
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:
    A__      :	Any          =    None


A__      :	Optional[int]          =    logging.get_logger(__name__)


A__      :	List[str]          =    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A__      :	Dict          =    {
    "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",
    },
}

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

# fmt: off
A__      :	Optional[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'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							['input_ids', 'attention_mask']
     _A								=							MBartTokenizer

     _A								=							[]
     _A								=							[]



     def __init__(      self ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="</s>" ,			__UpperCamelCase="<s>" ,			__UpperCamelCase="<unk>" ,			__UpperCamelCase="<pad>" ,			__UpperCamelCase="<mask>" ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			**__UpperCamelCase ,			)->					List[str]:
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__							: Union[str, 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__							: List[Any] 							=						vocab_file
          UpperCAmelCase__							: List[Any] 							=						False if not self.vocab_file else True

          UpperCAmelCase__							: Optional[int] 							=						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__							: Tuple 							=						{
              lang_code: self.convert_tokens_to_ids(_lowercase  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

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



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



     @src_lang.setter
     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					None:
          UpperCAmelCase__							: int 							=						new_src_lang
          self.set_src_lang_special_tokens(self._src_lang  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = 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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:
          UpperCAmelCase__							: Union[str, 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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Union[str, 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"  )
          UpperCAmelCase__							: int 							=						src_lang
          UpperCAmelCase__							: Any 							=						self(_lowercase ,			add_special_tokens=_lowercase ,			return_tensors=_lowercase ,			**_lowercase  )
          UpperCAmelCase__							: Optional[int] 							=						self.convert_tokens_to_ids(_lowercase  )
          UpperCAmelCase__							: Optional[int] 							=						tgt_lang_id
          return inputs



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = "en_XX" ,			__UpperCamelCase = None ,			__UpperCamelCase = "ro_RO" ,			**__UpperCamelCase ,			)->					BatchEncoding:
          UpperCAmelCase__							: Optional[int] 							=						src_lang
          UpperCAmelCase__							: Union[str, Any] 							=						tgt_lang
          return super().prepare_seqaseq_batch(_lowercase ,			_lowercase ,			**_lowercase  )



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          return self.set_src_lang_special_tokens(self.src_lang  )



     def  lowerCAmelCase__       (      self  )->					int:
          return self.set_tgt_lang_special_tokens(self.tgt_lang  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					None:
          UpperCAmelCase__							: str 							=						self.convert_tokens_to_ids(_lowercase  )
          UpperCAmelCase__							: Any 							=						[]
          UpperCAmelCase__							: Optional[int] 							=						[self.eos_token_id, self.cur_lang_code]

          UpperCAmelCase__							: Optional[int] 							=						self.convert_ids_to_tokens(self.prefix_tokens  )
          UpperCAmelCase__							: Optional[int] 							=						self.convert_ids_to_tokens(self.suffix_tokens  )

          UpperCAmelCase__							: 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  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					None:
          UpperCAmelCase__							: Union[str, Any] 							=						self.convert_tokens_to_ids(_lowercase  )
          UpperCAmelCase__							: List[str] 							=						[]
          UpperCAmelCase__							: Optional[int] 							=						[self.eos_token_id, self.cur_lang_code]

          UpperCAmelCase__							: Any 							=						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  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = 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(_lowercase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory."  )
               return
          UpperCAmelCase__							: Tuple 							=						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,)





"""simple docstring"""




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

A__      :	Dict          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[Any]		):






     '''simple docstring'''
     if isinstance(lowerCAmelCase       ,    (list, tuple)		) and isinstance(videos[0]       ,    (list, tuple)		) and is_valid_image(videos[0][0]		):
          return videos

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

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

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







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							['pixel_values']



     def __init__(      self ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			__UpperCamelCase = 1 / 2_55 ,			__UpperCamelCase = True ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          super().__init__(**__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						size if size is not None else {"shortest_edge": 2_56}
          UpperCAmelCase__							: List[Any] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCAmelCase__							: int 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = PILImageResampling.BILINEAR ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          if "shortest_edge" in size:
               UpperCAmelCase__							: Union[str, Any] 							=						get_resize_output_image_size(__UpperCamelCase ,			size["shortest_edge"] ,			default_to_square=__UpperCamelCase  )
          elif "height" in size and "width" in size:
               UpperCAmelCase__							: Any 							=						(size["height"], size["width"])
          else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
          return resize(__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          UpperCAmelCase__							: Optional[Any] 							=						get_size_dict(__UpperCamelCase  )
          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(__UpperCamelCase ,			size=(size["height"], size["width"]) ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = True ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					Tuple:
          UpperCAmelCase__							: str 							=						image.astype(np.floataa  )
          if offset:
               UpperCAmelCase__							: Tuple 							=						image - (scale / 2)
          return rescale(__UpperCamelCase ,			scale=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					np.ndarray:
          return normalize(__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase ,			data_format=__UpperCamelCase ,			**__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			)->					np.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_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__							: Optional[Any] 							=						to_numpy_array(__UpperCamelCase  )

          if do_resize:
               UpperCAmelCase__							: Union[str, Any] 							=						self.resize(image=__UpperCamelCase ,			size=__UpperCamelCase ,			resample=__UpperCamelCase  )

          if do_center_crop:
               UpperCAmelCase__							: int 							=						self.center_crop(__UpperCamelCase ,			size=__UpperCamelCase  )

          if do_rescale:
               UpperCAmelCase__							: List[str] 							=						self.rescale(image=__UpperCamelCase ,			scale=__UpperCamelCase ,			offset=__UpperCamelCase  )

          if do_normalize:
               UpperCAmelCase__							: List[Any] 							=						self.normalize(image=__UpperCamelCase ,			mean=__UpperCamelCase ,			std=__UpperCamelCase  )

          UpperCAmelCase__							: Dict 							=						to_channel_dimension_format(__UpperCamelCase ,			__UpperCamelCase  )
          return image



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			__UpperCamelCase = ChannelDimension.FIRST ,			**__UpperCamelCase ,			)->					PIL.Image.Image:
          UpperCAmelCase__							: Optional[int] 							=						do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase__							: int 							=						resample if resample is not None else self.resample
          UpperCAmelCase__							: Tuple 							=						do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase__							: int 							=						do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase__							: Any 							=						rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase__							: Optional[int] 							=						offset if offset is not None else self.offset
          UpperCAmelCase__							: Dict 							=						do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase__							: Dict 							=						image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase__							: Optional[int] 							=						image_std if image_std is not None else self.image_std

          UpperCAmelCase__							: List[str] 							=						size if size is not None else self.size
          UpperCAmelCase__							: Optional[int] 							=						get_size_dict(__UpperCamelCase ,			default_to_square=__UpperCamelCase  )
          UpperCAmelCase__							: Dict 							=						crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase__							: Tuple 							=						get_size_dict(__UpperCamelCase ,			param_name="crop_size"  )

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

          UpperCAmelCase__							: List[str] 							=						make_batched(__UpperCamelCase  )

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

          UpperCAmelCase__							: Dict 							=						{"pixel_values": videos}
          return BatchFeature(data=__UpperCamelCase ,			tensor_type=__UpperCamelCase  )



