





import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCamelCase :    int  							= {
    '''sample_size''': 3_2,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [3_2, 6_4],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Dict  							= {
    '''sample_size''': 6_4,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_0_0_0,
    '''block_out_channels''': [1_9_2, 1_9_2 * 2, 1_9_2 * 3, 1_9_2 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Tuple  							= {
    '''sample_size''': 2_5_6,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [2_5_6, 2_5_6, 2_5_6 * 2, 2_5_6 * 2, 2_5_6 * 4, 2_5_6 * 4],
    '''attention_head_dim''': 6_4,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_lowerCamelCase :    Dict  							= {
    '''num_train_timesteps''': 4_0,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_lowerCamelCase :    Tuple  							= {
    '''num_train_timesteps''': 2_0_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_lowerCamelCase :    Any  							= {
    '''num_train_timesteps''': 1_5_1,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Dict:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("boolean value expected"       )




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Optional[int]			,				SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=False       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[str]     =						checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							SCREAMING_SNAKE_CASE__       :      str     =						checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							SCREAMING_SNAKE_CASE__       :      int     =						checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint[f'''{old_prefix}.skip_connection.weight''']
														SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  List[str]=None       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3			,				dim=0       )
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3			,				dim=0       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint[f'''{old_prefix}.norm.weight''']
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						checkpoint[f'''{old_prefix}.norm.bias''']

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						weight_q.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      str     =						bias_q.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						weight_k.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      int     =						bias_k.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						weight_v.squeeze(-1       ).squeeze(-1       )
							SCREAMING_SNAKE_CASE__       :      Dict     =						bias_v.squeeze(-1       ).squeeze(-1       )

							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1       ).squeeze(-1       )
							)
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1       ).squeeze(-1       )

							return new_checkpoint




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    Any:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.load(SCREAMING_SNAKE_CASE__			,				map_location="cpu"       )
							SCREAMING_SNAKE_CASE__       :      str     =						{}

							SCREAMING_SNAKE_CASE__       :      List[str]     =						checkpoint["time_embed.0.weight"]
							SCREAMING_SNAKE_CASE__       :      str     =						checkpoint["time_embed.0.bias"]
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						checkpoint["time_embed.2.weight"]
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						checkpoint["time_embed.2.bias"]

							if unet_config["num_class_embeds"] is not None:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						checkpoint["label_emb.weight"]

							SCREAMING_SNAKE_CASE__       :      Dict     =						checkpoint["input_blocks.0.0.weight"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint["input_blocks.0.0.bias"]

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						unet_config["down_block_types"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						unet_config["layers_per_block"]
							SCREAMING_SNAKE_CASE__       :      Dict     =						unet_config["attention_head_dim"]
							SCREAMING_SNAKE_CASE__       :      Any     =						unet_config["block_out_channels"]
							SCREAMING_SNAKE_CASE__       :      str     =						1
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						channels_list[0]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Any     =						channels_list[i]
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE__       ):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''down_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      int     =						f'''input_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						True if j == 0 and downsample_block_has_skip else False
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				has_skip=SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(SCREAMING_SNAKE_CASE__       ):
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''down_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''input_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      int     =						True if j == 0 and downsample_block_has_skip else False
																												SCREAMING_SNAKE_CASE__       :      Any     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				has_skip=SCREAMING_SNAKE_CASE__       )
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''down_blocks.{i}.attentions.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''input_blocks.{current_layer}.1'''
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						convert_attention(
																												    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

														if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''down_blocks.{i}.downsamplers.0'''
																					SCREAMING_SNAKE_CASE__       :      int     =						f'''input_blocks.{current_layer}.0'''
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
																					current_layer += 1

