






'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {'vocab_file': 'vocab.txt'}

__lowerCAmelCase									=   {
    'vocab_file': {
        'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt',
        'YituTech/conv-bert-medium-small': (
            'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'
        ),
        'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt',
    }
}

__lowerCAmelCase									=   {
    'YituTech/conv-bert-base': 512,
    'YituTech/conv-bert-medium-small': 512,
    'YituTech/conv-bert-small': 512,
}


__lowerCAmelCase									=   {
    'YituTech/conv-bert-base': {'do_lower_case': True},
    'YituTech/conv-bert-medium-small': {'do_lower_case': True},
    'YituTech/conv-bert-small': {'do_lower_case': True},
}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					VOCAB_FILES_NAMES
			snake_case__	=					PRETRAINED_VOCAB_FILES_MAP
			snake_case__	=					PRETRAINED_INIT_CONFIGURATION
			snake_case__	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__	=					ConvBertTokenizer






			def __init__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=None	,    __SCREAMING_SNAKE_CASE   :      Any=None	,    __SCREAMING_SNAKE_CASE   :      List[str]=True	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]="[UNK]"	,    __SCREAMING_SNAKE_CASE   :      Optional[int]="[SEP]"	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]="[PAD]"	,    __SCREAMING_SNAKE_CASE   :      str="[CLS]"	,    __SCREAMING_SNAKE_CASE   :      str="[MASK]"	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=True	,    __SCREAMING_SNAKE_CASE   :      List[Any]=None	,    **__SCREAMING_SNAKE_CASE   :      int	,    )      ->      Union[str, Any]:
							super().__init__(
							    __SCREAMING_SNAKE_CASE	,    tokenizer_file=__SCREAMING_SNAKE_CASE	,    do_lower_case=__SCREAMING_SNAKE_CASE	,    unk_token=__SCREAMING_SNAKE_CASE	,    sep_token=__SCREAMING_SNAKE_CASE	,    pad_token=__SCREAMING_SNAKE_CASE	,    cls_token=__SCREAMING_SNAKE_CASE	,    mask_token=__SCREAMING_SNAKE_CASE	,    tokenize_chinese_chars=__SCREAMING_SNAKE_CASE	,    strip_accents=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE	,    )

							a_		:	List[Any]									=   json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
							if (
							    normalizer_state.get('''lowercase'''	,    __SCREAMING_SNAKE_CASE       ) != do_lower_case
							    or normalizer_state.get('''strip_accents'''	,    __SCREAMING_SNAKE_CASE       ) != strip_accents
							    or normalizer_state.get('''handle_chinese_chars'''	,    __SCREAMING_SNAKE_CASE       ) != tokenize_chinese_chars
							):
											a_		:	List[str]									=   getattr(__SCREAMING_SNAKE_CASE	,    normalizer_state.pop('''type'''       )       )
											a_		:	str									=   do_lower_case
											a_		:	Tuple									=   strip_accents
											a_		:	int									=   tokenize_chinese_chars
											a_		:	str									=   normalizer_class(**__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   do_lower_case






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      int=None       )      ->      int:
							a_		:	List[Any]									=   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

							return output






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      List[int]	,    __SCREAMING_SNAKE_CASE   :      Optional[List[int]] = None       )      ->      List[int]:
							a_		:	List[str]									=   [self.sep_token_id]
							a_		:	str									=   [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   :      int	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[str] = None       )      ->      Tuple[str]:
							a_		:	Any									=   self._tokenizer.model.save(__SCREAMING_SNAKE_CASE	,    name=__SCREAMING_SNAKE_CASE       )
							return tuple(__SCREAMING_SNAKE_CASE       )












'''simple docstring'''







# 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




