



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


a_       :str				  =				logging.get_logger(__name__)

if is_vision_available():
   import PIL.Image
   from PIL import Image
   from PIL.Image import Image as ImageType
else:
   a_       :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  :  Dict,			_snake_case  :  Any )       ->int:
            snake_case__							:     Dict            =     value






     def __str__(	self  :  str )       ->List[Any]:
            return self.to_string()






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






     def        lowercase_       (	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  snake_case__						(	__A    ,					__A					):






     """simple docstring"""


     def        lowercase_       (	self  :  Tuple )       ->Tuple:
            return self._value






     def        lowercase_       (	self  :  str )       ->Any:
            return self._value




class  snake_case__						(	__A    ,					__A					):






     """simple docstring"""


     def __init__(	self  :  Union[str, Any],			_snake_case  :  List[Any] )       ->str:
            super().__init__(_snake_case )

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

            snake_case__							:     Dict            =     None
            snake_case__							:     Any            =     None
            snake_case__							:     List[str]            =     None

            if isinstance(_snake_case,			_snake_case ):
                   snake_case__							:     int            =     value
            elif isinstance(_snake_case,			(str, pathlib.Path) ):
                   snake_case__							:     int            =     value
            elif isinstance(_snake_case,			torch.Tensor ):
                   snake_case__							:     Optional[int]            =     value
            else:
                   raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(_snake_case )}''' )






     def        lowercase_       (	self  :  str,			_snake_case  :  List[str]=None,			_snake_case  :  str=None )       ->Tuple:
            from IPython.display import Image, display

            display(Image(self.to_string() ) )






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

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

            if self._path is not None:
                   snake_case__							:     Optional[int]            =     Image.open(self._path )
                   return self._raw






     def        lowercase_       (	self  :  Any )       ->int:

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

            if self._raw is not None:
                   snake_case__							:     Union[str, Any]            =     tempfile.mkdtemp()
                   snake_case__							:     Any            =     os.path.join(_snake_case,			str(uuid.uuida() ) + '.png' )
                   self._raw.save(self._path )

                   return self._path

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

                   # There is likely simpler than load into image into save
                   snake_case__							:     str            =     Image.fromarray((array * 2_5_5).astype(np.uinta ) )

                   snake_case__							:     List[str]            =     tempfile.mkdtemp()
                   snake_case__							:     List[str]            =     os.path.join(_snake_case,			str(uuid.uuida() ) + '.png' )

                   img.save(self._path )

                   return self._path




class  snake_case__						(	__A					):






     """simple docstring"""


     def __init__(	self  :  int,			_snake_case  :  List[str],			_snake_case  :  str=1_6_0_0_0 )       ->Dict:
            super().__init__(_snake_case )

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

            snake_case__							:     str            =     None
            snake_case__							:     int            =     None

            snake_case__							:     Tuple            =     samplerate

            if isinstance(_snake_case,			(str, pathlib.Path) ):
                   snake_case__							:     Any            =     value
            elif isinstance(_snake_case,			torch.Tensor ):
                   snake_case__							:     Union[str, Any]            =     value
            else:
                   raise ValueError(F'''Unsupported audio type: {type(_snake_case )}''' )






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

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






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

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

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






     def        lowercase_       (	self  :  Optional[int] )       ->Tuple:

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

            if self._tensor is not None:
                   snake_case__							:     Optional[Any]            =     tempfile.mkdtemp()
                   snake_case__							:     Union[str, Any]            =     os.path.join(_snake_case,			str(uuid.uuida() ) + '.wav' )
                   sf.write(self._path,			self._tensor,			samplerate=self.samplerate )
                   return self._path


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

if is_vision_available():
   a_       :Any				  =				AgentImage
def 	lowercase_							(*A			:			Union[str, Any]		, **A			:			Any				):
       snake_case__							:     str            =     [(arg.to_raw() if isinstance(_a		, _a				) else arg) for arg in args]
       snake_case__							:     Union[str, Any]            =     {k: (v.to_raw() if isinstance(_a		, _a				) else v) for k, v in kwargs.items()}
       return args, kwargs





def 	lowercase_							(A			:			int		, A			:			str=None				):
       if isinstance(_a		, _a				):
              snake_case__							:     List[Any]            =     {}
              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:
                                   snake_case__							:     Tuple            =     AGENT_TYPE_MAPPING[output_types[i]](_a				)
                            else:
                                   snake_case__							:     Optional[int]            =     AgentType(_a				)

