





# 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  :    Tuple						    =  'facebook/wmt19-en-de'
_snake_case  :    List[str]						    =  FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_snake_case  :    Tuple						    =  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  :    int						    =  tokenizer(['Making tiny model'], return_tensors='pt')
_snake_case  :    str						    =  tiny_model(**batch)

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

# Save
_snake_case  :    Dict						    =  '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








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __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								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






  @property
  def 				lowercase		(				self :		Tuple     )							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]     )






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

