




from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase     :		Union[str, Any]								=       logging.get_logger(__name__)

_lowerCamelCase     :		Tuple								=       {
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''xlm-roberta'''


    def __init__(      self				:     str ,			UpperCAmelCase__				:     Union[str, Any]=30_522 ,			UpperCAmelCase__				:     Any=768 ,			UpperCAmelCase__				:     Optional[Any]=12 ,			UpperCAmelCase__				:     Optional[int]=12 ,			UpperCAmelCase__				:     List[str]=3_072 ,			UpperCAmelCase__				:     List[str]="gelu" ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     int=0.1 ,			UpperCAmelCase__				:     Optional[Any]=512 ,			UpperCAmelCase__				:     int=2 ,			UpperCAmelCase__				:     Any=0.02 ,			UpperCAmelCase__				:     Optional[int]=1e-12 ,			UpperCAmelCase__				:     Union[str, Any]=1 ,			UpperCAmelCase__				:     Any=0 ,			UpperCAmelCase__				:     Optional[int]=2 ,			UpperCAmelCase__				:     str="absolute" ,			UpperCAmelCase__				:     Tuple=True ,			UpperCAmelCase__				:     List[Any]=None ,			**UpperCAmelCase__				:     Optional[Any] ,			)						->Dict:





           '''simple docstring'''

           super().__init__(pad_token_id=UpperCAmelCase__ ,			bos_token_id=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__ ,			**UpperCAmelCase__)

           A__							=   vocab_size
           A__							=   hidden_size
           A__							=   num_hidden_layers
           A__							=   num_attention_heads
           A__							=   hidden_act
           A__							=   intermediate_size
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   type_vocab_size
           A__							=   initializer_range
           A__							=   layer_norm_eps
           A__							=   position_embedding_type
           A__							=   use_cache
           A__							=   classifier_dropout


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           if self.task == "multiple-choice":
                  A__							=   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
           else:
                  A__							=   {0: '''batch''', 1: '''sequence'''}
           return OrderedDict(
               [
                   ('''input_ids''', dynamic_axis),
                   ('''attention_mask''', dynamic_axis),
               ])








# 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

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

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

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

# Save
_lowerCamelCase     :		str								=       """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


