






from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__snake_case    :    Dict         =     {
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class     __SCREAMING_SNAKE_CASE					(   __lowerCamelCase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					'''canine'''






						def __init__(	self     ,						_UpperCamelCase=7_68     ,						_UpperCamelCase=12     ,						_UpperCamelCase=12     ,						_UpperCamelCase=30_72     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=1_63_84     ,						_UpperCamelCase=16     ,						_UpperCamelCase=0.02     ,						_UpperCamelCase=1E-12     ,						_UpperCamelCase=0     ,						_UpperCamelCase=0XE000     ,						_UpperCamelCase=0XE001     ,						_UpperCamelCase=4     ,						_UpperCamelCase=4     ,						_UpperCamelCase=8     ,						_UpperCamelCase=1_63_84     ,						_UpperCamelCase=1_28     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(pad_token_id=a_     ,						bos_token_id=a_     ,						eos_token_id=a_     ,						**a_   )

												lowerCAmelCase__    						=						max_position_embeddings
												lowerCAmelCase__    						=						hidden_size
												lowerCAmelCase__    						=						num_hidden_layers
												lowerCAmelCase__    						=						num_attention_heads
												lowerCAmelCase__    						=						intermediate_size
												lowerCAmelCase__    						=						hidden_act
												lowerCAmelCase__    						=						hidden_dropout_prob
												lowerCAmelCase__    						=						attention_probs_dropout_prob
												lowerCAmelCase__    						=						initializer_range
												lowerCAmelCase__    						=						type_vocab_size
												lowerCAmelCase__    						=						layer_norm_eps

												# Character config:
												lowerCAmelCase__    						=						downsampling_rate
												lowerCAmelCase__    						=						upsampling_kernel_size
												lowerCAmelCase__    						=						num_hash_functions
												lowerCAmelCase__    						=						num_hash_buckets
												lowerCAmelCase__    						=						local_transformer_stride













# 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

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

__snake_case    :    Optional[Any]         =     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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

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

# Save
__snake_case    :    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





