"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict						)					->       Union[str, Any]:
      '''simple docstring'''


      lowercase								=      SwinvaConfig()
      lowercase								=      swinva_name.split("""_"""						)

      lowercase								=      name_split[1]
      if "to" in name_split[3]:
            lowercase								=      int(name_split[3][-3:]						)
      else:
            lowercase								=      int(name_split[3]						)
      if "to" in name_split[2]:
            lowercase								=      int(name_split[2][-2:]						)
      else:
            lowercase								=      int(name_split[2][6:]						)

      if model_size == "tiny":
            lowercase								=      9_6
            lowercase								=      (2, 2, 6, 2)
            lowercase								=      (3, 6, 1_2, 2_4)
      elif model_size == "small":
            lowercase								=      9_6
            lowercase								=      (2, 2, 1_8, 2)
            lowercase								=      (3, 6, 1_2, 2_4)
      elif model_size == "base":
            lowercase								=      1_2_8
            lowercase								=      (2, 2, 1_8, 2)
            lowercase								=      (4, 8, 1_6, 3_2)
      else:
            lowercase								=      1_9_2
            lowercase								=      (2, 2, 1_8, 2)
            lowercase								=      (6, 1_2, 2_4, 4_8)

      if "to" in swinva_name:
            lowercase								=      (1_2, 1_2, 1_2, 6)

      if ("22k" in swinva_name) and ("to" not in swinva_name):
            lowercase								=      2_1_8_4_1
            lowercase								=      'huggingface/label-files'
            lowercase								=      'imagenet-22k-id2label.json'
            lowercase								=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						repo_type="""dataset"""						)   ,						"""r"""						)						)
            lowercase								=      {int(_SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
            lowercase								=      idalabel
            lowercase								=      {v: k for k, v in idalabel.items()}

      else:
            lowercase								=      1_0_0_0
            lowercase								=      'huggingface/label-files'
            lowercase								=      'imagenet-1k-id2label.json'
            lowercase								=      json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						repo_type="""dataset"""						)   ,						"""r"""						)						)
            lowercase								=      {int(_SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
            lowercase								=      idalabel
            lowercase								=      {v: k for k, v in idalabel.items()}

      lowercase								=      img_size
      lowercase								=      num_classes
      lowercase								=      embed_dim
      lowercase								=      depths
      lowercase								=      num_heads
      lowercase								=      window_size

      return config


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[str, Any]						)					->       Optional[Any]:
      '''simple docstring'''


      if "patch_embed.proj" in name:
            lowercase								=      name.replace("""patch_embed.proj"""   ,						"""embeddings.patch_embeddings.projection"""						)
      if "patch_embed.norm" in name:
            lowercase								=      name.replace("""patch_embed.norm"""   ,						"""embeddings.norm"""						)
      if "layers" in name:
            lowercase								=      'encoder.' + name
      if "attn.proj" in name:
            lowercase								=      name.replace("""attn.proj"""   ,						"""attention.output.dense"""						)
      if "attn" in name:
            lowercase								=      name.replace("""attn"""   ,						"""attention.self"""						)
      if "norm1" in name:
            lowercase								=      name.replace("""norm1"""   ,						"""layernorm_before"""						)
      if "norm2" in name:
            lowercase								=      name.replace("""norm2"""   ,						"""layernorm_after"""						)
      if "mlp.fc1" in name:
            lowercase								=      name.replace("""mlp.fc1"""   ,						"""intermediate.dense"""						)
      if "mlp.fc2" in name:
            lowercase								=      name.replace("""mlp.fc2"""   ,						"""output.dense"""						)
      if "q_bias" in name:
            lowercase								=      name.replace("""q_bias"""   ,						"""query.bias"""						)
      if "k_bias" in name:
            lowercase								=      name.replace("""k_bias"""   ,						"""key.bias"""						)
      if "v_bias" in name:
            lowercase								=      name.replace("""v_bias"""   ,						"""value.bias"""						)
      if "cpb_mlp" in name:
            lowercase								=      name.replace("""cpb_mlp"""   ,						"""continuous_position_bias_mlp"""						)
      if name == "norm.weight":
            lowercase								=      'layernorm.weight'
      if name == "norm.bias":
            lowercase								=      'layernorm.bias'

      if "head" in name:
            lowercase								=      name.replace("""head"""   ,						"""classifier"""						)
      else:
            lowercase								=      'swinv2.' + name

