

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_mbart import MBartTokenizer
else:
					_UpperCAmelCase						:  List[str]									=			None


_UpperCAmelCase						:  str									=			logging.get_logger(__name__)


_UpperCAmelCase						:  Union[str, Any]									=			{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase						:  str									=			{
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase						:  List[Any]									=			{
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
_UpperCAmelCase						:  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"""]







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Any			     =     VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     ['''input_ids''', '''attention_mask''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     MBartTokenizer

				__SCREAMING_SNAKE_CASE   :					List[int]			     =     []
				__SCREAMING_SNAKE_CASE   :					List[int]			     =     []




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

											super().__init__(
											    vocab_file=snake_case ,  tokenizer_file=snake_case ,  bos_token=snake_case ,  eos_token=snake_case ,  sep_token=snake_case ,  cls_token=snake_case ,  unk_token=snake_case ,  pad_token=snake_case ,  mask_token=snake_case ,  src_lang=snake_case ,  tgt_lang=snake_case ,  additional_special_tokens=snake_case ,  **snake_case ,  )

											snake_case_     							=       vocab_file
											snake_case_     							=       False if not self.vocab_file else True

											snake_case_     							=       FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
																		# Only add those special tokens if they are not already there.
																		_additional_special_tokens.extend(
																		    [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

											self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
											snake_case_     							=       {
											    lang_code: self.convert_tokens_to_ids(snake_case			) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											snake_case_     							=       src_lang if src_lang is not None else 'en_XX'
											snake_case_     							=       self.convert_tokens_to_ids(self._src_lang			)
											snake_case_     							=       tgt_lang
											self.set_src_lang_special_tokens(self._src_lang			)




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




				@src_lang.setter
				def        a				(   self ,  snake_case			):
											snake_case_     							=       new_src_lang
											self.set_src_lang_special_tokens(self._src_lang			)




				def        a				(   self ,  snake_case ,  snake_case = 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        a				(   self ,  snake_case ,  snake_case = None			):
											snake_case_     							=       [self.sep_token_id]
											snake_case_     							=       [self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep			) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case ,  **snake_case			):
											if src_lang is None or tgt_lang is None:
																		raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
											snake_case_     							=       src_lang
											snake_case_     							=       self(snake_case ,  add_special_tokens=snake_case ,  return_tensors=snake_case ,  **snake_case			)
											snake_case_     							=       self.convert_tokens_to_ids(snake_case			)
											snake_case_     							=       tgt_lang_id
											return inputs




				def        a				(   self ,  snake_case ,  snake_case = "en_XX" ,  snake_case = None ,  snake_case = "ro_RO" ,  **snake_case ,  ):
											snake_case_     							=       src_lang
											snake_case_     							=       tgt_lang
											return super().prepare_seqaseq_batch(snake_case ,  snake_case ,  **snake_case			)




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




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




				def        a				(   self ,  snake_case			):
											snake_case_     							=       self.convert_tokens_to_ids(snake_case			)
											snake_case_     							=       []
											snake_case_     							=       [self.eos_token_id, self.cur_lang_code]

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

											snake_case_     							=       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 ,  snake_case			):
											snake_case_     							=       self.convert_tokens_to_ids(snake_case			)
											snake_case_     							=       []
											snake_case_     							=       [self.eos_token_id, self.cur_lang_code]

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

											snake_case_     							=       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 ,  snake_case ,  snake_case = 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(snake_case			):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''			)
																		return
											snake_case_     							=       os.path.join(
											    snake_case ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

											return (out_vocab_file,)







import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_UpperCAmelCase						:  Dict									=			"""sshleifer/bart-tiny-random"""
_UpperCAmelCase						:  Optional[Any]									=			"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 					lowercase							(			unittest.TestCase     ):




				@cached_property
				def        a				(   self			):
											return AutoConfig.from_pretrained(snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.num_hidden_layers ,  1			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  self.teacher_config.encoder_layers			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  1			)




				def        a				(   self			):
											with self.assertRaises(snake_case			):
																		create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=snake_case ,  d=snake_case			)




