
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:
			__A				=None


__A				=logging.get_logger(__name__)


__A				={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A				={
    '''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''',
    },
}

__A				={
    '''facebook/mbart-large-en-ro''': 1_0_2_4,
    '''facebook/mbart-large-cc25''': 1_0_2_4,
}

# fmt: off
__A				=['''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     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = VOCAB_FILES_NAMES
	lowerCAmelCase__         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__         = PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__         = ['input_ids', 'attention_mask']
	lowerCAmelCase__         = MBartTokenizer

	lowerCAmelCase__         = []
	lowerCAmelCase__         = []



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

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

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

					lowerCamelCase_						=			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} )
					lowerCamelCase_						=			{
					    lang_code: self.convert_tokens_to_ids(lowercase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

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



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					return self._src_lang



	@src_lang.setter
	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			new_src_lang
					self.set_src_lang_special_tokens(self._src_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       List[int]:
					lowerCamelCase_						=			[self.sep_token_id]
					lowerCamelCase_						=			[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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  **lowercase )				->       List[Any]:
					if src_lang is None or tgt_lang is None:
									raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			self(lowercase						,  add_special_tokens=lowercase						,  return_tensors=lowercase						,  **lowercase )
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			tgt_lang_id
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = "en_XX"						,  lowercase = None						,  lowercase = "ro_RO"						,  **lowercase						,  )				->       BatchEncoding:
					lowerCamelCase_						=			src_lang
					lowerCamelCase_						=			tgt_lang
					return super().prepare_seqaseq_batch(lowercase						,  lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return self.set_src_lang_special_tokens(self.src_lang )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					return self.set_tgt_lang_special_tokens(self.tgt_lang )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					lowerCamelCase_						=			self.convert_tokens_to_ids(lowercase )
					lowerCamelCase_						=			[]
					lowerCamelCase_						=			[self.eos_token_id, self.cur_lang_code]

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None )				->       Tuple[str]:
					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(lowercase ):
									logger.error(f'Vocabulary path ({save_directory}) should be a directory.' )
									return
					lowerCamelCase_						=			os.path.join(
					    lowercase						,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

					return (out_vocab_file,)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


