



"""simple docstring"""







from manim import *





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   Rectangle(height=0.5				,width=0.5   )
			_lowercase				: int     =   Rectangle(height=0.46				,width=0.46   ).set_stroke(width=0   )
			_lowercase				: Optional[int]     =   Rectangle(height=0.25				,width=0.25   )

			_lowercase				: str     =   [mem.copy() for i in range(6   )]
			_lowercase				: Dict     =   [mem.copy() for i in range(6   )]
			_lowercase				: Optional[int]     =   VGroup(*UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: List[str]     =   VGroup(*UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: str     =   VGroup(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: Any     =   Text("""CPU"""				,font_size=24   )
			_lowercase				: List[Any]     =   Group(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0.5				,aligned_edge=UpperCAmelCase_   )
			cpu.move_to([-2.5, -0.5, 0]   )
			self.add(UpperCAmelCase_   )

			_lowercase				: str     =   [mem.copy() for i in range(4   )]
			_lowercase				: Optional[Any]     =   VGroup(*UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: Tuple     =   Text("""GPU"""				,font_size=24   )
			_lowercase				: str     =   Group(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0.5				,aligned_edge=UpperCAmelCase_   )
			gpu.move_to([-1, -1, 0]   )
			self.add(UpperCAmelCase_   )

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

			_lowercase				: Any     =   Text("""Model"""				,font_size=24   )
			_lowercase				: str     =   Group(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0.5				,aligned_edge=UpperCAmelCase_   )
			model.move_to([3, -1.0, 0]   )
			self.add(UpperCAmelCase_   )

			_lowercase				: List[str]     =   []
			_lowercase				: int     =   []

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

					_lowercase				: str     =   Rectangle(height=0.46 / 4				,width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(UpperCAmelCase_				,opacity=0.7   )

					if i == 0:
							cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )				,buff=0.02				,direction=UpperCAmelCase_   )
							cpu_target.set_x(cpu_target.get_x() + 0.1   )
					elif i == 3:
							cpu_target.next_to(model_cpu_arr[0]				,direction=UpperCAmelCase_				,buff=0.0   )
					else:
							cpu_target.next_to(model_cpu_arr[i - 1]				,direction=UpperCAmelCase_				,buff=0.0   )
					self.add(UpperCAmelCase_   )
					model_cpu_arr.append(UpperCAmelCase_   )

			self.add(*UpperCAmelCase_				,*UpperCAmelCase_   )

			_lowercase				: Dict     =   [meta_mem.copy() for i in range(6   )]
			_lowercase				: List[Any]     =   [meta_mem.copy() for i in range(6   )]
			_lowercase				: Optional[Any]     =   VGroup(*UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: int     =   VGroup(*UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: Any     =   VGroup(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0   )
			_lowercase				: List[Any]     =   Text("""Disk"""				,font_size=24   )
			_lowercase				: Optional[Any]     =   Group(UpperCAmelCase_				,UpperCAmelCase_   ).arrange(UpperCAmelCase_				,buff=0.5				,aligned_edge=UpperCAmelCase_   )
			disk.move_to([-4.0, -1.25, 0]   )
			self.add(UpperCAmelCase_				,UpperCAmelCase_   )

			_lowercase				: Any     =   []

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

			self.add(*UpperCAmelCase_   )

			_lowercase				: List[Any]     =   Square(side_length=2.2   )
			key.move_to([-5, 2, 0]   )

			_lowercase				: Any     =   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_   )

			_lowercase				: Tuple     =   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_   )

			_lowercase				: Any     =   MarkupText(f"""The offloaded weights are all sent to the CPU."""				,font_size=24   )
			step_a.move_to([2, 2, 0]   )

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

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

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

							self.remove(
							    *UpperCAmelCase_				,*UpperCAmelCase_				,)

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

			_lowercase				: List[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=24				,)
			step_a.move_to([2, 2, 0]   )

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

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










from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


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


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case			,    **__snake_case					)

							if self.framework == "tf":
												raise ValueError(f"""The {self.__class__} is only available in PyTorch."""					)

							requires_backends(self			,    "vision"					)
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)







		def 							lowercase							(     self			,    **__snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

							return super().__call__(__snake_case			,    **__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= load_image(__snake_case					)
							_snake_case							:       Tuple												= [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	_snake_case							:       List[Any]												= {}
												else:
																	_snake_case							:       int												= {"task_inputs": [subtask]}
												_snake_case							:       Tuple												= self.image_processor(images=[image]			,    return_tensors="pt"			,    **__snake_case					)
												_snake_case							:       Union[str, Any]												= self.tokenizer(
												    inputs["task_inputs"]			,    padding="max_length"			,    max_length=self.model.config.task_seq_len			,    return_tensors=self.framework			,    )["input_ids"]
							else:
												_snake_case							:       str												= self.image_processor(images=[image]			,    return_tensors="pt"					)
							_snake_case							:       Optional[int]												= target_size
							return inputs







		def 							lowercase							(     self			,    __snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

							if fn is not None:
												_snake_case							:       Optional[Any]												= fn(
												    __snake_case			,    threshold=__snake_case			,    mask_threshold=__snake_case			,    overlap_mask_area_threshold=__snake_case			,    target_sizes=model_outputs["target_size"]			,    )[0]

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

							elif subtask in {"semantic", None} and hasattr(self.image_processor			,    "post_process_semantic_segmentation"					):
												_snake_case							:       Tuple												= self.image_processor.post_process_semantic_segmentation(
												    __snake_case			,    target_sizes=model_outputs["target_size"]					)[0]

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= self.model.config.idalabel[label]
																	annotation.append({"score": None, "label": label, "mask": mask}					)
							else:
												raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model					)}"""					)
							return annotation