





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

__lowerCAmelCase				:      List[Any]						      =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
__lowerCAmelCase				:      Tuple						      ="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










'''simple docstring'''

__lowerCAmelCase				:      List[str]						      ="Alexander Joslin"

import operator as op

from .stack import Stack





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    A__							=     {"*": op.mul, "/": op.truediv, "+": op.add, "-": op.sub}

    A__							=     Stack()
    A__							=     Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(_lowerCamelCase			)			)
        elif i in operators:
            # RULE 2
            operator_stack.push(_lowerCamelCase			)
        elif i == ")":
            # RULE 4
            A__							=     operator_stack.peek()
            operator_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()
            A__							=     operand_stack.peek()
            operand_stack.pop()

            A__							=     operators[opr](_lowerCamelCase		,			_lowerCamelCase			)
            operand_stack.push(_lowerCamelCase			)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  __lowerCAmelCase				:      Dict						      ="(5 + ((4 * 2) * (2 + 3)))"
  # answer = 45
  print(f"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")



