





"""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()
UpperCAmelCase     :     Any							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]=False						):





  """simple docstring"""



  a__     : List[Any]  =[]
  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"
    a__     : str  =[(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 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       List[Any]=False						):





  """simple docstring"""



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





  """simple docstring"""



  a__     : List[str]  =["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(__A	,	__A						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  a__     : List[Any]  =dct.pop(__A						)
  a__     : List[Any]  =val
def 					_A	(		):





  """simple docstring"""



  a__     : List[Any]  ="http://images.cocodataset.org/val2017/000000039769.jpg"
  a__     : List[str]  =Image.open(requests.get(__A	,	stream=__A						).raw						)
  return im
@torch.no_grad()
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : List[str]  =ViTConfig()
  a__     : int  =False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    a__     : Tuple  =True
    a__     : List[Any]  =int(vit_name[-12:-10]						)
    a__     : Any  =int(vit_name[-9:-6]						)
  else:
    a__     : Any  =1_000
    a__     : int  ="huggingface/label-files"
    a__     : List[Any]  ="imagenet-1k-id2label.json"
    a__     : str  =json.load(open(hf_hub_download(__A	,	__A	,	repo_type="dataset"						)	,	"r"						)						)
    a__     : str  ={int(__A						): v for k, v in idalabel.items()}
    a__     : str  =idalabel
    a__     : str  ={v: k for k, v in idalabel.items()}
    a__     : str  =int(vit_name[-6:-4]						)
    a__     : List[str]  =int(vit_name[-3:]						)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith("tiny"						):
      a__     : Tuple  =192
      a__     : str  =768
      a__     : List[Any]  =12
      a__     : Optional[Any]  =3
    elif vit_name[9:].startswith("small"						):
      a__     : Tuple  =384
      a__     : str  =1_536
      a__     : Optional[int]  =12
      a__     : Union[str, Any]  =6
    else:
      pass
  else:
    if vit_name[4:].startswith("small"						):
      a__     : Union[str, Any]  =768
      a__     : List[str]  =2_304
      a__     : List[Any]  =8
      a__     : List[str]  =8
    elif vit_name[4:].startswith("base"						):
      pass
    elif vit_name[4:].startswith("large"						):
      a__     : Optional[int]  =1_024
      a__     : Optional[Any]  =4_096
      a__     : Union[str, Any]  =24
      a__     : Tuple  =16
    elif vit_name[4:].startswith("huge"						):
      a__     : List[Any]  =1_280
      a__     : Optional[int]  =5_120
      a__     : List[Any]  =32
      a__     : Tuple  =16

    # load original model from timm
  a__     : Tuple  =timm.create_model(__A	,	pretrained=__A						)
  timm_model.eval()

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

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    a__     : Union[str, Any]  =ViTModel(__A						).eval()
  else:
    a__     : Optional[Any]  =ViTForImageClassification(__A						).eval()
  model.load_state_dict(__A						)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    a__     : List[Any]  =DeiTImageProcessor(size=config.image_size						)
  else:
    a__     : str  =ViTImageProcessor(size=config.image_size						)
  a__     : Dict  =image_processor(images=prepare_img()	,	return_tensors="pt"						)
  a__     : Union[str, Any]  =encoding["pixel_values"]
  a__     : str  =model(__A						)

  if base_model:
    a__     : Union[str, Any]  =timm_model.forward_features(__A						)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(__A	,	outputs.pooler_output	,	atol=1e-3						)
  else:
    a__     : Tuple  =timm_model(__A						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__A	,	outputs.logits	,	atol=1e-3						)

  Path(__A						).mkdir(exist_ok=__A						)
  print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''						)
  model.save_pretrained(__A						)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
  image_processor.save_pretrained(__A						)


if __name__ == "__main__":
     UpperCAmelCase     :     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."""
     )

     UpperCAmelCase     :     int							=	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Tuple  =set()
  a__     : Optional[Any]  =[]

  def parse_line(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    for line in fp:
      if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : str  =line.decode("UTF-8"						)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(" "						):
        # process a single warning and move it to `selected_warnings`.
        if len(SCREAMING_SNAKE_CASE						) > 0:
          a__     : Union[str, Any]  ="\n".join(SCREAMING_SNAKE_CASE						)
          # Only keep the warnings specified in `targets`
          if any(f''': {x}: ''' in warning for x in targets						):
            selected_warnings.add(SCREAMING_SNAKE_CASE						)
          buffer.clear()
        continue
      else:
        a__     : Optional[Any]  =line.strip()
        buffer.append(SCREAMING_SNAKE_CASE						)

  if from_gh:
    for filename in os.listdir(SCREAMING_SNAKE_CASE						):
      a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(SCREAMING_SNAKE_CASE						) as fp:
          parse_line(SCREAMING_SNAKE_CASE						)
  else:
    try:
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE						) as z:
        for filename in z.namelist():
          if not os.path.isdir(SCREAMING_SNAKE_CASE						):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(SCREAMING_SNAKE_CASE						) as fp:
              parse_line(SCREAMING_SNAKE_CASE						)
    except Exception:
      logger.warning(
          f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''						)

  return selected_warnings
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =set()

  a__     : Any  =[os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for p in os.listdir(SCREAMING_SNAKE_CASE						) if (p.endswith(".zip"						) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  return selected_warnings


if __name__ == "__main__":
     def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





       """simple docstring"""



       return values.split(","						)

     UpperCAmelCase     :     Any							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
     parser.add_argument(
         """--output_dir""",
         type=str,
         required=True,
         help="""Where to store the downloaded artifacts and other result files.""",
     )
     parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
     # optional parameters
     parser.add_argument(
         """--targets""",
         default="""DeprecationWarning,UserWarning,FutureWarning""",
         type=list_str,
         help="""Comma-separated list of target warning(s) which we want to extract.""",
     )
     parser.add_argument(
         """--from_gh""",
         action="""store_true""",
         help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
     )

     UpperCAmelCase     :     List[Any]							=	parser.parse_args()

     UpperCAmelCase     :     str							=	args.from_gh
     if from_gh:
          # The artifacts have to be downloaded using `actions/download-artifact@v3`
          pass
     else:
          os.makedirs(args.output_dir, exist_ok=True)

          # get download links
          UpperCAmelCase     :     Dict							=	get_artifacts_links(args.workflow_run_id, token=args.token)
          with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
               json.dump(artifacts, fp, ensure_ascii=False, indent=4)

          # download artifacts
          for idx, (name, url) in enumerate(artifacts.items()):
               print(name)
               print(url)
               print("""=""" * 80)
               download_artifact(name, url, args.output_dir, args.token)
               # Be gentle to GitHub
               time.sleep(1)

    # extract warnings from artifacts
     UpperCAmelCase     :     Tuple							=	extract_warnings(args.output_dir, args.targets)
     UpperCAmelCase     :     Optional[Any]							=	sorted(selected_warnings)
     with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
          json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






