




'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_snake_case : Dict  				=						logging.get_logger(__name__)

_snake_case : int  				=						{
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class      A       (						_a       ):
							lowercase_							     =							'dpt'







							def __init__(    self			:    List[Any]				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    str=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[Any]=30_72				,     lowerCAmelCase_			:    Any="gelu"				,     lowerCAmelCase_			:    Any=0.0				,     lowerCAmelCase_			:    Union[str, Any]=0.0				,     lowerCAmelCase_			:    List[str]=0.0_2				,     lowerCAmelCase_			:    Any=1e-12				,     lowerCAmelCase_			:    List[Any]=3_84				,     lowerCAmelCase_			:    Optional[Any]=16				,     lowerCAmelCase_			:    Optional[int]=3				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    Dict=[2, 5, 8, 11]				,     lowerCAmelCase_			:    Optional[Any]="project"				,     lowerCAmelCase_			:    int=[4, 2, 1, 0.5]				,     lowerCAmelCase_			:    Optional[Any]=[96, 1_92, 3_84, 7_68]				,     lowerCAmelCase_			:    List[Any]=2_56				,     lowerCAmelCase_			:    Optional[int]=-1				,     lowerCAmelCase_			:    int=False				,     lowerCAmelCase_			:    Optional[int]=True				,     lowerCAmelCase_			:    Any=0.4				,     lowerCAmelCase_			:    List[str]=2_55				,     lowerCAmelCase_			:    Any=0.1				,     lowerCAmelCase_			:    Tuple=[1, 10_24, 24, 24]				,     lowerCAmelCase_			:    Optional[int]=[0, 1]				,     lowerCAmelCase_			:    int=None				,     **lowerCAmelCase_			:    Dict				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )

														_a      	=							hidden_size
														_a      	=							is_hybrid

														if self.is_hybrid:
																					if backbone_config is None:
																												logger.info('''Initializing the config with a `BiT` backbone.'''   )
																												_a      	=							{
																												    '''global_padding''': '''same''',
																												    '''layer_type''': '''bottleneck''',
																												    '''depths''': [3, 4, 9],
																												    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
																												    '''embedding_dynamic_padding''': True,
																												}
																												_a      	=							BitConfig(**lowerCAmelCase_   )
																					elif isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																												logger.info('''Initializing the config with a `BiT` backbone.'''   )
																												_a      	=							BitConfig(**lowerCAmelCase_   )
																					elif isinstance(lowerCAmelCase_				,     lowerCAmelCase_   ):
																												_a      	=							backbone_config
																					else:
																												raise ValueError(
																												    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'   )

																					_a      	=							backbone_featmap_shape
																					_a      	=							neck_ignore_stages

																					if readout_type != "project":
																												raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''   )
														else:
																					_a      	=							None
																					_a      	=							None
																					_a      	=							[]

														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							intermediate_size
														_a      	=							hidden_act
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							image_size
														_a      	=							patch_size
														_a      	=							num_channels
														_a      	=							qkv_bias
														_a      	=							backbone_out_indices
														if readout_type not in ["ignore", "add", "project"]:
																					raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''   )
														_a      	=							readout_type
														_a      	=							reassemble_factors
														_a      	=							neck_hidden_sizes
														_a      	=							fusion_hidden_size
														_a      	=							head_in_index
														_a      	=							use_batch_norm_in_fusion_residual
														# auxiliary head attributes (semantic segmentation)
														_a      	=							use_auxiliary_head
														_a      	=							auxiliary_loss_weight
														_a      	=							semantic_loss_ignore_index
														_a      	=							semantic_classifier_dropout







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							copy.deepcopy(self.__dict__   )

														if output["backbone_config"] is not None:
																					_a      	=							self.backbone_config.to_dict()

														_a      	=							self.__class__.model_type
														return output





from manim import *




class       lowercase_					(lowercase__   ):





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =Rectangle(height=0.5       ,  width=0.5)
											a__      =Rectangle(height=0.46       ,  width=0.46).set_stroke(width=0)

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

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

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

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

											a__      =[]
											for i, rect in enumerate(lowercase_):
																		rect.set_stroke(lowercase_)
																		# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																		# target.move_to(rect)
																		# self.add(target)

																		a__      =Rectangle(height=0.46 / 4       ,  width=0.46 / 3).set_stroke(width=0.0).set_fill(lowercase_       ,  opacity=0.7)

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

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

											a__      =Text('Loaded Checkpoint'       ,  font_size=24)
											a__      =Group(lowercase_       ,  lowercase_).arrange(lowercase_       ,  aligned_edge=lowercase_       ,  buff=0.4)
											checkpoint.move_to([3, 0.5, 0])

											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(lowercase_       ,  lowercase_)

											a__      =MarkupText(
											    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""       ,  font_size=18       ,  )

											blue_text.next_to(lowercase_       ,  DOWN * 2.4       ,  aligned_edge=key_text.get_left())

											a__      =MarkupText(
											    F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""       ,  font_size=24       ,  )
											step_a.move_to([2, 2, 0])
											self.play(Write(lowercase_)       ,  Write(lowercase_))

											self.play(Write(lowercase_       ,  run_time=1)       ,  Create(lowercase_       ,  run_time=1))

											a__      =[]
											a__      =[]
											for i, rect in enumerate(lowercase_):
																		a__      =fill.copy().set_fill(lowercase_       ,  opacity=0.7)
																		target.move_to(lowercase_)
																		first_animations.append(GrowFromCenter(lowercase_       ,  run_time=1))

																		a__      =target.copy()
																		cpu_target.generate_target()
																		if i < 5:
																									cpu_target.target.move_to(cpu_left_col_base[i + 1])
																		else:
																									cpu_target.target.move_to(cpu_right_col_base[i - 5])
																		second_animations.append(MoveToTarget(lowercase_       ,  run_time=1.5))

											self.play(*lowercase_)
											self.play(*lowercase_)
											self.wait()

