






import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_ta import TaTokenizer
else:
       a		:       Dict			     = None


a		:       Tuple			     = logging.get_logger(__name__)

a		:       List[Any]			     = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a		:       Any			     = {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a		:       Union[str, Any]			     = {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}
class     a    ( lowercase__   ):



      """simple docstring"""


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

      a  : List[int]							       =	[]
      def __init__(      self					:			Dict		,      __lowercase					:			int=None		,      __lowercase					:			Tuple=None		,      __lowercase					:			Optional[Any]="</s>"		,      __lowercase					:			Union[str, Any]="<unk>"		,      __lowercase					:			Union[str, Any]="<pad>"		,      __lowercase					:			Any=100		,      __lowercase					:			int=None		,      **__lowercase					:			Dict		,      )							->      Any:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
          __UpperCAmelCase							:	Optional[Any]           =  [f"""<extra_id_{i}>""" for i in range(__lowercase					)]
        elif extra_ids > 0 and additional_special_tokens is not None:
          # Check that we have the right number of extra special tokens
          __UpperCAmelCase							:	List[str]           =  len(set(filter(lambda __lowercase					: bool("""extra_id_""" in str(__lowercase					)					)		,      __lowercase					)					)					)
          if extra_tokens != extra_ids:
            raise ValueError(
                f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                """ tokens"""					)

        super().__init__(
            __lowercase		,      tokenizer_file=__lowercase		,      eos_token=__lowercase		,      unk_token=__lowercase		,      pad_token=__lowercase		,      extra_ids=__lowercase		,      additional_special_tokens=__lowercase		,      **__lowercase		,      )

        __UpperCAmelCase							:	str           =  vocab_file
        __UpperCAmelCase							:	List[Any]           =  False if not self.vocab_file else True
        __UpperCAmelCase							:	Any           =  extra_ids
      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			Optional[int]		,      __lowercase					:			Dict		,      __lowercase					:			Union[str, Any]					)							->      Dict:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
          __UpperCAmelCase							:	List[str]           =  TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
          if init_max_model_length is not None and init_max_model_length != max_model_length:
            return init_max_model_length
          elif init_max_model_length is None:
            warnings.warn(
                """This tokenizer was incorrectly instantiated with a model max length of"""
                f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                """ instantiate this tokenizer with `model_max_length` set to your preferred value."""		,      __lowercase		,      )

        return max_model_length
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str		,      __lowercase					:			Optional[str] = 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(__lowercase					):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
          return
        __UpperCAmelCase							:	List[Any]           =  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					)
          logger.info(f"""Copy vocab file to {out_vocab_file}"""					)

        return (out_vocab_file,)
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			List[int]		,      __lowercase					:			Optional[List[int]] = None					)							->      List[int]:
        __UpperCAmelCase							:	Optional[int]           =  token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a
        else:
          __UpperCAmelCase							:	List[Any]           =  token_ids_a + [self.eos_token_id]
          return self.prefix_tokens + token_ids_a + token_ids_a
      def 	UpperCAmelCase      (      self					:			List[str]		,      __lowercase					:			List[int]		,      __lowercase					:			Optional[List[int]] = None					)							->      List[int]:
        __UpperCAmelCase							:	List[Any]           =  [self.eos_token_id]

        if token_ids_a is None:
          return len(token_ids_a + eos					) * [0]
        return len(token_ids_a + eos + token_ids_a + eos					) * [0]
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Optional[int]:
        return list(
            set(filter(lambda __lowercase					: bool(re.search(r"""<extra_id_\d+>"""		,      __lowercase					)					) is not None		,      self.additional_special_tokens					)					)					)




      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Dict:
        return [self.convert_tokens_to_ids(__lowercase					) for token in self.get_sentinel_tokens()]










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


