






'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__A							:							str  							=		logging.get_logger(__name__)

__A							:							Optional[Any]  							=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__A							:							Union[str, Any]  							=		[
    """small""",
    """small-base""",
    """medium""",
    """medium-base""",
    """intermediate""",
    """intermediate-base""",
    """large""",
    """large-base""",
    """xlarge""",
    """xlarge-base""",
]

__A							:							Dict  							=		{
    """vocab_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt""",
        """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt""",
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt""",
        """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json""",
        """funnel-transformer/small-base""": (
            """https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json""",
        """funnel-transformer/large-base""": (
            """https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"""
        ),
    },
}
__A							:							Any  							=		{F"""funnel-transformer/{name}""": 5_12 for name in _model_names}
__A							:							List[Any]  							=		{F"""funnel-transformer/{name}""": {"""do_lower_case""": True} for name in _model_names}
class 				lowercase       (							lowerCAmelCase__      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							VOCAB_FILES_NAMES
    lowerCAmelCase__      				=							PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase__      				=							PRETRAINED_INIT_CONFIGURATION
    lowerCAmelCase__      				=							FunnelTokenizer
    lowerCAmelCase__      				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase__      				=							2

    def __init__(					self     :     Optional[Any]				,     __lowerCamelCase     :     str=None				,     __lowerCamelCase     :     Optional[int]=None				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     Optional[Any]="<unk>"				,     __lowerCamelCase     :     str="<sep>"				,     __lowerCamelCase     :     Optional[Any]="<pad>"				,     __lowerCamelCase     :     Tuple="<cls>"				,     __lowerCamelCase     :     List[Any]="<mask>"				,     __lowerCamelCase     :     Tuple="<s>"				,     __lowerCamelCase     :     List[str]="</s>"				,     __lowerCamelCase     :     List[str]=True				,     __lowerCamelCase     :     Optional[Any]=True				,     __lowerCamelCase     :     int=None				,     __lowerCamelCase     :     Any="##"				,     **__lowerCamelCase     :     str				,     )							->							Optional[int]:

     '''simple docstring'''




     super().__init__(
         _lowerCamelCase				,     tokenizer_file=_lowerCamelCase				,     do_lower_case=_lowerCamelCase				,     unk_token=_lowerCamelCase				,     sep_token=_lowerCamelCase				,     pad_token=_lowerCamelCase				,     cls_token=_lowerCamelCase				,     mask_token=_lowerCamelCase				,     bos_token=_lowerCamelCase				,     eos_token=_lowerCamelCase				,     clean_text=_lowerCamelCase				,     tokenize_chinese_chars=_lowerCamelCase				,     strip_accents=_lowerCamelCase				,     wordpieces_prefix=_lowerCamelCase				,     **_lowerCamelCase				,     )

     lowerCamelCase__  =     json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
     if (
         normalizer_state.get("lowercase"				,     _lowerCamelCase     ) != do_lower_case
         or normalizer_state.get("strip_accents"				,     _lowerCamelCase     ) != strip_accents
         or normalizer_state.get("handle_chinese_chars"				,     _lowerCamelCase     ) != tokenize_chinese_chars
     ):
      lowerCamelCase__  =     getattr(_lowerCamelCase				,     normalizer_state.pop("type"     )     )
      lowerCamelCase__  =     do_lower_case
      lowerCamelCase__  =     strip_accents
      lowerCamelCase__  =     tokenize_chinese_chars
      lowerCamelCase__  =     normalizer_class(**_lowerCamelCase     )

     lowerCamelCase__  =     do_lower_case

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[Any]=None     )							->							int:

     '''simple docstring'''




     lowerCamelCase__  =     [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    a__				(					self     :     Any				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     List[str] = None     )							->							List[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [self.sep_token_id]
     lowerCamelCase__  =     [self.cls_token_id]
     if token_ids_a is None:
      return len(cls     ) * [self.cls_token_type_id] + len(token_ids_a + sep     ) * [0]
     return len(cls     ) * [self.cls_token_type_id] + len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Dict = None     )							->							Tuple[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self._tokenizer.model.save(_lowerCamelCase				,     name=_lowerCamelCase     )
     return tuple(_lowerCamelCase     )









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

