
"""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': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# 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  lowerCAmelCase__							(		__a      ):
     __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     :     str       , _lowerCamelCase     :     Optional[int]=None       , _lowerCamelCase     :     int=None       , _lowerCamelCase     :     Tuple="<s>"       , _lowerCamelCase     :     str="</s>"       , _lowerCamelCase     :     Union[str, Any]="</s>"       , _lowerCamelCase     :     Union[str, Any]="<s>"       , _lowerCamelCase     :     Any="<unk>"       , _lowerCamelCase     :     Optional[Any]="<pad>"       , _lowerCamelCase     :     int="<mask>"       , _lowerCamelCase     :     Optional[Any]=None       , _lowerCamelCase     :     int=None       , _lowerCamelCase     :     Optional[int]=None       , **_lowerCamelCase     :     int       , ):
       _snake_case   	=			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       , )

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

       _snake_case   	=			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}     )
       _snake_case   	=			{
           lang_code: self.convert_tokens_to_ids(_lowercase     ) for lang_code in FAIRSEQ_LANGUAGE_CODES
       }

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







     @property
     def 	lowercase      (				self     :     Any     ):
       return self._src_lang







     @src_lang.setter
     def 	lowercase      (				self     :     str       , _lowerCamelCase     :     str     ):
       _snake_case   	=			new_src_lang
       self.set_src_lang_special_tokens(self._src_lang     )







     def 	lowercase      (				self     :     str       , _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 	lowercase      (				self     :     List[Any]       , _lowerCamelCase     :     List[int]       , _lowerCamelCase     :     Optional[List[int]] = None     ):
       _snake_case   	=			[self.sep_token_id]
       _snake_case   	=			[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 	lowercase      (				self     :     List[str]       , _lowerCamelCase     :     Tuple       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Optional[str]       , _lowerCamelCase     :     Optional[str]       , **_lowerCamelCase     :     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'''     )
       _snake_case   	=			src_lang
       _snake_case   	=			self(_lowercase       , add_special_tokens=_lowercase       , return_tensors=_lowercase       , **_lowercase     )
       _snake_case   	=			self.convert_tokens_to_ids(_lowercase     )
       _snake_case   	=			tgt_lang_id
       return inputs







     def 	lowercase      (				self     :     int       , _lowerCamelCase     :     List[str]       , _lowerCamelCase     :     str = "en_XX"       , _lowerCamelCase     :     Optional[List[str]] = None       , _lowerCamelCase     :     str = "ro_RO"       , **_lowerCamelCase     :     Optional[Any]       , ):
       _snake_case   	=			src_lang
       _snake_case   	=			tgt_lang
       return super().prepare_seqaseq_batch(_lowercase       , _lowercase       , **_lowercase     )







     def 	lowercase      (				self     :     List[str]     ):
       return self.set_src_lang_special_tokens(self.src_lang     )







     def 	lowercase      (				self     :     str     ):
       return self.set_tgt_lang_special_tokens(self.tgt_lang     )







     def 	lowercase      (				self     :     List[Any]       , _lowerCamelCase     :     Dict     ):
       _snake_case   	=			self.convert_tokens_to_ids(_lowercase     )
       _snake_case   	=			[]
       _snake_case   	=			[self.eos_token_id, self.cur_lang_code]

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

       _snake_case   	=			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 	lowercase      (				self     :     Tuple       , _lowerCamelCase     :     str     ):
       _snake_case   	=			self.convert_tokens_to_ids(_lowercase     )
       _snake_case   	=			[]
       _snake_case   	=			[self.eos_token_id, self.cur_lang_code]

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

       _snake_case   	=			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 	lowercase      (				self     :     Union[str, Any]       , _lowerCamelCase     :     str       , _lowerCamelCase     :     Optional[str] = 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
       _snake_case   	=			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,)




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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__)




