






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_mbartaa import MBartaaTokenizer
else:
      SCREAMING_SNAKE_CASE__		:       List[str]   							=							None


SCREAMING_SNAKE_CASE__		:       List[Any]   							=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       str   							=							{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__		:       int   							=							{
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__		:       Dict   							=							{
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
SCREAMING_SNAKE_CASE__		:       Any   							=							["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]







class 							UpperCAmelCase_     (		__lowerCamelCase		):
     __lowerCamelCase										=     VOCAB_FILES_NAMES
     __lowerCamelCase										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase										=     PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase										=     ['input_ids', 'attention_mask']
     __lowerCamelCase										=     MBartaaTokenizer

     __lowerCamelCase										=     []
     __lowerCamelCase										=     []




     def __init__(							self  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase=None  ,	_lowerCAmelCase="</s>"  ,	_lowerCAmelCase="</s>"  ,	_lowerCAmelCase="<s>"  ,	_lowerCAmelCase="<unk>"  ,	_lowerCAmelCase="<pad>"  ,	_lowerCAmelCase="<mask>"  ,	**_lowerCAmelCase  ,	):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__			:     int										= AddedToken(_lowerCAmelCase  ,	lstrip=_lowerCAmelCase  ,	rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		) else mask_token

          UpperCAmelCase__			:     Union[str, Any]										= kwargs.get("""additional_special_tokens"""  ,	[]		)
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              _lowerCAmelCase  ,	src_lang=_lowerCAmelCase  ,	tgt_lang=_lowerCAmelCase  ,	tokenizer_file=_lowerCAmelCase  ,	eos_token=_lowerCAmelCase  ,	sep_token=_lowerCAmelCase  ,	cls_token=_lowerCAmelCase  ,	unk_token=_lowerCAmelCase  ,	pad_token=_lowerCAmelCase  ,	mask_token=_lowerCAmelCase  ,	**_lowerCAmelCase  ,	)

          UpperCAmelCase__			:     Union[str, Any]										= vocab_file
          UpperCAmelCase__			:     Optional[int]										= False if not self.vocab_file else True

          UpperCAmelCase__			:     Any										= {
              lang_code: self.convert_tokens_to_ids(_lowerCAmelCase		) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          UpperCAmelCase__			:     List[str]										= src_lang if src_lang is not None else """en_XX"""
          UpperCAmelCase__			:     Optional[Any]										= tgt_lang
          UpperCAmelCase__			:     List[str]										= self.lang_code_to_id[self._src_lang]
          self.set_src_lang_special_tokens(self._src_lang		)




     @property
     def      __UpperCAmelCase     (							self		):
          return self._src_lang




     @src_lang.setter
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Dict										= new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
          if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = "en_XX"  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = "ro_RO"  ,	**_lowerCAmelCase  ,	):
          UpperCAmelCase__			:     int										= src_lang
          UpperCAmelCase__			:     Optional[int]										= tgt_lang
          return super().prepare_seqaseq_batch(_lowerCAmelCase  ,	_lowerCAmelCase  ,	**_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self		):
          return self.set_src_lang_special_tokens(self.src_lang		)




     def      __UpperCAmelCase     (							self		):
          return self.set_tgt_lang_special_tokens(self.tgt_lang		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Any										= self.convert_tokens_to_ids(_lowerCAmelCase		)
          UpperCAmelCase__			:     Optional[Any]										= [self.cur_lang_code_id]
          UpperCAmelCase__			:     Optional[Any]										= [self.eos_token_id]

          UpperCAmelCase__			:     Dict										= self.convert_ids_to_tokens(self.prefix_tokens		)
          UpperCAmelCase__			:     Union[str, Any]										= self.convert_ids_to_tokens(self.suffix_tokens		)

          UpperCAmelCase__			:     Tuple										= processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str  ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str  ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,	self.prefix_tokens + self.suffix_tokens		)		)  ,	)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Tuple										= self.convert_tokens_to_ids(_lowerCAmelCase		)
          UpperCAmelCase__			:     List[str]										= [self.cur_lang_code_id]
          UpperCAmelCase__			:     Optional[int]										= [self.eos_token_id]

          UpperCAmelCase__			:     str										= self.convert_ids_to_tokens(self.prefix_tokens		)
          UpperCAmelCase__			:     Any										= self.convert_ids_to_tokens(self.suffix_tokens		)

          UpperCAmelCase__			:     str										= processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str  ,	pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str  ,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,	self.prefix_tokens + self.suffix_tokens		)		)  ,	)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	**_lowerCAmelCase		):
          if src_lang is None or tgt_lang is None:
               raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
          UpperCAmelCase__			:     str										= src_lang
          UpperCAmelCase__			:     Tuple										= self(_lowerCAmelCase  ,	add_special_tokens=_lowerCAmelCase  ,	return_tensors=_lowerCAmelCase  ,	**_lowerCAmelCase		)
          UpperCAmelCase__			:     str										= self.convert_tokens_to_ids(_lowerCAmelCase		)
          UpperCAmelCase__			:     int										= tgt_lang_id
          return inputs




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
          if not self.can_save_slow_tokenizer:
               raise ValueError(
                   """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                   """tokenizer."""		)

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

          if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		):
               copyfile(self.vocab_file  ,	_lowerCAmelCase		)

          return (out_vocab_file,)














import math
import os

import cva
import numpy as np

SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:

     '''simple docstring'''
     UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
     if mse == 0:
          return 100

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)





def    _lowerCamelCase	(	)   -> None:

     '''simple docstring'''
     UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
     # Loading images (original image and compressed image)
     UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
     UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)

     UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
     UpperCAmelCase__			:     int										= cva.imread(
         os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)

     # Value expected: 29.73dB
     print("""-- First Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)

     # # Value expected: 31.53dB (Wikipedia Example)
     print("""\n-- Second Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)


if __name__ == "__main__":
      main()