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						current_channels

							# hardcoded the mid-block for now
							SCREAMING_SNAKE_CASE__       :      str     =						"mid_block.resnets.0"
							SCREAMING_SNAKE_CASE__       :      int     =						"middle_block.0"
							SCREAMING_SNAKE_CASE__       :      Any     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						"mid_block.attentions.0"
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"middle_block.1"
							SCREAMING_SNAKE_CASE__       :      Any     =						convert_attention(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"mid_block.resnets.1"
							SCREAMING_SNAKE_CASE__       :      str     =						"middle_block.2"
							SCREAMING_SNAKE_CASE__       :      List[str]     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      int     =						0
							SCREAMING_SNAKE_CASE__       :      int     =						unet_config["up_block_types"]

							for i, layer_type in enumerate(SCREAMING_SNAKE_CASE__       ):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1       ):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''up_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						f'''output_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				has_skip=SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						f'''up_blocks.{i}.upsamplers.0'''
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						f'''output_blocks.{current_layer-1}.1'''
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1       ):
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''up_blocks.{i}.resnets.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						f'''output_blocks.{current_layer}.0'''
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				has_skip=SCREAMING_SNAKE_CASE__       )
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						f'''up_blocks.{i}.attentions.{j}'''
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						f'''output_blocks.{current_layer}.1'''
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						convert_attention(
																												    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
																												current_layer += 1

																					if i != len(SCREAMING_SNAKE_CASE__       ) - 1:
																												SCREAMING_SNAKE_CASE__       :      int     =						f'''up_blocks.{i}.upsamplers.0'''
																												SCREAMING_SNAKE_CASE__       :      Dict     =						f'''output_blocks.{current_layer-1}.2'''
																												SCREAMING_SNAKE_CASE__       :      int     =						convert_resnet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Any     =						checkpoint["out.0.weight"]
							SCREAMING_SNAKE_CASE__       :      Tuple     =						checkpoint["out.0.bias"]
							SCREAMING_SNAKE_CASE__       :      Any     =						checkpoint["out.2.weight"]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						checkpoint["out.2.bias"]

							return new_checkpoint


if __name__ == "__main__":
		_lowerCamelCase :    Optional[Any]  							= argparse.ArgumentParser()

		parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
		parser.add_argument(
		    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
		)
		parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

		_lowerCamelCase :    List[Any]  							= parser.parse_args()
		_lowerCamelCase :    int  							= strabool(args.class_cond)

		_lowerCamelCase :    int  							= os.path.basename(args.unet_path)
		print(f"Checkpoint: {ckpt_name}")

		# Get U-Net config
		if "imagenet64" in ckpt_name:
				_lowerCamelCase :    str  							= IMAGENET_64_UNET_CONFIG
		elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowerCamelCase :    Any  							= LSUN_256_UNET_CONFIG
		elif "test" in ckpt_name:
				_lowerCamelCase :    Any  							= TEST_UNET_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		if not args.class_cond:
				_lowerCamelCase :    Any  							= None

		_lowerCamelCase :    Tuple  							= con_pt_to_diffuser(args.unet_path, unet_config)

		_lowerCamelCase :    str  							= UNetaDModel(**unet_config)
		image_unet.load_state_dict(converted_unet_ckpt)

		# Get scheduler config
		if "cd" in ckpt_name or "test" in ckpt_name:
				_lowerCamelCase :    Optional[int]  							= CD_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
				_lowerCamelCase :    Optional[int]  							= CT_IMAGENET_64_SCHEDULER_CONFIG
		elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
				_lowerCamelCase :    Optional[int]  							= CT_LSUN_256_SCHEDULER_CONFIG
		else:
				raise ValueError(f"Checkpoint type {ckpt_name} is not currently supported.")

		_lowerCamelCase :    Union[str, Any]  							= CMStochasticIterativeScheduler(**scheduler_config)

		_lowerCamelCase :    List[str]  							= ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
		consistency_model.save_pretrained(args.dump_path)













import requests
from bsa import BeautifulSoup




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__			,				params=SCREAMING_SNAKE_CASE__       ).content			,				"html.parser"       )
							SCREAMING_SNAKE_CASE__       :      str     =						soup.find("div"			,				attrs={"class": "gs_ri"}       )
							SCREAMING_SNAKE_CASE__       :      int     =						div.find("div"			,				attrs={"class": "gs_fl"}       ).find_all("a"       )
							return anchors[2].get_text()


if __name__ == "__main__":
		_lowerCamelCase :    Any  							= {
		    '''title''': (
		        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
		        '''capacitance, volumetric capacitance, and energy density'''
		    ),
		    '''journal''': '''Chem. Mater.''',
		    '''volume''': 3_0,
		    '''pages''': '''3979-3990''',
		    '''year''': 2_0_1_8,
		    '''hl''': '''en''',
		}
		print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))






