

'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_nllb import NllbTokenizer
else:
 lowercase__         =      None


lowercase__         =      logging.get_logger(__name__)


lowercase__         =      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase__         =      {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__         =      {
    "facebook/nllb-large-en-ro": 1024,
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
lowercase__         =      ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Union[str, Any]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     List[Any]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     Union[str, Any]  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     Optional[Any]  		=  ["""input_ids""", """attention_mask"""]
     UpperCAmelCase_						:     Dict  		=  NllbTokenizer

     UpperCAmelCase_						:     Tuple  		=  []
     UpperCAmelCase_						:     Any  		=  []


     def __init__(		self			:					Optional[Any]					,      lowercase_			:					Any=None					,      lowercase_			:					str=None					,      lowercase_			:					Any="<s>"					,      lowercase_			:					Any="</s>"					,      lowercase_			:					Any="</s>"					,      lowercase_			:					Optional[Any]="<s>"					,      lowercase_			:					Union[str, Any]="<unk>"					,      lowercase_			:					int="<pad>"					,      lowercase_			:					int="<mask>"					,      lowercase_			:					List[str]=None					,      lowercase_			:					Any=None					,      lowercase_			:					List[Any]=None					,      lowercase_			:					Optional[Any]=False					,      **lowercase_			:					Optional[Any]					,      )						->     List[str]:
      UpperCAmelCase	:     Any						 =			AddedToken(_SCREAMING_SNAKE_CASE					,      lstrip=_SCREAMING_SNAKE_CASE					,      rstrip=_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE	) else mask_token
      UpperCAmelCase	:     Union[str, Any]						 =			legacy_behaviour
      super().__init__(
          vocab_file=_SCREAMING_SNAKE_CASE					,      tokenizer_file=_SCREAMING_SNAKE_CASE					,      bos_token=_SCREAMING_SNAKE_CASE					,      eos_token=_SCREAMING_SNAKE_CASE					,      sep_token=_SCREAMING_SNAKE_CASE					,      cls_token=_SCREAMING_SNAKE_CASE					,      unk_token=_SCREAMING_SNAKE_CASE					,      pad_token=_SCREAMING_SNAKE_CASE					,      mask_token=_SCREAMING_SNAKE_CASE					,      src_lang=_SCREAMING_SNAKE_CASE					,      tgt_lang=_SCREAMING_SNAKE_CASE					,      additional_special_tokens=_SCREAMING_SNAKE_CASE					,      legacy_behaviour=_SCREAMING_SNAKE_CASE					,      **_SCREAMING_SNAKE_CASE					,      )

      UpperCAmelCase	:     Tuple						 =			vocab_file
      UpperCAmelCase	:     List[Any]						 =			False if not self.vocab_file else True

      UpperCAmelCase	:     Union[str, Any]						 =			FAIRSEQ_LANGUAGE_CODES.copy()

      if additional_special_tokens is not None:
       # Only add those special tokens if they are not already there.
       _additional_special_tokens.extend(
           [t for t in additional_special_tokens if t not in _additional_special_tokens]	)

      self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}	)
      UpperCAmelCase	:     Optional[int]						 =			{
          lang_code: self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      UpperCAmelCase	:     Optional[Any]						 =			src_lang if src_lang is not None else '''eng_Latn'''
      UpperCAmelCase	:     Any						 =			self.convert_tokens_to_ids(self._src_lang	)
      UpperCAmelCase	:     List[Any]						 =			tgt_lang
      self.set_src_lang_special_tokens(self._src_lang	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     str:
      return self._src_lang


     @src_lang.setter
     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					str	)						->     None:
      UpperCAmelCase	:     int						 =			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[int]					,      lowercase_			:					Tuple = None	)						->     List[int]:





