


"""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_       :       Tuple      						=  logging.get_logger(__name__)
def _A						(						lowercase		):







    """simple docstring"""
    a					='''huggingface/label-files'''
    a					='''imagenet-1k-id2label.json'''
    a					=json.load(open(hf_hub_download(lowercase	,   lowercase	,   repo_type='''dataset'''		)	,   '''r'''		)		)
    a					={int(lowercase		): v for k, v in idalabel.items()}
    a					={v: k for k, v in idalabel.items()}

    a					='''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					=BitConfig(
        conv_layer=lowercase	,   num_labels=10_00	,   idalabel=lowercase	,   labelaid=lowercase	,   )

    return config
def _A						(						lowercase		):







    """simple docstring"""
    if "stem.conv" in name:
        a					=name.replace('''stem.conv'''	,   '''bit.embedder.convolution'''		)
    if "blocks" in name:
        a					=name.replace('''blocks'''	,   '''layers'''		)
    if "head.fc" in name:
        a					=name.replace('''head.fc'''	,   '''classifier.1'''		)
    if name.startswith('''norm'''		):
        a					='''bit.''' + name
    if "bit" not in name and "classifier" not in name:
        a					='''bit.encoder.''' + name

    return name
def _A						(						):







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(lowercase	,   stream=lowercase		).raw		)
    return im
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase=False		):







    """simple docstring"""
    a					=get_config(lowercase		)

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

    # load state_dict of original model
    a					=timm_model.state_dict()
    for key in state_dict.copy().keys():
        a					=state_dict.pop(lowercase		)
        a					=val.squeeze() if '''head''' in key else val

    # load HuggingFace model
    a					=BitForImageClassification(lowercase		)
    model.eval()
    model.load_state_dict(lowercase		)

    # create image processor
    a					=create_transform(**resolve_data_config({}	,   model=lowercase		)		)
    a					=transform.transforms

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

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

    a					=prepare_img()
    a					=transform(lowercase		).unsqueeze(0		)
    a					=processor(lowercase	,   return_tensors='''pt'''		).pixel_values

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

    # verify logits
    with torch.no_grad():
        a					=model(lowercase		)
        a					=outputs.logits

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

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

    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_       :       List[str]      						=  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 Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase_       :       List[Any]      						=  logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  ["pixel_values"]


 def __init__(		self			,   __A = True			,   __A = None			,   __A = PILImageResampling.BICUBIC			,   __A = True			,   __A = None			,   __A = True			,   __A = 1 / 255			,   __A = True			,   __A = None			,   __A = None			,   __A = True			,   **__A			,   ) ->					None:
     super().__init__(**__A						)
     a					=size if size is not None else {'''shortest_edge''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A						)
     a					=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     a					=get_size_dict(__A			,   default_to_square=__A			,   param_name='''crop_size'''						)

     a					=do_resize
     a					=size
     a					=resample
     a					=do_center_crop
     a					=crop_size
     a					=do_rescale
     a					=rescale_factor
     a					=do_normalize
     a					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     a					=image_std if image_std is not None else OPENAI_CLIP_STD
     a					=do_convert_rgb


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = PILImageResampling.BICUBIC			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A			,   default_to_square=__A						)
     if "shortest_edge" not in size:
         raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
     a					=get_resize_output_image_size(__A			,   size=size['''shortest_edge''']			,   default_to_square=__A						)
     return resize(__A			,   size=__A			,   resample=__A			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     a					=get_size_dict(__A						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
     return center_crop(__A			,   size=(size['''height'''], size['''width'''])			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					Any:
     return rescale(__A			,   scale=__A			,   data_format=__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A = None			,   **__A			,   ) ->					np.ndarray:
     return normalize(__A			,   mean=__A			,   std=__A			,   data_format=__A			,   **__A						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = None			,   __A = ChannelDimension.FIRST			,   **__A			,   ) ->					PIL.Image.Image:
     a					=do_resize if do_resize is not None else self.do_resize
     a					=size if size is not None else self.size
     a					=get_size_dict(__A			,   param_name='''size'''			,   default_to_square=__A						)
     a					=resample if resample is not None else self.resample
     a					=do_center_crop if do_center_crop is not None else self.do_center_crop
     a					=crop_size if crop_size is not None else self.crop_size
     a					=get_size_dict(__A			,   param_name='''crop_size'''			,   default_to_square=__A						)
     a					=do_rescale if do_rescale is not None else self.do_rescale
     a					=rescale_factor if rescale_factor is not None else self.rescale_factor
     a					=do_normalize if do_normalize is not None else self.do_normalize
     a					=image_mean if image_mean is not None else self.image_mean
     a					=image_std if image_std is not None else self.image_std
     a					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     a					=make_list_of_images(__A						)

     if not valid_images(__A						):
         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.'''						)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         a					=[convert_to_rgb(__A						) for image in images]

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

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

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

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

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

     a					=[to_channel_dimension_format(__A			,   __A						) for image in images]

     a					={'''pixel_values''': images}
     return BatchFeature(data=__A			,   tensor_type=__A						)