





'''simple docstring'''

import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowerCAmelCase__      :   Optional[Any]      	=			logging.get_logger(__name__)

if is_vision_available():
			import PIL.Image
			from PIL import Image
			from PIL.Image import Image as ImageType
else:
			lowerCAmelCase__      :   Optional[Any]      	=			object

if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf


class      SCREAMING_SNAKE_CASE__      :







					"""simple docstring"""






					def __init__(   self       :      str		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     value
					def __str__(   self       :      int		):







							"""simple docstring"""
							return self.to_string()
					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							logger.error(
							    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"		)
							return self._value






					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							logger.error(
							    "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"		)
							return str(self._value		)






class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self._value






					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							return self._value






class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      List[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							super().__init__(UpperCAmelCase_		)

							if not is_vision_available():
									raise ImportError("PIL must be installed in order to handle images."		)

							__UpperCAmelCase	:							str     =     None
							__UpperCAmelCase	:							Any     =     None
							__UpperCAmelCase	:							Any     =     None

							if isinstance(UpperCAmelCase_		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							List[str]     =     value
							elif isinstance(UpperCAmelCase_		,	(str, pathlib.Path)		):
									__UpperCAmelCase	:							str     =     value
							elif isinstance(UpperCAmelCase_		,	torch.Tensor		):
									__UpperCAmelCase	:							Tuple     =     value
							else:
									raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(UpperCAmelCase_		)}"		)
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Tuple=None		):







							"""simple docstring"""
							from IPython.display import Image, display

							display(Image(self.to_string()		)		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							if self._raw is not None:
									return self._raw

							if self._path is not None:
									__UpperCAmelCase	:							int     =     Image.open(self._path		)
									return self._raw






					def 		lowerCamelCase_   (   self       :      Optional[Any]		):







							"""simple docstring"""
							if self._path is not None:
									return self._path

							if self._raw is not None:
									__UpperCAmelCase	:							List[str]     =     tempfile.mkdtemp()
									__UpperCAmelCase	:							int     =     os.path.join(UpperCAmelCase_		,	str(uuid.uuida()		) + ".png"		)
									self._raw.save(self._path		)

									return self._path

							if self._tensor is not None:
									__UpperCAmelCase	:							List[Any]     =     self._tensor.cpu().detach().numpy()

									# There is likely simpler than load into image into save
									__UpperCAmelCase	:							List[Any]     =     Image.fromarray((array * 255).astype(np.uinta		)		)

									__UpperCAmelCase	:							Tuple     =     tempfile.mkdtemp()
									__UpperCAmelCase	:							List[str]     =     os.path.join(UpperCAmelCase_		,	str(uuid.uuida()		) + ".png"		)

									img.save(self._path		)

									return self._path






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Optional[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[Any]=16_000		):







							"""simple docstring"""
							super().__init__(UpperCAmelCase_		)

							if not is_soundfile_availble():
									raise ImportError("soundfile must be installed in order to handle audio."		)

							__UpperCAmelCase	:							List[str]     =     None
							__UpperCAmelCase	:							List[Any]     =     None

							__UpperCAmelCase	:							Dict     =     samplerate

							if isinstance(UpperCAmelCase_		,	(str, pathlib.Path)		):
									__UpperCAmelCase	:							Optional[int]     =     value
							elif isinstance(UpperCAmelCase_		,	torch.Tensor		):
									__UpperCAmelCase	:							Optional[int]     =     value
							else:
									raise ValueError(f"Unsupported audio type: {type(UpperCAmelCase_		)}"		)
					def 		lowerCamelCase_   (   self       :      str		,	UpperCAmelCase_       :      List[str]=None		,	UpperCAmelCase_       :      Any=None		):







							"""simple docstring"""
							from IPython.display import Audio, display

							display(Audio(self.to_string()		,	rate=self.samplerate		)		)
					def 		lowerCamelCase_   (   self       :      Any		):







							"""simple docstring"""
							if self._tensor is not None:
									return self._tensor

							if self._path is not None:
									__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     sf.read(self._path		)
									__UpperCAmelCase	:							Any     =     torch.tensor(UpperCAmelCase_		)
									return self._tensor






					def 		lowerCamelCase_   (   self       :      List[Any]		):







