






"""simple docstring"""


import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_UpperCamelCase :    str      =			logging.get_logger(__name__)

_UpperCamelCase :    int      =			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_UpperCamelCase :    Tuple      =			{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

_UpperCamelCase :    Optional[int]      =			{'Helsinki-NLP/opus-mt-en-de': 5_12}
_UpperCamelCase :    Tuple      =			{}






class __a ( SCREAMING_SNAKE_CASE__					):
  __lowercase     :     int       						=       VOCAB_FILES_NAMES
  __lowercase     :     Optional[int]       						=       PRETRAINED_VOCAB_FILES_MAP
  __lowercase     :     List[str]       						=       PRETRAINED_INIT_CONFIGURATION
  __lowercase     :     Optional[int]       						=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowercase     :     Dict       						=       ['input_ids', 'attention_mask']
  __lowercase     :     Optional[int]       						=       re.compile('>>.+<<'					)  # type: re.Pattern



  def __init__(self	:		Optional[Any]					,      lowercase_	:		Tuple					,      lowercase_	:		Tuple					,      lowercase_	:		Any					,      lowercase_	:		Optional[Any]=None					,      lowercase_	:		str=None					,      lowercase_	:		Dict=None					,      lowercase_	:		Union[str, Any]="<unk>"					,      lowercase_	:		Optional[Any]="</s>"					,      lowercase_	:		Dict="<pad>"					,      lowercase_	:		Dict=512					,      lowercase_	:		Optional[Dict[str, Any]] = None					,      lowercase_	:		Any=False					,      **lowercase_	:		int					,      )		->				None:
   __A						:							List[Any]      =						{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
       source_lang=lowercase_					,      target_lang=lowercase_					,      unk_token=lowercase_					,      eos_token=lowercase_					,      pad_token=lowercase_					,      model_max_length=lowercase_					,      sp_model_kwargs=self.sp_model_kwargs					,      target_vocab_file=lowercase_					,      separate_vocabs=lowercase_					,      **lowercase_					,      )
   assert Path(lowercase_ ).exists(), f"cannot find spm source {source_spm}"

   __A						:							List[str]      =						separate_vocabs
   __A						:							Optional[Any]      =						load_json(lowercase_ )
   if self.unk_token not in self.encoder:
    raise KeyError('<unk> token must be in vocab' )
   assert self.pad_token in self.encoder

   if separate_vocabs:
    __A						:							Optional[Any]      =						load_json(lowercase_ )
    __A						:							Optional[Any]      =						{v: k for k, v in self.target_encoder.items()}
    __A						:							List[str]      =						[]
   else:
    __A						:							Optional[int]      =						{v: k for k, v in self.encoder.items()}
    __A						:							list      =						[k for k in self.encoder if k.startswith('>>' ) and k.endswith('<<' )]

   __A						:							List[str]      =						source_lang
   __A						:							Optional[Any]      =						target_lang
   __A						:							Any      =						[source_spm, target_spm]

   # load SentencePiece model for pre-processing
   __A						:							Union[str, Any]      =						load_spm(lowercase_					,      self.sp_model_kwargs )
   __A						:							int      =						load_spm(lowercase_					,      self.sp_model_kwargs )
   __A						:							Any      =						self.spm_source
   __A						:							str      =						self.encoder

   # Multilingual target side: default to using first supported language code.

   self._setup_normalizer()



  def     A_					(self	:		List[Any] )		->				int:
   try:
    from sacremoses import MosesPunctNormalizer

    __A						:							Any      =						MosesPunctNormalizer(self.source_lang ).normalize
   except (ImportError, FileNotFoundError):
    warnings.warn('Recommended: pip install sacremoses.' )
    __A						:							Optional[Any]      =						lambda lowercase_ : x



  def     A_					(self	:		List[Any]					,      lowercase_	:		str )		->				str:
   return self.punc_normalizer(lowercase_ ) if x else ""



  def     A_					(self	:		Dict					,      lowercase_	:		Tuple )		->				List[Any]:
   return self.current_encoder.get(lowercase_					,      self.current_encoder[self.unk_token] )



  def     A_					(self	:		Optional[int]					,      lowercase_	:		str )		->				List[Any]:
   __A						:							Any      =						self.language_code_re.match(lowercase_ )
   __A						:							list      =						[match.group(0 )] if match else []
   return code, self.language_code_re.sub(''					,      lowercase_ )



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		str )		->				List[str]:
   __A						:							Any      =						self.remove_language_code(lowercase_ )
   __A						:							Any      =						self.current_spm.encode(lowercase_					,      out_type=lowercase_ )
   return code + pieces



  def     A_					(self	:		List[Any]					,      lowercase_	:		int )		->				str:
   return self.decoder.get(lowercase_					,      self.unk_token )



  def     A_					(self	:		List[Any]					,      lowercase_	:		str					,      **lowercase_	:		Dict )		->				List[Any]:
   return super().batch_decode(lowercase_					,      **lowercase_ )



  def     A_					(self	:		str					,      lowercase_	:		Any					,      **lowercase_	:		Optional[Any] )		->				str:
   return super().decode(lowercase_					,      **lowercase_ )



  def     A_					(self	:		Optional[int]					,      lowercase_	:		List[str] )		->				str:
   __A						:							Any      =						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
   __A						:							str      =						[]
   __A						:							Optional[Any]      =						''
   for token in tokens:
    # make sure that special tokens are not decoded using sentencepiece model
    if token in self.all_special_tokens:
     out_string += sp_model.decode_pieces(lowercase_ ) + token + " "
     __A						:							int      =						[]
    else:
     current_sub_tokens.append(lowercase_ )
   out_string += sp_model.decode_pieces(lowercase_ )
   return out_string.strip()



