


'''simple docstring'''



import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__UpperCAmelCase    :Optional[int]				       =     "\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

__UpperCAmelCase    :Dict				       =     "\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

__UpperCAmelCase    :Optional[Any]				       =     "\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							str:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Sequence(datasets.Value('''string'''							,				id='''token'''    )							,				id='''sequence'''    ),
                      '''references''': datasets.Sequence(
                          datasets.Sequence(datasets.Value('''string'''							,				id='''token'''    )							,				id='''sequence'''    )							,				id='''references'''    ),
                  }    )							,				codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py''']							,				reference_urls=[
                  '''https://en.wikipedia.org/wiki/BLEU''',
                  '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
              ]							,				)






     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Tuple							,				snake_case  :	int							,				snake_case  :	List[Any]=4							,				snake_case  :	List[str]=False    )		->							List[Any]:
          __UpperCAmelCase			:				List[Any]													=    compute_bleu(
              reference_corpus=snake_case							,				translation_corpus=snake_case							,				max_order=snake_case							,				smooth=snake_case    )
          ((__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase)							,     (__UpperCAmelCase))			:				Union[str, Any]													=    score
          return {
              "bleu": bleu,
              "precisions": precisions,
              "brevity_penalty": bp,
              "length_ratio": ratio,
              "translation_length": translation_length,
              "reference_length": reference_length,
          }



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




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				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"
     __UpperCAmelCase			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     return name




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase			:				Optional[Any]													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     return im




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

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

     __UpperCAmelCase			:				List[Any]													=    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()    ,      )

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

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

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

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

      __UpperCAmelCase    :str				       =     parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)