
"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			List[Any]  						=			logging.get_logger(__name__)

_A     :			List[str]  						=			{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""camembert"""
   def __init__(     self      ,       _a=30_522      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu"      ,       _a=0.1      ,       _a=0.1      ,       _a=512      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=1      ,       _a=0      ,       _a=2      ,       _a="absolute"      ,       _a=True      ,       _a=None      ,       **_a      ,       ):
          super().__init__(pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       **_a							)

          lowercase       :							List[str]									=				vocab_size
          lowercase       :							Dict									=				hidden_size
          lowercase       :							Optional[Any]									=				num_hidden_layers
          lowercase       :							Tuple									=				num_attention_heads
          lowercase       :							str									=				hidden_act
          lowercase       :							str									=				intermediate_size
          lowercase       :							Dict									=				hidden_dropout_prob
          lowercase       :							Any									=				attention_probs_dropout_prob
          lowercase       :							List[str]									=				max_position_embeddings
          lowercase       :							Optional[Any]									=				type_vocab_size
          lowercase       :							int									=				initializer_range
          lowercase       :							Dict									=				layer_norm_eps
          lowercase       :							List[Any]									=				position_embedding_type
          lowercase       :							List[Any]									=				use_cache
          lowercase       :							Any									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							int									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Optional[int]									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)




"""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

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			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,
    )
)

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

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

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

# Save
_A     :			List[Any]  						=			"""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


