



'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase:					Optional[Any]						       =						logging.get_logger(__name__)
def       lowerCamelCase__       (						_A				):
  a						:					Optional[Any]        =     'huggingface/label-files'
  a						:					Tuple        =     'imagenet-1k-id2label.json'
  a						:					int        =     json.load(open(hf_hub_download(A__ ,			A__ ,			repo_type='dataset'				) ,			'r'				)				)
  a						:					Union[str, Any]        =     {int(A__				): v for k, v in idalabel.items()}
  a						:					Dict        =     {v: k for k, v in idalabel.items()}

  a						:					Optional[Any]        =     'std_conv' if 'bit' in model_name else False

  # note that when using BiT as backbone for ViT-hybrid checkpoints,
  # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
  # config.conv_layer = "std_conv_same"
  a						:					str        =     BitConfig(
      conv_layer=A__ ,			num_labels=1000 ,			idalabel=A__ ,			labelaid=A__ ,			)

  return config
def       lowerCamelCase__       (						_A				):
  if "stem.conv" in name:
    a						:					Dict        =     name.replace('stem.conv' ,			'bit.embedder.convolution'				)
  if "blocks" in name:
    a						:					Optional[Any]        =     name.replace('blocks' ,			'layers'				)
  if "head.fc" in name:
    a						:					Tuple        =     name.replace('head.fc' ,			'classifier.1'				)
  if name.startswith('norm'				):
    a						:					Optional[Any]        =     'bit.' + name
  if "bit" not in name and "classifier" not in name:
    a						:					str        =     'bit.encoder.' + name

  return name
def       lowerCamelCase__       (						):
  a						:					List[str]        =     'http://images.cocodataset.org/val2017/000000039769.jpg'
  a						:					Union[str, Any]        =     Image.open(requests.get(A__ ,			stream=A__				).raw				)
  return im
@torch.no_grad()
def       lowerCamelCase__       (						_A ,			_A ,			_A=False				):
  a						:					Any        =     get_config(A__				)

  # load original model from timm
  a						:					Dict        =     create_model(A__ ,			pretrained=A__				)
  timm_model.eval()

  # load state_dict of original model
  a						:					Dict        =     timm_model.state_dict()
  for key in state_dict.copy().keys():
    a						:					Tuple        =     state_dict.pop(A__				)
    a						:					Optional[int]        =     val.squeeze() if 'head' in key else val

  # load HuggingFace model
  a						:					Optional[Any]        =     BitForImageClassification(A__				)
  model.eval()
  model.load_state_dict(A__				)

  # create image processor
  a						:					Optional[Any]        =     create_transform(**resolve_data_config({} ,			model=A__				)				)
  a						:					List[str]        =     transform.transforms

  a						:					Tuple        =     {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  a						:					Dict        =     BitImageProcessor(
      do_resize=A__ ,			size={'shortest_edge': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=A__ ,			crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]} ,			do_normalize=A__ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

  a						:					List[str]        =     prepare_img()
  a						:					Union[str, Any]        =     transform(A__				).unsqueeze(0				)
  a						:					List[Any]        =     processor(A__ ,			return_tensors='pt'				).pixel_values

  # verify pixel values
  assert torch.allclose(A__ ,			A__				)

  # verify logits
  with torch.no_grad():
    a						:					List[str]        =     model(A__				)
    a						:					Dict        =     outputs.logits

  print('Logits:' ,			logits[0, :3]				)
  print('Predicted class:' ,			model.config.idalabel[logits.argmax(-1				).item()]				)
  a						:					Union[str, Any]        =     timm_model(A__				)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(A__ ,			outputs.logits ,			atol=1E-3				)
  print('Looks ok!'				)

  if pytorch_dump_folder_path is not None:
    Path(A__				).mkdir(exist_ok=A__				)
    print(f"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""				)
    model.save_pretrained(A__				)
    processor.save_pretrained(A__				)

  if push_to_hub:
    print(f"""Pushing model {model_name} and processor to the hub"""				)
    model.push_to_hub(f"""ybelkada/{model_name}"""				)
    processor.push_to_hub(f"""ybelkada/{model_name}"""				)


if __name__ == "__main__":
     lowerCAmelCase:					Any						       =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='resnetv2_50x1_bitm',
         type=str,
         help='Name of the BiT 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.'
     )
     parser.add_argument(
         '--push_to_hub',
         action='store_true',
         help='Whether to push the model to the hub.',
     )

     lowerCAmelCase:					Tuple						       =						parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase	:       List[Any]      	=	{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       int      	=	[
       "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
       "M2M100ForConditionalGeneration",
       "M2M100Model",
       "M2M100PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
   from .tokenization_mam_aaa import MaMaaaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mam_aaa import (
          M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaMaaaForConditionalGeneration,
          MaMaaaModel,
          MaMaaaPreTrainedModel,
      )


else:
   import sys

   _lowerCamelCase	:       Optional[int]      	=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

