






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


a		:       Any			     = logging.get_logger(__name__)

a		:       Any			     = "▁"

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

a		:       Dict			     = {
    "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",
    },
}

a		:       Any			     = {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
a		:       Dict			     = {
    "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    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	VOCAB_FILES_NAMES
      a  : Optional[Any]							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a  : List[Any]							       =	PRETRAINED_VOCAB_FILES_MAP
      a  : str							       =	['input_ids', 'attention_mask']

      a  : List[int]							       =	[]
      a  : List[int]							       =	[]
      def __init__(      self					:			Tuple		,      __lowercase					:			Optional[Any]		,      __lowercase					:			int		,      __lowercase					:			Optional[int]=None		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			Any="<s>"		,      __lowercase					:			Dict="</s>"		,      __lowercase					:			List[Any]="</s>"		,      __lowercase					:			Union[str, Any]="<pad>"		,      __lowercase					:			Optional[Any]="<unk>"		,      __lowercase					:			str="m2m100"		,      __lowercase					:			Optional[Dict[str, Any]] = None		,      __lowercase					:			Optional[Any]=8		,      **__lowercase					:			Union[str, Any]		,      )							->      None:
        __UpperCAmelCase							:	Tuple           =  {} if sp_model_kwargs is None else sp_model_kwargs

        __UpperCAmelCase							:	Tuple           =  language_codes
        __UpperCAmelCase							:	Dict           =  FAIRSEQ_LANGUAGE_CODES[language_codes]
        __UpperCAmelCase							:	Optional[Any]           =  {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}

        __UpperCAmelCase							:	int           =  kwargs.get("""additional_special_tokens"""		,      []					)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(__lowercase					)
            for lang_code in fairseq_language_code
            if self.get_lang_token(__lowercase					) not in kwargs["additional_special_tokens"]
        ]

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

        __UpperCAmelCase							:	int           =  vocab_file
        __UpperCAmelCase							:	Optional[int]           =  load_json(__lowercase					)
        __UpperCAmelCase							:	List[str]           =  {v: k for k, v in self.encoder.items()}
        __UpperCAmelCase							:	Optional[int]           =  spm_file
        __UpperCAmelCase							:	Optional[Any]           =  load_spm(__lowercase		,      self.sp_model_kwargs					)

        __UpperCAmelCase							:	Dict           =  len(self.encoder					)

        __UpperCAmelCase							:	int           =  {
            self.get_lang_token(__lowercase					): self.encoder_size + i for i, lang_code in enumerate(__lowercase					)
        }
        __UpperCAmelCase							:	Union[str, Any]           =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowercase					)}
        __UpperCAmelCase							:	Any           =  {v: k for k, v in self.lang_token_to_id.items()}

        __UpperCAmelCase							:	Dict           =  src_lang if src_lang is not None else """en"""
        __UpperCAmelCase							:	Dict           =  tgt_lang
        __UpperCAmelCase							:	Optional[int]           =  self.get_lang_id(self._src_lang					)
        self.set_src_lang_special_tokens(self._src_lang					)

        __UpperCAmelCase							:	Any           =  num_madeup_words
      @property
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      int:
        return len(self.encoder					) + len(self.lang_token_to_id					)
      @property
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      str:
        return self._src_lang
      @src_lang.setter
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			str					)							->      None:
        __UpperCAmelCase							:	Dict           =  new_src_lang
        self.set_src_lang_special_tokens(self._src_lang					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			str					)							->      List[str]:
        return self.sp_model.encode(__lowercase		,      out_type=__lowercase					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[str]					)							->      List[Any]:
        if token in self.lang_token_to_id:
          return self.lang_token_to_id[token]
        return self.encoder.get(__lowercase		,      self.encoder[self.unk_token]					)
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			int					)							->      str:
        if index in self.id_to_lang_token:
          return self.id_to_lang_token[index]
        return self.decoder.get(__lowercase		,      self.unk_token					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]		,      __lowercase					:			Any					)							->      List[Any]:
        __UpperCAmelCase							:	List[str]           =  []
        __UpperCAmelCase							:	List[str]           =  """"""
        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(__lowercase					) + token
            __UpperCAmelCase							:	List[str]           =  []
          else:
            current_sub_tokens.append(__lowercase					)
        out_string += self.sp_model.decode(__lowercase					)
        return out_string.strip()
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			List[int]		,      __lowercase					:			Optional[List[int]] = None		,      __lowercase					:			bool = False					)							->      List[int]:

