




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase	:		Optional[Any]							       =   logging.get_logger(__name__)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False						)   ->       int:



       """simple docstring"""

       A__       =    []
       for i in range(config.num_hidden_layers						):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")						)
              rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")						)
              rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")						)

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('''cls_token''', '''vit.embeddings.cls_token'''),
               ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
               ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
               ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
           ]						)

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''layernorm.weight'''),
                      ('''norm.bias''', '''layernorm.bias'''),
                      ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                      ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                  ]						)

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              A__       =    [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''						) else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''vit.layernorm.weight'''),
                      ('''norm.bias''', '''vit.layernorm.bias'''),
                      ('''head.weight''', '''classifier.weight'''),
                      ('''head.bias''', '''classifier.bias'''),
                  ]						)

       return rename_keys







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_=False						)   ->       List[str]:



       """simple docstring"""

       for i in range(config.num_hidden_layers						):
              if base_model:
                     A__       =    """"""
              else:
                     A__       =    """vit."""
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A__       =    state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""						)
              A__       =    state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""						)
              # next, add query, keys and values (in that order) to the state dict
              A__       =    in_proj_weight[
                  : config.hidden_size, :
              ]
              A__       =    in_proj_bias[: config.hidden_size]
              A__       =    in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A__       =    in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A__       =    in_proj_weight[
                  -config.hidden_size :, :
              ]
              A__       =    in_proj_bias[-config.hidden_size :]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[int]:



       """simple docstring"""

       A__       =    ["""head.weight""", """head.bias"""]
       for k in ignore_keys:
              state_dict.pop(lowercase_ ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    dct.pop(lowercase_						)
       A__       =    val







def SCREAMING_SNAKE_CASE				(       )   ->       str:



       """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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    ViTConfig()
       A__       =    False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              A__       =    True
              A__       =    int(vit_name[-12:-10]						)
              A__       =    int(vit_name[-9:-6]						)
       else:
              A__       =    1_000
              A__       =    """huggingface/label-files"""
              A__       =    """imagenet-1k-id2label.json"""
              A__       =    json.load(open(hf_hub_download(lowercase_ ,				lowercase_ ,				repo_type='''dataset'''						) ,				'''r'''						)						)
              A__       =    {int(lowercase_						): v for k, v in idalabel.items()}
              A__       =    idalabel
              A__       =    {v: k for k, v in idalabel.items()}
              A__       =    int(vit_name[-6:-4]						)
              A__       =    int(vit_name[-3:]						)
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith('''tiny'''						):
                     A__       =    192
                     A__       =    768
                     A__       =    12
                     A__       =    3
              elif vit_name[9:].startswith('''small'''						):
                     A__       =    384
                     A__       =    1_536
                     A__       =    12
                     A__       =    6
              else:
                     pass
       else:
              if vit_name[4:].startswith('''small'''						):
                     A__       =    768
                     A__       =    2_304
                     A__       =    8
                     A__       =    8
              elif vit_name[4:].startswith('''base'''						):
                     pass
              elif vit_name[4:].startswith('''large'''						):
                     A__       =    1_024
                     A__       =    4_096
                     A__       =    24
                     A__       =    16
              elif vit_name[4:].startswith('''huge'''						):
                     A__       =    1_280
                     A__       =    5_120
                     A__       =    32
                     A__       =    16

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

       # load state_dict of original model, remove and rename some keys
       A__       =    timm_model.state_dict()
       if base_model:
              remove_classification_head_(lowercase_						)
       A__       =    create_rename_keys(lowercase_ ,				lowercase_						)
       for src, dest in rename_keys:
              rename_key(lowercase_ ,				lowercase_ ,				lowercase_						)
       read_in_q_k_v(lowercase_ ,				lowercase_ ,				lowercase_						)

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              A__       =    ViTModel(lowercase_						).eval()
       else:
              A__       =    ViTForImageClassification(lowercase_						).eval()
       model.load_state_dict(lowercase_						)

       # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
       if "deit" in vit_name:
              A__       =    DeiTImageProcessor(size=config.image_size						)
       else:
              A__       =    ViTImageProcessor(size=config.image_size						)
       A__       =    image_processor(images=prepare_img() ,				return_tensors='''pt'''						)
       A__       =    encoding["""pixel_values"""]
       A__       =    model(lowercase_						)

       if base_model:
              A__       =    timm_model.forward_features(lowercase_						)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(lowercase_ ,				outputs.pooler_output ,				atol=1E-3						)
       else:
              A__       =    timm_model(lowercase_						)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(lowercase_ ,				outputs.logits ,				atol=1E-3						)

