

"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


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

lowerCAmelCase							:  Any            =						"""▁"""

lowerCAmelCase							:  int            =						{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase							:  int            =						{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase							:  Optional[Any]            =						{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
lowerCAmelCase							:  Union[str, Any]            =						["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							int      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							str      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							Optional[Any]      							=					['''input_ids''', '''attention_mask''']

			_UpperCAmelCase       :							List[int]      							=					[]
			_UpperCAmelCase       :							List[int]      							=					[]



			def __init__(			self  :					int       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Tuple="<s>"       ,						lowerCAmelCase__  :					Optional[int]="</s>"       ,						lowerCAmelCase__  :					Union[str, Any]="</s>"       ,						lowerCAmelCase__  :					List[str]="<s>"       ,						lowerCAmelCase__  :					Union[str, Any]="<unk>"       ,						lowerCAmelCase__  :					Optional[int]="<pad>"       ,						lowerCAmelCase__  :					Dict="<mask>"       ,						lowerCAmelCase__  :					Union[str, Any]=None       ,						lowerCAmelCase__  :					Optional[Any]=None       ,						lowerCAmelCase__  :					Dict=None       ,						lowerCAmelCase__  :					Optional[Dict[str, Any]] = None       ,						lowerCAmelCase__  :					List[str]=None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									SCREAMING_SNAKE_CASE_:			Any  		=	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						unk_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						tokenizer_file=lowerCAmelCase__       ,						src_lang=lowerCAmelCase__       ,						tgt_lang=lowerCAmelCase__       ,						additional_special_tokens=lowerCAmelCase__       ,						sp_model_kwargs=self.sp_model_kwargs       ,						**lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.Load(str(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	vocab_file

									# Original fairseq vocab and spm vocab must be "aligned":
									# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
									# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
									# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
									# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

									# Mimic fairseq token-to-id alignment for the first 4 token
									SCREAMING_SNAKE_CASE_:			int  		=	{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

									# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
									SCREAMING_SNAKE_CASE_:			List[str]  		=	1

									SCREAMING_SNAKE_CASE_:			int  		=	len(self.sp_model)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__)
									}
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{v: k for k, v in self.lang_code_to_id.items()}
									SCREAMING_SNAKE_CASE_:			int  		=	len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
									SCREAMING_SNAKE_CASE_:			Tuple  		=	list(self.lang_code_to_id.keys())

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

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	src_lang if src_lang is not None else "en_XX"
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.lang_code_to_id[self._src_lang]
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tgt_lang
									self.set_src_lang_special_tokens(self._src_lang)



			def __getstate__(			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Any  		=	self.__dict__.copy()
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	None
									SCREAMING_SNAKE_CASE_:			str  		=	self.sp_model.serialized_model_proto()
									return state



			def __setstate__(			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	d

									# for backward compatibility
									if not hasattr(self       ,						"sp_model_kwargs"):
															SCREAMING_SNAKE_CASE_:			List[str]  		=	{}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return self._src_lang



			@src_lang.setter
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									SCREAMING_SNAKE_CASE_:			int  		=	new_src_lang
									self.set_src_lang_special_tokens(self._src_lang)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):

									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[1] * len(self.prefix_tokens)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[1] * len(self.suffix_tokens)
									if token_ids_a is None:
															return prefix_ones + ([0] * len(lowerCAmelCase__)) + suffix_ones
									return prefix_ones + ([0] * len(lowerCAmelCase__)) + ([0] * len(lowerCAmelCase__)) + suffix_ones



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									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 	_SCREAMING_SNAKE_CASE      (			self  :					Any       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Dict  		=	[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 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Optional[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str]       ,						lowerCAmelCase__  :					Optional[str]       ,						**lowerCAmelCase__  :					Optional[Any]):
									if src_lang is None or tgt_lang is None:
															raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
									SCREAMING_SNAKE_CASE_:			str  		=	src_lang
									SCREAMING_SNAKE_CASE_:			Any  		=	self(lowerCAmelCase__       ,						add_special_tokens=lowerCAmelCase__       ,						return_tensors=lowerCAmelCase__       ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	self.convert_tokens_to_ids(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	tgt_lang_id
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									SCREAMING_SNAKE_CASE_:			Any  		=	{self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					str):
									return self.sp_model.encode(lowerCAmelCase__       ,						out_type=lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Tuple):
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									SCREAMING_SNAKE_CASE_:			Any  		=	self.sp_model.PieceToId(lowerCAmelCase__)

									# Need to return unknown token if the SP model returned 0
									return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					Union[str, Any]):
									if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
									return self.sp_model.IdToPiece(index - self.fairseq_offset)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"".join(lowerCAmelCase__).replace(lowerCAmelCase__       ,						" ").strip()
									return out_string



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	os.path.join(
									    lowerCAmelCase__       ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

									if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(self.vocab_file):
															copyfile(self.vocab_file       ,						lowerCAmelCase__)
									elif not os.path.isfile(self.vocab_file):
															with open(lowerCAmelCase__       ,						"wb") as fi:
																					SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.sp_model.serialized_model_proto()
																					fi.write(lowerCAmelCase__)

									return (out_vocab_file,)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					List[str]       ,						lowerCAmelCase__  :					str = "en_XX"       ,						lowerCAmelCase__  :					Optional[List[str]] = None       ,						lowerCAmelCase__  :					str = "ro_RO"       ,						**lowerCAmelCase__  :					Optional[Any]       ,						):
									SCREAMING_SNAKE_CASE_:			str  		=	src_lang
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tgt_lang
									return super().prepare_seqaseq_batch(lowerCAmelCase__       ,						lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.set_src_lang_special_tokens(self.src_lang)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self.set_tgt_lang_special_tokens(self.tgt_lang)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	self.lang_code_to_id[src_lang]
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[]
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.eos_token_id, self.cur_lang_code]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.lang_code_to_id[lang]
									SCREAMING_SNAKE_CASE_:			str  		=	[]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.eos_token_id, self.cur_lang_code]









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 convert_to_rgb, 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


if is_vision_available():
						import PIL


lowerCAmelCase							:  Optional[Any]            =						logging.get_logger(__name__)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

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



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									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_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															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 or resample is None:
															raise ValueError("Size and resample must be specified if do_resize 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:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

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

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

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






