



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 SPIECE_UNDERLINE, logging


lowerCamelCase		       =      logging.get_logger(__name__)

lowerCamelCase		       =      {"""vocab_file""": """spiece.model"""}

lowerCamelCase		       =      {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

lowerCamelCase		       =      {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
lowerCamelCase		       =      0
lowerCamelCase		       =      1
lowerCamelCase		       =      2
lowerCamelCase		       =      3
lowerCamelCase		       =      4


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Optional[Any] =       VOCAB_FILES_NAMES
							A				:Tuple =       PRETRAINED_VOCAB_FILES_MAP
							A				:Union[str, Any] =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A				:Tuple =       "left"



							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=False ,					__UpperCAmelCase=True ,					__UpperCAmelCase=False ,					__UpperCAmelCase="<s>" ,					__UpperCAmelCase="</s>" ,					__UpperCAmelCase="<unk>" ,					__UpperCAmelCase="<sep>" ,					__UpperCAmelCase="<pad>" ,					__UpperCAmelCase="<cls>" ,					__UpperCAmelCase="<mask>" ,					__UpperCAmelCase=["<eop>", "<eod>"] ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= AddedToken(__UpperCAmelCase ,					lstrip=__UpperCAmelCase ,					rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				) else mask_token

											a__	:							int      						= {} if sp_model_kwargs is None else sp_model_kwargs

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

											a__	:							Tuple      						= 3

											a__	:							int      						= do_lower_case
											a__	:							Optional[Any]      						= remove_space
											a__	:							str      						= keep_accents
											a__	:							int      						= vocab_file

											a__	:							Optional[int]      						= spm.SentencePieceProcessor(**self.sp_model_kwargs				)
											self.sp_model.Load(__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return len(self.sp_model				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= {self.convert_ids_to_tokens(__UpperCAmelCase				): i for i in range(self.vocab_size				)}
											vocab.update(self.added_tokens_encoder				)
											return vocab



							def __getstate__(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.__dict__.copy()
											a__	:							Any      						= None
											return state



							def __setstate__(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= d

											# for backward compatibility
											if not hasattr(self ,					"sp_model_kwargs"				):
															a__	:							Optional[int]      						= {}

											a__	:							Tuple      						= spm.SentencePieceProcessor(**self.sp_model_kwargs				)
											self.sp_model.Load(self.vocab_file				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											if self.remove_space:
															a__	:							Optional[Any]      						= " ".join(inputs.strip().split()				)
											else:
															a__	:							Optional[Any]      						= inputs
											a__	:							Tuple      						= outputs.replace("``" ,					"\""				).replace("''" ,					"\""				)

											if not self.keep_accents:
															a__	:							Tuple      						= unicodedata.normalize("NFKD" ,					__UpperCAmelCase				)
															a__	:							Union[str, Any]      						= "".join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase				)]				)
											if self.do_lower_case:
															a__	:							Optional[int]      						= outputs.lower()

											return outputs



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							int      						= self.preprocess_text(__UpperCAmelCase				)
											a__	:							Optional[int]      						= self.sp_model.encode(__UpperCAmelCase ,					out_type=__UpperCAmelCase				)
											a__	:							str      						= []
											for piece in pieces:
															if len(__UpperCAmelCase				) > 1 and piece[-1] == str(","				) and piece[-2].isdigit():
																			a__	:							Optional[int]      						= self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase ,					""				)				)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																							if len(cur_pieces[0]				) == 1:
																											a__	:							List[Any]      						= cur_pieces[1:]
																							else:
																											a__	:							Union[str, Any]      						= cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]				)
																			new_pieces.extend(__UpperCAmelCase				)
															else:
																			new_pieces.append(__UpperCAmelCase				)

											return new_pieces



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return self.sp_model.PieceToId(__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return self.sp_model.IdToPiece(__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							List[str]      						= "".join(__UpperCAmelCase				).replace(__UpperCAmelCase ,					" "				).strip()
											return out_string



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[int]      						= kwargs.pop("use_source_tokenizer" ,					__UpperCAmelCase				)

											a__	:							Optional[int]      						= self.convert_ids_to_tokens(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase				)

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											a__	:							List[str]      						= []
											a__	:							Optional[Any]      						= []
											for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																			continue
															if token in self.added_tokens_encoder:
																			if current_sub_text:
																							sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase				)				)
																							a__	:							Union[str, Any]      						= []
																			sub_texts.append(__UpperCAmelCase				)
															else:
																			current_sub_text.append(__UpperCAmelCase				)
											if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(__UpperCAmelCase				)				)

											# Mimic the behavior of the Rust tokenizer:
											# By default, there are no spaces between special tokens
											a__	:							Optional[Any]      						= "".join(__UpperCAmelCase				)

											a__	:							Optional[int]      						= (
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
															a__	:							List[Any]      						= self.clean_up_tokenization(__UpperCAmelCase				)
															return clean_text
											else:
															return text



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False				):







											"""simple docstring"""



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

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



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



											a__	:							str      						= [self.sep_token_id]
											a__	:							Tuple      						= [2]

											if token_ids_a is None:
															return len(token_ids_a + sep				) * [0] + cls_segment_id
											return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None				):







											"""simple docstring"""



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

											if os.path.abspath(self.vocab_file				) != os.path.abspath(__UpperCAmelCase				) and os.path.isfile(self.vocab_file				):
															copyfile(self.vocab_file ,					__UpperCAmelCase				)
											elif not os.path.isfile(self.vocab_file				):
															with open(__UpperCAmelCase ,					"wb"				) as fi:
																			a__	:							Optional[int]      						= self.sp_model.serialized_model_proto()
																			fi.write(__UpperCAmelCase				)

											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

lowerCamelCase		       =      """facebook/wmt19-en-de"""
lowerCamelCase		       =      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCamelCase		       =      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,
    )
)

lowerCamelCase		       =      FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase		       =      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
lowerCamelCase		       =      tiny_model(**batch)

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

# Save
lowerCamelCase		       =      """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


