





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()
lowercase_					     =	logging.get_logger(__name__)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=False	)       ->     int:
  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						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=False	)       ->     Optional[Any]:
  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						(_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						dct.pop(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						val




def     __UpperCamelCase						()       ->     str:
  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						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  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[-12:-10]	)
    lowercase__			    =						int(vit_name[-9:-6]	)
  else:
    lowercase__			    =						1000
    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__			    =						192
      lowercase__			    =						768
      lowercase__			    =						12
      lowercase__			    =						3
    elif vit_name[9:].startswith('small'	):
      lowercase__			    =						384
      lowercase__			    =						1536
      lowercase__			    =						12
      lowercase__			    =						6
    else:
      pass
  else:
    if vit_name[4:].startswith('small'	):
      lowercase__			    =						768
      lowercase__			    =						2304
      lowercase__			    =						8
      lowercase__			    =						8
    elif vit_name[4:].startswith('base'	):
      pass
    elif vit_name[4:].startswith('large'	):
      lowercase__			    =						1024
      lowercase__			    =						4096
      lowercase__			    =						24
      lowercase__			    =						16
    elif vit_name[4:].startswith('huge'	):
      lowercase__			    =						1280
      lowercase__			    =						5120
      lowercase__			    =						32
      lowercase__			    =						16

    # 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__":
      lowercase_					     =	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."""
      )

      lowercase_					     =	parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)











import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						[]

  lowercase__			    =						(
      F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
      ' https://api.github.com/repos/huggingface/transformers/actions/runners'
  )
  lowercase__			    =						subprocess.run(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

  lowercase__			    =						status['runners']
  for runner in runners:
    if runner["name"] in target_runners:
      if runner["status"] == "offline":
        offline_runners.append(_SCREAMING_SNAKE_CASE	)

    # save the result so we can report them on Slack
  with open('offline_runners.txt'	,		'w'	) as fp:
    fp.write(json.dumps(_SCREAMING_SNAKE_CASE	)	)

  if len(_SCREAMING_SNAKE_CASE	) > 0:
    lowercase__			    =						'\n'.join([x['name'] for x in offline_runners]	)
    raise ValueError(F"""The following runners are offline:\n{failed}"""	)


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--target_runners""",
          default=None,
          type=list_str,
          required=True,
          help="""Comma-separated list of runners to check status.""",
      )

      parser.add_argument(
          """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
      )
      lowercase_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


