

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase :	Any     				=    logging.get_logger()


@dataclass
class     lowerCAmelCase				:


       '''simple docstring'''







       snake_case    							=	42
       snake_case    							=	field(default_factory=__UpperCamelCase				)
       snake_case    							=	field(default_factory=__UpperCamelCase				)
       snake_case    							=	field(default_factory=__UpperCamelCase				)

       def   lowerCamelCase__						(	self					:  List[Any]						,	__snake_case					:  Optional[int]						,	__snake_case					:  Tensor						,	__snake_case					:  Tensor						,	__snake_case					:  str     )   -> List[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    len(list(m.modules()     )     ) == 1 or isinstance(__snake_case						,	nn.Convad     ) or isinstance(__snake_case						,	nn.BatchNormad     )
        if has_not_submodules:
         self.traced.append(__snake_case     )
         lowerCamelCase      						=    m

       def __call__(	self					:  List[Any]						,	__snake_case					:  Tensor     )   -> Tuple:







        '''simple docstring'''

        for name, m in self.module.named_modules():
         self.handles.append(m.register_forward_hook(partial(self._forward_hook						,	name=__snake_case     )     )     )
        self.module(__snake_case     )
        [x.remove() for x in self.handles]
        return self





       @property
       def   lowerCamelCase__						(	self					:  Dict     )   -> Optional[Any]:







        '''simple docstring'''

        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()     )     ) > 0}


class     lowerCAmelCase				(    nn.Module				):


       '''simple docstring'''







       def __init__(	self					:  int						,	__snake_case					:  nn.Module     )   -> List[Any]:







        '''simple docstring'''

        super().__init__()

        lowerCamelCase      						=    []
        # - get the stem
        feature_blocks.append(('conv1', model.stem)     )
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
         assert k.startswith('block'     ), F'''Unexpected layer name {k}'''
         lowerCamelCase      						=    len(__snake_case     ) + 1
         feature_blocks.append((F'''res{block_index}''', v)     )

        lowerCamelCase      						=    nn.ModuleDict(__snake_case     )





       def   lowerCamelCase__						(	self					:  Union[str, Any]						,	__snake_case					:  Tensor     )   -> str:







        '''simple docstring'''

        return get_trunk_forward_outputs(
            __snake_case						,	out_feat_keys=__snake_case						,	feature_blocks=self._feature_blocks						,	)


class     lowerCAmelCase				(    __UpperCamelCase				):


       '''simple docstring'''







       def   lowerCamelCase__						(	self					:  Any     )   -> Optional[int]:







        '''simple docstring'''

        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def a_		(       UpperCamelCase_   :							str  )							->   Dict[str, str]:







 """simple docstring"""

 lowerCamelCase      						=    RegNetConfig(depths=[2, 7, 1_7, 1]   ,					hidden_sizes=[8, 8, 8, 8]   ,					groups_width=8  )
 if "in1k" in model_name:
  lowerCamelCase      						=    RegNetForImageClassification(UpperCamelCase_  )
 else:
  lowerCamelCase      						=    RegNetModel(UpperCamelCase_  )
 # create from model (with small weights)
 lowerCamelCase      						=    FakeRegNetVisslWrapper(
     RegNet(FakeRegNetParams(depth=2_7   ,					group_width=1_0_1_0   ,					w_a=1_7_4_4   ,					w_a=620.83   ,					w_m=2.52  )  )  )

 with torch.no_grad():
  lowerCamelCase      						=    from_model.eval()
  lowerCamelCase      						=    our_model.eval()

  lowerCamelCase      						=    torch.randn((1, 3, 3_2, 3_2)  )
  # trace both
  lowerCamelCase      						=    Tracker(UpperCamelCase_  )
  lowerCamelCase      						=    dest_tracker(UpperCamelCase_  ).parametrized

  pprint(dest_tracker.nameamodule  )
  lowerCamelCase      						=    Tracker(UpperCamelCase_  )
  lowerCamelCase      						=    src_tracker(UpperCamelCase_  ).parametrized

 # convert the keys -> module dict to keys -> params
 def to_params_dict(UpperCamelCase_   :							Union[str, Any]  ):
  lowerCamelCase      						=    OrderedDict()
  for name, module in dict_with_modules.items():
   for param_name, param in module.state_dict().items():
    lowerCamelCase      						=    param
  return params_dict

 lowerCamelCase      						=    {}

 lowerCamelCase      						=    to_params_dict(UpperCamelCase_  )
 lowerCamelCase      						=    to_params_dict(UpperCamelCase_  )

 for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()   ,					dst_state_dict.items()  ):
  lowerCamelCase      						=    dest_key
  logger.info(f'''{src_key} -> {dest_key}'''  )
 # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
 if "in1k" in model_name:
  lowerCamelCase      						=    'classifier.1.weight'
  lowerCamelCase      						=    'classifier.1.bias'

 return from_to_ours_keys







def a_		(       UpperCamelCase_   :							Path   ,					UpperCamelCase_   :							str = None   ,					UpperCamelCase_   :							bool = True  )							->   int:







 """simple docstring"""

 lowerCamelCase      						=    'imagenet-1k-id2label.json'
 lowerCamelCase      						=    1_0_0_0

 lowerCamelCase      						=    'huggingface/label-files'
 lowerCamelCase      						=    num_labels
 lowerCamelCase      						=    json.load(open(cached_download(hf_hub_url(UpperCamelCase_   ,					UpperCamelCase_   ,					repo_type='dataset'  )  )   ,					'r'  )  )
 lowerCamelCase      						=    {int(UpperCamelCase_  ): v for k, v in idalabel.items()}

 lowerCamelCase      						=    idalabel
 lowerCamelCase      						=    {v: k for k, v in idalabel.items()}

 lowerCamelCase      						=    partial(UpperCamelCase_   ,					num_labels=UpperCamelCase_   ,					idalabel=UpperCamelCase_   ,					labelaid=UpperCamelCase_  )

 lowerCamelCase      						=    {
     'regnet-y-10b-seer': ImageNetPreTrainedConfig(
         depths=[2, 7, 1_7, 1]   ,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]   ,					groups_width=1_0_1_0  ),
     # finetuned on imagenet
     'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
         depths=[2, 7, 1_7, 1]   ,					hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]   ,					groups_width=1_0_1_0  ),
 }

 # add seer weights logic
 def load_using_classy_vision(UpperCamelCase_   :							str  ) -> Tuple[Dict, Dict]:
  lowerCamelCase      						=    torch.hub.load_state_dict_from_url(UpperCamelCase_   ,					model_dir=str(UpperCamelCase_  )   ,					map_location='cpu'  )
  # check if we have a head, if yes add it
  lowerCamelCase      						=    files['classy_state_dict']['base_model']['model']
  return model_state_dict["trunk"], model_state_dict["heads"]

 lowerCamelCase      						=    {
     'regnet-y-10b-seer': partial(
         UpperCamelCase_   ,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'   ,					),
     'regnet-y-10b-seer-in1k': partial(
         UpperCamelCase_   ,					'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'   ,					),
 }

 lowerCamelCase      						=    get_from_to_our_keys(UpperCamelCase_  )

 if not (save_directory / f'''{model_name}.pth''').exists():
  logger.info('Loading original state_dict.'  )
  lowerCamelCase						,	lowerCamelCase      						=    names_to_from_model[model_name]()
  lowerCamelCase      						=    from_state_dict_trunk
  if "in1k" in model_name:
   # add the head
   lowerCamelCase      						=    {**from_state_dict_trunk, **from_state_dict_head}
  logger.info('Done!'  )

  lowerCamelCase      						=    {}

  lowerCamelCase      						=    list(from_state_dict.keys()  )
  lowerCamelCase      						=    R'\.block.-part.'
  # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
  for key in from_state_dict.keys():
   # remove the weird "block[0,1]-part" from the key
   lowerCamelCase      						=    re.sub(UpperCamelCase_   ,					''   ,					UpperCamelCase_  )
   # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
   lowerCamelCase      						=    from_to_ours_keys[src_key]
   # store the parameter with our key
   lowerCamelCase      						=    from_state_dict[key]
   not_used_keys.remove(UpperCamelCase_  )
  # check that all keys have been updated
  assert len(UpperCamelCase_  ) == 0, f'''Some keys where not used {",".join(UpperCamelCase_  )}'''

  logger.info(f'''The following keys were not used: {",".join(UpperCamelCase_  )}'''  )

  # save our state dict to disk
  torch.save(UpperCamelCase_   ,					save_directory / f'''{model_name}.pth'''  )

  del converted_state_dict
 else:
  logger.info('The state_dict was already stored on disk.'  )
 if push_to_hub:
  logger.info(f'''Token is {os.environ["HF_TOKEN"]}'''  )
  logger.info('Loading our model.'  )
  # create our model
  lowerCamelCase      						=    names_to_config[model_name]
  lowerCamelCase      						=    RegNetModel
  if "in1k" in model_name:
   lowerCamelCase      						=    RegNetForImageClassification
  lowerCamelCase      						=    our_model_func(UpperCamelCase_  )
  # place our model to the meta device (so remove all the weights)
  our_model.to(torch.device('meta'  )  )
  logger.info('Loading state_dict in our model.'  )
  # load state dict
  lowerCamelCase      						=    our_model.state_dict().keys()
  PreTrainedModel._load_pretrained_model_low_mem(
      UpperCamelCase_   ,					UpperCamelCase_   ,					[save_directory / f'''{model_name}.pth''']  )
  logger.info('Finally, pushing!'  )
  # push it to hub
  our_model.push_to_hub(
      repo_path_or_name=save_directory / model_name   ,					commit_message='Add model'   ,					output_dir=save_directory / model_name   ,					)
  lowerCamelCase      						=    3_8_4
  # we can use the convnext one
  lowerCamelCase      						=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'   ,					size=UpperCamelCase_  )
  image_processor.push_to_hub(
      repo_path_or_name=save_directory / model_name   ,					commit_message='Add image processor'   ,					output_dir=save_directory / model_name   ,					)


if __name__ == "__main__":
     _lowerCAmelCase :	Optional[Any]     				=    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 regnet* architecture,'
             ' currently: regnetx-*, regnety-*. 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(
         '--push_to_hub',
         default=True,
         type=bool,
         required=False,
         help='If True, push model and image processor to the hub.',
     )

     _lowerCAmelCase :	Union[str, Any]     				=    parser.parse_args()

     _lowerCAmelCase :	Path     				=    args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import requests
from bsa import BeautifulSoup







def a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							str  )							->   str:







 """simple docstring"""

 lowerCamelCase      						=    (
     'https://www.horoscope.com/us/horoscopes/general/'
     f'''horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'''
 )
 lowerCamelCase      						=    BeautifulSoup(requests.get(UpperCamelCase_  ).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',
     )
     _lowerCAmelCase :	Dict     				=    int(input('number> ').strip())
     print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
     _lowerCAmelCase :	Optional[Any]     				=    input('enter the day> ')
     _lowerCAmelCase :	int     				=    horoscope(zodiac_sign, day)
     print(horoscope_text)




