




import math
class 			_UpperCamelCase      :
      def __init__(			self   :Optional[Any] ,      lowerCamelCase   :List[str]=0      )     ->			List[str]:  # a graph with Node 0,1,...,N-1
       UpperCAmelCase__							 =						n
       UpperCAmelCase__							 =						[
           [math.inf for j in range(0 ,      __UpperCamelCase      )] for i in range(0 ,      __UpperCamelCase      )
       ]  # adjacency matrix for weight
       UpperCAmelCase__							 =						[
           [math.inf for j in range(0 ,      __UpperCamelCase      )] for i in range(0 ,      __UpperCamelCase      )
       ]  # dp[i][j] stores minimum distance from i to j
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						w
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			List[Any]:
       for k in range(0 ,      self.n      ):
        for i in range(0 ,      self.n      ):
         for j in range(0 ,      self.n      ):
          UpperCAmelCase__							 =						min(self.dp[i][j] ,      self.dp[i][k] + self.dp[k][j]      )







      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[int]      )     ->			Dict:
       return self.dp[u][v]


if __name__ == "__main__":
  _lowerCAmelCase						:   List[str]		=   Graph(5)
  graph.add_edge(0, 2, 9)
  graph.add_edge(0, 4, 1_0)
  graph.add_edge(1, 3, 5)
  graph.add_edge(2, 3, 7)
  graph.add_edge(3, 0, 1_0)
  graph.add_edge(3, 1, 2)
  graph.add_edge(3, 2, 1)
  graph.add_edge(3, 4, 6)
  graph.add_edge(4, 1, 3)
  graph.add_edge(4, 2, 4)
  graph.add_edge(4, 3, 9)
  graph.floyd_warshall()
  graph.show_min(1, 4)
  graph.show_min(0, 3)











# 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						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   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						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

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

# Save
_lowerCAmelCase						:   Optional[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