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

        __UpperCAmelCase							:	str           =  [1] * len(self.prefix_tokens					)
        __UpperCAmelCase							:	Optional[int]           =  [1] * len(self.suffix_tokens					)
        if token_ids_a is None:
          return prefix_ones + ([0] * len(__lowercase					)) + suffix_ones
        return prefix_ones + ([0] * len(__lowercase					)) + ([0] * len(__lowercase					)) + suffix_ones
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			List[int]		,      __lowercase					:			Optional[List[int]] = 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					:			Any					)							->      Dict:
        __UpperCAmelCase							:	Any           =  {self.convert_ids_to_tokens(__lowercase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab
      def __getstate__(      self					:			Dict					)							->      Dict:
        __UpperCAmelCase							:	Tuple           =  self.__dict__.copy()
        __UpperCAmelCase							:	List[str]           =  None
        return state
      def __setstate__(      self					:			str		,      __lowercase					:			Dict					)							->      None:
        __UpperCAmelCase							:	List[str]           =  d

        # for backward compatibility
        if not hasattr(self		,      """sp_model_kwargs"""					):
          __UpperCAmelCase							:	Optional[Any]           =  {}

        __UpperCAmelCase							:	Union[str, Any]           =  load_spm(self.spm_file		,      self.sp_model_kwargs					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str		,      __lowercase					:			Optional[str] = None					)							->      Tuple[str]:
        __UpperCAmelCase							:	List[str]           =  Path(__lowercase					)
        if not save_dir.is_dir():
          raise OSError(f"""{save_directory} should be a directory"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  save_dir / (
            (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
        )
        __UpperCAmelCase							:	Optional[int]           =  save_dir / (
            (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
        )

        save_json(self.encoder		,      __lowercase					)

        if os.path.abspath(self.spm_file					) != os.path.abspath(__lowercase					) and os.path.isfile(self.spm_file					):
          copyfile(self.spm_file		,      __lowercase					)
        elif not os.path.isfile(self.spm_file					):
          with open(__lowercase		,      """wb"""					) as fi:
            __UpperCAmelCase							:	Optional[Any]           =  self.sp_model.serialized_model_proto()
            fi.write(__lowercase					)

        return (str(__lowercase					), str(__lowercase					))
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			List[str]		,      __lowercase					:			str = "en"		,      __lowercase					:			Optional[List[str]] = None		,      __lowercase					:			str = "ro"		,      **__lowercase					:			Any		,      )							->      BatchEncoding:
        __UpperCAmelCase							:	List[Any]           =  src_lang
        __UpperCAmelCase							:	Tuple           =  tgt_lang
        self.set_src_lang_special_tokens(self.src_lang					)
        return super().prepare_seqaseq_batch(__lowercase		,      __lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			Any		,      __lowercase					:			Optional[str]		,      __lowercase					:			Optional[str]		,      **__lowercase					:			Tuple					)							->      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							:	Union[str, Any]           =  src_lang
        __UpperCAmelCase							:	Dict           =  self(__lowercase		,      add_special_tokens=__lowercase		,      **__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.get_lang_id(__lowercase					)
        __UpperCAmelCase							:	str           =  tgt_lang_id
        return inputs
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      List[str]:
        self.set_src_lang_special_tokens(self.src_lang					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Tuple:
        self.set_tgt_lang_special_tokens(self.tgt_lang					)
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			str					)							->      None:
        __UpperCAmelCase							:	List[Any]           =  self.get_lang_token(__lowercase					)
        __UpperCAmelCase							:	Optional[int]           =  self.lang_token_to_id[lang_token]
        __UpperCAmelCase							:	Tuple           =  [self.cur_lang_id]
        __UpperCAmelCase							:	Union[str, Any]           =  [self.eos_token_id]
      def 	UpperCAmelCase      (      self					:			List[Any]		,      __lowercase					:			str					)							->      None:
        __UpperCAmelCase							:	Optional[int]           =  self.get_lang_token(__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  self.lang_token_to_id[lang_token]
        __UpperCAmelCase							:	int           =  [self.cur_lang_id]
        __UpperCAmelCase							:	Optional[int]           =  [self.eos_token_id]
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			str					)							->      str:
        return self.lang_code_to_token[lang]




