'''simple docstring'''

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCAmelCase_ : List[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "spiece.model"}

lowerCAmelCase_ : Any						  =      {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

lowerCAmelCase_ : Dict						  =      {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=[]


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Dict="<unk>"   ,		__lowerCAmelCase				:	List[str]="<s>"   ,		__lowerCAmelCase				:	str="</s>"   ,		__lowerCAmelCase				:	Optional[Any]="<pad>"   ,		__lowerCAmelCase				:	List[str]="[SEP]"   ,		__lowerCAmelCase				:	Union[str, Any]="[MASK]"   ,		__lowerCAmelCase				:	Tuple="[CLS]"   ,		__lowerCAmelCase				:	Optional[Dict[str, Any]] = None   ,		**__lowerCAmelCase				:	Optional[Any]   ,		)      ->					None:
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else bos_token
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else eos_token
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else unk_token
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else pad_token
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else cls_token
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else sep_token

         # Mask token behave like a normal word, i.e. include the space before it
         lowercase_     =      AddedToken(__lowerCAmelCase   ,		lstrip=__lowerCAmelCase   ,		rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase) else mask_token

         lowercase_     =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		pad_token=__lowerCAmelCase   ,		sep_token=__lowerCAmelCase   ,		mask_token=__lowerCAmelCase   ,		cls_token=__lowerCAmelCase   ,		sp_model_kwargs=self.sp_model_kwargs   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      vocab_file

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(__lowerCAmelCase)


   @property
   def __UpperCAmelCase       (   self				:	List[Any])      ->					Tuple:
         return self.sp_model.get_piece_size()


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					List[str]:
         lowercase_     =      {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				:	Dict)      ->					int:
         lowercase_     =      self.__dict__.copy()
         lowercase_     =      None
         return state


   def __setstate__(   self				:	str   ,		__lowerCAmelCase				:	List[Any])      ->					str:
         lowercase_     =      d

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

         lowercase_     =      spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	str)      ->					List[str]:
         return self.sp_model.encode(__lowerCAmelCase   ,		out_type=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	int)      ->					Dict:
         return self.sp_model.piece_to_id(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Tuple)      ->					Union[str, Any]:
         lowercase_     =      self.sp_model.IdToPiece(__lowerCAmelCase)
         return token


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	int)      ->					str:
         lowercase_     =      []
         lowercase_     =      ""
         lowercase_     =      False
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     if not prev_is_special:
                           out_string += " "
                     out_string += self.sp_model.decode(__lowerCAmelCase) + token
                     lowercase_     =      True
                     lowercase_     =      []
               else:
                     current_sub_tokens.append(__lowerCAmelCase)
                     lowercase_     =      False
         out_string += self.sp_model.decode(__lowerCAmelCase)
         return out_string.strip()


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	bool = False   ,		__lowerCAmelCase				:	bool = None   ,		__lowerCAmelCase				:	bool = True   ,		**__lowerCAmelCase				:	Optional[int]   ,		)      ->					str:
         lowercase_     =      kwargs.pop("use_source_tokenizer"   ,		__lowerCAmelCase)

         lowercase_     =      self.convert_ids_to_tokens(__lowerCAmelCase   ,		skip_special_tokens=__lowerCAmelCase)

         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         lowercase_     =      []
         lowercase_     =      []
         for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                     continue
               if token in self.added_tokens_encoder:
                     if current_sub_text:
                           sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))
                           lowercase_     =      []
                     sub_texts.append(__lowerCAmelCase)
               else:
                     current_sub_text.append(__lowerCAmelCase)
         if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCAmelCase))

         # Mimic the behavior of the Rust tokenizer:
         # No space before [MASK] and [SEP]
         if spaces_between_special_tokens:
               lowercase_     =      re.sub(R" (\[(MASK|SEP)\])"   ,		R"\1"   ,		" ".join(__lowerCAmelCase))
         else:
               lowercase_     =      "".join(__lowerCAmelCase)

         lowercase_     =      (
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
               lowercase_     =      self.clean_up_tokenization(__lowerCAmelCase)
               return clean_text
         else:
               return text


