





"""simple docstring"""


from heapq import heappop, heappush

import numpy as np





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Optional[Any]		,    snake_case  :   Optional[int]		,    snake_case  :   int		,    snake_case  :   Dict		,    )->						int:
       _lowerCamelCase      ,      _lowerCamelCase             =   grid.shape
       _lowerCamelCase             =   [-1, 1, 0, 0]
       _lowerCamelCase             =   [0, 0, -1, 1]
       if allow_diagonal:
              dx += [-1, -1, 1, 1]
              dy += [-1, 1, -1, 1]

       _lowerCamelCase      ,      _lowerCamelCase             =   [(0, source)], set()
       _lowerCamelCase             =   np.full((rows, cols)		,    np.inf						)
       _lowerCamelCase             =   0
       _lowerCamelCase             =   np.empty((rows, cols)		,    dtype=_UpperCamelCase						)
       _lowerCamelCase             =   None

       while queue:
              ((_lowerCamelCase)      ,      (_lowerCamelCase))             =   heappop(_UpperCamelCase						)
              if (x, y) in visited:
                     continue
              visited.add((x, y)						)

              if (x, y) == destination:
                     _lowerCamelCase             =   []
                     while (x, y) != source:
                            path.append((x, y)						)
                            _lowerCamelCase      ,      _lowerCamelCase             =   predecessors[x, y]
                     path.append(_UpperCamelCase						)  # add the source manually
                     path.reverse()
                     return matrix[destination], path

              for i in range(len(_UpperCamelCase						)						):
                     _lowerCamelCase      ,      _lowerCamelCase             =   x + dx[i], y + dy[i]
                     if 0 <= nx < rows and 0 <= ny < cols:
                            _lowerCamelCase             =   grid[nx][ny]
                            if next_node == 1 and matrix[nx, ny] > dist + 1:
                                   heappush(_UpperCamelCase		,    (dist + 1, (nx, ny))						)
                                   _lowerCamelCase             =   dist + 1
                                   _lowerCamelCase             =   (x, y)

       return np.inf, []


if __name__ == "__main__":
      import doctest

      doctest.testmod()










# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

a			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
a			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					build_dir / VOCAB_FILES_NAMES['''merges_file''']
	with open(src_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(tgt_vocab_file, '''w''') as fp:
		fp.write(json.dumps(vocab_tokens))
	with open(merges_file, '''w''') as fp:
		fp.write('''\n'''.join(merges))

	a			:			Optional[int]												=					FSMTTokenizer(
	    langs=['''en''', '''ru'''],
	    src_vocab_size=len(vocab),
	    tgt_vocab_size=len(vocab),
	    src_vocab_file=src_vocab_file,
	    tgt_vocab_file=tgt_vocab_file,
	    merges_file=merges_file,
	)

a			:			List[str]												=					FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

a			:			Union[str, Any]												=					FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

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

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

# Save
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-ru