      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Any = None	)						->     List[int]:
      UpperCAmelCase	:     Any						 =			[self.sep_token_id]
      UpperCAmelCase	:     int						 =			[self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Dict					,      **lowercase_			:					Dict	)						->     List[str]:





      if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'	)
      UpperCAmelCase	:     Dict						 =			src_lang
      UpperCAmelCase	:     str						 =			self(_SCREAMING_SNAKE_CASE					,      add_special_tokens=_SCREAMING_SNAKE_CASE					,      return_tensors=_SCREAMING_SNAKE_CASE					,      **_SCREAMING_SNAKE_CASE	)
      UpperCAmelCase	:     Tuple						 =			self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
      UpperCAmelCase	:     Any						 =			tgt_lang_id
      return inputs


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					List[str]					,      lowercase_			:					Union[str, Any] = "eng_Latn"					,      lowercase_			:					Dict = None					,      lowercase_			:					str = "fra_Latn"					,      **lowercase_			:					Optional[Any]					,      )						->     BatchEncoding:
      UpperCAmelCase	:     Optional[Any]						 =			src_lang
      UpperCAmelCase	:     List[Any]						 =			tgt_lang
      return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE					,      _SCREAMING_SNAKE_CASE					,      **_SCREAMING_SNAKE_CASE	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     List[str]:
      return self.set_src_lang_special_tokens(self.src_lang	)


     def        UpperCAmelCase_	(		self			:					str	)						->     Optional[int]:
      return self.set_tgt_lang_special_tokens(self.tgt_lang	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					Optional[Any]	)						->     None:
      UpperCAmelCase	:     str						 =			self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)

      if self.legacy_behaviour:
       UpperCAmelCase	:     Union[str, Any]						 =			[]
       UpperCAmelCase	:     Any						 =			[self.eos_token_id, self.cur_lang_code]
      else:
       UpperCAmelCase	:     str						 =			[self.cur_lang_code]
       UpperCAmelCase	:     List[Any]						 =			[self.eos_token_id]

      UpperCAmelCase	:     List[str]						 =			self.convert_ids_to_tokens(self.prefix_tokens	)
      UpperCAmelCase	:     List[Any]						 =			self.convert_ids_to_tokens(self.suffix_tokens	)

      UpperCAmelCase	:     Union[str, Any]						 =			processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str					,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str					,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,      self.prefix_tokens + self.suffix_tokens	)	)					,      )


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					Optional[int]	)						->     None:
      UpperCAmelCase	:     Optional[int]						 =			self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE	)
      if self.legacy_behaviour:
       UpperCAmelCase	:     List[Any]						 =			[]
       UpperCAmelCase	:     Optional[Any]						 =			[self.eos_token_id, self.cur_lang_code]
      else:
       UpperCAmelCase	:     List[str]						 =			[self.cur_lang_code]
       UpperCAmelCase	:     str						 =			[self.eos_token_id]

      UpperCAmelCase	:     Any						 =			self.convert_ids_to_tokens(self.prefix_tokens	)
      UpperCAmelCase	:     List[str]						 =			self.convert_ids_to_tokens(self.suffix_tokens	)

      UpperCAmelCase	:     Union[str, Any]						 =			processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str					,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str					,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str					,      self.prefix_tokens + self.suffix_tokens	)	)					,      )




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Optional[int]					,      lowercase_			:					str = None	)						->     Tuple[str]:





      if not self.can_save_slow_tokenizer:
       raise ValueError(
           'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
           'tokenizer.'	)

      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['vocab_file']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(_SCREAMING_SNAKE_CASE	):
       copyfile(self.vocab_file					,      _SCREAMING_SNAKE_CASE	)

      return (out_vocab_file,)






'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase__         =      logging.get_logger(__name__)


if is_vision_available():
 import PIL




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     str  		=  ["""pixel_values"""]


     def __init__(		self			:					Dict					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Union[int, float] = 1 / 255					,      lowercase_			:					bool = True					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					bool = True					,      **lowercase_			:					Any					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Dict						 =			size if size is not None else {'shortest_edge': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     List[str]						 =			crop_size if crop_size is not None else {'height': 224, 'width': 224}
      UpperCAmelCase	:     Any						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_					,      param_name='crop_size'	)