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         if not os.path.isdir(__lowerCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
         lowercase_     =      os.path.join(
             __lowerCAmelCase   ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

         return (out_vocab_file,)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None)      ->					List[int]:
         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         lowercase_     =      [self.cls_token_id]
         lowercase_     =      [self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None   ,		__lowerCAmelCase				:	bool = False)      ->					List[int]:
         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCAmelCase   ,		token_ids_a=__lowerCAmelCase   ,		already_has_special_tokens=__lowerCAmelCase)

         if token_ids_a is None:
               return [1] + ([0] * len(__lowerCAmelCase)) + [1]
         return [1] + ([0] * len(__lowerCAmelCase)) + [1] + ([0] * len(__lowerCAmelCase)) + [1]


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[int]   ,		__lowerCAmelCase				:	Optional[List[int]] = None)      ->					List[int]:
         lowercase_     =      [self.sep_token_id]
         lowercase_     =      [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) * [0] + len(token_ids_a + sep) * [1]







'''simple docstring'''

import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


lowerCAmelCase_ : Optional[List[str]]						  =      None
lowerCAmelCase_ : str						  =      "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase_ : Any						  =      [
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=True
   lowerCamelCase_    					=None
   # Automatically constructed
   lowerCamelCase_    					="PIL.Image.Image"
   lowerCamelCase_    					=pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
   lowerCamelCase_    					=field(default='Image' ,				init=__lowerCamelCase ,				repr=__lowerCamelCase )


   def __call__(   self				:	List[Any])      ->					List[Any]:
         return self.pa_type


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"])      ->					dict:
         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support encoding images, please install 'Pillow'.")

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               lowercase_     =      np.array(__lowerCAmelCase)

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": value, "bytes": None}
         elif isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               return {"path": None, "bytes": value}
         elif isinstance(__lowerCAmelCase   ,		np.ndarray):
               # convert the image array to PNG/TIFF bytes
               return encode_np_array(__lowerCAmelCase)
         elif isinstance(__lowerCAmelCase   ,		PIL.Image.Image):
               # convert the PIL image to bytes (default format is PNG/TIFF)
               return encode_pil_image(__lowerCAmelCase)
         elif value.get("path") is not None and os.path.isfile(value["path"]):
               # we set "bytes": None to not duplicate the data if they're already available locally
               return {"bytes": None, "path": value.get("path")}
         elif value.get("bytes") is not None or value.get("path") is not None:
               # store the image bytes, and path is used to infer the image format using the file extension
               return {"bytes": value.get("bytes"), "path": value.get("path")}
         else:
               raise ValueError(
                   F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.')


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	dict   ,		__lowerCAmelCase				:	Dict=None)      ->					"PIL.Image.Image":
         if not self.decode:
               raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead.")

         if config.PIL_AVAILABLE:
               import PIL.Image
         else:
               raise ImportError("To support decoding images, please install 'Pillow'.")

         if token_per_repo_id is None:
               lowercase_     =      {}

         lowercase_ ,							lowercase_     =      value["path"], value["bytes"]
         if bytes_ is None:
               if path is None:
                     raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.')
               else:
                     if is_local_path(__lowerCAmelCase):
                           lowercase_     =      PIL.Image.open(__lowerCAmelCase)
                     else:
                           lowercase_     =      path.split("::")[-1]
                           try:
                                 lowercase_     =      string_to_dict(__lowerCAmelCase   ,		config.HUB_DATASETS_URL)["repo_id"]
                                 lowercase_     =      token_per_repo_id.get(__lowerCAmelCase)
                           except ValueError:
                                 lowercase_     =      None
                           with xopen(__lowerCAmelCase   ,		"rb"   ,		use_auth_token=__lowerCAmelCase) as f:
                                 lowercase_     =      BytesIO(f.read())
                           lowercase_     =      PIL.Image.open(bytes_)
         else:
               lowercase_     =      PIL.Image.open(BytesIO(bytes_))
         image.load()  # to avoid "Too many open files" errors
         return image


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value("binary"),
                 "path": Value("string"),
             }
         )


