
# 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

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

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

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

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

# Save
snake_case :		Dict							      =  "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


def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  str   )				->		bool:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) + 1
	__magic_name__		:		List[str]	   =     len(_snake_case   ) + 1

	# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
	# length i of input_string matches with prefix string of length j of
	# given pattern.
	# "dp" stands for dynamic programming.
	__magic_name__		:		str	   =     [[0 for i in range(_snake_case   )] for j in range(_snake_case   )]

	# since string of zero length match pattern of zero length
	__magic_name__		:		Optional[int]	   =     1

	# since pattern of zero length will never match with string of non-zero length
	for i in range(1      ,   _snake_case   ):
		__magic_name__		:		Optional[int]	   =     0

	# since string of zero length will match with pattern where there
	# is at least one * alternatively
	for j in range(1      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     dp[0][j - 2] if pattern[j - 1] == "*" else 0

	# now using bottom-up approach to find for all remaining lengths
	for i in range(1      ,   _snake_case   ):
		for j in range(1      ,   _snake_case   ):
			if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
				__magic_name__		:		Optional[int]	   =     dp[i - 1][j - 1]

			elif pattern[j - 1] == "*":
				if dp[i][j - 2] == 1:
					__magic_name__		:		Optional[Any]	   =     1
				elif pattern[j - 2] in (input_string[i - 1], "."):
					__magic_name__		:		List[Any]	   =     dp[i - 1][j]
				else:
					__magic_name__		:		Union[str, Any]	   =     0
			else:
				__magic_name__		:		Dict	   =     0

	return bool(dp[-1][-1]   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				snake_case :		Optional[Any]							      =  "aab"
				snake_case :		List[str]							      =  "c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F"{input_string} matches the given pattern {pattern}")
				else:
								print(F"{input_string} does not match with the given pattern {pattern}")
