



"""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


snake_case_								=			logging.get_logger(__name__)

if is_vision_available():
				import PIL.Image
				from PIL import Image
				from PIL.Image import Image as ImageType
else:
				snake_case_								=			object

if is_torch_available():
				import torch

if is_soundfile_availble():
				import soundfile as sf







class  A_     :



						"""simple docstring"""






						def __init__(   self					:List[Any]  ,    lowercase_					:Optional[Any]   )							->     Any:
												UpperCAmelCase			 =					value






						def __str__(   self					:str   )							->     int:
												return self.to_string()






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Dict:
												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					:Any   )							->     str:
												logger.error(
												    'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'   )
												return str(self._value   )







class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Optional[Any]:
												return self._value






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Union[str, Any]:
												return self._value







class  A_     (	SCREAMING_SNAKE_CASE_      ,  SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Optional[Any]  ,    lowercase_					:Optional[int]   )							->     str:
												super().__init__(lowercase_   )

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

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

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






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Any=None  ,    lowercase_					:Any=None   )							->     Optional[int]:
												from IPython.display import Image, display

												display(Image(self.to_string()   )   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     str:
												if self._raw is not None:
																		return self._raw

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






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     List[str]:
												if self._path is not None:
																		return self._path

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

																		return self._path

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

																		# There is likely simpler than load into image into save
																		UpperCAmelCase			 =					Image.fromarray((array * 2_55).astype(np.uinta   )   )

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

																		img.save(self._path   )

																		return self._path







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Optional[int]  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Dict=1_60_00   )							->     Optional[Any]:
												super().__init__(lowercase_   )

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

												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None

												UpperCAmelCase			 =					samplerate

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






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:Union[str, Any]=None  ,    lowercase_					:str=None   )							->     Optional[Any]:
												from IPython.display import Audio, display

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






						def       UpperCAmelCase__	(   self					:str   )							->     List[Any]:
												if self._tensor is not None:
																		return self._tensor

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






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Any:
												if self._path is not None:
																		return self._path

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


snake_case_								=			{"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
snake_case_								=			{str: AgentText}

if is_vision_available():
				snake_case_								=			AgentImage

def 					_lowerCAmelCase (  *lowercase_						,      **lowercase_		):
						UpperCAmelCase			 =					[(arg.to_raw() if isinstance(lowercase_						,      lowercase_		) else arg) for arg in args]
						UpperCAmelCase			 =					{k: (v.to_raw() if isinstance(lowercase_						,      lowercase_		) else v) for k, v in kwargs.items()}
						return args, kwargs






def 					_lowerCAmelCase (  lowercase_						,      lowercase_=None		):
						if isinstance(lowercase_						,      lowercase_		):
												UpperCAmelCase			 =					{}
												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			 =					AGENT_TYPE_MAPPING[output_types[i]](lowercase_		)
																								else:
																														UpperCAmelCase			 =					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		):
																																				UpperCAmelCase			 =					_v(lowercase_		)
																								if k not in decoded_outputs:
																														UpperCAmelCase			 =					AgentType[v]

						elif isinstance(lowercase_						,      (list, tuple)		):
												UpperCAmelCase			 =					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
																								UpperCAmelCase			 =					False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(lowercase_						,      _k		):
																																				decoded_outputs.append(_v(lowercase_		)		)
																																				UpperCAmelCase			 =					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
																		UpperCAmelCase			 =					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









'''simple docstring'''




import math
from collections.abc import Generator

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)					))
	for i in (n for n in numbers if n > 1):
		for j in range(2       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)					))
	for i in (n for n in numbers if n > 1):
		# only need to check for factors up to sqrt(i)
		lowerCAmelCase       :     List[str]   				=			int(math.sqrt(_A					)					) + 1
		for j in range(2       , _A					):
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     _A     :					int					)     ->   Generator[int, None, None]:




	"""simple docstring"""





	lowerCAmelCase       :     Generator   				=			(i for i in range(1       , (max_n + 1)       , 2					))
	# It's useless to test even numbers as they will not be prime
	if max_n > 2:
		yield 2  # Because 2 will not be tested, it's necessary to yield it now
	for i in (n for n in numbers if n > 1):
		lowerCAmelCase       :     int   				=			int(math.sqrt(_A					)					) + 1
		for j in range(3       , _A       , 2					):
			# As we removed the even numbers, we don't need them now
			if (i % j) == 0:
				break
		else:
			yield i

def       __UpperCamelCase     (     )     ->   Dict:




	"""simple docstring"""





	from timeit import timeit

	lowerCAmelCase       :     Tuple   				=			'from __main__ import slow_primes, primes, fast_primes'
	print(timeit('slow_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)
	print(timeit('fast_primes(1_000_000_000_000)'       , setup=_A       , number=1_00_00_00					)					)


if __name__ == "__main__":
				_lowerCAmelCase	:		Optional[Any]		  =						int(input('Calculate primes up to:\n>> ').strip())
				for ret in primes(number):
								print(ret)
				benchmark()
