





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_						   =    logging.get_logger(__name__)


def     lowerCamelCase_	(      lowerCAmelCase:	List[str]				)->		Dict:
  _snake_case   :				Tuple														=	'huggingface/label-files'
  _snake_case   :				Tuple														=	'imagenet-1k-id2label.json'
  _snake_case   :				Dict														=	json.load(open(hf_hub_download(__snake_case    ,							__snake_case    ,							repo_type='dataset'				)    ,							'r'				)				)
  _snake_case   :				List[str]														=	{int(__snake_case				): v for k, v in idalabel.items()}
  _snake_case   :				Optional[int]														=	{v: k for k, v in idalabel.items()}

  _snake_case   :				List[str]														=	'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"
  _snake_case   :				Optional[int]														=	BitConfig(
      conv_layer=__snake_case    ,							num_labels=10_00    ,							idalabel=__snake_case    ,							labelaid=__snake_case    ,							)

  return config


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		Any:
  if "stem.conv" in name:
    _snake_case   :				List[str]														=	name.replace('stem.conv'    ,							'bit.embedder.convolution'				)
  if "blocks" in name:
    _snake_case   :				str														=	name.replace('blocks'    ,							'layers'				)
  if "head.fc" in name:
    _snake_case   :				Any														=	name.replace('head.fc'    ,							'classifier.1'				)
  if name.startswith('norm'				):
    _snake_case   :				List[str]														=	'bit.' + name
  if "bit" not in name and "classifier" not in name:
    _snake_case   :				Tuple														=	'bit.encoder.' + name

  return name


def     lowerCamelCase_	(      )->		Optional[int]:
  _snake_case   :				Tuple														=	'http://images.cocodataset.org/val2017/000000039769.jpg'
  _snake_case   :				str														=	Image.open(requests.get(__snake_case    ,							stream=__snake_case				).raw				)
  return im


@torch.no_grad()
def     lowerCamelCase_	(      lowerCAmelCase:	Dict    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	List[str]=False				)->		Tuple:
  _snake_case   :				List[str]														=	get_config(__snake_case				)

  # load original model from timm
  _snake_case   :				List[Any]														=	create_model(__snake_case    ,							pretrained=__snake_case				)
  timm_model.eval()

  # load state_dict of original model
  _snake_case   :				List[Any]														=	timm_model.state_dict()
  for key in state_dict.copy().keys():
    _snake_case   :				List[str]														=	state_dict.pop(__snake_case				)
    _snake_case   :				Optional[int]														=	val.squeeze() if 'head' in key else val

  # load HuggingFace model
  _snake_case   :				Union[str, Any]														=	BitForImageClassification(__snake_case				)
  model.eval()
  model.load_state_dict(__snake_case				)

  # create image processor
  _snake_case   :				Tuple														=	create_transform(**resolve_data_config({}    ,							model=__snake_case				)				)
  _snake_case   :				Tuple														=	transform.transforms

  _snake_case   :				str														=	{
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

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

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

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

  # verify logits
  with torch.no_grad():
    _snake_case   :				str														=	model(__snake_case				)
    _snake_case   :				List[str]														=	outputs.logits

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

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

  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_						   =    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_						   =    parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCAmelCase_						   =    logging.get_logger(__name__)


lowerCAmelCase_						   =    {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def     lowerCamelCase_	(      lowerCAmelCase:	List[Any]    ,							lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	Union[str, Any]				)->		Optional[int]:
  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(F"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()				)}."""				)

  if tokenizer_name is None:
    _snake_case   :				Tuple														=	TOKENIZER_CLASSES
  else:
    _snake_case   :				Union[str, Any]														=	{tokenizer_name: getattr(lowerCAmelCase    ,							tokenizer_name + 'Fast'				)}

  logger.info(F"""Loading tokenizer classes: {tokenizer_names}"""				)

  for tokenizer_name in tokenizer_names:
    _snake_case   :				Dict														=	TOKENIZER_CLASSES[tokenizer_name]

    _snake_case   :				Optional[Any]														=	True
    if checkpoint_name is None:
      _snake_case   :				Union[str, Any]														=	list(tokenizer_class.max_model_input_sizes.keys()				)
    else:
      _snake_case   :				Optional[int]														=	[checkpoint_name]

    logger.info(F"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""				)

    for checkpoint in checkpoint_names:
      logger.info(F"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""				)

      # Load tokenizer
      _snake_case   :				str														=	tokenizer_class.from_pretrained(lowerCAmelCase    ,							force_download=lowerCAmelCase				)

      # Save fast tokenizer
      logger.info(F"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""				)

      # For organization names we create sub-directories
      if "/" in checkpoint:
        _snake_case							,					_snake_case   :				Tuple														=	checkpoint.split('/'				)
        _snake_case   :				int														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
      elif add_prefix:
        _snake_case   :				Dict														=	checkpoint
        _snake_case   :				Optional[Any]														=	dump_path
      else:
        _snake_case   :				str														=	None
        _snake_case   :				Union[str, Any]														=	dump_path

      logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""				)

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()				)[0]:
        _snake_case   :				Optional[Any]														=	list(tokenizer.pretrained_vocab_files_map.values()				)[0][checkpoint]
        _snake_case   :				Optional[int]														=	file_path.split(lowerCAmelCase				)[-1][0]
        if next_char == "/":
          _snake_case   :				Union[str, Any]														=	os.path.join(lowerCAmelCase    ,							lowerCAmelCase				)
          _snake_case   :				str														=	None

        logger.info(F"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""				)

      _snake_case   :				Optional[int]														=	tokenizer.save_pretrained(
          lowerCAmelCase    ,							legacy_format=lowerCAmelCase    ,							filename_prefix=lowerCAmelCase				)
      logger.info(F"""=> File names {file_names}"""				)

      for file_name in file_names:
        if not file_name.endswith('tokenizer.json'				):
          os.remove(lowerCAmelCase				)
          logger.info(F"""=> removing {file_name}"""				)


if __name__ == "__main__":
      lowerCAmelCase_						   =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
      )
      parser.add_argument(
          """--tokenizer_name""",
          default=None,
          type=str,
          help=(
              F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
              """download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--checkpoint_name""",
          default=None,
          type=str,
          help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
      )
      parser.add_argument(
          """--force_download""",
          action="""store_true""",
          help="""Re-download checkpoints.""",
      )
      lowerCAmelCase_						   =    parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)




