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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					'▁'

UpperCamelCase__											=					{'vocab_file': 'prophetnet.tokenizer'}

UpperCamelCase__											=					{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

UpperCamelCase__											=					{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

UpperCamelCase__											=					{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def    lowerCAmelCase_ (     __A							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  collections.OrderedDict()
      with open(__A,  "r",  encoding="utf-8"							) as reader:
            UpperCAmelCase__								=  reader.readlines()
      for index, token in enumerate(__A							):
            UpperCAmelCase__								=  token.rstrip("\n"							)
            UpperCAmelCase__								=  index
      return vocab







class      A							(				_lowerCamelCase     ):
   __UpperCAmelCase : Tuple      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : str      =			['input_ids', 'attention_mask']


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[Any]="[SEP]"       ,  __UpperCAmelCase     :						List[str]="[SEP]"       ,  __UpperCAmelCase     :						str="[SEP]"       ,  __UpperCAmelCase     :						Tuple="[UNK]"       ,  __UpperCAmelCase     :						Union[str, Any]="[PAD]"       ,  __UpperCAmelCase     :						List[str]="[CLS]"       ,  __UpperCAmelCase     :						List[Any]="[MASK]"       ,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None       ,  **__UpperCAmelCase     :						List[str]       ,  )						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  {} if sp_model_kwargs is None else sp_model_kwargs

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

         try:
               import sentencepiece as spm
         except ImportError:
               logger.warning(
                   "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
                   " pip install sentencepiece"		)
               raise

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(_UpperCamelCase		)		)
         UpperCAmelCase__								=  vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # put special tokens and [unused] tokens into the vocab
         UpperCAmelCase__								=  {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

         for i in range(1_0		):
               UpperCAmelCase__								=  f"""[unused{i}]"""
               UpperCAmelCase__								=  5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         UpperCAmelCase__								=  1_2
         UpperCAmelCase__								=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
               self.unique_no_split_tokens.append(_UpperCamelCase		)


   def __getstate__(self     :						Any		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.__dict__.copy()
         UpperCAmelCase__								=  None
         return state


   def __setstate__(self     :						int       ,  __UpperCAmelCase     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  d
         try:
               import sentencepiece as spm
         except ImportError:
               logger.warning(
                   "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
                   " pip install sentencepiece"		)
               raise

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

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


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						bool = False		)						->				List[int]:
         """simple docstring"""



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

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


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]

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


   @property
   def 			lowercase_      (self     :						List[str]		)						->				Optional[int]:
         """simple docstring"""



         return len(self.sp_model		) + self.fairseq_offset


   def 			lowercase_      (self     :						Dict		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {self.convert_ids_to_tokens(_UpperCamelCase		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						str		)						->				str:
         """simple docstring"""



         return self.sp_model.encode(_UpperCamelCase       ,  out_type=_UpperCamelCase		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__								=  self.sp_model.PieceToId(_UpperCamelCase		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Dict		)						->				Dict:
         """simple docstring"""



         if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						List[str]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  "".join(_UpperCamelCase		).replace(_UpperCamelCase       ,  " "		).strip()
         return out_string


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(_UpperCamelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             _UpperCamelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

         return (out_vocab_file,)





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         if token_ids_a is None:
               return token_ids_a + [self.sep_token_id]
         UpperCAmelCase__								=  [self.sep_token_id]
         return token_ids_a + sep + token_ids_a + sep










from pathlib import Path

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



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


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

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[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
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['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()





