



"""simple docstring"""






from typing import Dict, List, Optional

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


snake_case   					=				logging.get_logger(__name__)


snake_case   					=				{
    '''nielsr/canine-s''': 2_0_4_8,
}

# Unicode defines 1,114,112 total “codepoints”
snake_case   					=				1_1_1_4_1_1_2

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
snake_case   					=				0

snake_case   					=				0Xe0_00
snake_case   					=				0Xe0_01
snake_case   					=				0Xe0_02
snake_case   					=				0Xe0_03
snake_case   					=				0Xe0_04

# Maps special codepoints to human-readable names.
snake_case   					=				{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
snake_case   					=				{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	List[str]           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



   def __init__(       self    :	List[Any]							,       __lowerCamelCase    :	Any=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	Any=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	Optional[Any]=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	Any=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	Tuple=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	str=chr(__lowerCamelCase				)							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	List[str]=2_0_4_8							,       **__lowerCamelCase    :	Optional[Any]							,       ):



     """simple docstring"""
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else bos_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else eos_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else sep_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else cls_token
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     _snake_case   	=      AddedToken(__lowerCamelCase							,       lstrip=__lowerCamelCase							,       rstrip=__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else mask_token

     super().__init__(
         bos_token=__lowerCamelCase							,       eos_token=__lowerCamelCase							,       sep_token=__lowerCamelCase							,       cls_token=__lowerCamelCase							,       pad_token=__lowerCamelCase							,       mask_token=__lowerCamelCase							,       add_prefix_space=__lowerCamelCase							,       model_max_length=__lowerCamelCase							,       **__lowerCamelCase							,       )

     # Creates a mapping for looking up the IDs of special symbols.
     _snake_case   	=      {}
     for codepoint, name in SPECIAL_CODEPOINTS.items():
       _snake_case   	=      codepoint

     # Creates a mapping for looking up the string forms of special symbol IDs.
     _snake_case   	=      {
         codepoint: name for name, codepoint in self._special_codepoints.items()
     }

     _snake_case   	=      UNICODE_VOCAB_SIZE
     _snake_case   	=      len(self._special_codepoints				)



   @property
   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     return self._unicode_vocab_size



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     return list(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     try:
       return ord(__lowerCamelCase				)
     except TypeError:
       raise ValueError(f"""invalid token: '{token}'"""				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     try:
       if index in SPECIAL_CODEPOINTS:
         return SPECIAL_CODEPOINTS[index]
       return chr(__lowerCamelCase				)
     except TypeError:
       raise ValueError(f"""invalid id: {index}"""				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return "".join(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [self.cls_token_id]

     _snake_case   	=      cls + token_ids_a + sep
     if token_ids_a is not None:
       result += token_ids_a + sep
     return result



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None							,       __lowerCamelCase    :	bool = False				):



     """simple docstring"""
     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__lowerCamelCase							,       token_ids_a=__lowerCamelCase							,       already_has_special_tokens=__lowerCamelCase				)

     _snake_case   	=      [1] + ([0] * len(__lowerCamelCase				)) + [1]
     if token_ids_a is not None:
       result += ([0] * len(__lowerCamelCase				)) + [1]
     return result



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [self.sep_token_id]
     _snake_case   	=      [self.cls_token_id]

     _snake_case   	=      len(cls + token_ids_a + sep				) * [0]
     if token_ids_a is not None:
       result += len(token_ids_a + sep				) * [1]
     return result





   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     return ()










"""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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

snake_case   					=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
snake_case   					=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
snake_case   					=				dict(zip(vocab, range(len(vocab))))
snake_case   					=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case   					=				Path(tmpdirname)
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
     with open(src_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, '''w''') as fp:
          fp.write('''\n'''.join(merges))

     snake_case   					=				FSMTTokenizer(
         langs=['''en''', '''ru'''],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

snake_case   					=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    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   					=				FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

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

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

# Save
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-ru





