
import unittest

import numpy as np






def   lowerCAmelCase_						(					_snake_case      :  np.ndarray      ,   _snake_case      :  np.ndarray      ,   _snake_case      :  np.ndarray      ,   _snake_case      :  np.ndarray | None = None      ,   )				->		np.ndarray:







	'''simple docstring'''


	__magic_name__		:		Any	   =     np.shape(_snake_case   )
	__magic_name__		:		List[str]	   =     np.shape(_snake_case   )
	__magic_name__		:		Dict	   =     np.shape(_snake_case   )

	if shape_a[0] != shape_b[0]:
		__magic_name__		:		List[Any]	   =     (
		    "Expected the same number of rows for A and B. "
		    F'''Instead found A of size {shape_a} and B of size {shape_b}'''
		)
		raise ValueError(_snake_case   )

	if shape_b[1] != shape_c[1]:
		__magic_name__		:		Any	   =     (
		    "Expected the same number of columns for B and C. "
		    F'''Instead found B of size {shape_b} and C of size {shape_c}'''
		)
		raise ValueError(_snake_case   )

	__magic_name__		:		str	   =     pseudo_inv
	if a_inv is None:
		try:
			__magic_name__		:		int	   =     np.linalg.inv(_snake_case   )
		except np.linalg.LinAlgError:
			raise ValueError(
			    "Input matrix A is not invertible. Cannot compute Schur complement."   )

	return mat_c - mat_b.T @ a_inv @ mat_b





class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
				__magic_name__		:		Dict	   =     np.array([[0, 3], [3, 0], [2, 3]]		)
				__magic_name__		:		List[Any]	   =     np.array([[2, 1], [6, 3]]		)

				__magic_name__		:		Dict	   =     schur_complement(_a						,   _a						,   _a		)

				__magic_name__		:		int	   =     np.block([[a, b], [b.T, c]]		)

				__magic_name__		:		Dict	   =     np.linalg.det(_a		)
				__magic_name__		:		str	   =     np.linalg.det(_a		)
				__magic_name__		:		Dict	   =     np.linalg.det(_a		)

				self.assertAlmostEqual(_a						,   det_a * det_s		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Any	   =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
				__magic_name__		:		str	   =     np.array([[0, 3], [3, 0], [2, 3]]		)
				__magic_name__		:		Union[str, Any]	   =     np.array([[2, 1], [6, 3]]		)

				with self.assertRaises(_a		):
					schur_complement(_a						,   _a						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]		)
				__magic_name__		:		Optional[Any]	   =     np.array([[0, 3], [3, 0], [2, 3]]		)
				__magic_name__		:		List[Any]	   =     np.array([[2, 1, 3], [6, 3, 5]]		)

				with self.assertRaises(_a		):
					schur_complement(_a						,   _a						,   _a		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()


# 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
