





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


SCREAMING_SNAKE_CASE_      :			Dict		  =    logging.get_logger(__name__)

if is_vision_available():
		import PIL.Image
		from PIL import Image
		from PIL.Image import Image as ImageType
else:
		SCREAMING_SNAKE_CASE_      :			List[str]		  =    object

if is_torch_available():
		import torch

if is_soundfile_availble():
		import soundfile as sf

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    Union[str, Any]				,			__lowerCamelCase       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			__lowercase					=    value




	def __str__(	self       :    Optional[int]      )					->   List[str]:

			'''simple docstring'''




			return self.to_string()




	def       UpperCAmelCase (	self       :    Optional[int]      )					->   int:

			'''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       UpperCAmelCase (	self       :    Optional[Any]      )					->   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 		snake_case_      (       __UpperCAmelCase    ,    __UpperCAmelCase	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Optional[int]:

			'''simple docstring'''




			return self._value






	def       UpperCAmelCase (	self       :    str      )					->   Dict:

			'''simple docstring'''




			return self._value





class 		snake_case_      (       __UpperCAmelCase    ,    __UpperCAmelCase	):







	'''simple docstring'''




	def __init__(	self       :    Optional[int]				,			__lowerCamelCase       :    Union[str, Any]      )					->   Optional[Any]:

			'''simple docstring'''




			super().__init__(lowerCamelCase__      )

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

			__lowercase					=    None
			__lowercase					=    None
			__lowercase					=    None

			if isinstance(lowerCamelCase__				,			lowerCamelCase__      ):
					__lowercase					=    value
			elif isinstance(lowerCamelCase__				,			(str, pathlib.Path)      ):
					__lowercase					=    value
			elif isinstance(lowerCamelCase__				,			torch.Tensor      ):
					__lowercase					=    value
			else:
					raise ValueError(F"Unsupported type for {self.__class__.__name__}: {type(lowerCamelCase__      )}"      )




	def       UpperCAmelCase (	self       :    Optional[Any]				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    List[str]=None      )					->   List[Any]:

			'''simple docstring'''




			from IPython.display import Image, display

			display(Image(self.to_string()      )      )




	def       UpperCAmelCase (	self       :    Dict      )					->   Union[str, Any]:

			'''simple docstring'''




			if self._raw is not None:
					return self._raw

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






	def       UpperCAmelCase (	self       :    List[str]      )					->   Optional[Any]:

			'''simple docstring'''




			if self._path is not None:
					return self._path

			if self._raw is not None:
					__lowercase					=    tempfile.mkdtemp()
					__lowercase					=    os.path.join(lowerCamelCase__				,			str(uuid.uuida()      ) + '.png'      )
					self._raw.save(self._path      )

					return self._path

			if self._tensor is not None:
					__lowercase					=    self._tensor.cpu().detach().numpy()

					# There is likely simpler than load into image into save
					__lowercase					=    Image.fromarray((array * 255).astype(np.uinta      )      )

					__lowercase					=    tempfile.mkdtemp()
					__lowercase					=    os.path.join(lowerCamelCase__				,			str(uuid.uuida()      ) + '.png'      )

					img.save(self._path      )

					return self._path





class 		snake_case_      (       __UpperCAmelCase	):







	'''simple docstring'''




	def __init__(	self       :    Tuple				,			__lowerCamelCase       :    str				,			__lowerCamelCase       :    Optional[int]=16_000      )					->   Union[str, Any]:

			'''simple docstring'''




			super().__init__(lowerCamelCase__      )

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

			__lowercase					=    None
			__lowercase					=    None

			__lowercase					=    samplerate

			if isinstance(lowerCamelCase__				,			(str, pathlib.Path)      ):
					__lowercase					=    value
			elif isinstance(lowerCamelCase__				,			torch.Tensor      ):
					__lowercase					=    value
			else:
					raise ValueError(F"Unsupported audio type: {type(lowerCamelCase__      )}"      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]=None				,			__lowerCamelCase       :    Tuple=None      )					->   str:

			'''simple docstring'''




			from IPython.display import Audio, display

			display(Audio(self.to_string()				,			rate=self.samplerate      )      )




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			if self._tensor is not None:
					return self._tensor

			if self._path is not None:
					__lowercase	,			__lowercase					=    sf.read(self._path      )
					__lowercase					=    torch.tensor(lowerCamelCase__      )
					return self._tensor






