




"""simple docstring"""







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							lowerCamelCase__        =		"""platform"""

							import jax
							import jax.numpy as jnp

							from transformers import BlenderbotTokenizer
							from transformers.models.blenderbot.modeling_flax_blenderbot import (
							    FlaxBlenderbotForConditionalGeneration,
							    FlaxBlenderbotModel,
							    shift_tokens_right,
							)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  _UpperCamelCase=None	,  ):
	if attention_mask is None:
		__lowerCAmelCase      :						int						      =				np.where(input_ids != config.pad_token_id	,  1	,  0		)
	if decoder_attention_mask is None:
		__lowerCAmelCase      :						List[str]						      =				np.where(decoder_input_ids != config.pad_token_id	,  1	,  0		)
	if head_mask is None:
		__lowerCAmelCase      :						List[str]						      =				np.ones((config.encoder_layers, config.encoder_attention_heads)		)
	if decoder_head_mask is None:
		__lowerCAmelCase      :						List[str]						      =				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
	if cross_attn_head_mask is None:
		__lowerCAmelCase      :						int						      =				np.ones((config.decoder_layers, config.decoder_attention_heads)		)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}




class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=13	,		_SCREAMING_SNAKE_CASE=7	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=99	,		_SCREAMING_SNAKE_CASE=16	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE=4	,		_SCREAMING_SNAKE_CASE="gelu"	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=0.1	,		_SCREAMING_SNAKE_CASE=32	,		_SCREAMING_SNAKE_CASE=2	,		_SCREAMING_SNAKE_CASE=1	,		_SCREAMING_SNAKE_CASE=0	,		_SCREAMING_SNAKE_CASE=0.02	,		):
			__lowerCAmelCase      :						Dict						      =				parent
			__lowerCAmelCase      :						Dict						      =				batch_size
			__lowerCAmelCase      :						Tuple						      =				seq_length
			__lowerCAmelCase      :						Optional[int]						      =				is_training
			__lowerCAmelCase      :						Optional[int]						      =				use_labels
			__lowerCAmelCase      :						Tuple						      =				vocab_size
			__lowerCAmelCase      :						Any						      =				hidden_size
			__lowerCAmelCase      :						Tuple						      =				num_hidden_layers
			__lowerCAmelCase      :						Any						      =				num_attention_heads
			__lowerCAmelCase      :						Dict						      =				intermediate_size
			__lowerCAmelCase      :						Tuple						      =				hidden_act
			__lowerCAmelCase      :						Any						      =				hidden_dropout_prob
			__lowerCAmelCase      :						List[str]						      =				attention_probs_dropout_prob
			__lowerCAmelCase      :						List[str]						      =				max_position_embeddings
			__lowerCAmelCase      :						Optional[Any]						      =				eos_token_id
			__lowerCAmelCase      :						int						      =				pad_token_id
			__lowerCAmelCase      :						int						      =				bos_token_id
			__lowerCAmelCase      :						Optional[Any]						      =				initializer_range

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,		self.vocab_size			)	,		3	,		self.vocab_size			)
			__lowerCAmelCase      :						Tuple						      =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,		dtype=np.intaa			))	,		-1			)

			__lowerCAmelCase      :						Optional[Any]						      =				shift_tokens_right(_SCREAMING_SNAKE_CASE	,		1	,		2			)

			__lowerCAmelCase      :						Dict						      =				BlenderbotConfig(
			    vocab_size=self.vocab_size	,		d_model=self.hidden_size	,		encoder_layers=self.num_hidden_layers	,		decoder_layers=self.num_hidden_layers	,		encoder_attention_heads=self.num_attention_heads	,		decoder_attention_heads=self.num_attention_heads	,		encoder_ffn_dim=self.intermediate_size	,		decoder_ffn_dim=self.intermediate_size	,		dropout=self.hidden_dropout_prob	,		attention_dropout=self.attention_probs_dropout_prob	,		max_position_embeddings=self.max_position_embeddings	,		eos_token_id=self.eos_token_id	,		bos_token_id=self.bos_token_id	,		pad_token_id=self.pad_token_id	,		initializer_range=self.initializer_range	,		use_cache=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Optional[int]						      =				prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			return config, inputs_dict

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self.prepare_config_and_inputs()
			return config, inputs_dict

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				20
			__lowerCAmelCase      :						Union[str, Any]						      =				model_class_name(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				model.encode(inputs_dict['input_ids']			)

			__lowerCAmelCase      :						List[Any]						      =				(
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			__lowerCAmelCase      :						Tuple						      =				model.init_cache(decoder_input_ids.shape[0]	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,		dtype='i4'			)