                     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(_a		, _k				):
                                          snake_case__							:     str            =     _v(_a				)
                            if k not in decoded_outputs:
                                   snake_case__							:     Union[str, Any]            =     AgentType[v]

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

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

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

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

       return decoded_outputs











from manim import *




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[Any] )       ->Dict:
            snake_case__							:     str            =     Rectangle(height=0.5,			width=0.5 )
            snake_case__							:     str            =     Rectangle(height=0.4_6,			width=0.4_6 ).set_stroke(width=0 )
            snake_case__							:     List[Any]            =     Rectangle(height=0.2_5,			width=0.2_5 )

            snake_case__							:     Any            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Optional[Any]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[int]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Any            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[Any]            =     Text('CPU',			font_size=2_4 )
            snake_case__							:     str            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            cpu.move_to([-2.5, -0.5, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(4 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     str            =     Text('GPU',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            gpu.move_to([-1, -1, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Union[str, Any]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )

            snake_case__							:     int            =     Text('Model',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            model.move_to([3, -1.0, 0] )
            self.add(_snake_case )

            snake_case__							:     Any            =     []
            snake_case__							:     Optional[int]            =     []

            for i, rect in enumerate(_snake_case ):
                   rect.set_stroke(_snake_case )

                   snake_case__							:     Union[str, Any]            =     Rectangle(height=0.4_6 / 4,			width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case,			opacity=0.7 )

                   if i == 0:
                          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ),			buff=0.0_2,			direction=_snake_case )
                          cpu_target.set_x(cpu_target.get_x() + 0.1 )
                   elif i == 3:
                          cpu_target.next_to(model_cpu_arr[0],			direction=_snake_case,			buff=0.0 )
                   else:
                          cpu_target.next_to(model_cpu_arr[i - 1],			direction=_snake_case,			buff=0.0 )
                   self.add(_snake_case )
                   model_cpu_arr.append(_snake_case )

            self.add(*_snake_case,			*_snake_case )

            snake_case__							:     Optional[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     List[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     Dict            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Tuple            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     int            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Union[str, Any]            =     Text('Disk',			font_size=2_4 )
            snake_case__							:     int            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            disk.move_to([-4.0, -1.2_5, 0] )
            self.add(_snake_case,			_snake_case )

            snake_case__							:     str            =     []

            for i in range(6 ):
                   snake_case__							:     str            =     fill.copy().set_fill(_snake_case,			opacity=0.8 )
                   target.move_to(disk_left_col_base[i] ).scale(0.5 )
                   cpu_disk_arr.append(_snake_case )

            self.add(*_snake_case )

            snake_case__							:     int            =     Square(side_length=2.2 )
            key.move_to([-5, 2, 0] )

            snake_case__							:     int            =     MarkupText(
                F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',			font_size=1_8,			)

            key_text.move_to([-5, 2.4, 0] )

            self.add(_snake_case,			_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(
                F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',			font_size=1_8,			)

            blue_text.next_to(_snake_case,			DOWN * 2.4,			aligned_edge=key_text.get_left() )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(F'''The offloaded weights are all sent to the CPU.''',			font_size=2_4 )
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            for i in range(6 ):
                   snake_case__							:     int            =     cpu_disk_arr[i]
                   snake_case__							:     Optional[Any]            =     rect.copy().set_fill(_snake_case,			opacity=0.8 ).scale(2.0 )
                   cpa.generate_target()
                   cpa.target.move_to(model_base[i] )

                   if i == 0:
                          rect.set_fill(_snake_case,			opacity=0.8 )
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[0] ).scale(2.0 )

                          self.remove(
                              *_snake_case,			*_snake_case,			)

                   else:
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[i] ).scale(2.0 )
                   self.play(MoveToTarget(_snake_case ),			MoveToTarget(_snake_case ),			model_base[i].animate.set_stroke(_snake_case ) )
            self.play(FadeOut(_snake_case ) )

            snake_case__							:     Optional[Any]            =     MarkupText(
                F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.''',			font_size=2_4,			)
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            snake_case__							:     str            =     []
            snake_case__							:     Optional[Any]            =     []
            for i in range(6 ):
                   snake_case__							:     List[Any]            =     Arrow(start=_snake_case,			end=_snake_case,			color=_snake_case,			buff=0.5 )
                   a.next_to(model_base[i].get_left(),			_snake_case,			buff=0.2 )
                   arrows.append(_snake_case )
                   animations.append(Write(_snake_case ) )
            self.play(*_snake_case )
            self.wait()






