




import argparse
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


snake_case_ : Any    		=       "Run commands across TPU VMs for initial setup before running `accelerate launch`."


def    A						(__A   :    int=None       )   ->      Any:







     """simple docstring"""


     if subparsers is not None:
          UpperCAmelCase_          =							subparsers.add_parser('''tpu-config'''   ,				description=_description       )
     else:
          UpperCAmelCase_          =							argparse.ArgumentParser('''Accelerate tpu-config command'''   ,				description=_description       )
     # Core arguments
     UpperCAmelCase_          =							parser.add_argument_group(
         '''Config Arguments'''   ,				'''Arguments that can be configured through `accelerate config`.'''       )
     config_args.add_argument(
         '''--config_file'''   ,				type=__lowerCamelCase   ,				default=__lowerCamelCase   ,				help='''Path to the config file to use for accelerate.'''   ,				)
     config_args.add_argument(
         '''--tpu_name'''   ,				default=__lowerCamelCase   ,				help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''   ,				)
     config_args.add_argument(
         '''--tpu_zone'''   ,				default=__lowerCamelCase   ,				help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''   ,				)
     UpperCAmelCase_          =							parser.add_argument_group('''TPU Arguments'''   ,				'''Arguments for options ran inside the TPU.'''       )
     pod_args.add_argument(
         '''--use_alpha'''   ,				action='''store_true'''   ,				help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''   ,				)
     pod_args.add_argument(
         '''--command_file'''   ,				default=__lowerCamelCase   ,				help='''The path to the file containing the commands to run on the pod on startup.'''   ,				)
     pod_args.add_argument(
         '''--command'''   ,				action='''append'''   ,				nargs='''+'''   ,				help='''A command to run on the pod. Can be passed multiple times.'''   ,				)
     pod_args.add_argument(
         '''--install_accelerate'''   ,				action='''store_true'''   ,				help='''Whether to install accelerate on the pod. Defaults to False.'''   ,				)
     pod_args.add_argument(
         '''--accelerate_version'''   ,				default='''latest'''   ,				help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''   ,				)
     pod_args.add_argument(
         '''--debug'''   ,				action='''store_true'''   ,				help='''If set, will print the command that would be run instead of running it.'''       )

     if subparsers is not None:
          parser.set_defaults(func=__lowerCamelCase       )
     return parser


def    A						(__A   :    Union[str, Any]       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							None

     # Get the default from the config file if it exists.
     if args.config_file is not None or os.path.isfile(__lowerCamelCase       ):
          UpperCAmelCase_          =							load_config_from_file(args.config_file       )
          if not args.command_file and defaults.command_file is not None and not args.command:
               UpperCAmelCase_          =							defaults.command_file
          if not args.command and defaults.commands is not None:
               UpperCAmelCase_          =							defaults.commands
          if not args.tpu_name:
               UpperCAmelCase_          =							defaults.tpu_name
          if not args.tpu_zone:
               UpperCAmelCase_          =							defaults.tpu_zone
     if args.accelerate_version == "dev":
          UpperCAmelCase_          =							'''git+https://github.com/huggingface/accelerate.git'''
     elif args.accelerate_version == "latest":
          UpperCAmelCase_          =							'''accelerate -U'''
     elif isinstance(parse(args.accelerate_version       )   ,				__lowerCamelCase       ):
          UpperCAmelCase_          =							F"""accelerate=={args.accelerate_version}"""

     if not args.command_file and not args.command:
          raise ValueError('''You must specify either a command file or a command to run on the pod.'''       )

     if args.command_file:
          with open(args.command_file   ,				'''r'''       ) as f:
               UpperCAmelCase_          =							[f.read().splitlines()]

          # To turn list of lists into list of strings
     if isinstance(args.command[0]   ,				__lowerCamelCase       ):
          UpperCAmelCase_          =							[line for cmd in args.command for line in cmd]
     # Default to the shared folder and install accelerate
     UpperCAmelCase_          =							['''cd /usr/share''']
     if args.install_accelerate:
          new_cmd += [F"""pip install {args.accelerate_version}"""]
     new_cmd += args.command
     UpperCAmelCase_          =							'''; '''.join(__lowerCamelCase       )

     # Then send it to gcloud
     # Eventually try to use google-api-core to do this instead of subprocess
     UpperCAmelCase_          =							['''gcloud''']
     if args.use_alpha:
          cmd += ["alpha"]
     cmd += [
         "compute",
         "tpus",
         "tpu-vm",
         "ssh",
         args.tpu_name,
         "--zone",
         args.tpu_zone,
         "--command",
         args.command,
         "--worker",
         "all",
     ]
     if args.debug:
          print(F"""Running {" ".join(__lowerCamelCase       )}"""       )
          return
     subprocess.run(__lowerCamelCase       )
     print('''Successfully setup pod.'''       )


def    A						()   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							tpu_command_parser()
     UpperCAmelCase_          =							parser.parse_args()

     tpu_command_launcher(__lowerCamelCase       )








"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__          = logging.get_logger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Union[str, Any]=False		)     ->					Optional[int]:
  _snake_case					     =       []
  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'''deit.encoder.layer.{i}.layernorm_before.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')		)
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')		)
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')		)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('''cls_token''', '''deit.embeddings.cls_token'''),
          ('''dist_token''', '''deit.embeddings.distillation_token'''),
          ('''patch_embed.proj.weight''', '''deit.embeddings.patch_embeddings.projection.weight'''),
          ('''patch_embed.proj.bias''', '''deit.embeddings.patch_embeddings.projection.bias'''),
          ('''pos_embed''', '''deit.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 "deit" from all keys that start with "deit"
    _snake_case					     =       [(pair[0], pair[1][4:]) if pair[1].startswith('''deit'''		) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('''norm.weight''', '''deit.layernorm.weight'''),
            ('''norm.bias''', '''deit.layernorm.bias'''),
            ('''head.weight''', '''cls_classifier.weight'''),
            ('''head.bias''', '''cls_classifier.bias'''),
            ('''head_dist.weight''', '''distillation_classifier.weight'''),
            ('''head_dist.bias''', '''distillation_classifier.bias'''),
        ]		)

  return rename_keys
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Tuple=False		)     ->					Tuple:
  for i in range(config.num_hidden_layers		):
    if base_model:
      _snake_case					     =       ''''''
    else:
      _snake_case					     =       '''deit.'''
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
    _snake_case					     =       state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _snake_case					     =       in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case					     =       in_proj_bias[: config.hidden_size]
    _snake_case					     =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case					     =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case					     =       in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case					     =       in_proj_bias[-config.hidden_size :]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       Tuple		)     ->					Tuple:
  _snake_case					     =       dct.pop(__lowerCamelCase		)
  _snake_case					     =       val
def 					_UpperCAmelCase      ( )     ->					Dict:
  _snake_case					     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case					     =       Image.open(requests.get(__lowerCamelCase ,					stream=__lowerCamelCase		).raw		)
  return im
@torch.no_grad()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str		)     ->					str:
  _snake_case					     =       DeiTConfig()
  # all deit models have fine-tuned heads
  _snake_case					     =       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case					     =       10_00
  _snake_case					     =       '''huggingface/label-files'''
  _snake_case					     =       '''imagenet-1k-id2label.json'''
  _snake_case					     =       json.load(open(hf_hub_download(__lowerCamelCase ,					__lowerCamelCase ,					repo_type='''dataset'''		) ,					'''r'''		)		)
  _snake_case					     =       {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  _snake_case					     =       idalabel
  _snake_case					     =       {v: k for k, v in idalabel.items()}
  _snake_case					     =       int(deit_name[-6:-4]		)
  _snake_case					     =       int(deit_name[-3:]		)
  # size of the architecture
  if deit_name[9:].startswith('''tiny'''		):
    _snake_case					     =       1_92
    _snake_case					     =       7_68
    _snake_case					     =       12
    _snake_case					     =       3
  elif deit_name[9:].startswith('''small'''		):
    _snake_case					     =       3_84
    _snake_case					     =       15_36
    _snake_case					     =       12
    _snake_case					     =       6
  if deit_name[9:].startswith('''base'''		):
    pass
  elif deit_name[4:].startswith('''large'''		):
    _snake_case					     =       10_24
    _snake_case					     =       40_96
    _snake_case					     =       24
    _snake_case					     =       16

  # load original model from timm
  _snake_case					     =       timm.create_model(__lowerCamelCase ,					pretrained=__lowerCamelCase		)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  _snake_case					     =       timm_model.state_dict()
  _snake_case					     =       create_rename_keys(__lowerCamelCase ,					__lowerCamelCase		)
  for src, dest in rename_keys:
    rename_key(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)
  read_in_q_k_v(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		)

  # load HuggingFace model
  _snake_case					     =       DeiTForImageClassificationWithTeacher(__lowerCamelCase		).eval()
  model.load_state_dict(__lowerCamelCase		)

  # Check outputs on an image, prepared by DeiTImageProcessor
  _snake_case					     =       int(
      (2_56 / 2_24) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  _snake_case					     =       DeiTImageProcessor(size=__lowerCamelCase ,					crop_size=config.image_size		)
  _snake_case					     =       image_processor(images=prepare_img() ,					return_tensors='''pt'''		)
  _snake_case					     =       encoding['''pixel_values''']
  _snake_case					     =       model(__lowerCamelCase		)

  _snake_case					     =       timm_model(__lowerCamelCase		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__lowerCamelCase ,					outputs.logits ,					atol=1E-3		)

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


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT 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__          = parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)



