


import os

def    lowercase_	(	_A    :					str = "input.txt"		):

 """simple docstring"""




 with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
  lowerCamelCase__	:       List[Any]					=						[
      [int(_A		) for element in line.split(","		)]
      for line in input_file.readlines()
  ]

 lowerCamelCase__	:       Optional[Any]					=						len(_A		)
 lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

 lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
 for i in range(_A		):
  lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

 for j in range(1					,      _A		):
  for i in range(_A		):
   lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

  for i in range(1					,      _A		):
   lowerCamelCase__	:       Tuple					=						min(
       minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

  for i in range(rows - 2					,      -1					,      -1		):
   lowerCamelCase__	:       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() = }')






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


