

"""simple docstring"""

import sys



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Dict:
 lowerCAmelCase__   :				List[Any] 			=				len(lowercase__   )
 lowerCAmelCase__   :				str 			=				[[0 for x in range(lowercase__   )] for x in range(lowercase__   )]
 lowerCAmelCase__   :				List[str] 			=				[[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   ):
   lowerCAmelCase__   :				Any 			=				a + chain_length - 1

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



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__					,					lowercase__   )			->						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       SCREAMING_SNAKE_CASE							( )			->						List[str]:
 lowerCAmelCase__   :				Union[str, Any] 			=				[3_0, 3_5, 1_5, 5, 1_0, 2_0, 2_5]
 lowerCAmelCase__   :				Any 			=				len(lowercase__   )
 # Size of matrix created from above array will be
 # 30*35 35*15 15*5 5*10 10*20 20*25
 lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				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__,
    )