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				Union[str, Any]		)     ->					Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			b.T
 _UpperCamelCase:  Optional[Any]    						=			np.sum(np.square(lowercase		)	,       axis=1		)
 _UpperCamelCase:  List[Any]    						=			np.sum(np.square(lowercase		)	,       axis=0		)
 _UpperCamelCase:  Tuple    						=			np.matmul(lowercase	,       lowercase		)
 _UpperCamelCase:  Tuple    						=			aa[:, None] - 2 * ab + ba[None, :]
 return d




def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				List[Any]		)     ->					List[Any]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			x.reshape(-1	,       3		)
 _UpperCamelCase:  Dict    						=			squared_euclidean_distance(lowercase	,       lowercase		)
 return np.argmin(lowercase	,       axis=1		)





class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[Any]	      = ['''pixel_values''']



   def __init__(						self       :	List[str]		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	bool = True		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	bool = True		,	_lowercase       :	bool = True		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Tuple    						=			size if size is not None else {'''height''': 256, '''width''': 256}
    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Dict    						=			np.array(_lowercase				) if clusters is not None else None
    _UpperCamelCase:  Optional[Any]    						=			do_resize
    _UpperCamelCase:  Union[str, Any]    						=			size
    _UpperCamelCase:  Optional[int]    						=			resample
    _UpperCamelCase:  Any    						=			do_normalize
    _UpperCamelCase:  int    						=			do_color_quantize



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Dict[str, int]		,	_lowercase       :	PILImageResampling = PILImageResampling.BILINEAR		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	**_lowercase       :	Tuple		,	):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			get_size_dict(_lowercase				)
    if "height" not in size or "width" not in size:
     raise ValueError(f"""Size dictionary must contain both height and width keys. Got {size.keys()}"""				)
    return resize(
        _lowercase		,	size=(size['''height'''], size['''width'''])		,	resample=_lowercase		,	data_format=_lowercase		,	**_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	np.ndarray		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = None		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			rescale(image=_lowercase		,	scale=1 / 127.5		,	data_format=_lowercase				)
    _UpperCamelCase:  List[Any]    						=			image - 1
    return image



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	ImageInput		,	_lowercase       :	bool = None		,	_lowercase       :	Dict[str, int] = None		,	_lowercase       :	PILImageResampling = None		,	_lowercase       :	bool = None		,	_lowercase       :	Optional[bool] = None		,	_lowercase       :	Optional[Union[List[List[int]], np.ndarray]] = None		,	_lowercase       :	Optional[Union[str, TensorType]] = None		,	_lowercase       :	Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST		,	**_lowercase       :	List[str]		,	):



    """simple docstring"""



    _UpperCamelCase:  Any    						=			do_resize if do_resize is not None else self.do_resize
    _UpperCamelCase:  Optional[Any]    						=			size if size is not None else self.size
    _UpperCamelCase:  Optional[Any]    						=			get_size_dict(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			resample if resample is not None else self.resample
    _UpperCamelCase:  Union[str, Any]    						=			do_normalize if do_normalize is not None else self.do_normalize
    _UpperCamelCase:  Any    						=			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _UpperCamelCase:  Dict    						=			clusters if clusters is not None else self.clusters
    _UpperCamelCase:  Optional[Any]    						=			np.array(_lowercase				)

    _UpperCamelCase:  List[str]    						=			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 or resample is None:
     raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

    if do_color_quantize and clusters is None:
     raise ValueError('''Clusters must be specified if do_color_quantize is True.'''				)

    # All transformations expect numpy arrays.
    _UpperCamelCase:  Optional[int]    						=			[to_numpy_array(_lowercase				) for image in images]

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

    if do_normalize:
     _UpperCamelCase:  Any    						=			[self.normalize(image=_lowercase				) for image in images]

    if do_color_quantize:
     _UpperCamelCase:  Tuple    						=			[to_channel_dimension_format(_lowercase		,	ChannelDimension.LAST				) for image in images]
     # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
     _UpperCamelCase:  Tuple    						=			np.array(_lowercase				)
     _UpperCamelCase:  List[Any]    						=			color_quantize(_lowercase		,	_lowercase				).reshape(images.shape[:-1]				)

     # flatten to (batch_size, height*width)
     _UpperCamelCase:  Any    						=			images.shape[0]
     _UpperCamelCase:  Optional[int]    						=			images.reshape(_lowercase		,	-1				)

     # We need to convert back to a list of images to keep consistent behaviour across processors.
     _UpperCamelCase:  Tuple    						=			list(_lowercase				)
    else:
     _UpperCamelCase:  List[str]    						=			[to_channel_dimension_format(_lowercase		,	_lowercase				) for image in images]

    _UpperCamelCase:  List[Any]    						=			{'''input_ids''': images}
    return BatchFeature(data=_lowercase		,	tensor_type=_lowercase				)