

'''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		       =    '''facebook/wmt19-en-de'''
__lowerCamelCase		       =    FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCamelCase		       =    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		       =    FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

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

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

# Save
__lowerCamelCase		       =    '''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'''







from queue import PriorityQueue
from typing import Any

import numpy as np
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )			->			float | int:
       for nxt, d in graph[v]:
              if nxt in visited_forward:
                     continue
              A_						=      cst_fwd.get(UpperCAmelCase__,       np.inf				)
              A_						=      cst_fwd[v] + d
              if new_cost_f < old_cost_f:
                     queue.put((new_cost_f, nxt)				)
                     A_						=      new_cost_f
                     A_						=      v
              if nxt in visited_backward:
                     if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                            A_						=      cst_fwd[v] + d + cst_bwd[nxt]
       return shortest_distance
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			int:
       A_						=      -1

       A_						=      set()
       A_						=      set()
       A_						=      {source: 0}
       A_						=      {destination: 0}
       A_						=      {source: None}
       A_						=      {destination: None}
       A_						=      PriorityQueue()
       A_						=      PriorityQueue()

       A_						=      np.inf

       queue_forward.put((0, source)				)
       queue_backward.put((0, destination)				)

       if source == destination:
              return 0

       while not queue_forward.empty() and not queue_backward.empty():
              A_					,			A_						=      queue_forward.get()
              visited_forward.add(UpperCAmelCase__				)

              A_					,			A_						=      queue_backward.get()
              visited_backward.add(UpperCAmelCase__				)

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              A_						=      pass_and_relaxation(
                  UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       )

              if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
                     break

       if shortest_distance != np.inf:
              A_						=      shortest_distance
       return shortest_path_distance


__lowerCamelCase		       =    {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
__lowerCamelCase		       =    {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
      import doctest

      doctest.testmod()
