"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase       :			Union[str, Any]   	=logging.get_logger(__name__)


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


      lowercase								=      []
      for i in range(config.num_hidden_layers						):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')						)
            rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')						)
            rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')						)
            rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')						)
            rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')						)
            rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')						)
            rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')						)
            rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')						)
            rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')						)
            rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')						)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """vit.embeddings.cls_token"""),
              ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """vit.embeddings.position_embeddings"""),
          ]						)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                    ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                    ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                ]						)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            lowercase								=      [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""						) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ("""norm.weight""", """vit.layernorm.weight"""),
                    ("""norm.bias""", """vit.layernorm.bias"""),
                    ("""head.weight""", """classifier.weight"""),
                    ("""head.bias""", """classifier.bias"""),
                ]						)

      return rename_keys


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:str   ,						lowerCAmelCase__	:Optional[int]=False						)					->       int:
      '''simple docstring'''


      for i in range(config.num_hidden_layers						):
            if base_model:
                  lowercase								=      ''
            else:
                  lowercase								=      'vit.'
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowercase								=      state_dict.pop(f'blocks.{i}.attn.qkv.weight'						)
            lowercase								=      state_dict.pop(f'blocks.{i}.attn.qkv.bias'						)
            # next, add query, keys and values (in that order) to the state dict
            lowercase								=      in_proj_weight[
                : config.hidden_size, :
            ]
            lowercase								=      in_proj_bias[: config.hidden_size]
            lowercase								=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowercase								=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowercase								=      in_proj_weight[
                -config.hidden_size :, :
            ]
            lowercase								=      in_proj_bias[-config.hidden_size :]


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


      lowercase								=      ['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:str						)					->       int:
      '''simple docstring'''


      lowercase								=      dct.pop(_SCREAMING_SNAKE_CASE						)
      lowercase								=      val


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


      lowercase								=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowercase								=      Image.open(requests.get(_SCREAMING_SNAKE_CASE   ,						stream=_SCREAMING_SNAKE_CASE						).raw						)
      return im


@torch.no_grad()
def      UpperCAmelCase__			(							lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:int						)					->       Dict:
      '''simple docstring'''


      lowercase								=      ViTConfig()
      lowercase								=      False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            lowercase								=      True
            lowercase								=      int(vit_name[-1_2:-1_0]						)
            lowercase								=      int(vit_name[-9:-6]						)
      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								=      int(vit_name[-6:-4]						)
            lowercase								=      int(vit_name[-3:]						)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith("""tiny"""						):
                  lowercase								=      1_9_2
                  lowercase								=      7_6_8
                  lowercase								=      1_2
                  lowercase								=      3
            elif vit_name[9:].startswith("""small"""						):
                  lowercase								=      3_8_4
                  lowercase								=      1_5_3_6
                  lowercase								=      1_2
                  lowercase								=      6
            else:
                  pass
      else:
            if vit_name[4:].startswith("""small"""						):
                  lowercase								=      7_6_8
                  lowercase								=      2_3_0_4
                  lowercase								=      8
                  lowercase								=      8
            elif vit_name[4:].startswith("""base"""						):
                  pass
            elif vit_name[4:].startswith("""large"""						):
                  lowercase								=      1_0_2_4
                  lowercase								=      4_0_9_6
                  lowercase								=      2_4
                  lowercase								=      1_6
            elif vit_name[4:].startswith("""huge"""						):
                  lowercase								=      1_2_8_0
                  lowercase								=      5_1_2_0
                  lowercase								=      3_2
                  lowercase								=      1_6

            # load original model from timm
      lowercase								=      timm.create_model(_SCREAMING_SNAKE_CASE   ,						pretrained=_SCREAMING_SNAKE_CASE						)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowercase								=      timm_model.state_dict()
      if base_model:
            remove_classification_head_(_SCREAMING_SNAKE_CASE						)
      lowercase								=      create_rename_keys(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE						)
      for src, dest in rename_keys:
            rename_key(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE						)
      read_in_q_k_v(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE						)

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            lowercase								=      ViTModel(_SCREAMING_SNAKE_CASE						).eval()
      else:
            lowercase								=      ViTForImageClassification(_SCREAMING_SNAKE_CASE						).eval()
      model.load_state_dict(_SCREAMING_SNAKE_CASE						)

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            lowercase								=      DeiTImageProcessor(size=config.image_size						)
      else:
            lowercase								=      ViTImageProcessor(size=config.image_size						)
      lowercase								=      image_processor(images=prepare_img()   ,						return_tensors="""pt"""						)
      lowercase								=      encoding['pixel_values']
      lowercase								=      model(_SCREAMING_SNAKE_CASE						)

      if base_model:
            lowercase								=      timm_model.forward_features(_SCREAMING_SNAKE_CASE						)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_SCREAMING_SNAKE_CASE   ,						outputs.pooler_output   ,						atol=1e-3						)
      else:
            lowercase								=      timm_model(_SCREAMING_SNAKE_CASE						)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_SCREAMING_SNAKE_CASE   ,						outputs.logits   ,						atol=1e-3						)

      Path(_SCREAMING_SNAKE_CASE						).mkdir(exist_ok=_SCREAMING_SNAKE_CASE						)
      print(f'Saving model {vit_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						)


if __name__ == "__main__":
       __lowerCAmelCase       :			Union[str, Any]   	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--vit_name""",
           default="""vit_base_patch16_224""",
           type=str,
           help="""Name of the ViT 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       :			Tuple   	=parser.parse_args()
       convert_vit_checkpoint(args.vit_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""")
