import re
import string

import numpy as np

import datasets


lowerCAmelCase__     						=			'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

lowerCAmelCase__     						=			'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

lowerCAmelCase__     						=			'\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,					_KWARGS_DESCRIPTION						)
class     a_			(						datasets.Metric						):






 '''simple docstring'''






 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        return datasets.MetricInfo(
            description=_DESCRIPTION      ,   citation=_CITATION      ,   inputs_description=_KWARGS_DESCRIPTION      ,   features=datasets.Features(
                {
                    'predictions': datasets.Value('string'      ,   id='sequence'),
                    'references': datasets.Value('string'      ,   id='sequence'),
                })      ,   reference_urls=[]      ,   )




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  str      ,   lowercase__ :  Optional[int]      ,   lowercase__ :  int=None      ,   lowercase__ :  List[str]=False      ,   lowercase__ :  Optional[int]=False      ,   lowercase__ :  List[Any]=False      ,   ):







        '''simple docstring'''



        if regexes_to_ignore is not None:
               for s in regexes_to_ignore:
                      lowerCAmelCase__					     = np.array([re.sub(lowercase__      ,   ''      ,   lowercase__) for x in predictions])
                      lowerCAmelCase__					     = np.array([re.sub(lowercase__      ,   ''      ,   lowercase__) for x in references])
        else:
               lowerCAmelCase__					     = np.asarray(lowercase__)
               lowerCAmelCase__					     = np.asarray(lowercase__)

        if ignore_case:
               lowerCAmelCase__					     = np.char.lower(lowercase__)
               lowerCAmelCase__					     = np.char.lower(lowercase__)

        if ignore_punctuation:
               lowerCAmelCase__					     = string.punctuation.maketrans(''      ,   ''      ,   string.punctuation)
               lowerCAmelCase__					     = np.char.translate(lowercase__      ,   table=lowercase__)
               lowerCAmelCase__					     = np.char.translate(lowercase__      ,   table=lowercase__)

        if ignore_numbers:
               lowerCAmelCase__					     = string.digits.maketrans(''      ,   ''      ,   string.digits)
               lowerCAmelCase__					     = np.char.translate(lowercase__      ,   table=lowercase__)
               lowerCAmelCase__					     = np.char.translate(lowercase__      ,   table=lowercase__)

        lowerCAmelCase__					     = predictions == references

        return {"exact_match": np.mean(lowercase__) * 100}

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__     						=			logging.get_logger(__name__)






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 'huggingface/label-files'
       lowerCAmelCase__					     = 'imagenet-1k-id2label.json'
       lowerCAmelCase__					     = json.load(open(hf_hub_download(lowerCAmelCase__     ,  lowerCAmelCase__     ,  repo_type='dataset'     )     ,  'r'     )     )
       lowerCAmelCase__					     = {int(lowerCAmelCase__     ): v for k, v in idalabel.items()}
       lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       lowerCAmelCase__					     = '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"
       lowerCAmelCase__					     = BitConfig(
           conv_layer=lowerCAmelCase__     ,  num_labels=1_0_0_0     ,  idalabel=lowerCAmelCase__     ,  labelaid=lowerCAmelCase__     ,  )

       return config






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

       return name






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(lowerCAmelCase__     ,  stream=lowerCAmelCase__     ).raw     )
       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = get_config(lowerCAmelCase__     )

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

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

       # load HuggingFace model
       lowerCAmelCase__					     = BitForImageClassification(lowerCAmelCase__     )
       model.eval()
       model.load_state_dict(lowerCAmelCase__     )

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

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

       lowerCAmelCase__					     = 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()     ,  )

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

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

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

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