

"""simple docstring"""





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
   import torch


_a       =							logging.get_logger(__name__)




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	["pixel_values"]


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Dict[str, int]] = None,					UpperCAmelCase__							:			PILImageResampling = PILImageResampling.BILINEAR,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Dict[str, int] = None,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Union[int, float] = 1 / 2_5_5,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Union[float, List[float]]] = None,					UpperCAmelCase__							:			Optional[Union[float, List[float]]] = None,					**UpperCAmelCase__							:			Any,					):
    super().__init__(**UpperCAmelCase__	)
    __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
    __lowercase								=						get_size_dict(UpperCAmelCase__,					default_to_square=UpperCAmelCase__	)
    __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__,					param_name="crop_size"	)
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						resample
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_normalize
    __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			Dict[str, int],					UpperCAmelCase__							:			PILImageResampling = PILImageResampling.BICUBIC,					UpperCAmelCase__							:			Optional[Union[str, ChannelDimension]] = None,					**UpperCAmelCase__							:			Union[str, Any],					):
    __lowercase								=						get_size_dict(UpperCAmelCase__,					default_to_square=UpperCAmelCase__	)
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""	)
    __lowercase								=						get_resize_output_image_size(UpperCAmelCase__,					size=size["shortest_edge"],					default_to_square=UpperCAmelCase__	)
    return resize(UpperCAmelCase__,					size=UpperCAmelCase__,					resample=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			Dict[str, int],					UpperCAmelCase__							:			Optional[Union[str, ChannelDimension]] = None,					**UpperCAmelCase__							:			Optional[int],					):
    __lowercase								=						get_size_dict(UpperCAmelCase__	)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""	)
    return center_crop(UpperCAmelCase__,					size=(size["height"], size["width"]),					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			float,					UpperCAmelCase__							:			Optional[Union[str, ChannelDimension]] = None,					**UpperCAmelCase__							:			Union[str, Any]	):
    return rescale(UpperCAmelCase__,					scale=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			np.ndarray,					UpperCAmelCase__							:			Union[float, List[float]],					UpperCAmelCase__							:			Union[float, List[float]],					UpperCAmelCase__							:			Optional[Union[str, ChannelDimension]] = None,					**UpperCAmelCase__							:			Tuple,					):
    return normalize(UpperCAmelCase__,					mean=UpperCAmelCase__,					std=UpperCAmelCase__,					data_format=UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			ImageInput,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			Dict[str, int] = None,					UpperCAmelCase__							:			PILImageResampling = None,					UpperCAmelCase__							:			bool = None,					UpperCAmelCase__							:			Dict[str, int] = None,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			Optional[float] = None,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			Optional[Union[float, List[float]]] = None,					UpperCAmelCase__							:			Optional[Union[float, List[float]]] = None,					UpperCAmelCase__							:			Optional[Union[str, TensorType]] = None,					UpperCAmelCase__							:			Union[str, ChannelDimension] = ChannelDimension.FIRST,					**UpperCAmelCase__							:			Optional[Any],					):
    __lowercase								=						do_resize if do_resize is not None else self.do_resize
    __lowercase								=						size if size is not None else self.size
    __lowercase								=						get_size_dict(UpperCAmelCase__,					default_to_square=UpperCAmelCase__	)
    __lowercase								=						resample if resample is not None else self.resample
    __lowercase								=						do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowercase								=						crop_size if crop_size is not None else self.crop_size
    __lowercase								=						get_size_dict(UpperCAmelCase__,					param_name="crop_size"	)
    __lowercase								=						do_rescale if do_rescale is not None else self.do_rescale
    __lowercase								=						rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						image_mean if image_mean is not None else self.image_mean
    __lowercase								=						image_std if image_std is not None else self.image_std

    __lowercase								=						make_list_of_images(UpperCAmelCase__	)

    if not valid_images(UpperCAmelCase__	):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."	)

    if do_resize and size is None:
       raise ValueError("Size must be specified if do_resize is True."	)

    if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True."	)

    if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."	)

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."	)

    # All transformations expect numpy arrays.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__	) for image in images]

    if do_resize:
       __lowercase								=						[self.resize(image=UpperCAmelCase__,					size=UpperCAmelCase__,					resample=UpperCAmelCase__	) for image in images]

    if do_center_crop:
       __lowercase								=						[self.center_crop(image=UpperCAmelCase__,					size=UpperCAmelCase__	) for image in images]

    if do_rescale:
       __lowercase								=						[self.rescale(image=UpperCAmelCase__,					scale=UpperCAmelCase__	) for image in images]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__,					mean=UpperCAmelCase__,					std=UpperCAmelCase__	) for image in images]

    __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__,					UpperCAmelCase__	) for image in images]

    __lowercase								=						{"pixel_values": images}
    return BatchFeature(data=UpperCAmelCase__,					tensor_type=UpperCAmelCase__	)



 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			List[Tuple] = None	):
    __lowercase								=						outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
       if len(UpperCAmelCase__	) != len(UpperCAmelCase__	):
          raise ValueError(
              "Make sure that you pass in as many target sizes as the batch dimension of the logits"	)

       if is_torch_tensor(UpperCAmelCase__	):
          __lowercase								=						target_sizes.numpy()

       __lowercase								=						[]

       for idx in range(len(UpperCAmelCase__	)	):
          __lowercase								=						torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0	),					size=target_sizes[idx],					mode="bilinear",					align_corners=UpperCAmelCase__	)
          __lowercase								=						resized_logits[0].argmax(dim=0	)
          semantic_segmentation.append(UpperCAmelCase__	)
    else:
       __lowercase								=						logits.argmax(dim=1	)
       __lowercase								=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

    return semantic_segmentation





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_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						int =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					json.load(open(hf_hub_download(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)  ,  'r'	)	)
 UpperCamelCase_					:						str =					{int(lowerCamelCase	): v for k, v in idalabel.items()}
 UpperCamelCase_					:						Optional[int] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						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"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					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_					:						str =					timm_model.state_dict()
 for key in state_dict.copy().keys():
  UpperCamelCase_					:						Tuple =					state_dict.pop(lowerCamelCase	)
  UpperCamelCase_					:						str =					val.squeeze() if 'head' in key else val

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

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

 UpperCamelCase_					:						Tuple =					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_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					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_					:						Tuple =					outputs.logits

 print('Logits:'  ,  logits[0, :3]	)
 print('Predicted class:'  ,  model.config.idalabel[logits.argmax(-1	).item()]	)
 UpperCamelCase_					:						List[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_    					=       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_    					=       parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