      UpperCAmelCase	:     Optional[int]						 =			do_resize
      UpperCAmelCase	:     str						 =			size
      UpperCAmelCase	:     str						 =			resample
      UpperCAmelCase	:     Union[str, Any]						 =			do_center_crop
      UpperCAmelCase	:     Any						 =			crop_size
      UpperCAmelCase	:     Optional[Any]						 =			do_rescale
      UpperCAmelCase	:     List[Any]						 =			rescale_factor
      UpperCAmelCase	:     Any						 =			do_normalize
      UpperCAmelCase	:     int						 =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
      UpperCAmelCase	:     str						 =			image_std if image_std is not None else OPENAI_CLIP_STD
      UpperCAmelCase	:     Union[str, Any]						 =			do_convert_rgb


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BICUBIC					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					int					,      )						->     np.ndarray:
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_					,      default_to_square=lowercase_	)
      if "shortest_edge" not in size:
       raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
      UpperCAmelCase	:     str						 =			get_resize_output_image_size(lowercase_					,      size=size['shortest_edge']					,      default_to_square=lowercase_	)
      return resize(lowercase_					,      size=lowercase_					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Any					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Tuple					,      )						->     np.ndarray:
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      if "height" not in size or "width" not in size:
       raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
      return center_crop(lowercase_					,      size=(size['height'], size['width'])					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Union[int, float]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     Union[str, Any]:
      return rescale(lowercase_					,      scale=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Union[float, List[float]]					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Optional[int]					,      )						->     np.ndarray:
      return normalize(lowercase_					,      mean=lowercase_					,      std=lowercase_					,      data_format=lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					int = None					,      lowercase_			:					bool = None					,      lowercase_			:					float = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					Optional[Union[float, List[float]]] = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[ChannelDimension] = ChannelDimension.FIRST					,      **lowercase_			:					List[Any]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Any						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     List[str]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_					,      param_name='size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     int						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     List[Any]						 =			do_center_crop if do_center_crop is not None else self.do_center_crop
      UpperCAmelCase	:     List[Any]						 =			crop_size if crop_size is not None else self.crop_size
      UpperCAmelCase	:     Union[str, Any]						 =			get_size_dict(lowercase_					,      param_name='crop_size'					,      default_to_square=lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			do_rescale if do_rescale is not None else self.do_rescale
      UpperCAmelCase	:     Optional[int]						 =			rescale_factor if rescale_factor is not None else self.rescale_factor
      UpperCAmelCase	:     Tuple						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			image_mean if image_mean is not None else self.image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std if image_std is not None else self.image_std
      UpperCAmelCase	:     List[Any]						 =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      UpperCAmelCase	:     Union[str, Any]						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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.'	)

      if do_center_crop and crop_size is None:
       raise ValueError('Crop size must be specified if do_center_crop is True.'	)

      if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

      if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

      # PIL RGBA images are converted to RGB
      if do_convert_rgb:
       UpperCAmelCase	:     int						 =			[convert_to_rgb(lowercase_	) for image in images]

      # All transformations expect numpy arrays.
      UpperCAmelCase	:     List[Any]						 =			[to_numpy_array(lowercase_	) for image in images]

      if do_resize:
       UpperCAmelCase	:     Dict						 =			[self.resize(image=lowercase_					,      size=lowercase_					,      resample=lowercase_	) for image in images]

      if do_center_crop:
       UpperCAmelCase	:     List[str]						 =			[self.center_crop(image=lowercase_					,      size=lowercase_	) for image in images]

      if do_rescale:
       UpperCAmelCase	:     str						 =			[self.rescale(image=lowercase_					,      scale=lowercase_	) for image in images]

      if do_normalize:
       UpperCAmelCase	:     List[Any]						 =			[self.normalize(image=lowercase_					,      mean=lowercase_					,      std=lowercase_	) for image in images]

      UpperCAmelCase	:     Optional[int]						 =			[to_channel_dimension_format(lowercase_					,      lowercase_	) for image in images]

      UpperCAmelCase	:     str						 =			{'pixel_values': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)




