




'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


a		:	str       =      logging.get_logger(__name__)


a		:	List[Any]       =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
a		:	List[Any]       =      {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

a		:	Dict       =      {
    '''RUCAIBox/mvp''': 1_0_2_4,
}





@lru_cache()
def UpperCAmelCase    (       ):
    _a				:							str							       =      (
        list(range(ord("""!"""						)   ,	ord("""~"""						) + 1						)						) + list(range(ord("""¡"""						)   ,	ord("""¬"""						) + 1						)						) + list(range(ord("""®"""						)   ,	ord("""ÿ"""						) + 1						)						)
    )
    _a				:							str							       =      bs[:]
    _a				:							Dict							       =      0
    for b in range(2**8						):
        if b not in bs:
            bs.append(UpperCAmelCase_						)
            cs.append(2**8 + n						)
            n += 1
    _a				:							Tuple							       =      [chr(UpperCAmelCase_						) for n in cs]
    return dict(zip(UpperCAmelCase_   ,	UpperCAmelCase_						)						)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							str							       =      set()
    _a				:							Any							       =      word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)						)
        _a				:							List[str]							       =      char
    return pairs





class      UpperCamelCase       (	snake_case__       ):
  a     :Union[str, Any]          =					VOCAB_FILES_NAMES
  a     :Union[str, Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Union[str, Any]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Optional[Any]          =					['input_ids', 'attention_mask']



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase="replace"		,     __lowerCAmelCase="<s>"		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase="<pad>"		,     __lowerCAmelCase="<mask>"		,     __lowerCAmelCase=False		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      _a				:							int							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else bos_token
      _a				:							Tuple							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else eos_token
      _a				:							Tuple							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else sep_token
      _a				:							Tuple							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else cls_token
      _a				:							int							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else unk_token
      _a				:							List[Any]							       =      AddedToken(__lowerCAmelCase		,     lstrip=__lowerCAmelCase		,     rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else pad_token

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

      super().__init__(
          errors=__lowerCAmelCase		,     bos_token=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     sep_token=__lowerCAmelCase		,     cls_token=__lowerCAmelCase		,     pad_token=__lowerCAmelCase		,     mask_token=__lowerCAmelCase		,     add_prefix_space=__lowerCAmelCase		,     **__lowerCAmelCase		,     )
      with open(__lowerCAmelCase		,     encoding="""utf-8""" ) as vocab_handle:
          _a				:							Optional[Any]							       =      json.load(__lowerCAmelCase )
      _a				:							int							       =      {v: k for k, v in self.encoder.items()}
      _a				:							Union[str, Any]							       =      errors  # how to handle errors in decoding
      _a				:							List[str]							       =      bytes_to_unicode()
      _a				:							Optional[Any]							       =      {v: k for k, v in self.byte_encoder.items()}
      with open(__lowerCAmelCase		,     encoding="""utf-8""" ) as merges_handle:
          _a				:							List[Any]							       =      merges_handle.read().split("""\n""" )[1:-1]
      _a				:							Tuple							       =      [tuple(merge.split() ) for merge in bpe_merges]
      _a				:							Tuple							       =      dict(zip(__lowerCAmelCase		,     range(len(__lowerCAmelCase ) ) ) )
      _a				:							Optional[Any]							       =      {}
      _a				:							Tuple							       =      add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      _a				:							List[str]							       =      re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" )



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return len(self.encoder )



  def     a_							(			self ):







      '''simple docstring'''


      return dict(self.encoder		,     **self.added_tokens_encoder )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      if token in self.cache:
          return self.cache[token]
      _a				:							List[Any]							       =      tuple(__lowerCAmelCase )
      _a				:							Any							       =      get_pairs(__lowerCAmelCase )

      if not pairs:
          return token

      while True:
          _a				:							List[str]							       =      min(__lowerCAmelCase		,     key=lambda __lowerCAmelCase : self.bpe_ranks.get(__lowerCAmelCase		,     float("""inf""" ) ) )
          if bigram not in self.bpe_ranks:
              break
          _a       , _a				:							Optional[int]							       =      bigram
          _a				:							int							       =      []
          _a				:							List[Any]							       =      0
          while i < len(__lowerCAmelCase ):
              try:
                  _a				:							Optional[int]							       =      word.index(__lowerCAmelCase		,     __lowerCAmelCase )
              except ValueError:
                  new_word.extend(word[i:] )
                  break
              else:
                  new_word.extend(word[i:j] )
                  _a				:							Any							       =      j

              if word[i] == first and i < len(__lowerCAmelCase ) - 1 and word[i + 1] == second:
                  new_word.append(first + second )
                  i += 2
              else:
                  new_word.append(word[i] )
                  i += 1
          _a				:							List[str]							       =      tuple(__lowerCAmelCase )
          _a				:							List[str]							       =      new_word
          if len(__lowerCAmelCase ) == 1:
              break
          else:
              _a				:							Union[str, Any]							       =      get_pairs(__lowerCAmelCase )
      _a				:							Dict							       =      """ """.join(__lowerCAmelCase )
      _a				:							int							       =      word
      return word



