import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__=0      )	->Tuple:




  '''simple docstring'''

  if name is None:
    _UpperCamelCase				=  None
  else:
    _UpperCamelCase				=  "." * max(0  ,			spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
    _UpperCamelCase				=  fmt.format(a__      )

  # Print and recurse (if needed).
  if isinstance(a__  ,			a__      ):
    if msg is not None:
      print(a__      )
    for k in val.keys():
      recursive_print(a__  ,			val[k]  ,			spaces + 2      )
  elif isinstance(a__  ,			torch.Tensor      ):
    print(a__  ,			":"  ,			val.size()      )
  else:
    print(a__  ,			":"  ,			a__      )


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase				=  param.size()
  if checkpoint_version == 1.0:
    # version 1.0 stores [num_heads * hidden_size * num_splits, :]
    _UpperCamelCase				=  (num_heads, hidden_size, num_splits) + input_shape[1:]
    _UpperCamelCase				=  param.view(*a__      )
    _UpperCamelCase				=  param.transpose(0  ,			2      )
    _UpperCamelCase				=  param.transpose(1  ,			2      ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    _UpperCamelCase				=  (num_heads, num_splits, hidden_size) + input_shape[1:]
    _UpperCamelCase				=  param.view(*a__      )
    _UpperCamelCase				=  param.transpose(0  ,			1      ).contiguous()
  _UpperCamelCase				=  param.view(*a__      )
  return param


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase				=  {}

  # old versions did not store training args
  _UpperCamelCase				=  input_state_dict.get("args"  ,			a__      )
  if ds_args is not None:
    # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
    # from pprint import pprint
    # pprint(vars(ds_args))

    _UpperCamelCase				=  ds_args.tokenizer_type
    _UpperCamelCase				=  ds_args.padded_vocab_size
    _UpperCamelCase				=  ds_args.max_position_embeddings
    _UpperCamelCase				=  ds_args.hidden_size
    _UpperCamelCase				=  ds_args.num_layers
    _UpperCamelCase				=  ds_args.num_attention_heads
    _UpperCamelCase				=  ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
    # pprint(config)

  # The number of heads.
  _UpperCamelCase				=  config.num_attention_heads
  # The hidden_size per head.
  _UpperCamelCase				=  config.hidden_size // heads
  # Megatron-LM checkpoint version
  if "checkpoint_version" in input_state_dict.keys():
    _UpperCamelCase				=  input_state_dict["checkpoint_version"]
  else:
    _UpperCamelCase				=  0.0

  # The model.
  _UpperCamelCase				=  input_state_dict["model"]
  # The language model.
  _UpperCamelCase				=  model["language_model"]
  # The embeddings.
  _UpperCamelCase				=  lm["embedding"]

  # The word embeddings.
  _UpperCamelCase				=  embeddings["word_embeddings"]["weight"]
  # Truncate the embedding table to vocab_size rows.
  _UpperCamelCase				=  word_embeddings[: config.vocab_size, :]
  # Store the word embeddings.
  _UpperCamelCase				=  word_embeddings

  # The position embeddings.
  _UpperCamelCase				=  embeddings["position_embeddings"]["weight"]
  assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
  # Store the position embeddings.
  _UpperCamelCase				=  pos_embeddings

  # The token-type embeddings.
  _UpperCamelCase				=  embeddings["tokentype_embeddings"]["weight"]
  # Store the position embeddings.
  _UpperCamelCase				=  tokentype_embeddings

  # The transformer.
  _UpperCamelCase				=  lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

  # The regex to extract layer names.
  _UpperCamelCase				=  re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"      )

  # The simple map of names for "automated" rules.
  _UpperCamelCase				=  {
      "attention.dense": ".attention.output.dense.",
      "self_attention.dense": ".attention.output.dense.",
      "mlp.dense_h_to_4h": ".intermediate.dense.",
      "mlp.dense_4h_to_h": ".output.dense.",
  }

  # Keep track of the attention/query/value tensor.
  _UpperCamelCase				=  None

  # Extract the layers.
  for key, val in transformer.items():
    # Match the name.
    _UpperCamelCase				=  layer_re.match(a__      )

    # Stop if that's not a layer
    if m is None:
      break

    # The index of the layer.
    _UpperCamelCase				=  int(m.group(1      )      )
    # The name of the operation.
    _UpperCamelCase				=  m.group(2      )
    # Is it a weight or a bias?
    _UpperCamelCase				=  m.group(3      )

    # The name of the layer.
    _UpperCamelCase				=  f'bert.encoder.layer.{layer_idx}'

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("layernorm"      ):
      _UpperCamelCase				=  "attention.ln" if op_name.startswith("input"      ) else "ln"
      _UpperCamelCase				=  val

