
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

   if config.model_type == "t5":
      _SCREAMING_SNAKE_CASE      =				"""SelfAttention"""
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      _SCREAMING_SNAKE_CASE      =				"""LocalSelfAttention"""
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _SCREAMING_SNAKE_CASE      =				"""TransientGlobalSelfAttention"""
   else:
      raise ValueError(
          """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
          """ attribute with a value from ['local', 'transient-global]."""						)

   # Encoder
   for layer_index in range(config.num_layers						):
      _SCREAMING_SNAKE_CASE      =				F"layers_{str(SCREAMING_SNAKE_CASE_						)}"

      # Self-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
      else:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

      # Assigning
      _SCREAMING_SNAKE_CASE      =				flax_model.params["""encoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_						)]["""layer"""]
      _SCREAMING_SNAKE_CASE      =				tax_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         _SCREAMING_SNAKE_CASE      =				tax_global_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi

      _SCREAMING_SNAKE_CASE      =				tax_mlp_wo
      _SCREAMING_SNAKE_CASE      =				tax_mlp_layer_norm

      _SCREAMING_SNAKE_CASE      =				flax_model_encoder_layer_block

   # Only for layer 0:
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
   _SCREAMING_SNAKE_CASE      =				tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
      _SCREAMING_SNAKE_CASE      =				tax_encoder_global_rel_embedding

   # Assigning
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
   _SCREAMING_SNAKE_CASE      =				tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers						):
      _SCREAMING_SNAKE_CASE      =				F"layers_{str(SCREAMING_SNAKE_CASE_						)}"

      # Self-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
          """scale"""
      ]

      # Encoder-Decoder-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""value"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

      # MLP
      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
      else:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

      # Assigning
      _SCREAMING_SNAKE_CASE      =				flax_model.params["""decoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_						)]["""layer"""]
      _SCREAMING_SNAKE_CASE      =				tax_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_pre_attention_layer_norm

      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_cross_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi

      _SCREAMING_SNAKE_CASE      =				tax_mlp_wo

      _SCREAMING_SNAKE_CASE      =				txa_mlp_layer_norm

      _SCREAMING_SNAKE_CASE      =				flax_model_decoder_layer_block

   # Decoder Normalization
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
   _SCREAMING_SNAKE_CASE      =				txa_decoder_norm

   # Only for layer 0:
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
   _SCREAMING_SNAKE_CASE      =				tax_decoder_rel_embedding

   # Token Embeddings
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""token_embedder"""]["""embedding"""]
   _SCREAMING_SNAKE_CASE      =				txa_token_embeddings

   # LM Head (only in v1.1 and LongT5 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

   flax_model.save_pretrained(SCREAMING_SNAKE_CASE_						)
   print("""T5X Model was sucessfully converted!"""						)


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, Any]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
     )
     parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
     parser.add_argument(
         "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
     )
     UpperCamelCase__      :		int       							=  parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''



from sklearn.metrics import matthews_corrcoef

import datasets


UpperCamelCase__      :		List[str]       							=  "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

UpperCamelCase__      :		List[Any]       							=  "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

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

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





       """simple docstring"""




       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
                  {
                      """predictions""": datasets.Value("""int32"""      ),
                      """references""": datasets.Value("""int32"""      ),
                  }      )       ,							reference_urls=[
                  """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
              ]       ,							)






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None      )    ->			List[str]:
          return {
              "matthews_correlation": float(matthews_corrcoef(A__       ,							A__       ,							sample_weight=A__      )      ),
          }