			__lowerCAmelCase      :						int						      =				jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]	,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,		)
			__lowerCAmelCase      :						Dict						      =				model.decode(
			    decoder_input_ids[:, :-1]	,		_SCREAMING_SNAKE_CASE	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE	,		past_key_values=_SCREAMING_SNAKE_CASE	,		decoder_position_ids=_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						List[Any]						      =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,		dtype='i4'			)
			__lowerCAmelCase      :						Optional[Any]						      =				model.decode(
			    decoder_input_ids[:, -1:]	,		_SCREAMING_SNAKE_CASE	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE	,		past_key_values=outputs_cache.past_key_values	,		decoder_position_ids=_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						Tuple						      =				model.decode(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						int						      =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
			self.parent.assertTrue(diff < 1E-3	,		msg=f"Max diff is {diff}"			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Tuple						      =				20
			__lowerCAmelCase      :						Any						      =				model_class_name(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				model.encode(inputs_dict['input_ids']			)

			__lowerCAmelCase      :						Optional[Any]						      =				(
			    inputs_dict['decoder_input_ids'],
			    inputs_dict['decoder_attention_mask'],
			)

			__lowerCAmelCase      :						List[Any]						      =				jnp.concatenate(
			    [
			        decoder_attention_mask,
			        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
			    ]	,		axis=-1	,		)

			__lowerCAmelCase      :						Dict						      =				model.init_cache(decoder_input_ids.shape[0]	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				jnp.broadcast_to(
			    jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]	,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,		)

			__lowerCAmelCase      :						Optional[Any]						      =				model.decode(
			    decoder_input_ids[:, :-1]	,		_SCREAMING_SNAKE_CASE	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE	,		past_key_values=_SCREAMING_SNAKE_CASE	,		decoder_position_ids=_SCREAMING_SNAKE_CASE	,		)
			__lowerCAmelCase      :						Tuple						      =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,		dtype='i4'			)
			__lowerCAmelCase      :						Any						      =				model.decode(
			    decoder_input_ids[:, -1:]	,		_SCREAMING_SNAKE_CASE	,		past_key_values=outputs_cache.past_key_values	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE	,		decoder_position_ids=_SCREAMING_SNAKE_CASE	,		)

			__lowerCAmelCase      :						Dict						      =				model.decode(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
			self.parent.assertTrue(diff < 1E-3	,		msg=f"Max diff is {diff}"			)




@require_flax
class 			A__	(     unittest.TestCase):
		A_						:	Optional[Any]           =     9_9

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				np.array(
			    [
			        [71, 82, 18, 33, 46, 91, 2],
			        [68, 34, 26, 58, 30, 82, 2],
			        [5, 97, 17, 39, 94, 40, 2],
			        [76, 83, 94, 25, 70, 78, 2],
			        [87, 59, 41, 35, 48, 66, 2],
			        [55, 13, 16, 58, 5, 2, 1],  # note padding
			        [64, 27, 31, 51, 12, 75, 2],
			        [52, 64, 86, 17, 83, 39, 2],
			        [48, 61, 9, 24, 71, 82, 2],
			        [26, 1, 60, 48, 22, 13, 2],
			        [21, 5, 62, 28, 14, 76, 2],
			        [45, 98, 37, 86, 59, 48, 2],
			        [70, 70, 50, 9, 28, 0, 2],
			    ]	,		dtype=np.intaa	,		)

			__lowerCAmelCase      :						Dict						      =				input_ids.shape[0]
			__lowerCAmelCase      :						List[str]						      =				BlenderbotConfig(
			    vocab_size=self.vocab_size	,		d_model=24	,		encoder_layers=2	,		decoder_layers=2	,		encoder_attention_heads=2	,		decoder_attention_heads=2	,		encoder_ffn_dim=32	,		decoder_ffn_dim=32	,		max_position_embeddings=48	,		eos_token_id=2	,		pad_token_id=1	,		bos_token_id=0	,		)
			return config, input_ids, batch_size

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						int						      =				self._get_config_and_data()
			__lowerCAmelCase      :						Union[str, Any]						      =				FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[str]						      =				lm_model(input_ids=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				(batch_size, input_ids.shape[1], config.vocab_size)
			self.assertEqual(outputs['logits'].shape	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[Any]						      =				BlenderbotConfig(
			    vocab_size=self.vocab_size	,		d_model=14	,		encoder_layers=2	,		decoder_layers=2	,		encoder_attention_heads=2	,		decoder_attention_heads=2	,		encoder_ffn_dim=8	,		decoder_ffn_dim=8	,		max_position_embeddings=48	,		)
			__lowerCAmelCase      :						int						      =				FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,		dtype=np.intaa			)
			__lowerCAmelCase      :						str						      =				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,		dtype=np.intaa			)
			__lowerCAmelCase      :						Union[str, Any]						      =				lm_model(input_ids=_SCREAMING_SNAKE_CASE	,		decoder_input_ids=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				(*summary.shape, config.vocab_size)
			self.assertEqual(outputs['logits'].shape	,		_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,		dtype=np.intaa			)
			__lowerCAmelCase      :						Optional[Any]						      =				shift_tokens_right(_SCREAMING_SNAKE_CASE	,		1	,		2			)
			__lowerCAmelCase      :						Dict						      =				np.equal(_SCREAMING_SNAKE_CASE	,		1			).astype(np.floataa			).sum()
			__lowerCAmelCase      :						str						      =				np.equal(_SCREAMING_SNAKE_CASE	,		1			).astype(np.floataa			).sum()
			self.assertEqual(shifted.shape	,		input_ids.shape			)
			self.assertEqual(_SCREAMING_SNAKE_CASE	,		n_pad_before - 1			)
			self.assertTrue(np.equal(shifted[:, 0]	,		2			).all()			)




