


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


SCREAMING_SNAKE_CASE__					:     Optional[List[str]]       =     None
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     '<' 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
SCREAMING_SNAKE_CASE__					:     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 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    None
     # Automatically constructed
     __SCREAMING_SNAKE_CASE     					=    "PIL.Image.Image"
     __SCREAMING_SNAKE_CASE     					=    pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}     )
     __SCREAMING_SNAKE_CASE     					=    field(default='''Image'''							,	init=A							,	repr=A     )


     def __call__(  self    ):
       return self.pa_type


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

       if isinstance(A_							,					A_    ):
         _UpperCAmelCase		:							Optional[int]							=     np.array(A_    )

       if isinstance(A_							,					A_    ):
         return {"path": value, "bytes": None}
       elif isinstance(A_							,					A_    ):
         return {"path": None, "bytes": value}
       elif isinstance(A_							,					np.ndarray    ):
         # convert the image array to PNG/TIFF bytes
         return encode_np_array(A_    )
       elif isinstance(A_							,					PIL.Image.Image    ):
         # convert the PIL image to bytes (default format is PNG/TIFF)
         return encode_pil_image(A_    )
       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        __snake_case(  self							,					A_							,					A_=None    ):
       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:
         _UpperCAmelCase		:							str							=     {}

       _UpperCAmelCase,_UpperCAmelCase		:							str							=     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(A_    ):
             _UpperCAmelCase		:							Optional[Any]							=     PIL.Image.open(A_    )
           else:
             _UpperCAmelCase		:							List[Any]							=     path.split("""::"""    )[-1]
             try:
               _UpperCAmelCase		:							Optional[Any]							=     string_to_dict(A_							,					config.HUB_DATASETS_URL    )["""repo_id"""]
               _UpperCAmelCase		:							int							=     token_per_repo_id.get(A_    )
             except ValueError:
               _UpperCAmelCase		:							Optional[int]							=     None
             with xopen(A_							,					"""rb"""							,					use_auth_token=A_    ) as f:
               _UpperCAmelCase		:							str							=     BytesIO(f.read()    )
             _UpperCAmelCase		:							Union[str, Any]							=     PIL.Image.open(bytes_    )
       else:
         _UpperCAmelCase		:							Union[str, Any]							=     PIL.Image.open(BytesIO(bytes_    )    )
       image.load()  # to avoid "Too many open files" errors
       return image


     def        __snake_case(  self    ):
       from .features import Value

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


     def        __snake_case(  self							,					A_    ):
       if pa.types.is_string(storage.type    ):
         _UpperCAmelCase		:							Optional[Any]							=     pa.array([None] * len(A_    )							,					type=pa.binary()    )
         _UpperCAmelCase		:							Optional[int]							=     pa.StructArray.from_arrays([bytes_array, storage]							,					["""bytes""", """path"""]							,					mask=storage.is_null()    )
       elif pa.types.is_binary(storage.type    ):
         _UpperCAmelCase		:							List[str]							=     pa.array([None] * len(A_    )							,					type=pa.string()    )
         _UpperCAmelCase		:							List[str]							=     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:
           _UpperCAmelCase		:							Union[str, Any]							=     storage.field("""bytes"""    )
         else:
           _UpperCAmelCase		:							Optional[int]							=     pa.array([None] * len(A_    )							,					type=pa.binary()    )
         if storage.type.get_field_index("""path"""    ) >= 0:
           _UpperCAmelCase		:							int							=     storage.field("""path"""    )
         else:
           _UpperCAmelCase		:							Any							=     pa.array([None] * len(A_    )							,					type=pa.string()    )
         _UpperCAmelCase		:							Union[str, Any]							=     pa.StructArray.from_arrays([bytes_array, path_array]							,					["""bytes""", """path"""]							,					mask=storage.is_null()    )
       elif pa.types.is_list(storage.type    ):
         _UpperCAmelCase		:							Optional[int]							=     pa.array(
             [encode_np_array(np.array(A_    )    )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]							,					type=pa.binary()							,					)
         _UpperCAmelCase		:							Union[str, Any]							=     pa.array([None] * len(A_    )							,					type=pa.string()    )
         _UpperCAmelCase		:							Tuple							=     pa.StructArray.from_arrays(
             [bytes_array, path_array]							,					["""bytes""", """path"""]							,					mask=bytes_array.is_null()    )
       return array_cast(A_							,					self.pa_type    )




     def        __snake_case(  self							,					A_    ):

       @no_op_if_value_is_null
       def path_to_bytes(A_    ):
         with xopen(A_							,					"""rb"""    ) as f:
           _UpperCAmelCase		:							Optional[int]							=     f.read()
         return bytes_

