





'''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

_SCREAMING_SNAKE_CASE	 =							"tiny-wmt19-en-ru"

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
       _SCREAMING_SNAKE_CASE	 =							Path(tmpdirname)
       _SCREAMING_SNAKE_CASE	 =							build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
       _SCREAMING_SNAKE_CASE	 =							build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
       _SCREAMING_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))

       _SCREAMING_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,
       )

_SCREAMING_SNAKE_CASE	 =							FSMTConfig(
    langs=["ru", "en"],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    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,
)

_SCREAMING_SNAKE_CASE	 =							FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE	 =							tokenizer(["Making tiny model"], return_tensors="pt")
_SCREAMING_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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"bert-generation"




 def __init__(	self			,     _lowerCAmelCase=50358			,     _lowerCAmelCase=1024			,     _lowerCAmelCase=24			,     _lowerCAmelCase=16			,     _lowerCAmelCase=4096			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-12			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1			,     _lowerCAmelCase="absolute"			,     _lowerCAmelCase=True			,     **_lowerCAmelCase			,     )    ->							Tuple:
     super().__init__(pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     **_lowerCAmelCase		)

     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_dropout_prob
     _lowerCAmelCase				=      attention_probs_dropout_prob
     _lowerCAmelCase				=      max_position_embeddings
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      position_embedding_type
     _lowerCAmelCase				=      use_cache


