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


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

SCREAMING_SNAKE_CASE			:						Dict				  =		"""▁"""

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		{
    """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""",
    },
}

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

# fmt: off
SCREAMING_SNAKE_CASE			:						Any				  =		{
    """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  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      ["""input_ids""", """attention_mask"""]

  _SCREAMING_SNAKE_CASE     					=      []
  _SCREAMING_SNAKE_CASE     					=      []

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   List[str]=None  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=None  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="<s>"  ,				__SCREAMING_SNAKE_CASE			:   Tuple="</s>"  ,				__SCREAMING_SNAKE_CASE			:   str="</s>"  ,				__SCREAMING_SNAKE_CASE			:   int="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   List[str]="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   int="m2m100"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, Any]] = None  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   __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(__SCREAMING_SNAKE_CASE							)
       for lang_code in fairseq_language_code
       if self.get_lang_token(__SCREAMING_SNAKE_CASE							) not in kwargs["additional_special_tokens"]
   ]

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

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

   __a							=	len(self.encoder							)

   __a							=	{
       self.get_lang_token(__SCREAMING_SNAKE_CASE							): self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE							)
   }
   __a							=	{lang_code: self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE							)}
   __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 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return len(self.encoder							) + len(self.lang_token_to_id							)

  @property
  def 		_UpperCAmelCase (		self			:   int							):
   return self._src_lang

  @src_lang.setter
  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	new_src_lang
   self.set_src_lang_special_tokens(self._src_lang							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   str							):
   return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,				out_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   int							):
   if token in self.lang_token_to_id:
    return self.lang_token_to_id[token]
   return self.encoder.get(__SCREAMING_SNAKE_CASE  ,				self.encoder[self.unk_token]							)

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   int							):
   if index in self.id_to_lang_token:
    return self.id_to_lang_token[index]
   return self.decoder.get(__SCREAMING_SNAKE_CASE  ,				self.unk_token							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   __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(__SCREAMING_SNAKE_CASE							) + token
     __a							=	[]
    else:
     current_sub_tokens.append(__SCREAMING_SNAKE_CASE							)
   out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE							)
   return out_string.strip()

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = False							):

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

   __a							=	[1] * len(self.prefix_tokens							)
   __a							=	[1] * len(self.suffix_tokens							)
   if token_ids_a is None:
    return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE							)) + suffix_ones
   return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE							)) + ([0] * len(__SCREAMING_SNAKE_CASE							)) + suffix_ones

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   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 		_UpperCAmelCase (		self			:   Any							):
   __a							=	{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							): i for i in range(self.vocab_size							)}
   vocab.update(self.added_tokens_encoder							)
   return vocab

  def __getstate__(		self			:   Tuple							):
   __a							=	self.__dict__.copy()
   __a							=	None
   return state

  def __setstate__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Dict							):
   __a							=	d

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

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

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   __a							=	Path(__SCREAMING_SNAKE_CASE							)
   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  ,				__SCREAMING_SNAKE_CASE							)

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

   return (str(__SCREAMING_SNAKE_CASE							), str(__SCREAMING_SNAKE_CASE							))

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   str = "en"  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[str]] = None  ,				__SCREAMING_SNAKE_CASE			:   str = "ro"  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   __a							=	src_lang
   __a							=	tgt_lang
   self.set_src_lang_special_tokens(self.src_lang							)
   return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[str]  ,				__SCREAMING_SNAKE_CASE			:   Optional[str]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   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(__SCREAMING_SNAKE_CASE  ,				add_special_tokens=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)
   __a							=	self.get_lang_id(__SCREAMING_SNAKE_CASE							)
   __a							=	tgt_lang_id
   return inputs

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

  def 		_UpperCAmelCase (		self			:   List[str]							):
   self.set_tgt_lang_special_tokens(self.tgt_lang							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	self.get_lang_token(__SCREAMING_SNAKE_CASE							)
   __a							=	self.lang_token_to_id[lang_token]
   __a							=	[self.cur_lang_id]
   __a							=	[self.eos_token_id]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	self.get_lang_token(__SCREAMING_SNAKE_CASE							)
   __a							=	self.lang_token_to_id[lang_token]
   __a							=	[self.cur_lang_id]
   __a							=	[self.eos_token_id]

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   str							):
   return self.lang_code_to_token[lang]

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	self.get_lang_token(__SCREAMING_SNAKE_CASE							)
   return self.lang_token_to_id[lang_token]



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	sentencepiece.SentencePieceProcessor(**_A       )
 spm.Load(str(_A       )       )
 return spm



def 	__A		(      _A       ):



 """simple docstring"""


 with open(_A				,					"r"       ) as f:
  return json.load(_A       )






def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 with open(_A				,					"w"       ) as f:
  json.dump(_A				,					_A				,					indent=2       )



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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __a							=	image_std if image_std is not None else OPENAI_CLIP_STD
   __a							=	do_convert_rgb

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __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							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

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

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

   if not valid_images(__SCREAMING_SNAKE_CASE							):
    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_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."							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

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

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

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

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

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

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   return encoded_outputs