@require_flax
class 			A__	(     _lowerCamelCase	,					unittest.TestCase	,					_lowerCamelCase):
		A_						:	str           =     True
		A_						:	int           =     (
		    (
		        FlaxBlenderbotModel,
		        FlaxBlenderbotForConditionalGeneration,
		    )
		    if is_flax_available()
		    else ()
		)
		A_						:	List[str]           =     (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				FlaxBlenderbotModelTester(self			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
				self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.model_tester.prepare_config_and_inputs()
			for model_class in self.all_model_classes:
				self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__			):
					__lowerCAmelCase      :						List[str]						      =				self._prepare_for_class(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase      :						Any						      =				model_class(_SCREAMING_SNAKE_CASE			)

					@jax.jit
					def encode_jitted(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None	,		**_SCREAMING_SNAKE_CASE			):
						return model.encode(input_ids=_SCREAMING_SNAKE_CASE	,		attention_mask=_SCREAMING_SNAKE_CASE			)

					with self.subTest('JIT Enabled'			):
						__lowerCAmelCase      :						Any						      =				encode_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

					with self.subTest('JIT Disabled'			):
						with jax.disable_jit():
							__lowerCAmelCase      :						Union[str, Any]						      =				encode_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

					self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		len(_SCREAMING_SNAKE_CASE			)			)
					for jitted_output, output in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
						self.assertEqual(jitted_output.shape	,		output.shape			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Dict						      =				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				with self.subTest(model_class.__name__			):
					__lowerCAmelCase      :						List[Any]						      =				model_class(_SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase      :						Union[str, Any]						      =				model.encode(inputs_dict['input_ids']	,		inputs_dict['attention_mask']			)

					__lowerCAmelCase      :						Optional[int]						      =				{
					    'decoder_input_ids': inputs_dict['decoder_input_ids'],
					    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
					    'encoder_outputs': encoder_outputs,
					}

					@jax.jit
					def decode_jitted(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
						return model.decode(
						    decoder_input_ids=_SCREAMING_SNAKE_CASE	,		decoder_attention_mask=_SCREAMING_SNAKE_CASE	,		encoder_outputs=_SCREAMING_SNAKE_CASE	,		)

					with self.subTest('JIT Enabled'			):
						__lowerCAmelCase      :						Dict						      =				decode_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

					with self.subTest('JIT Disabled'			):
						with jax.disable_jit():
							__lowerCAmelCase      :						Dict						      =				decode_jitted(**_SCREAMING_SNAKE_CASE			).to_tuple()

					self.assertEqual(len(_SCREAMING_SNAKE_CASE			)	,		len(_SCREAMING_SNAKE_CASE			)			)
					for jitted_output, output in zip(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
						self.assertEqual(jitted_output.shape	,		output.shape			)

		@slow
		def 							__lowerCamelCase							(				self			):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase      :						Optional[Any]						      =				model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
				# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
				__lowerCAmelCase      :						Any						      =				np.ones((1, 1)			) * model.config.eos_token_id
				__lowerCAmelCase      :						int						      =				model(_SCREAMING_SNAKE_CASE			)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)







		@unittest.skipUnless(jax_device != 'cpu'	,		'3B test too slow on CPU.'			)
		@slow
		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Tuple						      =				{'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
			__lowerCAmelCase      :						List[Any]						      =				{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

			__lowerCAmelCase      :						Optional[Any]						      =				FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'	,		from_pt=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

			__lowerCAmelCase      :						List[str]						      =				['Sam']
			__lowerCAmelCase      :						int						      =				tokenizer(_SCREAMING_SNAKE_CASE	,		return_tensors='jax'			)

			__lowerCAmelCase      :						Optional[int]						      =				model.generate(**_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				'Sam is a great name. It means "sun" in Gaelic.'

			__lowerCAmelCase      :						Dict						      =				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)
			assert generated_txt[0].strip() == tgt_text

"""simple docstring"""







import os
def    __lowerCAmelCase    (_UpperCamelCase = "input.txt"		):

	with open(os.path.join(os.path.dirname(_UpperCamelCase		)	,  _UpperCamelCase		)		) as input_file:
		__lowerCAmelCase      :						Optional[Any]						      =				[
		    [int(_UpperCamelCase		) for element in line.split(','		)]
		    for line in input_file.readlines()
		]

	__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				len(matrix[0]		)

	__lowerCAmelCase      :						int						      =				[[-1 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				matrix[i][0]

	for j in range(1	,  _UpperCamelCase		):
		for i in range(_UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i - 1][j] + matrix[i][j]		)

		for i in range(rows - 2	,  -1	,  -1		):
			__lowerCAmelCase      :						List[str]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i + 1][j] + matrix[i][j]		)

	return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
							print(f'{solution() = }')