							"""simple docstring"""
							if self._path is not None:
									return self._path

							if self._tensor is not None:
									__UpperCAmelCase	:							Union[str, Any]     =     tempfile.mkdtemp()
									__UpperCAmelCase	:							Dict     =     os.path.join(UpperCAmelCase_		,	str(uuid.uuida()		) + ".wav"		)
									sf.write(self._path		,	self._tensor		,	samplerate=self.samplerate		)
									return self._path






lowerCAmelCase__      :   int      	=			{"text": AgentText, "image": AgentImage, "audio": AgentAudio}
lowerCAmelCase__      :   Optional[Any]      	=			{str: AgentText}

if is_vision_available():
			lowerCAmelCase__      :   Optional[int]      	=			AgentImage


def 						__UpperCamelCase						(			*_UpperCAmelCase,       **_UpperCAmelCase  ):
		__UpperCAmelCase	:							Optional[Any]     =     [(arg.to_raw() if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ) else arg) for arg in args]
		__UpperCAmelCase	:							Optional[Any]     =     {k: (v.to_raw() if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ) else v) for k, v in kwargs.items()}
		return args, kwargs






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=None  ):
		if isinstance(_UpperCAmelCase,       _UpperCAmelCase  ):
				__UpperCAmelCase	:							str     =     {}
				for i, (k, v) in enumerate(outputs.items()  ):
						if output_types is not None:
								# If the class has defined outputs, we can map directly according to the class definition
								if output_types[i] in AGENT_TYPE_MAPPING:
										__UpperCAmelCase	:							List[Any]     =     AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase  )
								else:
										__UpperCAmelCase	:							Union[str, Any]     =     AgentType(_UpperCAmelCase  )

						else:
								# If the class does not have defined output, then we map according to the type
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(_UpperCAmelCase,       _k  ):
												__UpperCAmelCase	:							List[Any]     =     _v(_UpperCAmelCase  )
								if k not in decoded_outputs:
										__UpperCAmelCase	:							str     =     AgentType[v]

		elif isinstance(_UpperCAmelCase,       (list, tuple)  ):
				__UpperCAmelCase	:							Optional[Any]     =     type(_UpperCAmelCase  )()
				for i, v in enumerate(_UpperCAmelCase  ):
						if output_types is not None:
								# If the class has defined outputs, we can map directly according to the class definition
								if output_types[i] in AGENT_TYPE_MAPPING:
										decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase  )  )
								else:
										decoded_outputs.append(AgentType(_UpperCAmelCase  )  )
						else:
								# If the class does not have defined output, then we map according to the type
								__UpperCAmelCase	:							Optional[int]     =     False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(_UpperCAmelCase,       _k  ):
												decoded_outputs.append(_v(_UpperCAmelCase  )  )
												__UpperCAmelCase	:							Dict     =     True

								if not found:
										decoded_outputs.append(AgentType(_UpperCAmelCase  )  )

		else:
				if output_types[0] in AGENT_TYPE_MAPPING:
						# If the class has defined outputs, we can map directly according to the class definition
						__UpperCAmelCase	:							Dict     =     AGENT_TYPE_MAPPING[output_types[0]](_UpperCAmelCase  )

				else:
						# If the class does not have defined output, then we map according to the type
						for _k, _v in INSTANCE_TYPE_MAPPING.items():
								if isinstance(_UpperCAmelCase,       _k  ):
										return _v(_UpperCAmelCase  )
						return AgentType(_UpperCAmelCase  )

		return decoded_outputs











'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

		for i in range(1,       len_destination_seq + 1  ):
				__UpperCAmelCase	:							Optional[int]     =     i * insert_cost
				__UpperCAmelCase	:							List[Any]     =     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]:
								__UpperCAmelCase	:							List[str]     =     costs[i - 1][j - 1] + copy_cost
								__UpperCAmelCase	:							Tuple     =     F"C{source_seq[i - 1]:c}"
						else:
								__UpperCAmelCase	:							Optional[int]     =     costs[i - 1][j - 1] + replace_cost
								__UpperCAmelCase	:							Dict     =     F"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]  )

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

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

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			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":
												lowerCAmelCase__      :   Optional[int]      	=			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))




