



"""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()
lowercase__					:						Dict												=						logging.get_logger(__name__)




def __lowercase    (							_a					):
    snake_case_				:  Union[str, Any]       =			"""huggingface/label-files"""
    snake_case_				:  Tuple       =			"""imagenet-1k-id2label.json"""
    snake_case_				:  Tuple       =			json.load(open(hf_hub_download(__lowerCAmelCase	,    __lowerCAmelCase	,    repo_type='''dataset'''					)	,    '''r'''					)					)
    snake_case_				:  str       =			{int(__lowerCAmelCase					): v for k, v in idalabel.items()}
    snake_case_				:  str       =			{v: k for k, v in idalabel.items()}

    snake_case_				:  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"
    snake_case_				:  Dict       =			BitConfig(
        conv_layer=__lowerCAmelCase	,    num_labels=1_000	,    idalabel=__lowerCAmelCase	,    labelaid=__lowerCAmelCase	,    )

    return config




def __lowercase    (							_a					):
    if "stem.conv" in name:
        snake_case_				:  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_				:  int       =			name.replace('''head.fc'''	,    '''classifier.1'''					)
    if name.startswith('''norm'''					):
        snake_case_				:  str       =			"""bit.""" + name
    if "bit" not in name and "classifier" not in name:
        snake_case_				:  Optional[int]       =			"""bit.encoder.""" + name

    return name




def __lowercase    (							):
    snake_case_				:  Union[str, Any]       =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case_				:  Tuple       =			Image.open(requests.get(__lowerCAmelCase	,    stream=__lowerCAmelCase					).raw					)
    return im




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a=False					):
    snake_case_				:  List[Any]       =			get_config(__lowerCAmelCase					)

    # load original model from timm
    snake_case_				:  str       =			create_model(__lowerCAmelCase	,    pretrained=__lowerCAmelCase					)
    timm_model.eval()

    # load state_dict of original model
    snake_case_				:  int       =			timm_model.state_dict()
    for key in state_dict.copy().keys():
        snake_case_				:  int       =			state_dict.pop(__lowerCAmelCase					)
        snake_case_				:  Union[str, Any]       =			val.squeeze() if """head""" in key else val

    # load HuggingFace model
    snake_case_				:  Any       =			BitForImageClassification(__lowerCAmelCase					)
    model.eval()
    model.load_state_dict(__lowerCAmelCase					)

    # create image processor
    snake_case_				:  Dict       =			create_transform(**resolve_data_config({}	,    model=__lowerCAmelCase					)					)
    snake_case_				:  Union[str, Any]       =			transform.transforms

    snake_case_				:  Union[str, Any]       =			{
        """bilinear""": PILImageResampling.BILINEAR,
        """bicubic""": PILImageResampling.BICUBIC,
        """nearest""": PILImageResampling.NEAREST,
    }

    snake_case_				:  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()	,    )

    snake_case_				:  Union[str, Any]       =			prepare_img()
    snake_case_				:  Optional[int]       =			transform(__lowerCAmelCase					).unsqueeze(0					)
    snake_case_				:  Any       =			processor(__lowerCAmelCase	,    return_tensors='''pt'''					).pixel_values

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

    # verify logits
    with torch.no_grad():
        snake_case_				:  Optional[int]       =			model(__lowerCAmelCase					)
        snake_case_				:  List[Any]       =			outputs.logits

    print('''Logits:'''	,    logits[0, :3]					)
    print('''Predicted class:'''	,    model.config.idalabel[logits.argmax(-1					).item()]					)
    snake_case_				:  Optional[int]       =			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__":
     lowercase__					:						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.''',
     )

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










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





