




'''simple docstring'''





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
  import torch




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					['image_processor', 'tokenizer']
  a     :int          =					'OneFormerImageProcessor'
  a     :int          =					('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase = 77		,     __lowerCAmelCase = 77		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if image_processor is None:
          raise ValueError("""You need to specify an `image_processor`.""" )
      if tokenizer is None:
          raise ValueError("""You need to specify a `tokenizer`.""" )

      _a				:							int							       =      max_seq_length
      _a				:							Optional[Any]							       =      task_seq_length

      super().__init__(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=77 ):







      '''simple docstring'''


      if text_list is None:
          raise ValueError("""tokens cannot be None.""" )

      _a				:							Any							       =      self.tokenizer(__lowerCAmelCase		,     padding="""max_length"""		,     max_length=__lowerCAmelCase		,     truncation=__lowerCAmelCase )

      _a       , _a				:							Optional[Any]							       =      tokens["""attention_mask"""], tokens["""input_ids"""]

      _a				:							str							       =      []
      for attn_mask, input_id in zip(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      torch.tensor(__lowerCAmelCase ) * torch.tensor(__lowerCAmelCase )
          token_inputs.append(token.unsqueeze(0 ) )

      _a				:							List[str]							       =      torch.cat(__lowerCAmelCase		,     dim=0 )
      return token_inputs



  def __call__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if task_inputs is None:
          raise ValueError("""You have to specify the task_input. Found None.""" )
      elif images is None:
          raise ValueError("""You have to specify the image. Found None.""" )

      if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
          raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

      _a				:							int							       =      self.image_processor(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							List[str]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							List[str]							       =      []
          for task in task_inputs:
              _a				:							Dict							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							str							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.task_seq_length )
      else:
          raise TypeError("""Task Inputs should be a string or a list of strings.""" )

      if hasattr(__lowerCAmelCase		,     """text_inputs""" ):
          _a				:							int							       =      encoded_inputs.text_inputs

          _a				:							Tuple							       =      []
          for texts in texts_list:
              _a				:							int							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.max_seq_length )
              text_inputs.append(text_input_list.unsqueeze(0 ) )

          _a				:							Any							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



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







      '''simple docstring'''


      if task_inputs is None:
          raise ValueError("""You have to specify the task_input. Found None.""" )
      elif images is None:
          raise ValueError("""You have to specify the image. Found None.""" )

      if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs ):
          raise ValueError("""task_inputs must be semantic, instance, or panoptic.""" )

      _a				:							Dict							       =      self.image_processor.encode_inputs(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							Dict							       =      []
          for task in task_inputs:
              _a				:							str							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							List[Any]							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.task_seq_length )
      else:
          raise TypeError("""Task Inputs should be a string or a list of strings.""" )

      if hasattr(__lowerCAmelCase		,     """text_inputs""" ):
          _a				:							Union[str, Any]							       =      encoded_inputs.text_inputs

          _a				:							int							       =      []
          for texts in texts_list:
              _a				:							Optional[Any]							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.max_seq_length )
              text_inputs.append(text_input_list.unsqueeze(0 ) )

          _a				:							Dict							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



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







      '''simple docstring'''


      return self.image_processor.post_process_semantic_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )



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







      '''simple docstring'''


      return self.image_processor.post_process_instance_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )



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







      '''simple docstring'''


      return self.image_processor.post_process_panoptic_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )







'''simple docstring'''

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

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


_A					:     Optional[Any]      =logging.get_logger(__name__)

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

_A					:     str      ={
    '''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'''},
}

_A					:     List[Any]      ={
    '''microsoft/biogpt''': 1_0_2_4,
}







def     __UpperCamelCase					(				_lowercase       )			->      Dict:
	_lowercase       :							List[str]  	=		set()
	_lowercase       :							str  	=		word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)       )
		_lowercase       :							str  	=		char
	return pairs






class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    VOCAB_FILES_NAMES
					A_         =    PRETRAINED_VOCAB_FILES_MAP
					A_         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_         =    ["""input_ids""", """attention_mask"""]




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



						'''simple docstring'''





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

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

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




					@property
					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						return len(self.encoder       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						int       )	->							Optional[Any]:



