






'''simple docstring'''







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


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"timesformer"






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=224	,    __SCREAMING_SNAKE_CASE   :      List[Any]=16	,    __SCREAMING_SNAKE_CASE   :      Dict=3	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=8	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=768	,    __SCREAMING_SNAKE_CASE   :      str=12	,    __SCREAMING_SNAKE_CASE   :      List[str]=12	,    __SCREAMING_SNAKE_CASE   :      Dict=3072	,    __SCREAMING_SNAKE_CASE   :      int="gelu"	,    __SCREAMING_SNAKE_CASE   :      Tuple=0.0	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=0.0	,    __SCREAMING_SNAKE_CASE   :      List[str]=0.02	,    __SCREAMING_SNAKE_CASE   :      Any=1e-6	,    __SCREAMING_SNAKE_CASE   :      Tuple=True	,    __SCREAMING_SNAKE_CASE   :      Optional[int]="divided_space_time"	,    __SCREAMING_SNAKE_CASE   :      List[str]=0	,    **__SCREAMING_SNAKE_CASE   :      Any	,    )      ->      int:
							super().__init__(**__SCREAMING_SNAKE_CASE       )

							a_		:	List[str]									=   image_size
							a_		:	int									=   patch_size
							a_		:	List[Any]									=   num_channels
							a_		:	List[str]									=   num_frames

							a_		:	Tuple									=   hidden_size
							a_		:	Optional[int]									=   num_hidden_layers
							a_		:	List[Any]									=   num_attention_heads
							a_		:	Dict									=   intermediate_size
							a_		:	str									=   hidden_act
							a_		:	str									=   hidden_dropout_prob
							a_		:	List[Any]									=   attention_probs_dropout_prob
							a_		:	int									=   initializer_range
							a_		:	List[str]									=   layer_norm_eps
							a_		:	Dict									=   qkv_bias

							a_		:	Any									=   attention_type
							a_		:	Tuple									=   drop_path_rate












'''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

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   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									=   FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCAmelCase									=   tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCAmelCase									=   tiny_model(**batch)

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

# Save
__lowerCAmelCase									=   '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




