






def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     str     ,			__lowercase				:     str     ,			__lowercase				:     int     ,			__lowercase				:     int     ,			__lowercase				:     int     ,			__lowercase				:     int     ,			)  -> tuple[list[list[int]], list[list[str]]]:





    """simple docstring"""

    __A             =		list(__lowercase      )
    __A             =		list(__lowercase      )
    __A             =		len(__lowercase      )
    __A             =		len(__lowercase      )

    __A             =		[
        [0 for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
    ]
    __A             =		[
        ["""0""" for _ in range(len_destination_seq + 1      )] for _ in range(len_source_seq + 1      )
    ]

    for i in range(1     ,			len_source_seq + 1      ):
        __A             =		i * delete_cost
        __A             =		f"D{source_seq[i - 1]:c}"

    for i in range(1     ,			len_destination_seq + 1      ):
        __A             =		i * insert_cost
        __A             =		f"I{destination_seq[i - 1]:c}"

    for i in range(1     ,			len_source_seq + 1      ):
        for j in range(1     ,			len_destination_seq + 1      ):
            if source_seq[i - 1] == destination_seq[j - 1]:
                __A             =		costs[i - 1][j - 1] + copy_cost
                __A             =		f"C{source_seq[i - 1]:c}"
            else:
                __A             =		costs[i - 1][j - 1] + replace_cost
                __A             =		f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]      )

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                __A             =		costs[i - 1][j] + delete_cost
                __A             =		f"D{source_seq[i - 1]:c}"

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                __A             =		costs[i][j - 1] + insert_cost
                __A             =		f"I{destination_seq[j - 1]:c}"

    return costs, ops



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[list[str]]     ,			__lowercase				:     int     ,			__lowercase				:     int      )  -> list[str]:





    """simple docstring"""

    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            __A             =		assemble_transformation(__lowercase     ,			i - 1     ,			j - 1      )
            seq.append(ops[i][j]      )
            return seq
        elif ops[i][j][0] == "D":
            __A             =		assemble_transformation(__lowercase     ,			i - 1     ,			__lowercase      )
            seq.append(ops[i][j]      )
            return seq
        else:
            __A             =		assemble_transformation(__lowercase     ,			__lowercase     ,			j - 1      )
            seq.append(ops[i][j]      )
            return seq


if __name__ == "__main__":
  __a					,__a :    List[Any]								=				compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

  __a :    Optional[Any]								=				len(operations)
  __a :    List[Any]								=				len(operations[0])
  __a :    Dict								=				assemble_transformation(operations, m - 1, n - 1)

  __a :    List[str]								=				list("Python")
  __a :    Optional[Any]								=				0
  __a :    str								=				0

  with open("min_cost.txt", "w") as file:
    for op in sequence:
      print("".join(string))

      if op[0] == "C":
        file.write("%-16s" % "Copy %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost -= 1
      elif op[0] == "R":
        __a :    List[str]								=				op[2]

        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
        file.write("\t\t" + "".join(string))
        file.write("\r\n")

        cost += 1
      elif op[0] == "D":
        string.pop(i)

        file.write("%-16s" % "Delete %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost += 2
      else:
        string.insert(i, op[1])

        file.write("%-16s" % "Insert %c" % op[1])
        file.write("\t\t\t" + "".join(string))
        file.write("\r\n")

        cost += 2

      i += 1

    print("".join(string))
    print("Cost: ", cost)

    file.write("\r\nMinimum cost: " + str(cost))












import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		eos_token_id=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		max_new_tokens=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		text_encoding_offset
         __A             =		text_pad_token
         __A             =		eos_token_id
         __A             =		semantic_infer_token
         __A             =		semantic_vocab_size
         __A             =		max_input_semantic_length
         __A             =		semantic_rate_hz


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		coarse_semantic_pad_token
         __A             =		coarse_rate_hz
         __A             =		n_coarse_codebooks
         __A             =		coarse_infer_token
         __A             =		max_coarse_input_length
         __A             =		max_coarse_history
         __A             =		sliding_window_len


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



         if semantic_config is None:
             __A             =		{}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

         if coarse_acoustics_config is None:
             __A             =		{}
             logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""			)

         if fine_acoustics_config is None:
             __A             =		{}
             logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""			)

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """simple docstring"""



         return cls(
             semantic_config=semantic_config.to_dict()	,		coarse_acoustics_config=coarse_acoustics_config.to_dict()	,		fine_acoustics_config=fine_acoustics_config.to_dict()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)

         __A             =		self.semantic_config.to_dict()
         __A             =		self.coarse_acoustics_config.to_dict()
         __A             =		self.fine_acoustics_config.to_dict()

         __A             =		self.__class__.model_type
         return output




