



import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


SCREAMING_SNAKE_CASE_:List[Any]             =						{
    """facebook/maskformer-swin-base-ade""": (
        """https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"""
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

SCREAMING_SNAKE_CASE_:int             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[Any]					=      "maskformer"
    __lowerCamelCase			: Any					=      {"hidden_size": "mask_feature_size"}
    __lowerCamelCase			: Optional[Any]					=      ["resnet", "swin"]
    __lowerCamelCase			: str					=      ["detr"]






    def __init__(       self,		lowerCamelCase__ = 256,		lowerCamelCase__ = 256,		lowerCamelCase__ = 0.1,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = 0.02,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = 20.0,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        if backbone_config is None:
            # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
            A :       int          =   SwinConfig(
                image_size=384,		in_channels=3,		patch_size=4,		embed_dim=128,		depths=[2, 2, 18, 2],		num_heads=[4, 8, 16, 32],		window_size=12,		drop_path_rate=0.3,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""],		)

        if isinstance(lowerCamelCase__,		lowerCamelCase__		):
            A :       Any          =   backbone_config.pop("""model_type"""		)
            A :       Any          =   CONFIG_MAPPING[backbone_model_type]
            A :       List[str]          =   config_class.from_dict(lowerCamelCase__		)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
            logger.warning_once(
                f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                f'''Supported model types: {",".join(self.backbones_supported		)}'''		)

        if decoder_config is None:
            # fall back to https://huggingface.co/facebook/detr-resnet-50
            A :       Optional[int]          =   DetrConfig()
        else:
            # verify that the decoder is supported
            A :       str          =   (
                decoder_config.pop("""model_type"""		) if isinstance(lowerCamelCase__,		lowerCamelCase__		) else decoder_config.model_type
            )
            if decoder_type not in self.decoders_supported:
                raise ValueError(
                    f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                    f''' {",".join(self.decoders_supported		)}'''		)
            if isinstance(lowerCamelCase__,		lowerCamelCase__		):
                A :       Any          =   CONFIG_MAPPING[decoder_type]
                A :       Optional[int]          =   config_class.from_dict(lowerCamelCase__		)

        A :       List[str]          =   backbone_config
        A :       Optional[int]          =   decoder_config
        # main feature dimension for the model
        A :       Optional[Any]          =   fpn_feature_size
        A :       List[str]          =   mask_feature_size
        # initializer
        A :       Optional[Any]          =   init_std
        A :       Optional[int]          =   init_xavier_std
        # Hungarian matcher && loss
        A :       Any          =   cross_entropy_weight
        A :       Dict          =   dice_weight
        A :       Tuple          =   mask_weight
        A :       str          =   use_auxiliary_loss
        A :       Union[str, Any]          =   no_object_weight
        A :       Any          =   output_auxiliary_logits

        A :       List[str]          =   self.decoder_config.encoder_attention_heads
        A :       Optional[int]          =   self.decoder_config.num_hidden_layers
        super().__init__(**lowerCamelCase__		)






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		):
        return cls(
            backbone_config=lowerCamelCase__,		decoder_config=lowerCamelCase__,		**lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   copy.deepcopy(self.__dict__		)
        A :       Union[str, Any]          =   self.backbone_config.to_dict()
        A :       Union[str, Any]          =   self.decoder_config.to_dict()
        A :       Tuple          =   self.__class__.model_type
        return output







from manim import *






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   Rectangle(height=0.5,		width=0.5		)
        A :       Optional[int]          =   Rectangle(height=0.25,		width=0.25		)
        A :       Optional[Any]          =   Rectangle(height=0.46,		width=0.46		).set_stroke(width=0		)

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

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

        A :       Optional[int]          =   [mem.copy() for i in range(6		)]
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       Optional[int]          =   Text("""Model""",		font_size=24		)
        A :       List[Any]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        model.move_to([3, -1.0, 0]		)
        self.add(lowerCamelCase__		)

        A :       Tuple          =   []
        A :       Tuple          =   []
        A :       Any          =   []

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

            A :       Any          =   Rectangle(height=0.46 / 4,		width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase__,		opacity=0.7		)

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

        self.add(*lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		)

        A :       int          =   [mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)

        A :       str          =   Text("""Loaded Checkpoint""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        checkpoint.move_to([3, 0.5, 0]		)

        self.add(lowerCamelCase__		)

        A :       Optional[int]          =   []
        A :       List[Any]          =   []

        for i, rect in enumerate(lowerCamelCase__		):
            A :       int          =   fill.copy().set_fill(lowerCamelCase__,		opacity=0.7		)
            target.move_to(lowerCamelCase__		)
            ckpt_arr.append(lowerCamelCase__		)

            A :       List[Any]          =   target.copy()
            if i < 5:
                cpu_target.move_to(cpu_left_col_base[i + 1]		)
            else:
                cpu_target.move_to(cpu_right_col_base[i - 5]		)
            ckpt_cpu_arr.append(lowerCamelCase__		)
        self.add(*lowerCamelCase__,		*lowerCamelCase__		)

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

        A :       List[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(lowerCamelCase__,		lowerCamelCase__		)

        A :       Union[str, Any]          =   MarkupText(
            f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',		font_size=18,		)

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

        A :       List[str]          =   MarkupText(
            f'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''',		font_size=24,		)
        step_a.move_to([2, 2, 0]		)

        A :       List[str]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   [meta_mem.copy() for i in range(6		)]
        A :       List[Any]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       List[str]          =   VGroup(*lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Dict          =   VGroup(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0		)
        A :       Optional[Any]          =   Text("""Disk""",		font_size=24		)
        A :       List[str]          =   Group(lowerCamelCase__,		lowerCamelCase__		).arrange(lowerCamelCase__,		buff=0.5,		aligned_edge=lowerCamelCase__		)
        disk.move_to([-4.0, -1.25, 0]		)
        self.play(Write(lowerCamelCase__,		run_time=3		),		Write(lowerCamelCase__,		run_time=1		),		Create(lowerCamelCase__,		run_time=1		)		)

        A :       str          =   []
        for i, rect in enumerate(lowerCamelCase__		):
            A :       Optional[Any]          =   rect.copy()
            target.generate_target()
            target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
            animations.append(MoveToTarget(lowerCamelCase__,		run_time=1.5		)		)
        self.play(*lowerCamelCase__		)

        self.play(FadeOut(lowerCamelCase__		)		)

        A :       List[str]          =   MarkupText(f'''Then, the checkpoint is removed from memory\nthrough garbage collection.''',		font_size=24		)
        step_a.move_to([2, 2, 0]		)

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

        self.play(
            FadeOut(lowerCamelCase__,		lowerCamelCase__,		*lowerCamelCase__,		*lowerCamelCase__		),		)

        self.wait()


