
import sys






def 			a_						(			__lowercase  :      List[Any]					)					->    Dict:
			_snake_case            =     len(lowercase__					)
			_snake_case            =     [[0 for x in range(lowercase__					)] for x in range(lowercase__					)]
			_snake_case            =     [[0 for x in range(lowercase__					)] for x in range(lowercase__					)]

			for chain_length in range(2			,   lowercase__					):
						for a in range(1			,   n - chain_length + 1					):
									_snake_case            =     a + chain_length - 1

									_snake_case            =     sys.maxsize
									for c in range(lowercase__			,   lowercase__					):
												_snake_case            =     (
												    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
												)
												if cost < matrix[a][b]:
															_snake_case            =     cost
															_snake_case            =     c
			return matrix, sol






def 			a_						(			__lowercase  :      Tuple			,   __lowercase  :      Tuple			,   __lowercase  :      Tuple					)					->    List[str]:
			if i == j:
						print('A' + str(lowercase__					)			,   end=' '					)
			else:
						print('('			,   end=' '					)
						print_optiomal_solution(lowercase__			,   lowercase__			,   optimal_solution[i][j]					)
						print_optiomal_solution(lowercase__			,   optimal_solution[i][j] + 1			,   lowercase__					)
						print(')'			,   end=' '					)






def 			a_						(			)					->    List[str]:
			_snake_case            =     [30, 35, 15, 5, 10, 20, 25]
			_snake_case            =     len(lowercase__					)
			# Size of matrix created from above array will be
			# 30*35 35*15 15*5 5*10 10*20 20*25
			_snake_case            =     matrix_chain_order(lowercase__					)

			print('No. of Operation required: ' + str(matrix[1][n - 1]					)					)
			print_optiomal_solution(lowercase__			,   1			,   n - 1					)


if __name__ == "__main__":
			main()


"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCamelCase		     =    TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """MT5EncoderModel""",
        """MT5ForConditionalGeneration""",
        """MT5ForQuestionAnswering""",
        """MT5Model""",
        """MT5PreTrainedModel""",
        """MT5Stack""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(
        __name__,
        globals()["""__file__"""],
        _import_structure,
        extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
        module_spec=__spec__,
    )






