


'''simple docstring'''

import os
from pathlib import Path







def 					lowercase__				(   )->				Any:
      from torch.utils.cpp_extension import load

      UpperCamelCase       						=      Path(__UpperCamelCase					).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
      UpperCamelCase       						=      [
          root / filename
          for filename in [
              """vision.cpp""",
              os.path.join("""cpu"""	,		"""ms_deform_attn_cpu.cpp"""					),
              os.path.join("""cuda"""	,		"""ms_deform_attn_cuda.cu"""					),
          ]
      ]

      load(
          """MultiScaleDeformableAttention"""	,		__UpperCamelCase	,		with_cuda=__UpperCamelCase	,		extra_include_paths=[str(__UpperCamelCase					)]	,		extra_cflags=["""-DWITH_CUDA=1"""]	,		extra_cuda_cflags=[
              """-DCUDA_HAS_FP16=1""",
              """-D__CUDA_NO_HALF_OPERATORS__""",
              """-D__CUDA_NO_HALF_CONVERSIONS__""",
              """-D__CUDA_NO_HALF2_OPERATORS__""",
          ]	,		)

      import MultiScaleDeformableAttention as MSDA

      return MSDA








'''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

SCREAMING_SNAKE_CASE__											=     'facebook/wmt19-en-de'
SCREAMING_SNAKE_CASE__											=     FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
SCREAMING_SNAKE_CASE__											=     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,
    )
)

SCREAMING_SNAKE_CASE__											=     FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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

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

# Save
SCREAMING_SNAKE_CASE__											=     '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
