






"""simple docstring"""



import requests
from bsa import BeautifulSoup


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str = "AAPL"				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Union[str, Any]							=							f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
     UpperCAmelCase__						:    List[Any]							=							BeautifulSoup(requests.get(snake_case				).text			,					"html.parser"				)
     UpperCAmelCase__						:    str							=							"My(6px) Pos(r) smartphone_Mt(6px)"
     return soup.find("div"			,					class_=class_				).find("span"				).text


if __name__ == "__main__":
 for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
  print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")










"""simple docstring"""



import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCAmelCase	:	Dict				=      {
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """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	:	str				=      {
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """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	:	Dict				=      {
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """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	:	int				=      {
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}

_lowerCAmelCase	:	List[Any]				=      {
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_02,
    """sigma_max""": 80.0,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str				)->		str:






     '''simple docstring'''



     if isinstance(snake_case			,					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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any			,					snake_case       : Optional[Any]			,					snake_case       : List[str]			,					snake_case       : Any			,					snake_case       : str=False				)->		Tuple:






     '''simple docstring'''



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

     if has_skip:
          UpperCAmelCase__						:    str							=							checkpoint[f'{old_prefix}.skip_connection.weight']
          UpperCAmelCase__						:    Any							=							checkpoint[f'{old_prefix}.skip_connection.bias']

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Union[str, Any]			,					snake_case       : Any			,					snake_case       : Tuple			,					snake_case       : Dict=None				)->		Tuple:






     '''simple docstring'''



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

     UpperCAmelCase__						:    Dict							=							checkpoint[f'{old_prefix}.norm.weight']
     UpperCAmelCase__						:    Tuple							=							checkpoint[f'{old_prefix}.norm.bias']

     UpperCAmelCase__						:    Tuple							=							weight_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    int							=							bias_q.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							weight_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[int]							=							bias_k.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Tuple							=							weight_v.squeeze(-1				).squeeze(-1				)
     UpperCAmelCase__						:    Optional[Any]							=							bias_v.squeeze(-1				).squeeze(-1				)

     UpperCAmelCase__						:    Union[str, Any]							=							(
         checkpoint[f'{old_prefix}.proj_out.weight'].squeeze(-1				).squeeze(-1				)
     )
     UpperCAmelCase__						:    List[str]							=							checkpoint[f'{old_prefix}.proj_out.bias'].squeeze(-1				).squeeze(-1				)

     return new_checkpoint


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Any							=							{}

     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.0.weight"]
     UpperCAmelCase__						:    Optional[int]							=							checkpoint["time_embed.0.bias"]
     UpperCAmelCase__						:    Any							=							checkpoint["time_embed.2.weight"]
     UpperCAmelCase__						:    Optional[Any]							=							checkpoint["time_embed.2.bias"]

     if unet_config["num_class_embeds"] is not None:
          UpperCAmelCase__						:    List[Any]							=							checkpoint["label_emb.weight"]

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

     UpperCAmelCase__						:    Any							=							unet_config["down_block_types"]
     UpperCAmelCase__						:    Tuple							=							unet_config["layers_per_block"]
     UpperCAmelCase__						:    str							=							unet_config["attention_head_dim"]
     UpperCAmelCase__						:    str							=							unet_config["block_out_channels"]
     UpperCAmelCase__						:    Optional[int]							=							1
     UpperCAmelCase__						:    Tuple							=							channels_list[0]

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

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

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

          UpperCAmelCase__						:    Dict							=							current_channels

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

     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    int							=							unet_config["up_block_types"]

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

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

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

     UpperCAmelCase__						:    Union[str, Any]							=							checkpoint["out.0.weight"]
     UpperCAmelCase__						:    Any							=							checkpoint["out.0.bias"]
     UpperCAmelCase__						:    int							=							checkpoint["out.2.weight"]
     UpperCAmelCase__						:    str							=							checkpoint["out.2.bias"]

     return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase	:	str				=      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	:	int				=      parser.parse_args()
 _lowerCAmelCase	:	Optional[int]				=      strabool(args.class_cond)

 _lowerCAmelCase	:	Optional[Any]				=      os.path.basename(args.unet_path)
 print(F"""Checkpoint: {ckpt_name}""")

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

 if not args.class_cond:
  _lowerCAmelCase	:	int				=      None

 _lowerCAmelCase	:	Optional[int]				=      con_pt_to_diffuser(args.unet_path, unet_config)

 _lowerCAmelCase	:	Optional[Any]				=      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	:	str				=      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	:	Any				=      CT_LSUN_256_SCHEDULER_CONFIG
 else:
  raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")

 _lowerCAmelCase	:	Dict				=      CMStochasticIterativeScheduler(**scheduler_config)

 _lowerCAmelCase	:	Tuple				=      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
 consistency_model.save_pretrained(args.dump_path)


