


'''simple docstring'''




import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


_a   :   Tuple												=   logging.get_logger(__name__)




class 			_lowercase :
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							List[str]:
        __snake_case           =						question_encoder
        __snake_case           =						generator
        __snake_case           =						self.question_encoder
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							int:
        if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
             raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'      )
        os.makedirs(SCREAMING_SNAKE_CASE_       ,   exist_ok=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   'question_encoder_tokenizer'      )
        __snake_case           =						os.path.join(SCREAMING_SNAKE_CASE_       ,   'generator_tokenizer'      )
        self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE_      )
        self.generator.save_pretrained(SCREAMING_SNAKE_CASE_      )
   @classmethod
   def a  (						cls       :      Any       ,   SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Optional[Any]      ) ->							str:
        # dynamically import AutoTokenizer
        from ..auto.tokenization_auto import AutoTokenizer

        __snake_case           =						kwargs.pop('config'       ,   SCREAMING_SNAKE_CASE_      )

        if config is None:
             __snake_case           =						RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )

        __snake_case           =						AutoTokenizer.from_pretrained(
            SCREAMING_SNAKE_CASE_       ,   config=config.question_encoder       ,   subfolder='question_encoder_tokenizer'      )
        __snake_case           =						AutoTokenizer.from_pretrained(
            SCREAMING_SNAKE_CASE_       ,   config=config.generator       ,   subfolder='generator_tokenizer'      )
        return cls(question_encoder=SCREAMING_SNAKE_CASE_       ,   generator=SCREAMING_SNAKE_CASE_      )
   def __call__(						self       :      str       ,   *SCREAMING_SNAKE_CASE_       :      Dict       ,   **SCREAMING_SNAKE_CASE_       :      Any      ) ->							str:
        return self.current_tokenizer(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Any       ,   *SCREAMING_SNAKE_CASE_       :      Tuple       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]      ) ->							Union[str, Any]:
        return self.generator.batch_decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[str]       ,   *SCREAMING_SNAKE_CASE_       :      str       ,   **SCREAMING_SNAKE_CASE_       :      Dict      ) ->							Dict:
        return self.generator.decode(*SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Union[str, Any]      ) ->							Tuple:
        __snake_case           =						self.question_encoder
   def a  (						self       :      Optional[int]      ) ->							List[str]:
        __snake_case           =						self.generator





   def a  (						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[List[str]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[int] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[int] = None       ,   SCREAMING_SNAKE_CASE_       :      str = "longest"       ,   SCREAMING_SNAKE_CASE_       :      str = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   **SCREAMING_SNAKE_CASE_       :      List[str]       ,   ) ->							BatchEncoding:
        warnings.warn(
            '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
            'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
            'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
            'details'       ,   SCREAMING_SNAKE_CASE_       ,   )
        if max_length is None:
             __snake_case           =						self.current_tokenizer.model_max_length
        __snake_case           =						self(
            SCREAMING_SNAKE_CASE_       ,   add_special_tokens=SCREAMING_SNAKE_CASE_       ,   return_tensors=SCREAMING_SNAKE_CASE_       ,   max_length=SCREAMING_SNAKE_CASE_       ,   padding=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )
        if tgt_texts is None:
             return model_inputs
        # Process tgt_texts
        if max_target_length is None:
             __snake_case           =						self.current_tokenizer.model_max_length
        __snake_case           =						self(
            text_target=SCREAMING_SNAKE_CASE_       ,   add_special_tokens=SCREAMING_SNAKE_CASE_       ,   return_tensors=SCREAMING_SNAKE_CASE_       ,   padding=SCREAMING_SNAKE_CASE_       ,   max_length=SCREAMING_SNAKE_CASE_       ,   truncation=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_       ,   )
        __snake_case           =						labels['input_ids']
        return model_inputs









"""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





