




'''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_mbart import MBartTokenizer
else:
  UpperCAmelCase_ 		=      None


UpperCAmelCase_ 		=      logging.get_logger(__name__)


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

UpperCAmelCase_ 		=      {
    "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",
    },
}

UpperCAmelCase_ 		=      {
    "facebook/mbart-large-en-ro": 10_24,
    "facebook/mbart-large-cc25": 10_24,
}

# fmt: off
UpperCAmelCase_ 		=      ["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						(  __magic_name__ ):
   _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      ,							)				->    Tuple:
     # Mask token behave like a normal word, i.e. include the space before it
     __a			=					AddedToken(UpperCamelCase      ,							lstrip=UpperCamelCase      ,							rstrip=UpperCamelCase      ) if isinstance(UpperCamelCase      ,							UpperCamelCase      ) else mask_token

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

     __a			=					vocab_file
     __a			=					False if not self.vocab_file else True

     __a			=					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}      )
     __a			=					{
         lang_code: self.convert_tokens_to_ids(UpperCamelCase      ) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     __a			=					src_lang if src_lang is not None else 'en_XX'
     __a			=					self.convert_tokens_to_ids(self._src_lang      )
     __a			=					tgt_lang
     self.set_src_lang_special_tokens(self._src_lang      )







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







   @src_lang.setter
   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					new_src_lang
     self.set_src_lang_special_tokens(self._src_lang      )







   def 		UpperCamelCase__			(	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 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    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'      )
     __a			=					src_lang
     __a			=					self(UpperCamelCase      ,							add_special_tokens=UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					tgt_lang_id
     return inputs







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = "en_XX"      ,							UpperCamelCase = None      ,							UpperCamelCase = "ro_RO"      ,							**UpperCamelCase      ,							)				->    BatchEncoding:
     __a			=					src_lang
     __a			=					tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Any:
     return self.set_src_lang_special_tokens(self.src_lang      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     return self.set_tgt_lang_special_tokens(self.tgt_lang      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    None:
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					[]
     __a			=					[self.eos_token_id, self.cur_lang_code]

     __a			=					self.convert_ids_to_tokens(self.prefix_tokens      )
     __a			=					self.convert_ids_to_tokens(self.suffix_tokens      )

     __a			=					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      ,							UpperCamelCase      )				->    None:
     __a			=					self.convert_tokens_to_ids(UpperCamelCase      )
     __a			=					[]
     __a			=					[self.eos_token_id, self.cur_lang_code]

     __a			=					self.convert_ids_to_tokens(self.prefix_tokens      )
     __a			=					self.convert_ids_to_tokens(self.suffix_tokens      )

     __a			=					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      ,							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(UpperCamelCase      ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory."      )
       return
     __a			=					os.path.join(
         UpperCamelCase      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

     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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							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:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					make_list_of_images(UpperCamelCase      )

     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.'      )

     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_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     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.'      )

     # All transformations expect numpy arrays.
     __a			=					[to_numpy_array(UpperCamelCase      ) for image in images]

     if do_resize:
       __a			=					[self.resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							crop_pct=UpperCamelCase      ,							resample=UpperCamelCase      ) for image in images]

     if do_rescale:
       __a			=					[self.rescale(image=UpperCamelCase      ,							scale=UpperCamelCase      ) for image in images]

     if do_normalize:
       __a			=					[self.normalize(image=UpperCamelCase      ,							mean=UpperCamelCase      ,							std=UpperCamelCase      ) for image in images]

     __a			=					[to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      ) for image in images]

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



