
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase  :					List[Any]						    =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase  :					int						    =							[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

  # q, k, v projections in self/cross-attention in decoder for conditional DETR
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
  )

  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
  )
  # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
  )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       lowercase							:     List[str]       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[int]       	=      val







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     str       	=      OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:     Tuple       	=      key.replace("""backbone.0.body"""					,	"""backbone.conv_encoder.model"""				)
                     lowercase							:     List[str]       	=      value
              else:
                     lowercase							:     Union[str, Any]       	=      value

       return new_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False				) ->    Optional[Any]:
       lowercase							:     List[str]       	=      """"""
       if is_panoptic:
              lowercase							:     Optional[Any]       	=      """conditional_detr."""

       # first: transformer encoder
       for i in range(6				):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              lowercase							:     int       	=      state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"				)
              lowercase							:     Any       	=      state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"				)
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:     str       	=      in_proj_weight[:256, :]
              lowercase							:     List[Any]       	=      in_proj_bias[:256]
              lowercase							:     Union[str, Any]       	=      in_proj_weight[256:512, :]
              lowercase							:     Optional[int]       	=      in_proj_bias[256:512]
              lowercase							:     List[Any]       	=      in_proj_weight[-256:, :]
              lowercase							:     str       	=      in_proj_bias[-256:]







def 						_snake_case(  ) ->    Optional[int]:
       lowercase							:     Dict       	=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:     List[Any]       	=      Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__				).raw				)

       return im







@torch.no_grad()
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    str:
       lowercase							:     Any       	=      ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowercase							:     Dict       	=      """resnet101"""
       if "dc5" in model_name:
              lowercase							:     str       	=      True
       lowercase							:     Tuple       	=      """panoptic""" in model_name
       if is_panoptic:
              lowercase							:     int       	=      250
       else:
              lowercase							:     Optional[Any]       	=      91
              lowercase							:     Optional[Any]       	=      """huggingface/label-files"""
              lowercase							:     Tuple       	=      """coco-detection-id2label.json"""
              lowercase							:     List[Any]       	=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="""dataset"""				)					,	"""r"""				)				)
              lowercase							:     Any       	=      {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}
              lowercase							:     int       	=      idalabel
              lowercase							:     Union[str, Any]       	=      {v: k for k, v in idalabel.items()}

       # load image processor
       lowercase							:     str       	=      """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:     List[Any]       	=      ConditionalDetrImageProcessor(format=SCREAMING_SNAKE_CASE__				)

       # prepare image
       lowercase							:     List[str]       	=      prepare_img()
       lowercase							:     Tuple       	=      image_processor(images=SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)
       lowercase							:     Tuple       	=      encoding["""pixel_values"""]

       logger.info(f"Converting model {model_name}..."				)

       # load original model from torch hub
       lowercase							:     List[Any]       	=      torch.hub.load("""DeppMeng/ConditionalDETR"""					,	SCREAMING_SNAKE_CASE__					,	pretrained=SCREAMING_SNAKE_CASE__				).eval()
       lowercase							:     Dict       	=      conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:     str       	=      """conditional_detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      rename_backbone_keys(SCREAMING_SNAKE_CASE__				)
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE__					,	is_panoptic=SCREAMING_SNAKE_CASE__				)
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase							:     Tuple       	=      """conditional_detr.model.""" if is_panoptic else """model."""
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("""conditional_detr"""				)
                         and not key.startswith("""class_labels_classifier"""				)
                         and not key.startswith("""bbox_predictor"""				)
                     ):
                            lowercase							:     str       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     Optional[int]       	=      val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     Tuple       	=      val
                     elif key.startswith("""bbox_attention"""				) or key.startswith("""mask_head"""				):
                            continue
                     else:
                            lowercase							:     List[str]       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     str       	=      val
              else:
                     if not key.startswith("""class_labels_classifier"""				) and not key.startswith("""bbox_predictor"""				):
                            lowercase							:     Dict       	=      state_dict.pop(SCREAMING_SNAKE_CASE__				)
                            lowercase							:     List[Any]       	=      val
    # finally, create HuggingFace model and load state dict
       lowercase							:     int       	=      ConditionalDetrForSegmentation(SCREAMING_SNAKE_CASE__				) if is_panoptic else ConditionalDetrForObjectDetection(SCREAMING_SNAKE_CASE__				)
       model.load_state_dict(SCREAMING_SNAKE_CASE__				)
       model.eval()
       model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__					,	organization="""DepuMeng"""					,	commit_message="""Add model"""				)
       # verify our conversion
       lowercase							:     List[str]       	=      conditional_detr(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      model(SCREAMING_SNAKE_CASE__				)
       assert torch.allclose(outputs.logits					,	original_outputs["""pred_logits"""]					,	atol=1e-4				)
       assert torch.allclose(outputs.pred_boxes					,	original_outputs["""pred_boxes"""]					,	atol=1e-4				)
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks					,	original_outputs["""pred_masks"""]					,	atol=1e-4				)

       # Save model and image processor
       logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."				)
       Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
       model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Tuple						    =							argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""conditional_detr_resnet50""",
      type=str,
      help="""Name of the CONDITIONAL_DETR model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  lowercase  :					Optional[int]						    =							parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


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

import datasets


lowercase  :					str						    =							"""\
@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}
}
"""

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

lowercase  :					int						    =							"""
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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return float((preds == labels).mean()				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Any       	=      simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      float(fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     Union[str, Any]       	=      float(pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       lowercase							:     Dict       	=      float(spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class __snake_case		(      datasets.Metric     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           if self.config_name == "cola":
                  return {"matthews_correlation": matthews_corrcoef(snake_case		,snake_case      )}
           elif self.config_name == "stsb":
                  return pearson_and_spearman(snake_case		,snake_case      )
           elif self.config_name in ["mrpc", "qqp"]:
                  return acc_and_fa(snake_case		,snake_case      )
           elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                  return {"accuracy": simple_accuracy(snake_case		,snake_case      )}
           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\"]"""      )