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		str					,      lowercase_	:		Any=None )		->				List[int]:
   if token_ids_a is None:
    return token_ids_a + [self.eos_token_id]
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return token_ids_a + token_ids_a + [self.eos_token_id]



  def     A_					(self	:		Optional[int] )		->				int:
   __A						:							List[str]      =						self.spm_source
   __A						:							Optional[Any]      =						self.encoder



  def     A_					(self	:		Dict )		->				Tuple:
   __A						:							Dict      =						self.spm_target
   if self.separate_vocabs:
    __A						:							Optional[Any]      =						self.target_encoder



  @property
  def     A_					(self	:		Optional[int] )		->				int:
   return len(self.encoder )



  def     A_					(self	:		Tuple					,      lowercase_	:		str					,      lowercase_	:		Optional[str] = None )		->				Tuple[str]:
   if not os.path.isdir(lowercase_ ):
    logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
    return
   __A						:							List[str]      =						[]

   if self.separate_vocabs:
    __A						:							str      =						os.path.join(
        lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']					,      )
    __A						:							str      =						os.path.join(
        lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']					,      )
    save_json(self.encoder					,      lowercase_ )
    save_json(self.target_encoder					,      lowercase_ )
    saved_files.append(lowercase_ )
    saved_files.append(lowercase_ )
   else:
    __A						:							str      =						os.path.join(
        lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'] )
    save_json(self.encoder					,      lowercase_ )
    saved_files.append(lowercase_ )

   for spm_save_filename, spm_orig_path, spm_model in zip(
       [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]					,      self.spm_files					,      [self.spm_source, self.spm_target]					,      ):
    __A						:							Optional[Any]      =						os.path.join(
        lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + spm_save_filename )
    if os.path.abspath(lowercase_ ) != os.path.abspath(lowercase_ ) and os.path.isfile(lowercase_ ):
     copyfile(lowercase_					,      lowercase_ )
     saved_files.append(lowercase_ )
    elif not os.path.isfile(lowercase_ ):
     with open(lowercase_					,      'wb' ) as fi:
      __A						:							List[Any]      =						spm_model.serialized_model_proto()
      fi.write(lowercase_ )
     saved_files.append(lowercase_ )

   return tuple(lowercase_ )



  def     A_					(self	:		Any )		->				Dict:
   return self.get_src_vocab()



  def     A_					(self	:		Tuple )		->				Optional[int]:
   return dict(self.encoder					,      **self.added_tokens_encoder )



  def     A_					(self	:		List[str] )		->				Optional[int]:
   return dict(self.target_encoder					,      **self.added_tokens_decoder )



  def __getstate__(self	:		Dict )		->				Dict:
   __A						:							List[Any]      =						self.__dict__.copy()
   state.update(
       {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']} )
   return state



  def __setstate__(self	:		Union[str, Any]					,      lowercase_	:		Dict )		->				None:
   __A						:							int      =						d

   # for backward compatibility
   if not hasattr(self					,      'sp_model_kwargs' ):
    __A						:							str      =						{}

   __A						:							List[Any]      =						(load_spm(lowercase_					,      self.sp_model_kwargs ) for f in self.spm_files)
   __A						:							Tuple      =						self.spm_source
   self._setup_normalizer()



  def     A_					(self	:		Any					,      *lowercase_	:		Union[str, Any]					,      **lowercase_	:		Tuple )		->				Any:
   return 1



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		Tuple )		->				Optional[Any]:
   __A						:							Union[str, Any]      =						set(self.all_special_ids )  # call it once instead of inside list comp
   all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special
   return [1 if x in all_special_ids else 0 for x in seq]



  def     A_					(self	:		Any					,      lowercase_	:		List					,      lowercase_	:		Optional[List] = None					,      lowercase_	:		bool = False )		->				List[int]:
   if already_has_special_tokens:
    return self._special_token_mask(lowercase_ )
   elif token_ids_a is None:
    return self._special_token_mask(lowercase_ ) + [1]
   else:
    return self._special_token_mask(token_ids_a + token_ids_a ) + [1]






def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					str		)	->		sentencepiece.SentencePieceProcessor:
 __A						:							Dict      =						sentencepiece.SentencePieceProcessor(**_UpperCAmelCase		)
 spm.Load(_UpperCAmelCase		)
 return spm




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[Any]		)	->		None:


 with open(_UpperCAmelCase  ,						'w'		) as f:
  json.dump(_UpperCAmelCase  ,						_UpperCAmelCase  ,						indent=2		)




def 					a			(  UpperCamelCase_				:					int		)	->		Union[Dict, List]:


 with open(_UpperCAmelCase  ,						'r'		) as f:
  return json.load(_UpperCAmelCase		)










"""simple docstring"""




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 	a__		(    unittest.TestCase,	SCREAMING_SNAKE_CASE__  ):


   def 							lowercase					(				self   :				Dict      )     ->							List[str]:
         lowercase	:   str											= load_tool('image-captioning'      )
         self.tool.setup()
         lowercase	:   List[str]											= load_tool('image-captioning',  remote=lowerCAmelCase      )


   def 							lowercase					(				self   :				str      )     ->							int:
         lowercase	:   Union[str, Any]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   List[str]											= self.tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				str      )     ->							List[str]:
         lowercase	:   str											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Tuple											= self.remote_tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   Tuple											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Union[str, Any]											= self.tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )






   def 							lowercase					(				self   :				str      )     ->							Optional[int]:
         lowercase	:   List[str]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   str											= self.remote_tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )
