

'''simple docstring'''






import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_					  =	logging.get_logger(__name__)
@dataclass
class      SCREAMING_SNAKE_CASE__      :
    _A         =							42
    _A         =							field(default_factory=_UpperCAmelCase				)
    _A         =							field(default_factory=_UpperCAmelCase				)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	len(list(m.modules()      )      ) == 1 or isinstance(lowercase__    ,					nn.Convad      ) or isinstance(lowercase__    ,					nn.BatchNormad      )
          if has_not_submodules:
                if not isinstance(lowercase__    ,					lowercase__      ):
                      self.traced.append(lowercase__      )




    def __call__( self    ,					lowercase__      ):






          """simple docstring"""






          for m in self.module.modules():
                self.handles.append(m.register_forward_hook(self._forward_hook      )      )
          self.module(lowercase__      )
          [x.remove() for x in self.handles]
          return self







    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return list(filter(lambda lowercase__      : len(list(x.state_dict().keys()      )      ) > 0    ,					self.traced      )      )






@dataclass
class      SCREAMING_SNAKE_CASE__      :
    _A         =							42
    _A         =							42
    _A         =							0
    _A         =							field(default_factory=_UpperCAmelCase				)
    _A         =							field(default_factory=_UpperCAmelCase				)




    def __call__( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	Tracker(self.dest      )(lowercase__      ).parametrized
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	Tracker(self.src      )(lowercase__      ).parametrized

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	list(filter(lambda lowercase__      : type(lowercase__      ) not in self.src_skip    ,					lowercase__      )      )
          SCREAMING_SNAKE_CASE_				:      Any					=	list(filter(lambda lowercase__      : type(lowercase__      ) not in self.dest_skip    ,					lowercase__      )      )

          if len(lowercase__      ) != len(lowercase__      ):
                raise Exception(
                    F"Numbers of operations are different. Source module has {len(lowercase__      )} operations while"
                    F" destination module has {len(lowercase__      )}."      )

          for dest_m, src_m in zip(lowercase__    ,					lowercase__      ):
                dest_m.load_state_dict(src_m.state_dict()      )
                if self.verbose == 1:
                      print(F"Transfered from={src_m} to={dest_m}"      )






def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     nn.Module  ,  SCREAMING_SNAKE_CASE_					:     nn.Module   )							->       nn.Module:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      str					=	from_model.state_dict()
      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	our_model.state_dict()
      SCREAMING_SNAKE_CASE_				:      Tuple					=	our_model.config
      SCREAMING_SNAKE_CASE_				:      str					=	[]
      for stage_idx in range(len(config.hidden_sizes   )   ):
            for block_id in range(config.depths[stage_idx]   ):
                  SCREAMING_SNAKE_CASE_				:      Optional[int]					=	F"block{stage_idx + 1}.{block_id}.layer_scale_1"
                  SCREAMING_SNAKE_CASE_				:      int					=	F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

                  all_keys.append((from_key, to_key)   )
                  SCREAMING_SNAKE_CASE_				:      int					=	F"block{stage_idx + 1}.{block_id}.layer_scale_2"
                  SCREAMING_SNAKE_CASE_				:      Dict					=	F"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

                  all_keys.append((from_key, to_key)   )

      for from_key, to_key in all_keys:
            SCREAMING_SNAKE_CASE_				:      List[str]					=	from_state_dict.pop(SCREAMING_SNAKE_CASE_   )

      our_model.load_state_dict(SCREAMING_SNAKE_CASE_   )
      return our_model


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     VanConfig  ,  SCREAMING_SNAKE_CASE_					:     str  ,  SCREAMING_SNAKE_CASE_					:     nn.Module  ,  SCREAMING_SNAKE_CASE_					:     Path  ,  SCREAMING_SNAKE_CASE_					:     bool = True  ,  )							->       int:
      """simple docstring"""

      print(F"Downloading weights for {name}..."   )
      SCREAMING_SNAKE_CASE_				:      Any					=	cached_download(SCREAMING_SNAKE_CASE_   )
      print(F"Converting {name}..."   )
      SCREAMING_SNAKE_CASE_				:      Dict					=	torch.load(SCREAMING_SNAKE_CASE_   )["state_dict"]
      from_model.load_state_dict(SCREAMING_SNAKE_CASE_   )
      from_model.eval()
      with torch.no_grad():
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	VanForImageClassification(SCREAMING_SNAKE_CASE_   ).eval()
            SCREAMING_SNAKE_CASE_				:      Tuple					=	ModuleTransfer(src=SCREAMING_SNAKE_CASE_  ,  dest=SCREAMING_SNAKE_CASE_   )
            SCREAMING_SNAKE_CASE_				:      Tuple					=	torch.randn((1, 3, 2_2_4, 2_2_4)   )
            module_transfer(SCREAMING_SNAKE_CASE_   )
            SCREAMING_SNAKE_CASE_				:      Dict					=	copy_parameters(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_   )

      if not torch.allclose(from_model(SCREAMING_SNAKE_CASE_   )  ,  our_model(SCREAMING_SNAKE_CASE_   ).logits   ):
            raise ValueError("The model logits don't match the original one."   )

      SCREAMING_SNAKE_CASE_				:      List[str]					=	name
      print(SCREAMING_SNAKE_CASE_   )

      if push_to_hub:
            our_model.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name  ,  commit_message="Add model"  ,  use_temp_dir=SCREAMING_SNAKE_CASE_  ,  )

            # we can use the convnext one
            SCREAMING_SNAKE_CASE_				:      Dict					=	AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"   )
            image_processor.push_to_hub(
                repo_path_or_name=save_directory / checkpoint_name  ,  commit_message="Add image processor"  ,  use_temp_dir=SCREAMING_SNAKE_CASE_  ,  )

            print(F"Pushed {checkpoint_name}"   )


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     Path  ,  SCREAMING_SNAKE_CASE_					:     str = None  ,  SCREAMING_SNAKE_CASE_					:     bool = True   )							->       Union[str, Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      int					=	"imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_				:      List[str]					=	1_0_0_0

      SCREAMING_SNAKE_CASE_				:      Dict					=	"huggingface/label-files"
      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	num_labels
      SCREAMING_SNAKE_CASE_				:      List[str]					=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  repo_type="dataset"   )  ,  "r"   )   )
      SCREAMING_SNAKE_CASE_				:      Tuple					=	{int(SCREAMING_SNAKE_CASE_   ): v for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_				:      Dict					=	idalabel
      SCREAMING_SNAKE_CASE_				:      List[Any]					=	{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE_				:      str					=	partial(SCREAMING_SNAKE_CASE_  ,  num_labels=SCREAMING_SNAKE_CASE_  ,  idalabel=SCREAMING_SNAKE_CASE_  ,  labelaid=SCREAMING_SNAKE_CASE_   )

      SCREAMING_SNAKE_CASE_				:      str					=	{
          "van-tiny": ImageNetPreTrainedConfig(
              hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]  ,  depths=[3, 3, 5, 2]  ,  mlp_ratios=[8, 8, 4, 4]  ,  ),
          "van-small": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,  depths=[2, 2, 4, 2]  ,  mlp_ratios=[8, 8, 4, 4]  ,  ),
          "van-base": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,  depths=[3, 3, 1_2, 3]  ,  mlp_ratios=[8, 8, 4, 4]  ,  ),
          "van-large": ImageNetPreTrainedConfig(
              hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,  depths=[3, 5, 2_7, 3]  ,  mlp_ratios=[8, 8, 4, 4]  ,  ),
      }

      SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	{
          "van-tiny": van_tiny,
          "van-small": van_small,
          "van-base": van_base,
          "van-large": van_large,
      }

      SCREAMING_SNAKE_CASE_				:      List[str]					=	{
          "van-tiny": (
              "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
          ),
          "van-small": (
              "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
          ),
          "van-base": (
              "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
          ),
          "van-large": (
              "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
          ),
      }

      if model_name:
            convert_weight_and_push(
                SCREAMING_SNAKE_CASE_  ,  names_to_config[model_name]  ,  checkpoint=names_to_original_checkpoints[model_name]  ,  from_model=names_to_original_models[model_name]()  ,  save_directory=SCREAMING_SNAKE_CASE_  ,  push_to_hub=SCREAMING_SNAKE_CASE_  ,  )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(
                      SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  checkpoint=names_to_original_checkpoints[model_name]  ,  from_model=names_to_original_models[model_name]()  ,  save_directory=SCREAMING_SNAKE_CASE_  ,  push_to_hub=SCREAMING_SNAKE_CASE_  ,  )


if __name__ == "__main__":
      snake_case_					  =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--model-name',
          default=None,
          type=str,
          help=(
              'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
              ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
          ),
      )
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default=None,
          type=Path,
          required=True,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument(
          '--van_dir',
          required=True,
          type=Path,
          help=(
              'A path to VAN\'s original implementation directory. You can download from here:'
              ' https://github.com/Visual-Attention-Network/VAN-Classification'
          ),
      )
      parser.add_argument(
          '--push_to_hub',
          default=True,
          type=bool,
          required=False,
          help='If True, push model and image processor to the hub.',
      )

      snake_case_					  =	parser.parse_args()
      snake_case_					  =	args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      snake_case_					  =	args.van_dir
      # append the path to the parents to maskformer dir
      sys.path.append(str(van_dir.parent))
      from van.models.van import van_base, van_large, van_small, van_tiny

      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''






import requests
from bsa import BeautifulSoup


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     str   )							->       str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      List[str]					=	(
          "https://www.horoscope.com/us/horoscopes/general/"
          F"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
      )
      SCREAMING_SNAKE_CASE_				:      Dict					=	BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_   ).content  ,  "html.parser"   )
      return soup.find("div"  ,  class_="main-horoscope"   ).p.text


if __name__ == "__main__":
      print('Daily Horoscope. \n')
      print(
          'enter your Zodiac sign number:\n',
          '1. Aries\n',
          '2. Taurus\n',
          '3. Gemini\n',
          '4. Cancer\n',
          '5. Leo\n',
          '6. Virgo\n',
          '7. Libra\n',
          '8. Scorpio\n',
          '9. Sagittarius\n',
          '10. Capricorn\n',
          '11. Aquarius\n',
          '12. Pisces\n',
      )
      snake_case_					  =	int(input('number> ').strip())
      print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
      snake_case_					  =	input('enter the day> ')
      snake_case_					  =	horoscope(zodiac_sign, day)
      print(horoscope_text)