						'''simple docstring'''





						if lang not in self.cache_moses_tokenizer:
							_lowercase       :							Union[str, Any]  	=		self.sm.MosesTokenizer(lang=UpperCamelCase_       )
							_lowercase       :							int  	=		moses_tokenizer
						return self.cache_moses_tokenizer[lang].tokenize(
						    UpperCamelCase_  ,	aggressive_dash_splits=UpperCamelCase_  ,	return_str=UpperCamelCase_  ,	escape=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Any  ,	UpperCamelCase_					:						Dict  ,	UpperCamelCase_					:						str       )	->							List[str]:



						'''simple docstring'''





						if lang not in self.cache_moses_detokenizer:
							_lowercase       :							str  	=		self.sm.MosesDetokenizer(lang=UpperCamelCase_       )
							_lowercase       :							List[Any]  	=		moses_detokenizer
						return self.cache_moses_detokenizer[lang].detokenize(UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[int]       )	->							Optional[Any]:



						'''simple docstring'''





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

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

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

								if word[i] == first and i < len(UpperCamelCase_       ) - 1 and word[i + 1] == second:
									new_word.append(first + second       )
									i += 2
								else:
									new_word.append(word[i]       )
									i += 1
							_lowercase       :							Optional[int]  	=		tuple(UpperCamelCase_       )
							_lowercase       :							Tuple  	=		new_word
							if len(UpperCamelCase_       ) == 1:
								break
							else:
								_lowercase       :							List[str]  	=		get_pairs(UpperCamelCase_       )
						_lowercase       :							Optional[int]  	=		' '.join(UpperCamelCase_       )
						if word == "\n  </w>":
							_lowercase       :							Union[str, Any]  	=		'\n</w>'
						_lowercase       :							List[Any]  	=		word
						return word




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						List[str]=False       )	->							int:



						'''simple docstring'''





						if bypass_tokenizer:
							_lowercase       :							Optional[Any]  	=		text.split()
						else:
							_lowercase       :							List[str]  	=		self.moses_tokenize(UpperCamelCase_  ,	self.lang       )

						_lowercase       :							int  	=		[]
						for token in text:
							if token:
								split_tokens.extend(list(self.bpe(UpperCamelCase_       ).split(' '       )       )       )

						return split_tokens




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	UpperCamelCase_					:						Optional[int]       )	->							int:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[Any]  ,	UpperCamelCase_					:						Union[str, Any]       )	->							Tuple:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						Optional[Any]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							Dict  	=		[t.replace(' '  ,	''       ).replace('</w>'  ,	' '       ) for t in tokens]
						_lowercase       :							List[str]  	=		''.join(UpperCamelCase_       ).split()
						# detokenize
						_lowercase       :							Dict  	=		self.moses_detokenize(UpperCamelCase_  ,	self.lang       )
						return text




					def     __UpperCAmelCase  (	self					:						Optional[int]  ,	UpperCamelCase_					:						List[int]  ,	UpperCamelCase_					:						Optional[List[int]] = None       )	->							List[int]:



						'''simple docstring'''





						if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
						_lowercase       :							List[Any]  	=		[self.sep_token_id]
						return sep + token_ids_a + sep + token_ids_a




					def     __UpperCAmelCase  (	self					:						Union[str, Any]  ,	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_       )
						# no bos used in fairseq
						if token_ids_a is not None:
							return [1] + ([0] * len(UpperCamelCase_       )) + [1] + ([0] * len(UpperCamelCase_       ))
						return [1] + ([0] * len(UpperCamelCase_       ))




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



						'''simple docstring'''





						_lowercase       :							Dict  	=		[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					:						Tuple  ,	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
						_lowercase       :							Any  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
						_lowercase       :							Union[str, Any]  	=		os.path.join(
						    UpperCamelCase_  ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

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

						_lowercase       :							List[str]  	=		0
						with open(UpperCamelCase_  ,	'w'  ,	encoding='utf-8'       ) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,	key=lambda UpperCamelCase_       : 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!'       )
									_lowercase       :							Tuple  	=		token_index
								writer.write(' '.join(UpperCamelCase_       ) + '\n'       )
								index += 1

						return vocab_file, merge_file




					def __getstate__(	self					:						Optional[int]       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.__dict__.copy()
						_lowercase       :							Dict  	=		None
						return state




					def __setstate__(	self					:						List[str]  ,	UpperCamelCase_					:						Dict       )	->							List[Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		d

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

						_lowercase       :							str  	=		sacremoses
