

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

# Test
_SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_SCREAMING_SNAKE_CASE  :						Any         =    tiny_model(**batch)

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

# Save
_SCREAMING_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





import random
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					a[left_index]
     SCREAMING_SNAKE_CASE__					=					left_index + 1
     for j in range(left_index + 1   ,	_A       ):
          if a[j] < pivot:
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i], a[j]
               i += 1
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					a[i - 1], a[left_index]
     return i - 1
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     if left < right:
          SCREAMING_SNAKE_CASE__					=					random.randint(_A   ,	right - 1       )
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					(
              a[left],
              a[pivot],
          )  # switches the pivot with the left most bound
          SCREAMING_SNAKE_CASE__					=					partition(_A   ,	_A   ,	_A       )
          quick_sort_random(
              _A   ,	_A   ,	_A       )  # recursive quicksort to the left of the pivot point
          quick_sort_random(
              _A   ,	pivot_index + 1   ,	_A       )  # recursive quicksort to the right of the pivot point
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					input('''Enter numbers separated by a comma:\n'''       ).strip()
     SCREAMING_SNAKE_CASE__					=					[int(_A       ) for item in user_input.split(''','''       )]

     quick_sort_random(_A   ,	0   ,	len(_A       )       )

     print(_A       )


if __name__ == "__main__":
       main()