      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			str					)							->      int:
        __UpperCAmelCase							:	int           =  self.get_lang_token(__lowercase					)
        return self.lang_token_to_id[lang_token]






def 				lowerCamelCase__      (    __lowerCamelCase			:     str  ,		__lowerCamelCase			:     Dict[str, Any]		):
  __UpperCAmelCase							:	Optional[int]           =  sentencepiece.SentencePieceProcessor(**__lowerCamelCase		)
  spm.Load(str(__lowerCamelCase		)		)
  return spm






def 				lowerCamelCase__      (    __lowerCamelCase			:     str		):
  with open(__lowerCamelCase  ,		"""r"""		) as f:
    return json.load(__lowerCamelCase		)







def 				lowerCamelCase__      (    __lowerCamelCase			:     Union[str, Any]  ,		__lowerCamelCase			:     str		):
  with open(__lowerCamelCase  ,		"""w"""		) as f:
    json.dump(__lowerCamelCase  ,		__lowerCamelCase  ,		indent=2		)










import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


a		:       Optional[Any]			     = {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

a		:       Optional[Any]			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : str							       =	'maskformer'
      a  : Dict							       =	{'hidden_size': 'mask_feature_size'}
      a  : Optional[Any]							       =	['resnet', 'swin']
      a  : List[Any]							       =	['detr']
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int = 256		,      __lowercase					:			int = 256		,      __lowercase					:			float = 0.1		,      __lowercase					:			bool = False		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			Optional[Dict] = None		,      __lowercase					:			float = 0.02		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 1.0		,      __lowercase					:			float = 20.0		,      __lowercase					:			Optional[bool] = None		,      **__lowercase					:			Tuple		,      )							->      str:
        if backbone_config is None:
          # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
          __UpperCAmelCase							:	List[str]           =  SwinConfig(
              image_size=384		,      in_channels=3		,      patch_size=4		,      embed_dim=128		,      depths=[2, 2, 18, 2]		,      num_heads=[4, 8, 16, 32]		,      window_size=12		,      drop_path_rate=0.3		,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]		,      )

        if isinstance(__lowercase		,      __lowercase					):
          __UpperCAmelCase							:	Dict           =  backbone_config.pop("""model_type"""					)
          __UpperCAmelCase							:	Tuple           =  CONFIG_MAPPING[backbone_model_type]
          __UpperCAmelCase							:	List[str]           =  config_class.from_dict(__lowercase					)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
          logger.warning_once(
              f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
              f"""Supported model types: {",".join(self.backbones_supported					)}"""					)

        if decoder_config is None:
          # fall back to https://huggingface.co/facebook/detr-resnet-50
          __UpperCAmelCase							:	List[Any]           =  DetrConfig()
        else:
          # verify that the decoder is supported
          __UpperCAmelCase							:	List[Any]           =  (
              decoder_config.pop("""model_type"""					) if isinstance(__lowercase		,      __lowercase					) else decoder_config.model_type
          )
          if decoder_type not in self.decoders_supported:
            raise ValueError(
                f"""Transformer Decoder {decoder_type} not supported, please use one of"""
                f""" {",".join(self.decoders_supported					)}"""					)
          if isinstance(__lowercase		,      __lowercase					):
            __UpperCAmelCase							:	Dict           =  CONFIG_MAPPING[decoder_type]
            __UpperCAmelCase							:	Union[str, Any]           =  config_class.from_dict(__lowercase					)

        __UpperCAmelCase							:	Optional[Any]           =  backbone_config
        __UpperCAmelCase							:	List[str]           =  decoder_config
        # main feature dimension for the model
        __UpperCAmelCase							:	Union[str, Any]           =  fpn_feature_size
        __UpperCAmelCase							:	Optional[Any]           =  mask_feature_size
        # initializer
        __UpperCAmelCase							:	int           =  init_std
        __UpperCAmelCase							:	Any           =  init_xavier_std
        # Hungarian matcher && loss
        __UpperCAmelCase							:	Any           =  cross_entropy_weight
        __UpperCAmelCase							:	Optional[Any]           =  dice_weight
        __UpperCAmelCase							:	List[str]           =  mask_weight
        __UpperCAmelCase							:	Union[str, Any]           =  use_auxiliary_loss
        __UpperCAmelCase							:	int           =  no_object_weight
        __UpperCAmelCase							:	int           =  output_auxiliary_logits

        __UpperCAmelCase							:	Optional[Any]           =  self.decoder_config.encoder_attention_heads
        __UpperCAmelCase							:	Dict           =  self.decoder_config.num_hidden_layers
        super().__init__(**__lowercase					)
      @classmethod
      def 	UpperCAmelCase      (      cls					:			int		,      __lowercase					:			PretrainedConfig		,      __lowercase					:			PretrainedConfig		,      **__lowercase					:			str					)							->      Tuple:
        return cls(
            backbone_config=__lowercase		,      decoder_config=__lowercase		,      **__lowercase		,      )




      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Dict[str, any]:
        __UpperCAmelCase							:	Optional[Any]           =  copy.deepcopy(self.__dict__					)
        __UpperCAmelCase							:	Optional[int]           =  self.backbone_config.to_dict()
        __UpperCAmelCase							:	Any           =  self.decoder_config.to_dict()
        __UpperCAmelCase							:	Tuple           =  self.__class__.model_type
        return output


