


"""simple docstring"""




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

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


snake_case_  :     Union[str, Any]							  =							logging.get_logger(__name__)

snake_case_  :     Dict							  =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

snake_case_  :     Any							  =							{
    '''vocab_file''': {
        '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json''',
    },
    '''merges_file''': {'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'''},
}

snake_case_  :     Optional[int]							  =							{
    '''microsoft/biogpt''': 10_24,
}







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=set()
				_SCREAMING_SNAKE_CASE     					=word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								_SCREAMING_SNAKE_CASE     					=char
				return pairs


class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  VOCAB_FILES_NAMES
						UpperCAmelCase     =  PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase     =  ["input_ids", "attention_mask"]




						def __init__(    self   : Optional[Any]			,      _a   : Tuple			,      _a   : Dict			,      _a   : int="<unk>"			,      _a   : Union[str, Any]="<s>"			,      _a   : Union[str, Any]="</s>"			,      _a   : Any="</s>"			,      _a   : int="<pad>"			,      **_a   : List[Any]			,      )							->       Union[str, Any]:


										"""simple docstring"""





										super().__init__(
										    bos_token=_a			,      eos_token=_a			,      sep_token=_a			,      unk_token=_a			,      pad_token=_a			,      **_a			,      )

										try:
														import sacremoses
										except ImportError:
														raise ImportError(
														    '''You need to install sacremoses to use BioGptTokenizer. '''
														    '''See https://pypi.org/project/sacremoses/ for installation.'''					)

										_SCREAMING_SNAKE_CASE     					='''en'''
										_SCREAMING_SNAKE_CASE     					=sacremoses
										# cache of sm.MosesTokenizer instance
										_SCREAMING_SNAKE_CASE     					={}
										_SCREAMING_SNAKE_CASE     					={}
										with open(_a			,      encoding='''utf-8'''					) as vocab_handle:
														_SCREAMING_SNAKE_CASE     					=json.load(_a					)
										_SCREAMING_SNAKE_CASE     					={v: k for k, v in self.encoder.items()}
										with open(_a			,      encoding='''utf-8'''					) as merges_handle:
														_SCREAMING_SNAKE_CASE     					=merges_handle.read().split('''\n'''					)[:-1]
										_SCREAMING_SNAKE_CASE     					=[tuple(merge.split()[:2]					) for merge in merges]
										_SCREAMING_SNAKE_CASE     					=dict(zip(_a			,      range(len(_a					)					)					)					)
										_SCREAMING_SNAKE_CASE     					={}




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       Any:


										"""simple docstring"""





										return len(self.encoder					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       List[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Union[str, Any]			,      _a   : Any					)							->       int:


										"""simple docstring"""





										if lang not in self.cache_moses_tokenizer:
														_SCREAMING_SNAKE_CASE     					=self.sm.MosesTokenizer(lang=_a					)
														_SCREAMING_SNAKE_CASE     					=moses_tokenizer
										return self.cache_moses_tokenizer[lang].tokenize(
										    _a			,      aggressive_dash_splits=_a			,      return_str=_a			,      escape=_a					)




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Tuple			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										if lang not in self.cache_moses_detokenizer:
														_SCREAMING_SNAKE_CASE     					=self.sm.MosesDetokenizer(lang=_a					)
														_SCREAMING_SNAKE_CASE     					=moses_detokenizer
										return self.cache_moses_detokenizer[lang].detokenize(_a					)




						def 							__UpperCamelCase			(    self   : Any			,      _a   : List[str]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(token[:-1]					) + (token[-1] + '''</w>''',)
										if token in self.cache:
														return self.cache[token]
										_SCREAMING_SNAKE_CASE     					=get_pairs(_a					)

										if not pairs:
														return token + "</w>"

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

																		if word[i] == first and i < len(_a					) - 1 and word[i + 1] == second:
																						new_word.append(first + second					)
																						i += 2
																		else:
																						new_word.append(word[i]					)
																						i += 1
														_SCREAMING_SNAKE_CASE     					=tuple(_a					)
														_SCREAMING_SNAKE_CASE     					=new_word
														if len(_a					) == 1:
																		break
														else:
																		_SCREAMING_SNAKE_CASE     					=get_pairs(_a					)
										_SCREAMING_SNAKE_CASE     					=''' '''.join(_a					)
										if word == "\n  </w>":
														_SCREAMING_SNAKE_CASE     					='''\n</w>'''
										_SCREAMING_SNAKE_CASE     					=word
										return word




						def 							__UpperCamelCase			(    self   : str			,      _a   : int			,      _a   : Union[str, Any]=False					)							->       Union[str, Any]:


										"""simple docstring"""





										if bypass_tokenizer:
														_SCREAMING_SNAKE_CASE     					=text.split()
										else:
														_SCREAMING_SNAKE_CASE     					=self.moses_tokenize(_a			,      self.lang					)

										_SCREAMING_SNAKE_CASE     					=[]
										for token in text:
														if token:
																		split_tokens.extend(list(self.bpe(_a					).split(''' '''					)					)					)

										return split_tokens




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Optional[int]					)							->       str:


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Optional[int]			,      _a   : Any					)							->       Optional[Any]:


										"""simple docstring"""





										return self.decoder.get(_a			,      self.unk_token					)




						def 							__UpperCamelCase			(    self   : List[str]			,      _a   : List[str]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[t.replace(''' '''			,      ''''''					).replace('''</w>'''			,      ''' '''					) for t in tokens]
										_SCREAMING_SNAKE_CASE     					=''''''.join(_a					).split()
										# detokenize
										_SCREAMING_SNAKE_CASE     					=self.moses_detokenize(_a			,      self.lang					)
										return text