    # Transpose the QKV matrix.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "weight":
      # Make sure the QKV pointer is nil.
      assert attention_qkv_weight is None, ""

      _UpperCamelCase				=  fix_query_key_value_ordering(a__  ,			a__  ,			3  ,			a__  ,			a__      )
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      _UpperCamelCase				=  out_val

    # Transpose the bias.
    elif (
        op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
    ) and weight_or_bias == "bias":
      # Make sure we read the weight tensor.
      assert attention_qkv_weight is not None, ""

      # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
      _UpperCamelCase				=  attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      _UpperCamelCase				=  attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      _UpperCamelCase				=  attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

      _UpperCamelCase				=  fix_query_key_value_ordering(a__  ,			a__  ,			3  ,			a__  ,			a__      )
      # Split the bias.
      _UpperCamelCase				=  out_val[0 * config.hidden_size : 1 * config.hidden_size]
      _UpperCamelCase				=  out_val[1 * config.hidden_size : 2 * config.hidden_size]
      _UpperCamelCase				=  out_val[2 * config.hidden_size : 3 * config.hidden_size]

      # Store.
      _UpperCamelCase				=  q
      _UpperCamelCase				=  q_bias
      _UpperCamelCase				=  k
      _UpperCamelCase				=  k_bias
      _UpperCamelCase				=  v
      _UpperCamelCase				=  v_bias

      # Clear the stored tensor.
      _UpperCamelCase				=  None

    # Copy weights and biases as is.
    elif weight_or_bias in ["weight", "bias"]:
      _UpperCamelCase				=  megatron_to_transformers[op_name]
      _UpperCamelCase				=  val

    # The final layernorm.
  _UpperCamelCase				=  transformer["final_layernorm.weight"]
  _UpperCamelCase				=  transformer["final_layernorm.bias"]

  # The pooler.
  _UpperCamelCase				=  lm["pooler"]

  # Store the matrix and the bias.
  _UpperCamelCase				=  pooler["dense.weight"]
  _UpperCamelCase				=  pooler["dense.bias"]

  # The LM head from Megatron (for RACE).
  _UpperCamelCase				=  model["lm_head"]

  # The transform matrix.
  _UpperCamelCase				=  lm_head["dense.weight"]
  _UpperCamelCase				=  lm_head["dense.bias"]

  # The transform LN.
  _UpperCamelCase				=  lm_head["layernorm.weight"]
  _UpperCamelCase				=  lm_head["layernorm.bias"]

  # For the decoder, we replicate the weights.
  _UpperCamelCase				=  word_embeddings
  _UpperCamelCase				=  lm_head["bias"]

  # The classifier from Megatron (for MLNI).
  _UpperCamelCase				=  model["binary_head"]

  # Store the classifier.
  _UpperCamelCase				=  binary_head["weight"]
  _UpperCamelCase				=  binary_head["bias"]

  # It should be done!
  return output_state_dict


def 							lowerCAmelCase__    (       )	->Tuple:




  '''simple docstring'''

  _UpperCamelCase				=  argparse.ArgumentParser()
  parser.add_argument("--print-checkpoint-structure"  ,			action="store_true"      )
  parser.add_argument("path_to_checkpoint"  ,			type=a__  ,			help="Path to the ZIP file containing the checkpoint"      )
  parser.add_argument(
      "--config_file"  ,			default=""  ,			type=a__  ,			help="An optional config json file describing the pre-trained model."  ,			)
  _UpperCamelCase				=  parser.parse_args()

  # Extract the basename.
  _UpperCamelCase				=  os.path.dirname(args.path_to_checkpoint      )

  # Load the model.
  # the .zip is very optional, let's keep it for backward compatibility
  print(f'Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'      )
  if args.path_to_checkpoint.endswith(".zip"      ):
    with zipfile.ZipFile(args.path_to_checkpoint  ,			"r"      ) as checkpoint:
      with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"      ) as pytorch_dict:
        _UpperCamelCase				=  torch.load(a__  ,			map_location="cpu"      )
  else:
    _UpperCamelCase				=  torch.load(args.path_to_checkpoint  ,			map_location="cpu"      )

  if args.config_file == "":
    # Default config of megatron-bert 345m
    _UpperCamelCase				=  MegatronBertConfig()

    # different megatron-bert-*-345m models have different vocab sizes, so override the default
    # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
    _UpperCamelCase				=  input_state_dict["model"]["lm_head"]["bias"].numel()
  else:
    _UpperCamelCase				=  MegatronBertConfig.from_json_file(args.config_file      )

