
'''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()
a_											=						logging.get_logger(__name__)

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]							=						"huggingface/label-files"
    snake_case_ :    Optional[Any]							=						"imagenet-1k-id2label.json"
    snake_case_ :    List[Any]							=						json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		repo_type="dataset"       ),		"r"       )       )
    snake_case_ :    List[Any]							=						{int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    snake_case_ :    List[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_ :    Optional[int]							=						BitConfig(
        conv_layer=__SCREAMING_SNAKE_CASE,		num_labels=1_0_0_0,		idalabel=__SCREAMING_SNAKE_CASE,		labelaid=__SCREAMING_SNAKE_CASE,		)

    return config

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if "stem.conv" in name:
        snake_case_ :    List[str]							=						name.replace("stem.conv",		"bit.embedder.convolution"       )
    if "blocks" in name:
        snake_case_ :    Dict							=						name.replace("blocks",		"layers"       )
    if "head.fc" in name:
        snake_case_ :    str							=						name.replace("head.fc",		"classifier.1"       )
    if name.startswith("norm"       ):
        snake_case_ :    Union[str, Any]							=						"bit." + name
    if "bit" not in name and "classifier" not in name:
        snake_case_ :    Optional[Any]							=						"bit.encoder." + name

    return name

def 						UpperCamelCase_     (	):
    """simple docstring"""






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

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=False       ):
    """simple docstring"""






    snake_case_ :    Any							=						get_config(__SCREAMING_SNAKE_CASE       )

    # load original model from timm
    snake_case_ :    str							=						create_model(__SCREAMING_SNAKE_CASE,		pretrained=__SCREAMING_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_ :    Union[str, Any]							=						state_dict.pop(__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Tuple							=						val.squeeze() if "head" in key else val

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

    # create image processor
    snake_case_ :    Optional[int]							=						create_transform(**resolve_data_config({},		model=__SCREAMING_SNAKE_CASE       )       )
    snake_case_ :    str							=						transform.transforms

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

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

    snake_case_ :    str							=						prepare_img()
    snake_case_ :    Any							=						transform(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       )
    snake_case_ :    List[Any]							=						processor(__SCREAMING_SNAKE_CASE,		return_tensors="pt"       ).pixel_values

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

    # verify logits
    with torch.no_grad():
        snake_case_ :    List[Any]							=						model(__SCREAMING_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_ :    Tuple							=						timm_model(__SCREAMING_SNAKE_CASE       )
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(__SCREAMING_SNAKE_CASE,		outputs.logits,		atol=1E-3       )
    print("Looks ok!"       )

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






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

     if node.prev is None or node.next is None:
         return None

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						self.list.remove(self.cache[key])
         assert node == value_node

         # node is guaranteed not None because it is in self.cache
         assert node is not None
         self.list.add(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						self.list.head.next

             # guaranteed to have a non-None first node when num_keys > 0
             # explain to type checker via assertions
             assert first_node is not None
             assert first_node.key is not None
             assert (
                 self.list.remove(lowercase_) is not None
             )  # node guaranteed to be in list assert node.key is not None

             del self.cache[first_node.key]
             self.num_keys -= 1
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

     else:
         # bump node to the end of the list, update value
         snake_case_ :    List[Any]     			=						self.list.remove(self.cache[key])
         assert node is not None  # node guaranteed to be in list
         snake_case_ :    Optional[Any]     			=						value
         self.list.add(lowercase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

         def cache_info() -> LRUCache[T, U]:
             return cls.decorator_function_to_instance_map[func]

         setattr(lowercase_ ,       "cache_info" ,       lowercase_)  # noqa: B010

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




