
"""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


UpperCAmelCase__      :    Dict    							= logging.get_logger(__name__)

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

UpperCAmelCase__      :    List[Any]    							= {
    '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__      :    Union[str, Any]    							= {
    'jukebox': 5_1_2,
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Dict					       =   VOCAB_FILES_NAMES
				__UpperCamelCase      :						Tuple					       =   PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase      :						Optional[Any]					       =   PRETRAINED_LYRIC_TOKENS_SIZES
				__UpperCamelCase      :						Optional[Any]					       =   ['''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__=5_12   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__="<|endoftext|>"   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       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__   ,       )
											SCREAMING_SNAKE_CASE__						:       Any           =   version
											SCREAMING_SNAKE_CASE__						:       int           =   max_n_lyric_tokens
											SCREAMING_SNAKE_CASE__						:       List[str]           =   n_genres

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

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

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

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

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



				@property
				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




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

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



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											return list(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   self.prepare_for_tokenization(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self._tokenize(SCREAMING_SNAKE_CASE__    )
											return artist, genre, lyrics



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = False    )					->		Tuple[str, str, str, Dict[str, Any]]:



											"""simple docstring"""




											for idx in range(len(self.version    )    ):
																		if self.version[idx] == "v3":
																									SCREAMING_SNAKE_CASE__						:       List[str]           =   artists[idx].lower()
																									SCREAMING_SNAKE_CASE__						:       int           =   [genres[idx].lower()]
																		else:
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   self._normalize(artists[idx]    ) + """.v2"""
																									SCREAMING_SNAKE_CASE__						:       int           =   [
																									    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":
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   regex.compile(r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""    )
																		SCREAMING_SNAKE_CASE__						:       Dict           =   """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   {vocab[index]: index + 1 for index in range(len(SCREAMING_SNAKE_CASE__    )    )}
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   0
																		SCREAMING_SNAKE_CASE__						:       Dict           =   len(SCREAMING_SNAKE_CASE__    ) + 1
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.vocab
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   {v: k for k, v in self.vocab.items()}
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """"""
											else:
																		SCREAMING_SNAKE_CASE__						:       str           =   regex.compile(r"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"""    )

											SCREAMING_SNAKE_CASE__						:       Tuple           =   self._run_strip_accents(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   lyrics.replace("""\\"""   ,       """\n"""    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.out_of_vocab.sub(""""""   ,       SCREAMING_SNAKE_CASE__    ), [], []
											return artists, genres, lyrics



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   unicodedata.normalize("""NFD"""   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											for char in text:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   unicodedata.category(SCREAMING_SNAKE_CASE__    )
																		if cat == "Mn":
																									continue
																		output.append(SCREAMING_SNAKE_CASE__    )
											return "".join(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   (
											    [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    )]
											    + ["""."""]
											)
											SCREAMING_SNAKE_CASE__						:       Dict           =   frozenset(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   re.compile(r"""_+"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """""".join([c if c in accepted else """_""" for c in text.lower()]    )
											SCREAMING_SNAKE_CASE__						:       str           =   pattern.sub("""_"""   ,       SCREAMING_SNAKE_CASE__    ).strip("""_"""    )
											return text



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											return " ".join(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False    )					->		List[str]:



											"""simple docstring"""




											if not isinstance(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   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

																		SCREAMING_SNAKE_CASE__						:       str           =   tf.constant
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   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

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.tensor
																		SCREAMING_SNAKE_CASE__						:       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

																		SCREAMING_SNAKE_CASE__						:       int           =   jnp.array
																		SCREAMING_SNAKE_CASE__						:       Dict           =   _is_jax
											else:
																		SCREAMING_SNAKE_CASE__						:       Any           =   np.asarray
																		SCREAMING_SNAKE_CASE__						:       Dict           =   _is_numpy

