       )

          if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  Optional[int]							=	np.array(SCREAMING_SNAKE_CASE_       )

          if isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             return {"path": value, "bytes": None}
          elif isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
             return {"path": None, "bytes": value}
          elif isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ):
             # convert the image array to PNG/TIFF bytes
             return encode_np_array(SCREAMING_SNAKE_CASE_       )
          elif isinstance(SCREAMING_SNAKE_CASE_,			PIL.Image.Image       ):
             # convert the PIL image to bytes (default format is PNG/TIFF)
             return encode_pil_image(SCREAMING_SNAKE_CASE_       )
          elif value.get('path'       ) is not None and os.path.isfile(value['path']       ):
             # we set "bytes": None to not duplicate the data if they're already available locally
             return {"bytes": None, "path": value.get('path'       )}
          elif value.get('bytes'       ) is not None or value.get('path'       ) is not None:
             # store the image bytes, and path is used to infer the image format using the file extension
             return {"bytes": value.get('bytes'       ), "path": value.get('path'       )}
          else:
             raise ValueError(
                 F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		"PIL.Image.Image":
          if not self.decode:
             raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'       )

          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('To support decoding images, please install \'Pillow\'.'       )

          if token_per_repo_id is None:
             UpperCamelCase    :  Any							=	{}

          UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	value['path'], value['bytes']
          if bytes_ is None:
             if path is None:
                raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}."""       )
             else:
                if is_local_path(SCREAMING_SNAKE_CASE_       ):
                   UpperCamelCase    :  int							=	PIL.Image.open(SCREAMING_SNAKE_CASE_       )
                else:
                   UpperCamelCase    :  int							=	path.split('::'       )[-1]
                   try:
                      UpperCamelCase    :  Optional[Any]							=	string_to_dict(SCREAMING_SNAKE_CASE_,			config.HUB_DATASETS_URL       )['repo_id']
                      UpperCamelCase    :  str							=	token_per_repo_id.get(SCREAMING_SNAKE_CASE_       )
                   except ValueError:
                      UpperCamelCase    :  Tuple							=	None
                   with xopen(SCREAMING_SNAKE_CASE_,			'rb',			use_auth_token=SCREAMING_SNAKE_CASE_       ) as f:
                      UpperCamelCase    :  Optional[int]							=	BytesIO(f.read()       )
                   UpperCamelCase    :  int							=	PIL.Image.open(bytes_       )
          else:
             UpperCamelCase    :  Optional[int]							=	PIL.Image.open(BytesIO(bytes_       )       )
          image.load()  # to avoid "Too many open files" errors
          return image
       def 					snake_case_       (			self       )     ->		Union["FeatureType", Dict[str, "FeatureType"]]:
          from .features import Value

          return (
              self
              if self.decode
              else {
                  "bytes": Value('binary'       ),
                  "path": Value('string'       ),
              }
          )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		pa.StructArray:
          if pa.types.is_string(storage.type       ):
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             UpperCamelCase    :  Dict							=	pa.StructArray.from_arrays([bytes_array, storage],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_binary(storage.type       ):
             UpperCamelCase    :  Optional[int]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Union[str, Any]							=	pa.StructArray.from_arrays([storage, path_array],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_struct(storage.type       ):
             if storage.type.get_field_index('bytes'       ) >= 0:
                UpperCamelCase    :  List[str]							=	storage.field('bytes'       )
             else:
                UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.binary()       )
             if storage.type.get_field_index('path'       ) >= 0:
                UpperCamelCase    :  List[str]							=	storage.field('path'       )
             else:
                UpperCamelCase    :  Optional[Any]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  Optional[int]							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=storage.is_null()       )
          elif pa.types.is_list(storage.type       ):
             UpperCamelCase    :  Optional[Any]							=	pa.array(
                 [encode_np_array(np.array(SCREAMING_SNAKE_CASE_       )       )['bytes'] if arr is not None else None for arr in storage.to_pylist()],			type=pa.binary(),			)
             UpperCamelCase    :  List[str]							=	pa.array([None] * len(SCREAMING_SNAKE_CASE_       ),			type=pa.string()       )
             UpperCamelCase    :  int							=	pa.StructArray.from_arrays(
                 [bytes_array, path_array],			['bytes', 'path'],			mask=bytes_array.is_null()       )
          return array_cast(SCREAMING_SNAKE_CASE_,			self.pa_type       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		pa.StructArray:

          @no_op_if_value_is_null
          def path_to_bytes(SCREAMING_SNAKE_CASE_       ):
             with xopen(SCREAMING_SNAKE_CASE_,			'rb'       ) as f:
                UpperCamelCase    :  Optional[int]							=	f.read()
             return bytes_

          UpperCamelCase    :  Union[str, Any]							=	pa.array(
              [
                  (path_to_bytes(x['path']       ) if x['bytes'] is None else x['bytes']) if x is not None else None
                  for x in storage.to_pylist()
              ],			type=pa.binary(),			)
          UpperCamelCase    :  Any							=	pa.array(
              [os.path.basename(SCREAMING_SNAKE_CASE_       ) if path is not None else None for path in storage.field('path'       ).to_pylist()],			type=pa.string(),			)
          UpperCamelCase    :  int							=	pa.StructArray.from_arrays([bytes_array, path_array],			['bytes', 'path'],			mask=bytes_array.is_null()       )
          return array_cast(SCREAMING_SNAKE_CASE_,			self.pa_type       )






def 		UpperCamelCase	(   )	->     List[str]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      UpperCamelCase    :  Dict							=	list(set(PIL.Image.OPEN.keys()	) & set(PIL.Image.SAVE.keys()	)	)
   return _IMAGE_COMPRESSION_FORMATS
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     bytes:
   UpperCamelCase    :  Any							=	BytesIO()
   if image.format in list_image_compression_formats():
      UpperCamelCase    :  Tuple							=	image.format
   else:
      UpperCamelCase    :  List[str]							=	'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
   image.save(snake_case__       ,     format=snake_case__	)
   return buffer.getvalue()
def 		UpperCamelCase	(   snake_case__       :    "PIL.Image.Image"	)	->     dict:
   if hasattr(snake_case__       ,     'filename'	) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(snake_case__	)}
def 		UpperCamelCase	(   snake_case__       :    np.ndarray	)	->     dict:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   UpperCamelCase    :  Union[str, Any]							=	array.dtype
   UpperCamelCase    :  List[Any]							=	dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
   UpperCamelCase    :  Optional[Any]							=	dtype.kind
   UpperCamelCase    :  Any							=	dtype.itemsize

   UpperCamelCase    :  int							=	None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      UpperCamelCase    :  Optional[Any]							=	np.dtype('|u1'	)
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""	)
      if dtype is not dest_dtype:
         warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""	)
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      UpperCamelCase    :  List[Any]							=	dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         UpperCamelCase    :  Dict							=	dtype_byteorder + dtype_kind + str(snake_case__	)
         UpperCamelCase    :  str							=	np.dtype(snake_case__	)
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""	)
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""	)
   UpperCamelCase    :  Union[str, Any]							=	PIL.Image.fromarray(array.astype(snake_case__	)	)
   return {"path": None, "bytes": image_to_bytes(snake_case__	)}


def 		UpperCamelCase	(   snake_case__       :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]	)	->     List[dict]:
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('To support encoding images, please install \'Pillow\'.'	)

   if objs:
      UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	first_non_null_value(snake_case__	)
      if isinstance(snake_case__       ,     snake_case__	):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(snake_case__       ,     np.ndarray	):
         UpperCamelCase    :  List[Any]							=	no_op_if_value_is_null(snake_case__	)
         return [obj_to_image_dict_func(snake_case__	) for obj in objs]
      elif isinstance(snake_case__       ,     PIL.Image.Image	):
         UpperCamelCase    :  Optional[int]							=	no_op_if_value_is_null(snake_case__	)
         return [obj_to_image_dict_func(snake_case__	) for obj in objs]
      else:
         return objs
   else:
      return objs