      return name


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:Any						)					->       Union[str, Any]:
      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            lowercase								=      orig_state_dict.pop(_SCREAMING_SNAKE_CASE						)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  lowercase								=      key.split("""."""						)
                  lowercase								=      int(key_split[1]						)
                  lowercase								=      int(key_split[3]						)
                  lowercase								=      model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        lowercase								=      val[:dim, :]
                        lowercase								=      val[dim : dim * 2, :]
                        lowercase								=      val[-dim:, :]
                  else:
                        lowercase								=      val[:dim]
                        lowercase								=      val[
                            dim : dim * 2
                        ]
                        lowercase								=      val[-dim:]
            else:
                  lowercase								=      val

      return orig_state_dict


def      UpperCAmelCase__			(							lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Dict						)					->       Optional[int]:
      '''simple docstring'''


      lowercase								=      timm.create_model(_SCREAMING_SNAKE_CASE   ,						pretrained=_SCREAMING_SNAKE_CASE						)
      timm_model.eval()

      lowercase								=      get_swinva_config(_SCREAMING_SNAKE_CASE						)
      lowercase								=      SwinvaForImageClassification(_SCREAMING_SNAKE_CASE						)
      model.eval()

      lowercase								=      convert_state_dict(timm_model.state_dict()   ,						_SCREAMING_SNAKE_CASE						)
      model.load_state_dict(_SCREAMING_SNAKE_CASE						)

      lowercase								=      'http://images.cocodataset.org/val2017/000000039769.jpg'

      lowercase								=      AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   ,						"""-"""						)						)						)
      lowercase								=      Image.open(requests.get(_SCREAMING_SNAKE_CASE   ,						stream=_SCREAMING_SNAKE_CASE						).raw						)
      lowercase								=      image_processor(images=_SCREAMING_SNAKE_CASE   ,						return_tensors="""pt"""						)

      lowercase								=      timm_model(inputs["""pixel_values"""]						)
      lowercase								=      model(**_SCREAMING_SNAKE_CASE						).logits

      assert torch.allclose(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						atol=1e-3						)

      print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'						)
      model.save_pretrained(_SCREAMING_SNAKE_CASE						)

      print(f'Saving image processor to {pytorch_dump_folder_path}'						)
      image_processor.save_pretrained(_SCREAMING_SNAKE_CASE						)

      model.push_to_hub(
          repo_path_or_name=Path(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE						)   ,						organization="""nandwalritik"""   ,						commit_message="""Add model"""   ,						)


if __name__ == "__main__":
       __lowerCAmelCase       :			Any   	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--swinv2_name""",
           default="""swinv2_tiny_patch4_window8_256""",
           type=str,
           help="""Name of the Swinv2 timm model you'd like to convert.""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
       )

       __lowerCAmelCase       :			Optional[int]   	=parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










"""simple docstring"""





import csv

import tweepy

# Twitter API credentials
__A											=						""""""
__A											=						""""""
__A											=						""""""
__A											=						""""""
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->None:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				tweepy.OAuthHandler(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  auth.set_access_token(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				tweepy.API(_SCREAMING_SNAKE_CASE     )

  # initialize a list to hold all the tweepy Tweets
  lowerCAmelCase__     :Union[str, Any]											=				[]

  # make initial request for most recent tweets (200 is the maximum allowed count)
  lowerCAmelCase__     :Optional[Any]											=				api.user_timeline(screen_name=_SCREAMING_SNAKE_CASE	,  count=200     )

  # save most recent tweets
  alltweets.extend(_SCREAMING_SNAKE_CASE     )

  # save the id of the oldest tweet less one
  lowerCAmelCase__     :Tuple											=				alltweets[-1].id - 1

  # keep grabbing tweets until there are no tweets left to grab
  while len(_SCREAMING_SNAKE_CASE     ) > 0:
    print(F"getting tweets before {oldest}"     )

    # all subsequent requests use the max_id param to prevent duplicates
    lowerCAmelCase__     :Union[str, Any]											=				api.user_timeline(
        screen_name=_SCREAMING_SNAKE_CASE	,  count=200	,  max_id=_SCREAMING_SNAKE_CASE     )

    # save most recent tweets
    alltweets.extend(_SCREAMING_SNAKE_CASE     )

    # update the id of the oldest tweet less one
    lowerCAmelCase__     :Tuple											=				alltweets[-1].id - 1

    print(F"...{len(_SCREAMING_SNAKE_CASE     )} tweets downloaded so far"     )

  # transform the tweepy tweets into a 2D array that will populate the csv
  lowerCAmelCase__     :Optional[Any]											=				[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

  # write the csv
  with open(F"new_{screen_name}_tweets.csv"	,  'w'     ) as f:
    lowerCAmelCase__     :List[str]											=				csv.writer(_SCREAMING_SNAKE_CASE     )
    writer.writerow(['id', 'created_at', 'text']     )
    writer.writerows(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
  # pass in the username of the account you want to download
  get_all_tweets("""FirePing32""")