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	Union[pa.StringArray, pa.StructArray, pa.ListArray])      ->					pa.StructArray:
         if pa.types.is_string(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, storage]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_binary(storage.type):
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([storage, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_struct(storage.type):
               if storage.type.get_field_index("bytes") >= 0:
                     lowercase_     =      storage.field("bytes")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.binary())
               if storage.type.get_field_index("path") >= 0:
                     lowercase_     =      storage.field("path")
               else:
                     lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=storage.is_null())
         elif pa.types.is_list(storage.type):
               lowercase_     =      pa.array(
                   [encode_np_array(np.array(__lowerCAmelCase))["bytes"] if arr is not None else None for arr in storage.to_pylist()]   ,		type=pa.binary()   ,		)
               lowercase_     =      pa.array([None] * len(__lowerCAmelCase)   ,		type=pa.string())
               lowercase_     =      pa.StructArray.from_arrays(
                   [bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	pa.StructArray)      ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(__lowerCAmelCase				:	int):
               with xopen(__lowerCAmelCase   ,		"rb") as f:
                     lowercase_     =      f.read()
               return bytes_

         lowercase_     =      pa.array(
             [
                 (path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
                 for x in storage.to_pylist()
             ]   ,		type=pa.binary()   ,		)
         lowercase_     =      pa.array(
             [os.path.basename(__lowerCAmelCase) if path is not None else None for path in storage.field("path").to_pylist()]   ,		type=pa.string()   ,		)
         lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)




def __a (	)	->							List[str]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      global _IMAGE_COMPRESSION_FORMATS
      if _IMAGE_COMPRESSION_FORMATS is None:
            PIL.Image.init()
            lowercase_     =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
      return _IMAGE_COMPRESSION_FORMATS


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							bytes:
      '''simple docstring'''


      lowercase_     =      BytesIO()
      if image.format in list_image_compression_formats():
            lowercase_     =      image.format
      else:
            lowercase_     =      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(__lowerCamelCase ,		format=__lowerCamelCase				)
      return buffer.getvalue()


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							dict:
      '''simple docstring'''


      if hasattr(__lowerCamelCase ,		"filename"				) and image.filename != "":
            return {"path": image.filename, "bytes": None}
      else:
            return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}


def __a (	__lowerCamelCase			:						np.ndarray				)	->							dict:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      lowercase_     =      array.dtype
      lowercase_     =      dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
      lowercase_     =      dtype.kind
      lowercase_     =      dtype.itemsize

      lowercase_     =      None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            lowercase_     =      np.dtype("|u1"				)
            if dtype_kind not in ["u", "i"]:
                  raise TypeError(
                      f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'				)
            if dtype is not dest_dtype:
                  warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
    # Exact match
      elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
            lowercase_     =      dtype
      else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
            while dtype_itemsize >= 1:
                  lowercase_     =      dtype_byteorder + dtype_kind + str(__lowerCamelCase				)
                  lowercase_     =      np.dtype(__lowerCamelCase				)
                  if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                        warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''				)
                        break
                  else:
                        dtype_itemsize //= 2

      if dest_dtype is None:
            raise TypeError(
                f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'				)
      lowercase_     =      PIL.Image.fromarray(array.astype(__lowerCamelCase				)				)
      return {"path": None, "bytes": image_to_bytes(__lowerCamelCase				)}






def __a (	__lowerCamelCase			:						Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)	->							List[dict]:
      '''simple docstring'''


      if config.PIL_AVAILABLE:
            import PIL.Image
      else:
            raise ImportError("To support encoding images, please install 'Pillow'."				)

      if objs:
            lowercase_ ,							lowercase_     =      first_non_null_value(__lowerCamelCase				)
            if isinstance(__lowerCamelCase ,		__lowerCamelCase				):
                  return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
            if isinstance(__lowerCamelCase ,		np.ndarray				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            elif isinstance(__lowerCamelCase ,		PIL.Image.Image				):
                  lowercase_     =      no_op_if_value_is_null(__lowerCamelCase				)
                  return [obj_to_image_dict_func(__lowerCamelCase				) for obj in objs]
            else:
                  return objs
      else:
            return objs






