




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A     =			logging.get_logger(__name__)
_A     =			{"vocab_file": "spiece.model"}

_A     =			{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

_A     =			{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_A     =			"▁"


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   VOCAB_FILES_NAMES
		__snake_case									=   PRETRAINED_VOCAB_FILES_MAP
		__snake_case									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=True,	UpperCamelCase__=True,	UpperCamelCase__=False,	UpperCamelCase__="[CLS]",	UpperCamelCase__="[SEP]",	UpperCamelCase__="<unk>",	UpperCamelCase__="[SEP]",	UpperCamelCase__="<pad>",	UpperCamelCase__="[CLS]",	UpperCamelCase__="[MASK]",	UpperCamelCase__ = None,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  (
								    AddedToken(_a,	lstrip=_a,	rstrip=_a,	normalized=_a   )
								    if isinstance(_a,	_a   )
								    else mask_token
								)

								lowerCAmelCase_      			=  {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=_a,	remove_space=_a,	keep_accents=_a,	bos_token=_a,	eos_token=_a,	unk_token=_a,	sep_token=_a,	pad_token=_a,	cls_token=_a,	mask_token=_a,	sp_model_kwargs=self.sp_model_kwargs,	**_a,	)

								lowerCAmelCase_      			=  do_lower_case
								lowerCAmelCase_      			=  remove_space
								lowerCAmelCase_      			=  keep_accents
								lowerCAmelCase_      			=  vocab_file

								lowerCAmelCase_      			=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(_a   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return len(self.sp_model   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab



		def __getstate__(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.__dict__.copy()
								lowerCAmelCase_      			=  None
								return state



		def __setstate__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  d

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

								lowerCAmelCase_      			=  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
								self.sp_model.Load(self.vocab_file   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								if self.remove_space:
														lowerCAmelCase_      			=  " ".join(inputs.strip().split()   )
								else:
														lowerCAmelCase_      			=  inputs
								lowerCAmelCase_      			=  outputs.replace('''``''',	'''\"'''   ).replace('''\'\'''',	'''\"'''   )

								if not self.keep_accents:
														lowerCAmelCase_      			=  unicodedata.normalize('''NFKD''',	_a   )
														lowerCAmelCase_      			=  "".join([c for c in outputs if not unicodedata.combining(_a   )]   )
								if self.do_lower_case:
														lowerCAmelCase_      			=  outputs.lower()

								return outputs



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.preprocess_text(_a   )
								lowerCAmelCase_      			=  self.sp_model.encode(_a,	out_type=_a   )
								lowerCAmelCase_      			=  []
								for piece in pieces:
														if len(_a   ) > 1 and piece[-1] == str(''','''   ) and piece[-2].isdigit():
																				lowerCAmelCase_      			=  self.sp_model.EncodeAsPieces(piece[:-1].replace(_a,	''''''   )   )
																				if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																										if len(cur_pieces[0]   ) == 1:
																																lowerCAmelCase_      			=  cur_pieces[1:]
																										else:
																																lowerCAmelCase_      			=  cur_pieces[0][1:]
																				cur_pieces.append(piece[-1]   )
																				new_pieces.extend(_a   )
														else:
																				new_pieces.append(_a   )

								return new_pieces



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return self.sp_model.PieceToId(_a   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return self.sp_model.IdToPiece(_a   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  []
								lowerCAmelCase_      			=  ""
								lowerCAmelCase_      			=  False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(_a   ) + token
																				lowerCAmelCase_      			=  True
																				lowerCAmelCase_      			=  []
														else:
																				current_sub_tokens.append(_a   )
																				lowerCAmelCase_      			=  False
								out_string += self.sp_model.decode(_a   )
								return out_string.strip()



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]
								if token_ids_a is None:
														return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = False   ):







								"""simple docstring"""



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_a,	token_ids_a=_a,	already_has_special_tokens=_a   )

								if token_ids_a is not None:
														return [1] + ([0] * len(_a   )) + [1] + ([0] * len(_a   )) + [1]
								return [1] + ([0] * len(_a   )) + [1]



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								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   ) * [0] + len(token_ids_a + sep   ) * [1]







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



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

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ) and os.path.isfile(self.vocab_file   ):
														copyfile(self.vocab_file,	_a   )
								elif not os.path.isfile(self.vocab_file   ):
														with open(_a,	'''wb'''   ) as fi:
																				lowerCAmelCase_      			=  self.sp_model.serialized_model_proto()
																				fi.write(_a   )

								return (out_vocab_file,)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

snake_case :		List[str]							      =  "facebook/wmt19-en-de"
snake_case :		Dict							      =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
snake_case :		List[str]							      =  FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

snake_case :		int							      =  FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case :		Optional[Any]							      =  tokenizer(["Making tiny model"], return_tensors="pt")
snake_case :		List[str]							      =  tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
snake_case :		Dict							      =  "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de