  # Convert.
  print("Converting"      )
  _UpperCamelCase				=  convert_megatron_checkpoint(a__  ,			a__  ,			a__      )

  # Print the structure of converted state dict.
  if args.print_checkpoint_structure:
    recursive_print(a__  ,			a__      )

  # Store the config to file.
  print("Saving config"      )
  config.save_pretrained(a__      )

  # Store the state_dict to file.
  _UpperCamelCase				=  os.path.join(a__  ,			"pytorch_model.bin"      )
  print(f'Saving checkpoint to "{output_checkpoint_file}"'      )
  torch.save(a__  ,			a__      )


####################################################################################################

if __name__ == "__main__":
   main()

####################################################################################################











"""simple docstring"""


from sklearn.metrics import roc_auc_score

import datasets


A_     :      Optional[int]								="""
This metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.

This metric has three separate use cases:
    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.
    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.
    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.
"""

A_     :      Dict								="""
Args:
- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples,)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:
    - binary: expects an array-like of shape (n_samples,)
    - multiclass: expects an array-like of shape (n_samples, n_classes)
    - multilabel: expects an array-like of shape (n_samples, n_classes)
- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:
    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).
    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.
    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.
- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.
- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.
- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:
    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.
    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.
- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in
    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in
    `prediction_scores` is used. Defaults to `None`.
Returns:
    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.
Examples:
    Example 1:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")
        >>> refs = [1, 0, 1, 1, 0, 0]
        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)
        >>> print(round(results['roc_auc'], 2))
        0.78

    Example 2:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")
        >>> refs = [1, 0, 1, 2, 2, 0]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')
        >>> print(round(results['roc_auc'], 2))
        0.85

    Example 3:
        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")
        >>> refs = [[1, 1, 0],
        ...         [1, 1, 0],
        ...         [0, 1, 0],
        ...         [0, 0, 1],
        ...         [0, 1, 1],
        ...         [1, 0, 1]]
        >>> pred_scores = [[0.3, 0.5, 0.2],
        ...                 [0.7, 0.2, 0.1],
        ...                 [0.005, 0.99, 0.005],
        ...                 [0.2, 0.3, 0.5],
        ...                 [0.1, 0.1, 0.8],
        ...                 [0.1, 0.7, 0.2]]
        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)
        >>> print([round(res, 2) for res in results['roc_auc']])
        [0.83, 0.38, 0.94]
"""

A_     :      List[str]								="""\
@article{doi:10.1177/0272989X8900900307,
author = {Donna Katzman McClish},
title ={Analyzing a Portion of the ROC Curve},
journal = {Medical Decision Making},
volume = {9},
number = {3},
pages = {190-195},
year = {1989},
doi = {10.1177/0272989X8900900307},
    note ={PMID: 2668680},
URL = {https://doi.org/10.1177/0272989X8900900307},
eprint = {https://doi.org/10.1177/0272989X8900900307}
}


@article{10.1023/A:1010920819831,
author = {Hand, David J. and Till, Robert J.},
title = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},
year = {2001},
issue_date = {November 2001},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {45},
number = {2},
issn = {0885-6125},
url = {https://doi.org/10.1023/A:1010920819831},
doi = {10.1023/A:1010920819831},
journal = {Mach. Learn.},
month = {oct},
pages = {171–186},
numpages = {16},
keywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}
}


@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""







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



     def 						snake_case_			(       self   ):
            return datasets.MetricInfo(
                description=_DESCRIPTION					,     citation=_CITATION					,     inputs_description=_KWARGS_DESCRIPTION					,     features=datasets.Features(
                    {
                        'prediction_scores': datasets.Sequence(datasets.Value('float'   )   ),
                        'references': datasets.Value('int32'   ),
                    }
                    if self.config_name == 'multiclass'
                    else {
                        'references': datasets.Sequence(datasets.Value('int32'   )   ),
                        'prediction_scores': datasets.Sequence(datasets.Value('float'   )   ),
                    }
                    if self.config_name == 'multilabel'
                    else {
                        'references': datasets.Value('int32'   ),
                        'prediction_scores': datasets.Value('float'   ),
                    }   )					,     reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html']					,     )



     def 						snake_case_			(       self					,     a__					,     a__					,     a__="macro"					,     a__=None					,     a__=None					,     a__="raise"					,     a__=None					,     ):
            return {
                "roc_auc": roc_auc_score(
                    a__					,     a__					,     average=a__					,     sample_weight=a__					,     max_fpr=a__					,     multi_class=a__					,     labels=a__					,     )
            }