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      []
      for token in re.findall(self.pat		,     __lowerCAmelCase ):
          _a				:							Any							       =      """""".join(
              self.byte_encoder[b] for b in token.encode("""utf-8""" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCAmelCase ).split(""" """ ) )
      return bpe_tokens



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return self.encoder.get(__lowerCAmelCase		,     self.encoder.get(self.unk_token ) )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      return self.decoder.get(__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      """""".join(__lowerCAmelCase )
      _a				:							List[Any]							       =      bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8"""		,     errors=self.errors )
      return text



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


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

      with open(__lowerCAmelCase		,     """w"""		,     encoding="""utf-8""" ) as f:
          f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=__lowerCAmelCase		,     ensure_ascii=__lowerCAmelCase ) + """\n""" )

      _a				:							List[Any]							       =      0
      with open(__lowerCAmelCase		,     """w"""		,     encoding="""utf-8""" ) as writer:
          writer.write("""#version: 0.2\n""" )
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda __lowerCAmelCase : kv[1] ):
              if index != token_index:
                  logger.warning(
                      f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                      """ Please check that the tokenizer is not corrupted!""" )
                  _a				:							List[Any]							       =      token_index
              writer.write(""" """.join(__lowerCAmelCase ) + """\n""" )
              index += 1

      return vocab_file, merge_file



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _a				:							int							       =      [self.cls_token_id]
      _a				:							str							       =      [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None		,     __lowerCAmelCase = False ):







      '''simple docstring'''


      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, 1] + ([0] * len(__lowerCAmelCase )) + [1]



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      _a				:							str							       =      [self.sep_token_id]
      _a				:							int							       =      [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 + sep + token_ids_a + sep ) * [0]



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=False		,     **__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Any							       =      kwargs.pop("""add_prefix_space"""		,     self.add_prefix_space )
      if (is_split_into_words or add_prefix_space) and (len(__lowerCAmelCase ) > 0 and not text[0].isspace()):
          _a				:							List[str]							       =      """ """ + text
      return (text, kwargs)



'''simple docstring'''

import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






			def __call__(							self				,			snake_case_					):
									snake_case :       Optional[Any]				=    []
									for img in imgs:
															snake_case				,					snake_case :       Dict				=    img.shape[:2]
															# later: provide list and randomly choose index for resize
															snake_case :       Dict				=    np.random.randint(self.short_edge_length[0]				,			self.short_edge_length[1] + 1					)
															if size == 0:
																					return img
															snake_case :       Any				=    size * 1.0 / min(snake_case_				,			snake_case_					)
															if h < w:
																					snake_case				,					snake_case :       Optional[int]				=    size, scale * w
															else:
																					snake_case				,					snake_case :       Tuple				=    scale * h, size
															if max(snake_case_				,			snake_case_					) > self.max_size:
																					snake_case :       Optional[int]				=    self.max_size * 1.0 / max(snake_case_				,			snake_case_					)
																					snake_case :       Dict				=    newh * scale
																					snake_case :       List[Any]				=    neww * scale
															snake_case :       int				=    int(neww + 0.5					)
															snake_case :       List[Any]				=    int(newh + 0.5					)

															if img.dtype == np.uinta:
																					snake_case :       List[Any]				=    Image.fromarray(snake_case_					)
																					snake_case :       str				=    pil_image.resize((neww, newh)				,			PILImageResampling.BILINEAR					)
																					snake_case :       List[str]				=    np.asarray(snake_case_					)
															else:
																					snake_case :       List[Any]				=    img.permute(2				,			0				,			1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
																					snake_case :       Tuple				=    nn.functional.interpolate(
																					    snake_case_				,			(newh, neww)				,			mode=self.interp_method				,			align_corners=snake_case_					).squeeze(0					)
															img_augs.append(snake_case_					)

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    tuple(max(snake_case_					) for s in zip(*[img.shape for img in images]					)					)
									snake_case :       int				=    [im.shape[-2:] for im in images]
									snake_case :       List[Any]				=    [
									    nn.functional.pad(
									        snake_case_				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
									    for size, im in zip(snake_case_				,			snake_case_					)
									]

									return torch.stack(snake_case_					), torch.tensor(snake_case_					)






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

						assert torch.isfinite(_lowerCAmelCase).all(), "Box tensor contains infinite or NaN!"
						snake_case				,					snake_case :       Union[str, Any]				=    box_size
						tensor[:, 0].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 1].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 2].clamp_(min=0,      max=_lowerCAmelCase)
						tensor[:, 3].clamp_(min=0,      max=_lowerCAmelCase)





