"""simple docstring"""




import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     UpperCamelCase__(			__A ):
      lowerCAmelCase__  :						List[Any]													=		'MCTCTFeatureExtractor'
      lowerCAmelCase__  :						List[str]													=		'AutoTokenizer'





      def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				List[Any]:
             super().__init__(__UpperCAmelCase		,__UpperCAmelCase     )
             A__	     =							self.feature_extractor
             A__	     =							False





      def __call__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[int]:
             # For backward compatibility
             if self._in_target_context_manager:
                    return self.current_processor(*__UpperCAmelCase		,**__UpperCAmelCase     )

             if "raw_speech" in kwargs:
                    warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
                    A__	     =							kwargs.pop('raw_speech'     )
             else:
                    A__	     =							kwargs.pop('audio'		,__UpperCAmelCase     )
             A__	     =							kwargs.pop('sampling_rate'		,__UpperCAmelCase     )
             A__	     =							kwargs.pop('text'		,__UpperCAmelCase     )
             if len(__UpperCAmelCase     ) > 0:
                    A__	     =							args[0]
                    A__	     =							args[1:]

             if audio is None and text is None:
                    raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

             if audio is not None:
                    A__	     =							self.feature_extractor(__UpperCAmelCase		,*__UpperCAmelCase		,sampling_rate=__UpperCAmelCase		,**__UpperCAmelCase     )
             if text is not None:
                    A__	     =							self.tokenizer(__UpperCAmelCase		,**__UpperCAmelCase     )

             if text is None:
                    return inputs
             elif audio is None:
                    return encodings
             else:
                    A__	     =							encodings['input_ids']
                    return inputs





      def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Any:
             return self.tokenizer.batch_decode(*__UpperCAmelCase		,**__UpperCAmelCase     )





      def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Any:
             # For backward compatibility
             if self._in_target_context_manager:
                    return self.current_processor.pad(*__UpperCAmelCase		,**__UpperCAmelCase     )

             A__	     =							kwargs.pop('input_features'		,__UpperCAmelCase     )
             A__	     =							kwargs.pop('labels'		,__UpperCAmelCase     )
             if len(__UpperCAmelCase     ) > 0:
                    A__	     =							args[0]
                    A__	     =							args[1:]

             if input_features is not None:
                    A__	     =							self.feature_extractor.pad(__UpperCAmelCase		,*__UpperCAmelCase		,**__UpperCAmelCase     )
             if labels is not None:
                    A__	     =							self.tokenizer.pad(__UpperCAmelCase		,**__UpperCAmelCase     )

             if labels is None:
                    return input_features
             elif input_features is None:
                    return labels
             else:
                    A__	     =							labels['input_ids']
                    return input_features





      def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
             return self.tokenizer.decode(*__UpperCAmelCase		,**__UpperCAmelCase     )





      @contextmanager
      def 				snake_case__	(       self     )					->				Any:
             warnings.warn(
                 '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                 'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                 'your audio inputs, or in a separate call.'     )
             A__	     =							True
             A__	     =							self.tokenizer
             yield
             A__	     =							self.feature_extractor
             A__	     =							False






"""simple docstring"""




from manim import *
class     UpperCamelCase__(			__A ):





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							Rectangle(height=0.5		,width=0.5     )
													A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0     )

													A__	     =							Rectangle(height=0.2_5		,width=0.2_5     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('CPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													cpu.move_to([-2.5, -0.5, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(4     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('GPU'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													gpu.move_to([-1, -1, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )

													A__	     =							Text('Model'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													model.move_to([3, -1.0, 0]     )
													self.add(__UpperCAmelCase     )

													A__	     =							[]
													A__	     =							[]

													for i, rect in enumerate(__UpperCAmelCase     ):
																				A__	     =							fill.copy().set_fill(__UpperCAmelCase		,opacity=0.8     )
																				target.move_to(__UpperCAmelCase     )
																				model_arr.append(__UpperCAmelCase     )

																				A__	     =							Rectangle(height=0.4_6		,width=0.4_6     ).set_stroke(width=0.0     ).set_fill(__UpperCAmelCase		,opacity=0.8     )
																				cpu_target.move_to(cpu_left_col_base[i]     )
																				model_cpu_arr.append(__UpperCAmelCase     )

													self.add(*__UpperCAmelCase		,*__UpperCAmelCase     )

													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							[meta_mem.copy() for i in range(6     )]
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(*__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							VGroup(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0     )
													A__	     =							Text('Disk'		,font_size=24     )
													A__	     =							Group(__UpperCAmelCase		,__UpperCAmelCase     ).arrange(__UpperCAmelCase		,buff=0.5		,aligned_edge=__UpperCAmelCase     )
													disk.move_to([-4, -1.2_5, 0]     )
													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

