

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:
				_a:      str								=     None


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

_a:      Optional[int]								=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_a:      List[str]								=     {
    """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"""
        ),
    },
}

_a:      Optional[int]								=     {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
_a:      Optional[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      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__      			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__      			=       PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__      			=       ['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE__      			=       MBartaaTokenizer

	SCREAMING_SNAKE_CASE__      			=       []
	SCREAMING_SNAKE_CASE__      			=       []




	def __init__(						self		:  Optional[int]						,		lowerCAmelCase		:  Tuple=None						,		lowerCAmelCase		:  int=None						,		lowerCAmelCase		:  List[Any]=None						,		lowerCAmelCase		:  int=None						,		lowerCAmelCase		:  Optional[int]="</s>"						,		lowerCAmelCase		:  Union[str, Any]="</s>"						,		lowerCAmelCase		:  Any="<s>"						,		lowerCAmelCase		:  str="<unk>"						,		lowerCAmelCase		:  Union[str, Any]="<pad>"						,		lowerCAmelCase		:  List[str]="<mask>"						,		**lowerCAmelCase		:  Optional[Any]						,		):


		'''simple docstring'''







		UpperCAmelCase_    			=     AddedToken(lowerCAmelCase						,		lstrip=lowerCAmelCase						,		rstrip=lowerCAmelCase							) if isinstance(lowerCAmelCase						,		lowerCAmelCase							) else mask_token

		UpperCAmelCase_    			=     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_    			=     vocab_file
		UpperCAmelCase_    			=     False if not self.vocab_file else True

		UpperCAmelCase_    			=     {
		    lang_code: self.convert_tokens_to_ids(lowerCAmelCase							) for lang_code in FAIRSEQ_LANGUAGE_CODES
		}

		UpperCAmelCase_    			=     src_lang if src_lang is not None else "en_XX"
		UpperCAmelCase_    			=     tgt_lang
		UpperCAmelCase_    			=     self.lang_code_to_id[self._src_lang]
		self.set_src_lang_special_tokens(self._src_lang							)




	@property
	def  __A   (						self		:  int							):


		'''simple docstring'''







		return self._src_lang




	@src_lang.setter
	def  __A   (						self		:  List[str]						,		lowerCAmelCase		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     new_src_lang
		self.set_src_lang_special_tokens(self._src_lang							)




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  List[int]						,		lowerCAmelCase		:  Optional[List[int]] = None							):


		'''simple docstring'''







		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  __A   (						self		:  Dict						,		lowerCAmelCase		:  List[str]						,		lowerCAmelCase		:  str = "en_XX"						,		lowerCAmelCase		:  Optional[List[str]] = None						,		lowerCAmelCase		:  str = "ro_RO"						,		**lowerCAmelCase		:  List[str]						,		):


		'''simple docstring'''







		UpperCAmelCase_    			=     src_lang
		UpperCAmelCase_    			=     tgt_lang
		return super().prepare_seqaseq_batch(lowerCAmelCase						,		lowerCAmelCase						,		**lowerCAmelCase							)




	def  __A   (						self		:  List[str]							):


		'''simple docstring'''







		return self.set_src_lang_special_tokens(self.src_lang							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		return self.set_tgt_lang_special_tokens(self.tgt_lang							)




	def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.convert_tokens_to_ids(lowerCAmelCase							)
		UpperCAmelCase_    			=     [self.cur_lang_code_id]
		UpperCAmelCase_    			=     [self.eos_token_id]

		UpperCAmelCase_    			=     self.convert_ids_to_tokens(self.prefix_tokens							)
		UpperCAmelCase_    			=     self.convert_ids_to_tokens(self.suffix_tokens							)

		UpperCAmelCase_    			=     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  __A   (						self		:  str						,		lowerCAmelCase		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.convert_tokens_to_ids(lowerCAmelCase							)
		UpperCAmelCase_    			=     [self.cur_lang_code_id]
		UpperCAmelCase_    			=     [self.eos_token_id]

		UpperCAmelCase_    			=     self.convert_ids_to_tokens(self.prefix_tokens							)
		UpperCAmelCase_    			=     self.convert_ids_to_tokens(self.suffix_tokens							)

		UpperCAmelCase_    			=     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  __A   (						self		:  str						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  Optional[str]						,		lowerCAmelCase		:  Optional[str]						,		**lowerCAmelCase		:  Union[str, Any]							):


		'''simple docstring'''







		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_    			=     src_lang
		UpperCAmelCase_    			=     self(lowerCAmelCase						,		add_special_tokens=lowerCAmelCase						,		return_tensors=lowerCAmelCase						,		**lowerCAmelCase							)
		UpperCAmelCase_    			=     self.convert_tokens_to_ids(lowerCAmelCase							)
		UpperCAmelCase_    			=     tgt_lang_id
		return inputs




	def  __A   (						self		:  Optional[Any]						,		lowerCAmelCase		:  str						,		lowerCAmelCase		:  Optional[str] = None							):


		'''simple docstring'''







		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_    			=     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,)


from string import ascii_uppercase



def  __lowerCAmelCase (  A    ,       A    ,       A = False    ,       A = ascii_uppercase      ):
	UpperCAmelCase_    			=     keyword.upper()
	UpperCAmelCase_    			=     plaintext.upper()
	UpperCAmelCase_    			=     set(A      )

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	UpperCAmelCase_    			=     []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(A      )
    # the number of those unique characters will determine the number of rows
	UpperCAmelCase_    			=     len(A      )

	# create a shifted version of the alphabet
	UpperCAmelCase_    			=     unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	UpperCAmelCase_    			=     [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0    ,       26    ,       A      )
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	UpperCAmelCase_    			=     {}
	UpperCAmelCase_    			=     0
	for column in range(A      ):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(A      ) <= column:
				break

			# map current letter to letter in modified alphabet
			UpperCAmelCase_    			=     row[column]
			letter_index += 1

	if verbose:
		print(A      )
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext      )


if __name__ == "__main__":
				# example use
				print(mixed_keyword("""college""", """UNIVERSITY"""))