'''simple docstring'''


import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


snake_case__	:     int      =    '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

snake_case__	:     List[str]      =    '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

snake_case__	:     int      =    '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''






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







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return datasets.MetricInfo(
           description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
               {
                   'predictions': {
                       'id': datasets.Value('string'		),
                       'prediction_text': datasets.Value('string'		),
                       'no_answer_probability': datasets.Value('float32'		),
                   },
                   'references': {
                       'id': datasets.Value('string'		),
                       'answers': datasets.features.Sequence(
                           {'text': datasets.Value('string'		), 'answer_start': datasets.Value('int32'		)}		),
                   },
               }		) ,						codebase_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						reference_urls=['https://rajpurkar.github.io/SQuAD-explorer/'] ,						)






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_=1.0		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['no_answer_probability'] for p in predictions}
       UpperCAmelCase_				:  Dict  = [{'paragraphs': [{'qas': references}]}]
       UpperCAmelCase_				:  Optional[int]  = {p['id']: p['prediction_text'] for p in predictions}

       UpperCAmelCase_				:  Optional[int]  = make_qid_to_has_ans(snake_case_		)  # maps qid to True/False
       UpperCAmelCase_				:  List[Any]  = [k for k, v in qid_to_has_ans.items() if v]
       UpperCAmelCase_				:  Union[str, Any]  = [k for k, v in qid_to_has_ans.items() if not v]

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = get_raw_scores(snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Dict  = apply_no_ans_threshold(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  str  = make_eval_dict(snake_case_ ,						snake_case_		)

       if has_ans_qids:
            UpperCAmelCase_				:  Dict  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'HasAns'		)
       if no_ans_qids:
            UpperCAmelCase_				:  List[str]  = make_eval_dict(snake_case_ ,						snake_case_ ,						qid_list=snake_case_		)
            merge_eval(snake_case_ ,						snake_case_ ,						'NoAns'		)
       find_all_best_thresh(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)
       return dict(snake_case_		)











'''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 transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase											=      logging.get_logger(__name__)






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=ConvNextConfig()

     if "tiny" in checkpoint_url:
          a_									=[3, 3, 9, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "small" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[9_6, 1_9_2, 3_8_4, 7_6_8]
     if "base" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
     if "large" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
     if "xlarge" in checkpoint_url:
          a_									=[3, 3, 2_7, 3]
          a_									=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]

     if "1k" in checkpoint_url:
          a_									=1_0_0_0
          a_									="imagenet-1k-id2label.json"
          a_									=(1, 1_0_0_0)
     else:
          a_									=2_1_8_4_1
          a_									="imagenet-22k-id2label.json"
          a_									=(1, 2_1_8_4_1)

     a_									="huggingface/label-files"
     a_									=num_labels
     a_									=json.load(open(hf_hub_download(lowercase__							,						lowercase__							,						repo_type="dataset"							)							,						"r"							)							)
     a_									={int(lowercase__							): v for k, v in idalabel.items()}
     if "1k" not in checkpoint_url:
          # this dataset contains 21843 labels but the model only has 21841
          # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
          del idalabel[9_2_0_5]
          del idalabel[1_5_0_2_7]
     a_									=idalabel
     a_									={v: k for k, v in idalabel.items()}
     a_									=hidden_sizes
     a_									=depths

     return config, expected_shape






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     if "downsample_layers.0.0" in name:
          a_									=name.replace("downsample_layers.0.0"							,						"embeddings.patch_embeddings"							)
     if "downsample_layers.0.1" in name:
          a_									=name.replace("downsample_layers.0.1"							,						"embeddings.norm"							)  # we rename to layernorm later on
     if "downsample_layers.1.0" in name:
          a_									=name.replace("downsample_layers.1.0"							,						"stages.1.downsampling_layer.0"							)
     if "downsample_layers.1.1" in name:
          a_									=name.replace("downsample_layers.1.1"							,						"stages.1.downsampling_layer.1"							)
     if "downsample_layers.2.0" in name:
          a_									=name.replace("downsample_layers.2.0"							,						"stages.2.downsampling_layer.0"							)
     if "downsample_layers.2.1" in name:
          a_									=name.replace("downsample_layers.2.1"							,						"stages.2.downsampling_layer.1"							)
     if "downsample_layers.3.0" in name:
          a_									=name.replace("downsample_layers.3.0"							,						"stages.3.downsampling_layer.0"							)
     if "downsample_layers.3.1" in name:
          a_									=name.replace("downsample_layers.3.1"							,						"stages.3.downsampling_layer.1"							)
     if "stages" in name and "downsampling_layer" not in name:
          # stages.0.0. for instance should be renamed to stages.0.layers.0.
          a_									=name[: len("stages.0"							)] + ".layers" + name[len("stages.0"							) :]
     if "stages" in name:
          a_									=name.replace("stages"							,						"encoder.stages"							)
     if "norm" in name:
          a_									=name.replace("norm"							,						"layernorm"							)
     if "gamma" in name:
          a_									=name.replace("gamma"							,						"layer_scale_parameter"							)
     if "head" in name:
          a_									=name.replace("head"							,						"classifier"							)

     return name






def 			UpperCAmelCase_							(		):


     '''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 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_		,    a_									=get_convnext_config(lowercase__							)
     # load original state_dict from URL
     a_									=torch.hub.load_state_dict_from_url(lowercase__							)["model"]
     # rename keys
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          a_									=val
     # add prefix to all keys expect classifier head
     for key in state_dict.copy().keys():
          a_									=state_dict.pop(lowercase__							)
          if not key.startswith("classifier"							):
               a_									="convnext." + key
          a_									=val

     # load HuggingFace model
     a_									=ConvNextForImageClassification(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()

     # Check outputs on an image, prepared by ConvNextImageProcessor
     a_									=2_2_4 if "224" in checkpoint_url else 3_8_4
     a_									=ConvNextImageProcessor(size=lowercase__							)
     a_									=image_processor(images=prepare_img()							,						return_tensors="pt"							).pixel_values

     a_									=model(lowercase__							).logits

     # note: the logits below were obtained without center cropping
     if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
          a_									=torch.tensor([-0.1210, -0.6605, 0.1918]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
          a_									=torch.tensor([-0.4473, -0.1847, -0.6365]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
          a_									=torch.tensor([0.4525, 0.7539, 0.0308]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
          a_									=torch.tensor([0.3561, 0.6350, -0.0384]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
          a_									=torch.tensor([0.4174, -0.0989, 0.1489]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
          a_									=torch.tensor([0.2513, -0.1349, -0.1613]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
          a_									=torch.tensor([1.2980, 0.3631, -0.1198]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
          a_									=torch.tensor([1.2963, 0.1227, 0.1723]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
          a_									=torch.tensor([1.7956, 0.8390, 0.2820]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
          a_									=torch.tensor([-0.2822, -0.0502, -0.0878]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
          a_									=torch.tensor([-0.5672, -0.0730, -0.4348]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
          a_									=torch.tensor([0.2681, 0.2365, 0.6246]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
          a_									=torch.tensor([-0.2642, 0.3931, 0.5116]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
          a_									=torch.tensor([-0.6677, -0.1873, -0.8379]							)
     elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
          a_									=torch.tensor([-0.7749, -0.2967, -0.6444]							)
     else:
          raise ValueError(F"""Unknown URL: {checkpoint_url}"""							)

     assert torch.allclose(logits[0, :3]							,						lowercase__							,						atol=1E-3							)
     assert logits.shape == expected_shape

     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     print(F"""Saving model to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(lowercase__							)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(lowercase__							)

     print("Pushing model to the hub..."							)
     a_									="convnext"
     if "tiny" in checkpoint_url:
          model_name += "-tiny"
     elif "small" in checkpoint_url:
          model_name += "-small"
     elif "base" in checkpoint_url:
          model_name += "-base"
     elif "xlarge" in checkpoint_url:
          model_name += "-xlarge"
     elif "large" in checkpoint_url:
          model_name += "-large"
     if "224" in checkpoint_url:
          model_name += "-224"
     elif "384" in checkpoint_url:
          model_name += "-384"
     if "22k" in checkpoint_url and "1k" not in checkpoint_url:
          model_name += "-22k"
     if "22k" in checkpoint_url and "1k" in checkpoint_url:
          model_name += "-22k-1k"

     model.push_to_hub(
         repo_path_or_name=Path(lowercase__							,						lowercase__							)							,						organization="nielsr"							,						commit_message="Add model"							,						)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth''',
      type=str,
      help='''URL of the original ConvNeXT checkpoint you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default=None,
      type=str,
      required=True,
      help='''Path to the output PyTorch model directory.''',
  )

  lowercase											=      parser.parse_args()
  convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


