




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Optional[int]							 =     """▁"""

_lowerCamelCase			:	str							 =     {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowerCamelCase			:	Optional[int]							 =     {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_lowerCamelCase			:	Dict							 =     {
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
_lowerCamelCase			:	int							 =     {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}


class        UpperCamelCase_					(				_A  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']

    UpperCAmelCase__													=   []
    UpperCAmelCase__													=   []


    def __init__(    self   :			Dict      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Tuple=None      ,				UpperCAmelCase__   :			int=None      ,				UpperCAmelCase__   :			Union[str, Any]="<s>"      ,				UpperCAmelCase__   :			int="</s>"      ,				UpperCAmelCase__   :			str="</s>"      ,				UpperCAmelCase__   :			int="<pad>"      ,				UpperCAmelCase__   :			List[str]="<unk>"      ,				UpperCAmelCase__   :			str="m2m100"      ,				UpperCAmelCase__   :			Optional[Dict[str, Any]] = None      ,				UpperCAmelCase__   :			int=8      ,				**UpperCAmelCase__   :			List[Any]      ,				)		->None:





           '''simple docstring'''

           A__   				=					{} if sp_model_kwargs is None else sp_model_kwargs

           A__   				=					language_codes
           A__   				=					FAIRSEQ_LANGUAGE_CODES[language_codes]
           A__   				=					{lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

           A__   				=					kwargs.get('''additional_special_tokens'''      ,				[])
           kwargs["additional_special_tokens"] += [
               self.get_lang_token(__lowerCamelCase)
               for lang_code in fairseq_language_code
               if self.get_lang_token(__lowerCamelCase) not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               src_lang=__lowerCamelCase      ,				tgt_lang=__lowerCamelCase      ,				bos_token=__lowerCamelCase      ,				eos_token=__lowerCamelCase      ,				sep_token=__lowerCamelCase      ,				unk_token=__lowerCamelCase      ,				pad_token=__lowerCamelCase      ,				language_codes=__lowerCamelCase      ,				sp_model_kwargs=self.sp_model_kwargs      ,				num_madeup_words=__lowerCamelCase      ,				**__lowerCamelCase      ,				)

           A__   				=					vocab_file
           A__   				=					load_json(__lowerCamelCase)
           A__   				=					{v: k for k, v in self.encoder.items()}
           A__   				=					spm_file
           A__   				=					load_spm(__lowerCamelCase      ,				self.sp_model_kwargs)

           A__   				=					len(self.encoder)

           A__   				=					{
               self.get_lang_token(__lowerCamelCase): self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase)
           }
           A__   				=					{lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase)}
           A__   				=					{v: k for k, v in self.lang_token_to_id.items()}

           A__   				=					src_lang if src_lang is not None else "en"
           A__   				=					tgt_lang
           A__   				=					self.get_lang_id(self._src_lang)
           self.set_src_lang_special_tokens(self._src_lang)

           A__   				=					num_madeup_words


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''simple docstring'''

           return len(self.encoder) + len(self.lang_token_to_id)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           return self._src_lang


    @src_lang.setter
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str)		->None:





           '''simple docstring'''

           A__   				=					new_src_lang
           self.set_src_lang_special_tokens(self._src_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			str)		->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(__lowerCamelCase      ,				out_type=__lowerCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str)		->Tuple:





           '''simple docstring'''

           if token in self.lang_token_to_id:
                  return self.lang_token_to_id[token]
           return self.encoder.get(__lowerCamelCase      ,				self.encoder[self.unk_token])


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			int)		->str:





           '''simple docstring'''

           if index in self.id_to_lang_token:
                  return self.id_to_lang_token[index]
           return self.decoder.get(__lowerCamelCase      ,				self.unk_token)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[Any])		->List[Any]:





           '''simple docstring'''

           A__   				=					[]
           A__   				=					""
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(__lowerCamelCase) + token
                         A__   				=					[]
                  else:
                         current_sub_tokens.append(__lowerCamelCase)
           out_string += self.sp_model.decode(__lowerCamelCase)
           return out_string.strip()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None      ,				UpperCAmelCase__   :			bool = False)		->List[int]:





           '''simple docstring'''

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

           A__   				=					[1] * len(self.prefix_tokens)
           A__   				=					[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   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           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   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					{self.convert_ids_to_tokens(__lowerCamelCase): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def __getstate__(    self   :			Optional[int])		->Dict:





           '''simple docstring'''

           A__   				=					self.__dict__.copy()
           A__   				=					None
           return state


    def __setstate__(    self   :			str      ,				UpperCAmelCase__   :			Dict)		->None:





           '''simple docstring'''

           A__   				=					d

           # for backward compatibility
           if not hasattr(self      ,				'''sp_model_kwargs'''):
                  A__   				=					{}

           A__   				=					load_spm(self.spm_file      ,				self.sp_model_kwargs)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

           A__   				=					Path(__lowerCamelCase)
           if not save_dir.is_dir():
                  raise OSError(f"""{save_directory} should be a directory""")
           A__   				=					save_dir / (
               (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
           )
           A__   				=					save_dir / (
               (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
           )

           save_json(self.encoder      ,				__lowerCamelCase)

           if os.path.abspath(self.spm_file) != os.path.abspath(__lowerCamelCase) and os.path.isfile(self.spm_file):
                  copyfile(self.spm_file      ,				__lowerCamelCase)
           elif not os.path.isfile(self.spm_file):
                  with open(__lowerCamelCase      ,				'''wb''') as fi:
                         A__   				=					self.sp_model.serialized_model_proto()
                         fi.write(__lowerCamelCase)

           return (str(__lowerCamelCase), str(__lowerCamelCase))


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			str = "en"      ,				UpperCAmelCase__   :			Optional[List[str]] = None      ,				UpperCAmelCase__   :			str = "ro"      ,				**UpperCAmelCase__   :			Tuple      ,				)		->BatchEncoding:





           '''simple docstring'''

           A__   				=					src_lang
           A__   				=					tgt_lang
           self.set_src_lang_special_tokens(self.src_lang)
           return super().prepare_seqaseq_batch(__lowerCamelCase      ,				__lowerCamelCase      ,				**__lowerCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[str]      ,				UpperCAmelCase__   :			Optional[str]      ,				**UpperCAmelCase__   :			Dict)		->Optional[int]:





           '''simple docstring'''

           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(__lowerCamelCase      ,				add_special_tokens=__lowerCamelCase      ,				**__lowerCamelCase)
           A__   				=					self.get_lang_id(__lowerCamelCase)
           A__   				=					tgt_lang_id
           return inputs


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           self.set_src_lang_special_tokens(self.src_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           self.set_tgt_lang_special_tokens(self.tgt_lang)


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			str)		->None:





           '''simple docstring'''

           A__   				=					self.get_lang_token(__lowerCamelCase)
           A__   				=					self.lang_token_to_id[lang_token]
           A__   				=					[self.cur_lang_id]
           A__   				=					[self.eos_token_id]


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			str)		->None:





           '''simple docstring'''

           A__   				=					self.get_lang_token(__lowerCamelCase)
           A__   				=					self.lang_token_to_id[lang_token]
           A__   				=					[self.cur_lang_id]
           A__   				=					[self.eos_token_id]


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str)		->str:





           '''simple docstring'''

           return self.lang_code_to_token[lang]





    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str)		->int:





           '''simple docstring'''

           A__   				=					self.get_lang_token(__lowerCamelCase)
           return self.lang_token_to_id[lang_token]







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Any:



       """simple docstring"""

       A__   				=					sentencepiece.SentencePieceProcessor(**_lowerCamelCase		)
       spm.Load(str(_lowerCamelCase		)		)
       return spm







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       with open(_lowerCamelCase				,       '''r'''		) as f:
              return json.load(_lowerCamelCase		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       with open(_lowerCamelCase				,       '''w'''		) as f:
              json.dump(_lowerCamelCase				,       _lowerCamelCase				,       indent=2		)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) 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__)


