
"""simple docstring"""



import copy
import re

class       lowerCAmelCase__				:
     __a						=					"""hp"""
     __a						=					{}
     __a						=					None







     @classmethod
     def      lowercase	(							cls			:			Optional[Any]    ,      _lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       prefix
       _snake_case					     =       defaults
       cls.build_naming_info()







     @staticmethod
     def      lowercase	(							_lowerCamelCase			:			Dict    ,      _lowerCamelCase			:			List[str]			):
       if len(_lowerCamelCase			) == 0:
         return ""
       _snake_case					     =       None
       if any(char.isdigit() for char in word			):
         raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''			)
       if word in info["short_word"]:
         return info["short_word"][word]
       for prefix_len in range(1    ,      len(_lowerCamelCase			) + 1			):
         _snake_case					     =       word[:prefix_len]
         if prefix in info["reverse_short_word"]:
           continue
         else:
           _snake_case					     =       prefix
           break

       if short_word is None:
         # Paranoid fallback
         def int_to_alphabetic(_lowerCamelCase			:			List[str]			):
           _snake_case					     =       ''''''
           while integer != 0:
             _snake_case					     =       chr(ord('''A'''			) + integer % 10			) + s
             integer //= 10
           return s

         _snake_case					     =       0
         while True:
           _snake_case					     =       word + '''#''' + int_to_alphabetic(_lowerCamelCase			)
           if sword in info["reverse_short_word"]:
             continue
           else:
             _snake_case					     =       sword
             break

       _snake_case					     =       short_word
       _snake_case					     =       word
       return short_word







     @staticmethod
     def      lowercase	(							_lowerCamelCase			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       param_name.split('''_'''			)

       _snake_case					     =       [TrialShortNamer.shortname_for_word(_lowerCamelCase    ,      _lowerCamelCase			) for word in words]

       # We try to create a separatorless short name, but if there is a collision we have to fallback
       # to a separated short name
       _snake_case					     =       ['''''', '''_''']

       for separator in separators:
         _snake_case					     =       separator.join(_lowerCamelCase			)
         if shortname not in info["reverse_short_param"]:
           _snake_case					     =       shortname
           _snake_case					     =       param_name
           return shortname

       return param_name







     @staticmethod
     def      lowercase	(							_lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       TrialShortNamer.shortname_for_key(_lowerCamelCase    ,      _lowerCamelCase			)
       _snake_case					     =       short_name
       _snake_case					     =       param_name







     @classmethod
     def      lowercase	(							cls			:			List[Any]			):
       if cls.NAMING_INFO is not None:
         return

       _snake_case					     =       {
           '''short_word''': {},
           '''reverse_short_word''': {},
           '''short_param''': {},
           '''reverse_short_param''': {},
       }

       _snake_case					     =       list(cls.DEFAULTS.keys()			)

       for k in field_keys:
         cls.add_new_param_name(_lowerCamelCase    ,      _lowerCamelCase			)

       _snake_case					     =       info







     @classmethod
     def      lowercase	(							cls			:			List[Any]    ,      _lowerCamelCase			:			Union[str, Any]			):
       cls.build_naming_info()
       assert cls.PREFIX is not None
       _snake_case					     =       [copy.copy(cls.PREFIX			)]

       for k, v in params.items():
         if k not in cls.DEFAULTS:
           raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''			)
         if v == cls.DEFAULTS[k]:
           # The default value is not added to the name
           continue

         _snake_case					     =       cls.NAMING_INFO['''short_param'''][k]

         if isinstance(_lowerCamelCase    ,      _lowerCamelCase			):
           _snake_case					     =       1 if v else 0

         _snake_case					     =       '''''' if isinstance(_lowerCamelCase    ,      (int, float)			) else '''-'''
         _snake_case					     =       f'''{key}{sep}{v}'''
         name.append(_lowerCamelCase			)

       return "_".join(_lowerCamelCase			)







     @classmethod
     def      lowercase	(							cls			:			str    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       repr[len(cls.PREFIX			) + 1 :]
       if repr == "":
         _snake_case					     =       []
       else:
         _snake_case					     =       repr.split('''_'''			)

       _snake_case					     =       {}

       for value in values:
         if "-" in value:
           _snake_case     ,					_snake_case					     =       value.split('''-'''			)
         else:
           _snake_case					     =       re.sub('''[0-9.]'''    ,      ''''''    ,      _lowerCamelCase			)
           _snake_case					     =       float(re.sub('''[^0-9.]'''    ,      ''''''    ,      _lowerCamelCase			)			)

         _snake_case					     =       cls.NAMING_INFO['''reverse_short_param'''][p_k]

         _snake_case					     =       p_v

       for k in cls.DEFAULTS:
         if k not in parameters:
           _snake_case					     =       cls.DEFAULTS[k]

       return parameters





"""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

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

# borrowed from a test
UpperCAmelCase__          = [
    '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>',
]
UpperCAmelCase__          = dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__          = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase__          = Path(tmpdirname)
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 UpperCAmelCase__          = build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 UpperCAmelCase__          = 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))

 UpperCAmelCase__          = 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,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

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

# Test
UpperCAmelCase__          = tokenizer(['Making tiny model'], return_tensors='pt')
UpperCAmelCase__          = 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



