






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	:		Any										=   logging.get_logger(__name__)







def 			A__							(      _a  :					Tuple		,  _a  :					Union[str, Any]=False	):





 '''simple docstring'''







 snake_case__  :    int							       =[]
 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"
  snake_case__  :    int							       =[(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 			A__							(      _a  :					int		,  _a  :					Dict		,  _a  :					Optional[int]=False	):





 '''simple docstring'''







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







def 			A__							(      _a  :					Tuple	):





 '''simple docstring'''







 snake_case__  :    int							       =["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(_a		,  _a	)







def 			A__							(      _a  :					Tuple		,  _a  :					List[str]		,  _a  :					List[Any]	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =dct.pop(_a	)
 snake_case__  :    Optional[int]							       =val







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__  :    Optional[int]							       =Image.open(requests.get(_a		,  stream=_a	).raw	)
 return im







@torch.no_grad()
def 			A__							(      _a  :					Tuple		,  _a  :					List[Any]	):





 '''simple docstring'''







 snake_case__  :    Any							       =ViTConfig()
 snake_case__  :    List[str]							       =False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  snake_case__  :    str							       =True
  snake_case__  :    Optional[int]							       =int(vit_name[-12:-10]	)
  snake_case__  :    Dict							       =int(vit_name[-9:-6]	)
 else:
  snake_case__  :    Optional[Any]							       =1000
  snake_case__  :    int							       ="""huggingface/label-files"""
  snake_case__  :    Dict							       ="""imagenet-1k-id2label.json"""
  snake_case__  :    Any							       =json.load(open(hf_hub_download(_a		,  _a		,  repo_type="""dataset"""	)		,  """r"""	)	)
  snake_case__  :    Union[str, Any]							       ={int(_a	): v for k, v in idalabel.items()}
  snake_case__  :    Optional[Any]							       =idalabel
  snake_case__  :    List[str]							       ={v: k for k, v in idalabel.items()}
  snake_case__  :    Union[str, Any]							       =int(vit_name[-6:-4]	)
  snake_case__  :    Optional[int]							       =int(vit_name[-3:]	)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("""tiny"""	):
   snake_case__  :    int							       =192
   snake_case__  :    List[Any]							       =768
   snake_case__  :    str							       =12
   snake_case__  :    Optional[int]							       =3
  elif vit_name[9:].startswith("""small"""	):
   snake_case__  :    Tuple							       =384
   snake_case__  :    Dict							       =1536
   snake_case__  :    Any							       =12
   snake_case__  :    str							       =6
  else:
   pass
 else:
  if vit_name[4:].startswith("""small"""	):
   snake_case__  :    Optional[int]							       =768
   snake_case__  :    Dict							       =2304
   snake_case__  :    int							       =8
   snake_case__  :    List[Any]							       =8
  elif vit_name[4:].startswith("""base"""	):
   pass
  elif vit_name[4:].startswith("""large"""	):
   snake_case__  :    Tuple							       =1024
   snake_case__  :    List[str]							       =4096
   snake_case__  :    str							       =24
   snake_case__  :    Optional[int]							       =16
  elif vit_name[4:].startswith("""huge"""	):
   snake_case__  :    Dict							       =1280
   snake_case__  :    int							       =5120
   snake_case__  :    Optional[int]							       =32
   snake_case__  :    Optional[int]							       =16

    # load original model from timm
 snake_case__  :    int							       =timm.create_model(_a		,  pretrained=_a	)
 timm_model.eval()

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case__  :    str							       =ViTModel(_a	).eval()
 else:
  snake_case__  :    Dict							       =ViTForImageClassification(_a	).eval()
 model.load_state_dict(_a	)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  snake_case__  :    List[str]							       =DeiTImageProcessor(size=config.image_size	)
 else:
  snake_case__  :    Tuple							       =ViTImageProcessor(size=config.image_size	)
 snake_case__  :    Tuple							       =image_processor(images=prepare_img()		,  return_tensors="""pt"""	)
 snake_case__  :    Optional[int]							       =encoding["""pixel_values"""]
 snake_case__  :    Optional[int]							       =model(_a	)

 if base_model:
  snake_case__  :    Optional[Any]							       =timm_model.forward_features(_a	)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(_a		,  outputs.pooler_output		,  atol=1E-3	)
 else:
  snake_case__  :    List[Any]							       =timm_model(_a	)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_a		,  outputs.logits		,  atol=1E-3	)

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


if __name__ == "__main__":
       __lowerCamelCase	:		List[Any]										=   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	:		Any										=   parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)













from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


__lowerCamelCase	:		Any										=   """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

__lowerCamelCase	:		str										=   """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

__lowerCamelCase	:		str										=   """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def 			A__							(      _a  :					int		,  _a  :					str	):





 '''simple docstring'''







 return float((preds == labels).mean()	)







def 			A__							(      _a  :					Optional[Any]		,  _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    str							       =simple_accuracy(_a		,  _a	)
 snake_case__  :    str							       =float(fa_score(y_true=_a		,  y_pred=_a	)	)
 return {
     "accuracy": acc,
     "f1": fa,
 }







def 			A__							(      _a  :					int		,  _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__  :    str							       =float(pearsonr(_a		,  _a	)[0]	)
 snake_case__  :    Optional[int]							       =float(spearmanr(_a		,  _a	)[0]	)
 return {
     "pearson": pearson_corr,
     "spearmanr": spearman_corr,
 }







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       if self.config_name not in [
           "sst2",
           "mnli",
           "mnli_mismatched",
           "mnli_matched",
           "cola",
           "stsb",
           "mrpc",
           "qqp",
           "qnli",
           "rte",
           "wnli",
           "hans",
       ]:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
                   """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""   ),
               }   )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)



      def 						lowercase__					(     self				,			a				,			a   ):
       if self.config_name == "cola":
        return {"matthews_correlation": matthews_corrcoef(a				,			a   )}
       elif self.config_name == "stsb":
        return pearson_and_spearman(a				,			a   )
       elif self.config_name in ["mrpc", "qqp"]:
        return acc_and_fa(a				,			a   )
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
        return {"accuracy": simple_accuracy(a				,			a   )}
       else:
        raise KeyError(
            """You should supply a configuration name selected in """
            """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """
            """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"""   )