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


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Tuple			,      _a   : List[int]			,      _a   : Optional[List[int]] = None			,      _a   : bool = False					)							->       List[int]:


										"""simple docstring"""





										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_a			,      token_ids_a=_a			,      already_has_special_tokens=_a					)
										# no bos used in fairseq
										if token_ids_a is not None:
														return [1] + ([0] * len(_a					)) + [1] + ([0] * len(_a					))
										return [1] + ([0] * len(_a					))




						def 							__UpperCamelCase			(    self   : List[str]			,      _a   : List[int]			,      _a   : Optional[List[int]] = None					)							->       List[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=[self.sep_token_id]

										# no bos used in fairseq
										if token_ids_a is None:
														return len(token_ids_a + sep					) * [0]
										return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : str			,      _a   : Optional[str] = None					)							->       Tuple[str]:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE     					=0
										with open(_a			,      '''w'''			,      encoding='''utf-8'''					) as writer:
														for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,      key=lambda _a					: 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!'''					)
																						_SCREAMING_SNAKE_CASE     					=token_index
																		writer.write(''' '''.join(_a					) + '''\n'''					)
																		index += 1

										return vocab_file, merge_file




						def __getstate__(    self   : str					)							->       Union[str, Any]:


										"""simple docstring"""





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




						def __setstate__(    self   : Dict			,      _a   : int					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=d

										try:
														import sacremoses
										except ImportError:
														raise ImportError(
														    '''You need to install sacremoses to use XLMTokenizer. '''
														    '''See https://pypi.org/project/sacremoses/ for installation.'''					)

										_SCREAMING_SNAKE_CASE     					=sacremoses



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   A__							:




						def __init__(    self   : List[Any]			,      _a   : List[str]			,      _a   : Optional[Any]=sys.maxsize					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''bilinear'''
										_SCREAMING_SNAKE_CASE     					=max_size
										_SCREAMING_SNAKE_CASE     					=short_edge_length




						def __call__(    self   : Any			,      _a   : Tuple					)							->       List[str]:


										"""simple docstring"""





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

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

										return img_augs

class   A__							:




						def __init__(    self   : Any			,      _a   : Tuple					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,      cfg.INPUT.MAX_SIZE_TEST					)
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.FORMAT
										_SCREAMING_SNAKE_CASE     					=cfg.SIZE_DIVISIBILITY
										_SCREAMING_SNAKE_CASE     					=cfg.PAD_VALUE
										_SCREAMING_SNAKE_CASE     					=cfg.INPUT.MAX_SIZE_TEST
										_SCREAMING_SNAKE_CASE     					=cfg.MODEL.DEVICE
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)			,      1			,      1					)
										_SCREAMING_SNAKE_CASE     					=lambda _a					: (x - self.pixel_mean) / self.pixel_std




						def 							__UpperCamelCase			(    self   : int			,      _a   : Optional[Any]					)							->       Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=tuple(max(_a					) for s in zip(*[img.shape for img in images]					)					)
										_SCREAMING_SNAKE_CASE     					=[im.shape[-2:] for im in images]
										_SCREAMING_SNAKE_CASE     					=[
										    nn.functional.pad(
										        _a			,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,      value=self.pad_value			,      )
										    for size, im in zip(_a			,      _a					)
										]

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




						def __call__(    self   : str			,      _a   : Union[str, Any]			,      _a   : Any=False					)							->       Tuple:


										"""simple docstring"""





										with torch.no_grad():
														if not isinstance(_a			,      _a					):
																		_SCREAMING_SNAKE_CASE     					=[images]
														if single_image:
																		assert len(_a					) == 1
														for i in range(len(_a					)					):
																		if isinstance(images[i]			,      torch.Tensor					):
																						images.insert(_a			,      images.pop(_a					).to(self.device					).float()					)
																		elif not isinstance(images[i]			,      torch.Tensor					):
																						images.insert(
																						    _a			,      torch.as_tensor(img_tensorize(images.pop(_a					)			,      input_format=self.input_format					)					)
																						    .to(self.device					)
																						    .float()			,      )
            # resize smallest edge
														_SCREAMING_SNAKE_CASE     					=torch.tensor([im.shape[:2] for im in images]					)
														_SCREAMING_SNAKE_CASE     					=self.aug(_a					)
														# 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
														_SCREAMING_SNAKE_CASE     					=[self.normalizer(_a					) for x in images]
														# now pad them to do the following operations
														_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.pad(_a					)
														# Normalize

														if self.size_divisibility > 0:
																		raise NotImplementedError()
														# pad
														_SCREAMING_SNAKE_CASE     					=torch.true_divide(_a			,      _a					)
														if single_image:
																		return images[0], sizes[0], scales_yx[0]
														else:
																		return images, sizes, scales_yx







def   lowerCamelCase(				a__		,a__):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   lowerCamelCase(				a__		,a__):
				assert torch.isfinite(a__).all(), "Box tensor contains infinite or NaN!"
				_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=box_size
				tensor[:, 0].clamp_(min=0		,max=a__)
				tensor[:, 1].clamp_(min=0		,max=a__)
				tensor[:, 2].clamp_(min=0		,max=a__)
				tensor[:, 3].clamp_(min=0		,max=a__)