											# Do the tensor conversion in batch

											try:
																		if prepend_batch_axis:
																									SCREAMING_SNAKE_CASE__						:       Any           =   [inputs]

																		if not is_tensor(SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.tokenize(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   self._convert_token_to_id(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   [-INFINITY] * len(full_tokens[-1]    )
											SCREAMING_SNAKE_CASE__						:       str           =   [
											    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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None    )					->		Tuple[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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__    )    )

											SCREAMING_SNAKE_CASE__						:       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__    )    )

											SCREAMING_SNAKE_CASE__						:       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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




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






"""simple docstring"""




from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
					import PIL

# soft dependency
if is_pytesseract_available():
					import pytesseract

UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							return [
							    int(1_000 * (box[0] / width)						),
							    int(1_000 * (box[1] / height)						),
							    int(1_000 * (box[2] / width)						),
							    int(1_000 * (box[3] / height)						),
							]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = None						):
							SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else """"""

							# apply OCR
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   to_pil_image(_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   pil_image.size
							SCREAMING_SNAKE_CASE__						:       Tuple           =   pytesseract.image_to_data(_snake_case   ,lang=_snake_case   ,output_type="""dict"""   ,config=_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

							# filter empty words and corresponding coordinates
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [idx for idx, word in enumerate(_snake_case						) if not word.strip()]
							SCREAMING_SNAKE_CASE__						:       Dict           =   [word for idx, word in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       int           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   []
							for x, y, w, h in zip(_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [x, y, x + w, y + h]
														actual_boxes.append(_snake_case						)

							# finally, normalize the bounding boxes
							SCREAMING_SNAKE_CASE__						:       List[str]           =   []
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(_snake_case   ,_snake_case   ,_snake_case						)						)

							assert len(_snake_case						) == len(_snake_case						), "Not as many words as there are bounding boxes"

							return words, normalized_boxes



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   ['''pixel_values''']



				def __init__(self   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ""   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		None:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   size if size is not None else {"""height""": 2_24, """width""": 2_24}
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   get_size_dict(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize
											SCREAMING_SNAKE_CASE__						:       Any           =   size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   resample
											SCREAMING_SNAKE_CASE__						:       Dict           =   apply_ocr
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ocr_lang
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tesseract_config



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   get_size_dict(SCREAMING_SNAKE_CASE__    )
											if "height" not in size or "width" not in size:
																		raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''    )
											SCREAMING_SNAKE_CASE__						:       Any           =   (size["""height"""], size["""width"""])
											return resize(SCREAMING_SNAKE_CASE__   ,       size=SCREAMING_SNAKE_CASE__   ,       resample=SCREAMING_SNAKE_CASE__   ,       data_format=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		PIL.Image.Image:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   size if size is not None else self.size
											SCREAMING_SNAKE_CASE__						:       Dict           =   get_size_dict(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   make_list_of_images(SCREAMING_SNAKE_CASE__    )

											if not valid_images(SCREAMING_SNAKE_CASE__    ):
																		raise ValueError(
																		    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																		    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
																		raise ValueError("""Size must be specified if do_resize is True."""    )

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

											if apply_ocr:
																		requires_backends(self   ,       """pytesseract"""    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
																		SCREAMING_SNAKE_CASE__						:       Dict           =   []
																		for image in images:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   apply_tesseract(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									words_batch.append(SCREAMING_SNAKE_CASE__    )
																									boxes_batch.append(SCREAMING_SNAKE_CASE__    )

											if do_resize:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [self.resize(image=SCREAMING_SNAKE_CASE__   ,       size=SCREAMING_SNAKE_CASE__   ,       resample=SCREAMING_SNAKE_CASE__    ) for image in images]

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [flip_channel_order(SCREAMING_SNAKE_CASE__    ) for image in images]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) for image in images]

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   BatchFeature(data={"""pixel_values""": images}   ,       tensor_type=SCREAMING_SNAKE_CASE__    )

											if apply_ocr:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   words_batch
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   boxes_batch
											return data