       Path(lowercase_						).mkdir(exist_ok=lowercase_						)
       print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase	:		Dict							       =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_patch16_224""",
      type=str,
      help="""Name of the ViT 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."""
  )

  _lowerCamelCase	:		List[Any]							       =   parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







'''simple docstring'''





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


_lowerCAmelCase      						=	'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

_lowerCAmelCase      						=	'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

_lowerCAmelCase      						=	'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    return float((preds == labels).mean()			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase="binary"			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					simple_accuracy(UpperCamelCase							,    UpperCamelCase			)
    lowerCAmelCase__ :  Tuple							      =					float(fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average=UpperCamelCase			)			)
    return {
        "accuracy": acc,
        "f1": fa,
    }
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					{}
    for id_pred, label in zip(UpperCamelCase							,    UpperCamelCase			):
        lowerCAmelCase__ :  str							      =					f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
        lowerCAmelCase__ :  Dict							      =					id_pred["""prediction"""]
        if question_id in question_map:
            question_map[question_id].append((pred, label)			)
        else:
            lowerCAmelCase__ :  Optional[int]							      =					[(pred, label)]
    lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					[], []
    for question, preds_labels in question_map.items():
        lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					zip(*UpperCamelCase			)
        lowerCAmelCase__ :  List[Any]							      =					fa_score(y_true=UpperCamelCase							,    y_pred=UpperCamelCase							,    average="""macro"""			)
        fas.append(UpperCamelCase			)
        lowerCAmelCase__ :  Union[str, Any]							      =					int(sum(pred == label for pred, label in preds_labels			) == len(UpperCamelCase			)			)
        ems.append(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					float(sum(UpperCamelCase			) / len(UpperCamelCase			)			)
    lowerCAmelCase__ :  List[Any]							      =					sum(UpperCamelCase			) / len(UpperCamelCase			)
    lowerCAmelCase__ :  Dict							      =					float(fa_score(y_true=UpperCamelCase							,    y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]			)			)
    return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION     )
class        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      if self.config_name not in [
          "boolq",
          "cb",
          "copa",
          "multirc",
          "record",
          "rte",
          "wic",
          "wsc",
          "wsc.fixed",
          "axb",
          "axg",
      ]:
          raise KeyError(
              """You should supply a configuration name selected in """
              """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""      )
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types()      )  ,codebase_urls=[]  ,reference_urls=[]  ,format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None  ,)




  def 							UpperCAmelCase_	(    self      )    ->		str:
      if self.config_name == "record":
          return {
              "predictions": {
                  "idx": {
                      "passage": datasets.Value("""int64"""      ),
                      "query": datasets.Value("""int64"""      ),
                  },
                  "prediction_text": datasets.Value("""string"""      ),
              },
              "references": {
                  "idx": {
                      "passage": datasets.Value("""int64"""      ),
                      "query": datasets.Value("""int64"""      ),
                  },
                  "answers": datasets.Sequence(datasets.Value("""string"""      )      ),
              },
          }
      elif self.config_name == "multirc":
          return {
              "predictions": {
                  "idx": {
                      "answer": datasets.Value("""int64"""      ),
                      "paragraph": datasets.Value("""int64"""      ),
                      "question": datasets.Value("""int64"""      ),
                  },
                  "prediction": datasets.Value("""int64"""      ),
              },
              "references": datasets.Value("""int64"""      ),
          }
      else:
          return {
              "predictions": datasets.Value("""int64"""      ),
              "references": datasets.Value("""int64"""      ),
          }






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Any:
      if self.config_name == "axb":
          return {"matthews_correlation": matthews_corrcoef(__UpperCAmelCase  ,__UpperCAmelCase      )}
      elif self.config_name == "cb":
          return acc_and_fa(__UpperCAmelCase  ,__UpperCAmelCase  ,fa_avg="""macro"""      )
      elif self.config_name == "record":
          lowerCAmelCase__ :  Optional[Any]							      =					[
              {
                  """qas""": [
                      {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                      for ref in references
                  ]
              }
          ]
          lowerCAmelCase__ :  Union[str, Any]							      =					{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
          return evaluate_record(__UpperCAmelCase  ,__UpperCAmelCase      )[0]
      elif self.config_name == "multirc":
          return evaluate_multirc(__UpperCAmelCase  ,__UpperCAmelCase      )
      elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
          return {"accuracy": simple_accuracy(__UpperCAmelCase  ,__UpperCAmelCase      )}
      else:
          raise KeyError(
              """You should supply a configuration name selected in """
              """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""      )


