






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()
a		:       str			     = logging.get_logger(__name__)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict		):
  __UpperCAmelCase							:	Optional[Any]           =  """huggingface/label-files"""
  __UpperCAmelCase							:	Tuple           =  """imagenet-1k-id2label.json"""
  __UpperCAmelCase							:	Any           =  json.load(open(hf_hub_download(__lowerCamelCase  ,		__lowerCamelCase  ,		repo_type="""dataset"""		)  ,		"""r"""		)		)
  __UpperCAmelCase							:	Dict           =  {int(__lowerCamelCase		): v for k, v in idalabel.items()}
  __UpperCAmelCase							:	Union[str, Any]           =  {v: k for k, v in idalabel.items()}

  __UpperCAmelCase							:	Optional[int]           =  """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"
  __UpperCAmelCase							:	List[str]           =  BitConfig(
      conv_layer=__lowerCamelCase  ,		num_labels=1000  ,		idalabel=__lowerCamelCase  ,		labelaid=__lowerCamelCase  ,		)

  return config






def 				lowerCamelCase__      (    __lowerCamelCase			:     List[str]		):
  if "stem.conv" in name:
    __UpperCAmelCase							:	Union[str, Any]           =  name.replace("""stem.conv"""  ,		"""bit.embedder.convolution"""		)
  if "blocks" in name:
    __UpperCAmelCase							:	Tuple           =  name.replace("""blocks"""  ,		"""layers"""		)
  if "head.fc" in name:
    __UpperCAmelCase							:	int           =  name.replace("""head.fc"""  ,		"""classifier.1"""		)
  if name.startswith("""norm"""		):
    __UpperCAmelCase							:	int           =  """bit.""" + name
  if "bit" not in name and "classifier" not in name:
    __UpperCAmelCase							:	Union[str, Any]           =  """bit.encoder.""" + name

  return name






def 				lowerCamelCase__      (    ):
  __UpperCAmelCase							:	List[Any]           =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:	List[Any]           =  Image.open(requests.get(__lowerCamelCase  ,		stream=__lowerCamelCase		).raw		)
  return im






@torch.no_grad()
def 				lowerCamelCase__      (    __lowerCamelCase			:     int  ,		__lowerCamelCase			:     Optional[Any]  ,		__lowerCamelCase			:     Optional[int]=False		):
  __UpperCAmelCase							:	str           =  get_config(__lowerCamelCase		)

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

  # load state_dict of original model
  __UpperCAmelCase							:	Dict           =  timm_model.state_dict()
  for key in state_dict.copy().keys():
    __UpperCAmelCase							:	int           =  state_dict.pop(__lowerCamelCase		)
    __UpperCAmelCase							:	str           =  val.squeeze() if """head""" in key else val

  # load HuggingFace model
  __UpperCAmelCase							:	List[Any]           =  BitForImageClassification(__lowerCamelCase		)
  model.eval()
  model.load_state_dict(__lowerCamelCase		)

  # create image processor
  __UpperCAmelCase							:	List[str]           =  create_transform(**resolve_data_config({}  ,		model=__lowerCamelCase		)		)
  __UpperCAmelCase							:	str           =  transform.transforms

  __UpperCAmelCase							:	Any           =  {
      """bilinear""": PILImageResampling.BILINEAR,
      """bicubic""": PILImageResampling.BICUBIC,
      """nearest""": PILImageResampling.NEAREST,
  }

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

  __UpperCAmelCase							:	Optional[Any]           =  prepare_img()
  __UpperCAmelCase							:	int           =  transform(__lowerCamelCase		).unsqueeze(0		)
  __UpperCAmelCase							:	int           =  processor(__lowerCamelCase  ,		return_tensors="""pt"""		).pixel_values

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

  # verify logits
  with torch.no_grad():
    __UpperCAmelCase							:	Dict           =  model(__lowerCamelCase		)
    __UpperCAmelCase							:	Optional[Any]           =  outputs.logits

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

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

  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__":
       a		:       Union[str, 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.",
       )

       a		:       List[str]			     = parser.parse_args()
       convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[str]							       =	'MCTCTFeatureExtractor'
      a  : str							       =	'AutoTokenizer'
      def __init__(      self					:			Tuple		,      __lowercase					:			int		,      __lowercase					:			Dict					)							->      Any:
        super().__init__(__lowercase		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	Optional[int]           =  False
      def __call__(      self					:			int		,      *__lowercase					:			Tuple		,      **__lowercase					:			Optional[int]					)							->      Union[str, Any]:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor(*__lowercase		,      **__lowercase					)

        if "raw_speech" in kwargs:
          warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""					)
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""raw_speech"""					)
        else:
          __UpperCAmelCase							:	Dict           =  kwargs.pop("""audio"""		,      __lowercase					)
        __UpperCAmelCase							:	List[str]           =  kwargs.pop("""sampling_rate"""		,      __lowercase					)
        __UpperCAmelCase							:	Tuple           =  kwargs.pop("""text"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Tuple           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if audio is None and text is None:
          raise ValueError("""You need to specify either an `audio` or `text` input to process."""					)

        if audio is not None:
          __UpperCAmelCase							:	Tuple           =  self.feature_extractor(__lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      **__lowercase					)
        if text is not None:
          __UpperCAmelCase							:	str           =  self.tokenizer(__lowercase		,      **__lowercase					)

        if text is None:
          return inputs
        elif audio is None:
          return encodings
        else:
          __UpperCAmelCase							:	Dict           =  encodings["""input_ids"""]
          return inputs
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      *__lowercase					:			List[Any]		,      **__lowercase					:			int					)							->      List[Any]:
        return self.tokenizer.batch_decode(*__lowercase		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      *__lowercase					:			Optional[Any]		,      **__lowercase					:			List[str]					)							->      int:
        # For backward compatibility
        if self._in_target_context_manager:
          return self.current_processor.pad(*__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Optional[int]           =  kwargs.pop("""input_features"""		,      __lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  kwargs.pop("""labels"""		,      __lowercase					)
        if len(__lowercase					) > 0:
          __UpperCAmelCase							:	Union[str, Any]           =  args[0]
          __UpperCAmelCase							:	str           =  args[1:]

        if input_features is not None:
          __UpperCAmelCase							:	Any           =  self.feature_extractor.pad(__lowercase		,      *__lowercase		,      **__lowercase					)
        if labels is not None:
          __UpperCAmelCase							:	Union[str, Any]           =  self.tokenizer.pad(__lowercase		,      **__lowercase					)

        if labels is None:
          return input_features
        elif input_features is None:
          return labels
        else:
          __UpperCAmelCase							:	Any           =  labels["""input_ids"""]
          return input_features
      def 	UpperCAmelCase      (      self					:			Any		,      *__lowercase					:			Union[str, Any]		,      **__lowercase					:			Dict					)							->      List[Any]:
        return self.tokenizer.decode(*__lowercase		,      **__lowercase					)




      @contextmanager
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your audio inputs, or in a separate call."""					)
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	Optional[int]           =  self.tokenizer
        yield
        __UpperCAmelCase							:	List[Any]           =  self.feature_extractor
        __UpperCAmelCase							:	int           =  False