	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




			if self._path is not None:
					return self._path

			if self._tensor is not None:
					__lowercase					=    tempfile.mkdtemp()
					__lowercase					=    os.path.join(lowerCamelCase__				,			str(uuid.uuida()      ) + '.wav'      )
					sf.write(self._path				,			self._tensor				,			samplerate=self.samplerate      )
					return self._path





SCREAMING_SNAKE_CASE_      :			Optional[int]		  =    {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
SCREAMING_SNAKE_CASE_      :			Optional[int]		  =    {str: AgentText}

if is_vision_available():
		SCREAMING_SNAKE_CASE_      :			str		  =    AgentImage



def    SCREAMING_SNAKE_CASE							(			*snake_case	,      **snake_case       )		->						Optional[Any]:
		__lowercase					=    [(arg.to_raw() if isinstance(lowercase__	,      lowercase__       ) else arg) for arg in args]
		__lowercase					=    {k: (v.to_raw() if isinstance(lowercase__	,      lowercase__       ) else v) for k, v in kwargs.items()}
		return args, kwargs



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case=None       )		->						str:
		if isinstance(lowercase__	,      lowercase__       ):
				__lowercase					=    {}
				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:
										__lowercase					=    AGENT_TYPE_MAPPING[output_types[i]](lowercase__       )
								else:
										__lowercase					=    AgentType(lowercase__       )

						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(lowercase__	,      _k       ):
												__lowercase					=    _v(lowercase__       )
								if k not in decoded_outputs:
										__lowercase					=    AgentType[v]

		elif isinstance(lowercase__	,      (list, tuple)       ):
				__lowercase					=    type(lowercase__       )()
				for i, v in enumerate(lowercase__       ):
						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]](lowercase__       )       )
								else:
										decoded_outputs.append(AgentType(lowercase__       )       )
						else:
								# If the class does not have defined output, then we map according to the type
								__lowercase					=    False
								for _k, _v in INSTANCE_TYPE_MAPPING.items():
										if isinstance(lowercase__	,      _k       ):
												decoded_outputs.append(_v(lowercase__       )       )
												__lowercase					=    True

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

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

				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(lowercase__	,      _k       ):
										return _v(lowercase__       )
						return AgentType(lowercase__       )

		return decoded_outputs











SCREAMING_SNAKE_CASE_      :			str		  =      [chr(i) for i in range(32, 126)]
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Any		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			List[str]		  =      list(range(len(alphabets)))
SCREAMING_SNAKE_CASE_      :			Dict		  =      list(reversed(range(len(alphabets))))
SCREAMING_SNAKE_CASE_      :			int		  =      []
SCREAMING_SNAKE_CASE_      :			Union[str, Any]		  =      0



def    SCREAMING_SNAKE_CASE							(			)		->						int:
		global gear_one_pos
		global gear_two_pos
		global gear_three_pos
		__lowercase					=    gear_one[0]
		gear_one.append(snake_case       )
		del gear_one[0]
		gear_one_pos += 1
		if gear_one_pos % int(len(snake_case       )       ) == 0:
				__lowercase					=    gear_two[0]
				gear_two.append(snake_case       )
				del gear_two[0]
				gear_two_pos += 1
				if gear_two_pos % int(len(snake_case       )       ) == 0:
						__lowercase					=    gear_three[0]
						gear_three.append(snake_case       )
						del gear_three[0]
						gear_three_pos += 1



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    alphabets.index(snake_case       )
		__lowercase					=    gear_one[target]
		__lowercase					=    gear_two[target]
		__lowercase					=    gear_three[target]
		__lowercase					=    reflector[target]
		__lowercase					=    gear_three.index(snake_case       )
		__lowercase					=    gear_two.index(snake_case       )
		__lowercase					=    gear_one.index(snake_case       )
		code.append(alphabets[target]       )
		rotator()


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      list(input('''Type your message:\n'''))
		while True:
				try:
						SCREAMING_SNAKE_CASE_      :			List[str]		  =      int(input('''Please set token:(must be only digits)\n'''))
						break
				except Exception as error:
						print(error)
		for _ in range(token):
				rotator()
		for j in decode:
				engine(j)
		print('''\n''' + ''''''.join(code))
		print(
		    F"""\nYour Token is {token} please write it down.\nIf you want to decode """
		    '''this message again you should input same digits as token!'''
		)