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

													A__	     =							MarkupText(
													    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''		,font_size=18		,)

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

													self.add(__UpperCAmelCase		,__UpperCAmelCase     )

													A__	     =							MarkupText(
													    f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,font_size=18		,)

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

													A__	     =							MarkupText(
													    f'''Now watch as an input is passed through the model\nand how the memory is utilized and handled.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase     )     )

													A__	     =							Square(0.3     )
													input.set_fill(__UpperCAmelCase		,opacity=1.0     )
													input.set_stroke(width=0.0     )
													input.next_to(model_base[0]		,__UpperCAmelCase		,buff=0.5     )

													self.play(Write(__UpperCAmelCase     )     )

													input.generate_target()
													input.target.next_to(model_arr[0]		,direction=__UpperCAmelCase		,buff=0.0_2     )
													self.play(MoveToTarget(__UpperCAmelCase     )     )

													self.play(FadeOut(__UpperCAmelCase     )     )

													A__	     =							Arrow(start=__UpperCAmelCase		,end=__UpperCAmelCase		,color=__UpperCAmelCase		,buff=0.5     )
													a.next_to(model_arr[0].get_left()		,__UpperCAmelCase		,buff=0.2     )

													model_cpu_arr[0].generate_target()
													model_cpu_arr[0].target.move_to(gpu_rect[0]     )

													A__	     =							MarkupText(
													    f'''As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back.'''		,font_size=24		,)
													step_a.move_to([2, 2, 0]     )

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

													A__	     =							{'run_time': 1, 'fade_in': True, 'fade_out': True, 'buff': 0.0_2}

													self.play(
													    Write(__UpperCAmelCase     )		,Circumscribe(model_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_cpu_arr[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
													self.play(MoveToTarget(model_cpu_arr[0]     )     )

													A__	     =							a.copy()
													for i in range(6     ):
																				a_c.next_to(model_arr[i].get_right() + 0.0_2		,__UpperCAmelCase		,buff=0.2     )

																				input.generate_target()
																				input.target.move_to(model_arr[i].get_right() + 0.0_2     )

																				A__	     =							AnimationGroup(
																				    FadeOut(__UpperCAmelCase		,run_time=0.5     )		,MoveToTarget(__UpperCAmelCase		,run_time=0.5     )		,FadeIn(__UpperCAmelCase		,run_time=0.5     )		,lag_ratio=0.2     )

																				self.play(__UpperCAmelCase     )

																				model_cpu_arr[i].generate_target()
																				model_cpu_arr[i].target.move_to(cpu_left_col_base[i]     )

																				if i < 5:
																											model_cpu_arr[i + 1].generate_target()
																											model_cpu_arr[i + 1].target.move_to(gpu_rect[0]     )
																											if i >= 1:
																																		A__	     =							0.7

																											self.play(
																											    Circumscribe(model_arr[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i]		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(model_arr[i + 1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)
																											if i < 1:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]     )		,MoveToTarget(model_cpu_arr[i + 1]     )		,)
																											else:
																																		self.play(
																																		    MoveToTarget(model_cpu_arr[i]		,run_time=0.7     )		,MoveToTarget(model_cpu_arr[i + 1]		,run_time=0.7     )		,)
																				else:
																											model_cpu_arr[i].generate_target()
																											model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]     )
																											input.generate_target()
																											input.target.next_to(model_arr[-1].get_right()		,RIGHT + 0.0_2		,buff=0.2     )

																											self.play(
																											    Circumscribe(model_arr[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(cpu_left_col_base[-1]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,Circumscribe(gpu_rect[0]		,color=__UpperCAmelCase		,**__UpperCAmelCase     )		,)

																											self.play(MoveToTarget(model_cpu_arr[i]     )     )

																				A__	     =							a_c
																				A__	     =							a_c.copy()

													input.generate_target()
													input.target.next_to(model_base[-1]		,RIGHT + 0.0_2		,buff=0.5     )
													self.play(
													    FadeOut(__UpperCAmelCase     )		,FadeOut(__UpperCAmelCase		,run_time=0.5     )		,)

													A__	     =							MarkupText(f'''Inference on a model too large for GPU memory\nis successfully completed.'''		,font_size=24     )
													step_a.move_to([2, 2, 0]     )

													self.play(Write(__UpperCAmelCase		,run_time=3     )		,MoveToTarget(__UpperCAmelCase     )     )

													self.wait()





