


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

lowerCAmelCase       		=    logging.get_logger(__name__)




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     List[List[ImageInput]]:




						'''simple docstring'''







						if isinstance(lowercase_      ,       (list, tuple)      ) and isinstance(videos[0]      ,       (list, tuple)      ) and is_valid_image(videos[0][0]      ):
												return videos

						elif isinstance(lowercase_      ,       (list, tuple)      ) and is_valid_image(videos[0]      ):
												return [videos]

						elif is_valid_image(lowercase_      ):
												return [[videos]]

						raise ValueError(f"Could not make batched video from {videos}"      )





class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						['''pixel_values''']


	def __init__(    self				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = PILImageResampling.BILINEAR				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = True				,     lowercase__ = 1 / 2_5_5				,     lowercase__ = True				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = None				,     **lowercase__				,     ):
							super().__init__(**lowercase__)
							__UpperCAmelCase			:							int	    =	size if size is not None else {'''shortest_edge''': 2_5_6}
							__UpperCAmelCase			:							Optional[Any]	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__)
							__UpperCAmelCase			:							List[str]	    =	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase			:							int	    =	get_size_dict(lowercase__				,     param_name='''crop_size''')

							__UpperCAmelCase			:							Optional[Any]	    =	do_resize
							__UpperCAmelCase			:							Union[str, Any]	    =	size
							__UpperCAmelCase			:							List[Any]	    =	do_center_crop
							__UpperCAmelCase			:							Optional[int]	    =	crop_size
							__UpperCAmelCase			:							Union[str, Any]	    =	resample
							__UpperCAmelCase			:							str	    =	do_rescale
							__UpperCAmelCase			:							List[str]	    =	rescale_factor
							__UpperCAmelCase			:							Tuple	    =	offset
							__UpperCAmelCase			:							Any	    =	do_normalize
							__UpperCAmelCase			:							str	    =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__UpperCAmelCase			:							List[str]	    =	image_std if image_std is not None else IMAGENET_STANDARD_STD


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = PILImageResampling.BILINEAR				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__)
							if "shortest_edge" in size:
													__UpperCAmelCase			:							Optional[int]	    =	get_resize_output_image_size(lowercase__				,     size['''shortest_edge''']				,     default_to_square=lowercase__)
							elif "height" in size and "width" in size:
													__UpperCAmelCase			:							Dict	    =	(size['''height'''], size['''width'''])
							else:
													raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
							return resize(lowercase__				,     size=lowercase__				,     resample=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Union[str, Any]	    =	get_size_dict(lowercase__)
							if "height" not in size or "width" not in size:
													raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}")
							return center_crop(lowercase__				,     size=(size['''height'''], size['''width'''])				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							List[str]	    =	image.astype(np.floataa)
							if offset:
													__UpperCAmelCase			:							str	    =	image - (scale / 2)
							return rescale(lowercase__				,     scale=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return normalize(lowercase__				,     mean=lowercase__				,     std=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     ):
							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_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.''')

							if offset and not do_rescale:
													raise ValueError('''For offset, do_rescale must also be set to True.''')

							# All transformations expect numpy arrays.
							__UpperCAmelCase			:							str	    =	to_numpy_array(lowercase__)

							if do_resize:
													__UpperCAmelCase			:							str	    =	self.resize(image=lowercase__				,     size=lowercase__				,     resample=lowercase__)

							if do_center_crop:
													__UpperCAmelCase			:							Tuple	    =	self.center_crop(lowercase__				,     size=lowercase__)

							if do_rescale:
													__UpperCAmelCase			:							Union[str, Any]	    =	self.rescale(image=lowercase__				,     scale=lowercase__				,     offset=lowercase__)

							if do_normalize:
													__UpperCAmelCase			:							List[str]	    =	self.normalize(image=lowercase__				,     mean=lowercase__				,     std=lowercase__)

							__UpperCAmelCase			:							Union[str, Any]	    =	to_channel_dimension_format(lowercase__				,     lowercase__)
							return image


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Dict	    =	do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase			:							Dict	    =	resample if resample is not None else self.resample
							__UpperCAmelCase			:							int	    =	do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCAmelCase			:							str	    =	do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase			:							int	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase			:							List[Any]	    =	offset if offset is not None else self.offset
							__UpperCAmelCase			:							int	    =	do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase			:							Any	    =	image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase			:							int	    =	image_std if image_std is not None else self.image_std

							__UpperCAmelCase			:							int	    =	size if size is not None else self.size
							__UpperCAmelCase			:							Union[str, Any]	    =	get_size_dict(lowercase__				,     default_to_square=lowercase__)
							__UpperCAmelCase			:							List[str]	    =	crop_size if crop_size is not None else self.crop_size
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__				,     param_name='''crop_size''')

							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.''')