       _UpperCAmelCase		:							str							=     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()							,					)
       _UpperCAmelCase		:							Optional[int]							=     pa.array(
           [os.path.basename(A_    ) if path is not None else None for path in storage.field("""path"""    ).to_pylist()]							,					type=pa.string()							,					)
       _UpperCAmelCase		:							Tuple							=     pa.StructArray.from_arrays([bytes_array, path_array]							,					["""bytes""", """path"""]							,					mask=bytes_array.is_null()    )
       return array_cast(A_							,					self.pa_type    )




def    a__    (		):
  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()
    _UpperCAmelCase		:							Optional[Any]							=     list(set(PIL.Image.OPEN.keys()     ) & set(PIL.Image.SAVE.keys()     )     )
  return _IMAGE_COMPRESSION_FORMATS




def    a__    (		snake_case__					:							"PIL.Image.Image"     ):
  _UpperCAmelCase		:							str							=     BytesIO()
  if image.format in list_image_compression_formats():
    _UpperCAmelCase		:							int							=     image.format
  else:
    _UpperCAmelCase		:							Optional[int]							=     """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
  image.save(snake_case__						,   format=snake_case__     )
  return buffer.getvalue()




def    a__    (		snake_case__					:							"PIL.Image.Image"     ):
  if hasattr(snake_case__						,   """filename"""     ) and image.filename != "":
    return {"path": image.filename, "bytes": None}
  else:
    return {"path": None, "bytes": image_to_bytes(snake_case__     )}




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

  _UpperCAmelCase		:							int							=     array.dtype
  _UpperCAmelCase		:							int							=     dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
  _UpperCAmelCase		:							int							=     dtype.kind
  _UpperCAmelCase		:							Optional[int]							=     dtype.itemsize

  _UpperCAmelCase		:							List[str]							=     None

  # Multi-channel array case (only np.dtype("|u1") is allowed)
  if array.shape[2:]:
    _UpperCAmelCase		:							Optional[Any]							=     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:
    _UpperCAmelCase		:							int							=     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:
      _UpperCAmelCase		:							Any							=     dtype_byteorder + dtype_kind + str(snake_case__     )
      _UpperCAmelCase		:							str							=     np.dtype(snake_case__     )
      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}'''     )
  _UpperCAmelCase		:							int							=     PIL.Image.fromarray(array.astype(snake_case__     )     )
  return {"path": None, "bytes": image_to_bytes(snake_case__     )}





def    a__    (		snake_case__					:							Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]     ):
  if config.PIL_AVAILABLE:
    import PIL.Image
  else:
    raise ImportError("""To support encoding images, please install 'Pillow'."""     )

  if objs:
    _UpperCAmelCase,_UpperCAmelCase		:							Dict							=     first_non_null_value(snake_case__     )
    if isinstance(snake_case__						,   snake_case__     ):
      return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
    if isinstance(snake_case__						,   np.ndarray     ):
      _UpperCAmelCase		:							Optional[Any]							=     no_op_if_value_is_null(snake_case__     )
      return [obj_to_image_dict_func(snake_case__     ) for obj in objs]
    elif isinstance(snake_case__						,   PIL.Image.Image     ):
      _UpperCAmelCase		:							List[str]							=     no_op_if_value_is_null(snake_case__     )
      return [obj_to_image_dict_func(snake_case__     ) for obj in objs]
    else:
      return objs
  else:
    return objs









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


SCREAMING_SNAKE_CASE__					:     List[str]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'vocab_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'yjernite/retribert-base-uncased': (
            'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'yjernite/retribert-base-uncased': 512,
}


SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    'yjernite/retribert-base-uncased': {'do_lower_case': True},
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE     					=    PRETRAINED_INIT_CONFIGURATION
     __SCREAMING_SNAKE_CASE     					=    RetriBertTokenizer
     __SCREAMING_SNAKE_CASE     					=    ['''input_ids''', '''attention_mask''']


     def __init__(  self							,					A_=None							,					A_=None							,					A_=True							,					A_="[UNK]"							,					A_="[SEP]"							,					A_="[PAD]"							,					A_="[CLS]"							,					A_="[MASK]"							,					A_=True							,					A_=None							,					**A_							,					):
       super().__init__(
           A_							,					tokenizer_file=A_							,					do_lower_case=A_							,					unk_token=A_							,					sep_token=A_							,					pad_token=A_							,					cls_token=A_							,					mask_token=A_							,					tokenize_chinese_chars=A_							,					strip_accents=A_							,					**A_							,					)

       _UpperCAmelCase		:							List[str]							=     json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
       if (
           normalizer_state.get("""lowercase"""							,					A_    ) != do_lower_case
           or normalizer_state.get("""strip_accents"""							,					A_    ) != strip_accents
           or normalizer_state.get("""handle_chinese_chars"""							,					A_    ) != tokenize_chinese_chars
       ):
         _UpperCAmelCase		:							str							=     getattr(A_							,					normalizer_state.pop("""type"""    )    )
         _UpperCAmelCase		:							Optional[int]							=     do_lower_case
         _UpperCAmelCase		:							List[Any]							=     strip_accents
         _UpperCAmelCase		:							List[str]							=     tokenize_chinese_chars
         _UpperCAmelCase		:							Dict							=     normalizer_class(**A_    )

       _UpperCAmelCase		:							Dict							=     do_lower_case


     def        __snake_case(  self							,					A_							,					A_=None    ):
       _UpperCAmelCase		:							Tuple							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							List[Any]							=     [self.sep_token_id]
       _UpperCAmelCase		:							Tuple							=     [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]




     def        __snake_case(  self							,					A_							,					A_ = None    ):
       _UpperCAmelCase		:							Dict							=     self._tokenizer.model.save(A_							,					name=A_    )
       return tuple(A_    )





