

class 					lowercase							:  # Public class to implement a graph




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       row
											snake_case_     							=       col
											snake_case_     							=       graph




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											return (
											    0 <= i < self.ROW
											    and 0 <= j < self.COL
											    and not visited[i][j]
											    and self.graph[i][j]
											)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											# Checking all 8 elements surrounding nth element
											snake_case_     							=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
											snake_case_     							=       [-1, 0, 1, -1, 1, -1, 0, 1]
											snake_case_     							=       True  # Make those cells visited
											for k in range(8			):
																		if self.is_safe(i + row_nbr[k] ,  j + col_nbr[k] ,  _a			):
																									self.diffs(i + row_nbr[k] ,  j + col_nbr[k] ,  _a			)




				def        a				(   self			):  # And finally, count all islands.
											snake_case_     							=       [[False for j in range(self.COL			)] for i in range(self.ROW			)]
											snake_case_     							=       0
											for i in range(self.ROW			):
																		for j in range(self.COL			):
																									if visited[i][j] is False and self.graph[i][j] == 1:
																																self.diffs(_a ,  _a ,  _a			)
																																count += 1
											return count






"""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

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[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,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

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

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

# Save
_A     :			List[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