							__UpperCAmelCase			:							str	    =	make_batched(lowercase__)

							__UpperCAmelCase			:							Any	    =	[
							    [
							        self._preprocess_image(
							            image=lowercase__				,     do_resize=lowercase__				,     size=lowercase__				,     resample=lowercase__				,     do_center_crop=lowercase__				,     crop_size=lowercase__				,     do_rescale=lowercase__				,     rescale_factor=lowercase__				,     offset=lowercase__				,     do_normalize=lowercase__				,     image_mean=lowercase__				,     image_std=lowercase__				,     data_format=lowercase__				,     )
							        for img in video
							    ]
							    for video in videos
							]

							__UpperCAmelCase			:							int	    =	{'''pixel_values''': videos}
							return BatchFeature(data=lowercase__				,     tensor_type=lowercase__)





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_mbart import MBartTokenizer
else:
				lowerCAmelCase       		=    None


lowerCAmelCase       		=    logging.get_logger(__name__)


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

lowerCAmelCase       		=    {
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

lowerCAmelCase       		=    {
    """facebook/mbart-large-en-ro""": 1_024,
    """facebook/mbart-large-cc25""": 1_024,
}

# fmt: off
lowerCAmelCase       		=    ["""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 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		List[str]										=						VOCAB_FILES_NAMES
	_lowerCAmelCase     :		Union[str, Any]										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase     :		str										=						PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase     :		Optional[int]										=						['''input_ids''', '''attention_mask''']
	_lowerCAmelCase     :		Optional[int]										=						MBartTokenizer

	_lowerCAmelCase     :		List[int]										=						[]
	_lowerCAmelCase     :		List[int]										=						[]


	def __init__(    self				,     lowercase__=None				,     lowercase__=None				,     lowercase__="<s>"				,     lowercase__="</s>"				,     lowercase__="</s>"				,     lowercase__="<s>"				,     lowercase__="<unk>"				,     lowercase__="<pad>"				,     lowercase__="<mask>"				,     lowercase__=None				,     lowercase__=None				,     lowercase__=None				,     **lowercase__				,     ):
							# Mask token behave like a normal word, i.e. include the space before it
							__UpperCAmelCase			:							Any	    =	AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__) if isinstance(lowercase__				,     lowercase__) else mask_token

							super().__init__(
							    vocab_file=lowercase__				,     tokenizer_file=lowercase__				,     bos_token=lowercase__				,     eos_token=lowercase__				,     sep_token=lowercase__				,     cls_token=lowercase__				,     unk_token=lowercase__				,     pad_token=lowercase__				,     mask_token=lowercase__				,     src_lang=lowercase__				,     tgt_lang=lowercase__				,     additional_special_tokens=lowercase__				,     **lowercase__				,     )

							__UpperCAmelCase			:							Optional[Any]	    =	vocab_file
							__UpperCAmelCase			:							int	    =	False if not self.vocab_file else True

							__UpperCAmelCase			:							str	    =	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			:							Union[str, Any]	    =	{
							    lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

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


	@property
	def  A(    self):
							return self._src_lang


	@src_lang.setter
	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	new_src_lang
							self.set_src_lang_special_tokens(self._src_lang)


	def  A(    self				,     lowercase__				,     lowercase__ = 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  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							Optional[int]	    =	[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  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__):
							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			:							Optional[Any]	    =	src_lang
							__UpperCAmelCase			:							Union[str, Any]	    =	self(lowercase__				,     add_special_tokens=lowercase__				,     return_tensors=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							int	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	tgt_lang_id
							return inputs


	def  A(    self				,     lowercase__				,     lowercase__ = "en_XX"				,     lowercase__ = None				,     lowercase__ = "ro_RO"				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Any	    =	src_lang
							__UpperCAmelCase			:							Tuple	    =	tgt_lang
							return super().prepare_seqaseq_batch(lowercase__				,     lowercase__				,     **lowercase__)


	def  A(    self):
							return self.set_src_lang_special_tokens(self.src_lang)


	def  A(    self):
							return self.set_tgt_lang_special_tokens(self.tgt_lang)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[]
							__UpperCAmelCase			:							Dict	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							int	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Dict	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							str	    =	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  A(    self				,     lowercase__):
							__UpperCAmelCase			:							List[str]	    =	self.convert_tokens_to_ids(lowercase__)
							__UpperCAmelCase			:							List[Any]	    =	[]
							__UpperCAmelCase			:							List[Any]	    =	[self.eos_token_id, self.cur_lang_code]

							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.prefix_tokens)
							__UpperCAmelCase			:							Tuple	    =	self.convert_ids_to_tokens(self.suffix_tokens)

							__UpperCAmelCase			:							Optional[int]	    =	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  A(    self				,     lowercase__				,     lowercase__ = None):
							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(lowercase__):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
													return
							__UpperCAmelCase			:							List[Any]	    =	os.path.join(
							    lowercase__				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

							return (out_vocab_file,)

