import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def       __lowerCAmelCase      (						A_   : int						,	A_   : Optional[Any]						,	A_   : Tuple					)    ->  List[str]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def       __lowerCAmelCase      (						A_   : Optional[int]						,	A_   : Dict						,	A_   : Dict						,	A_   : Optional[int]="attention"					)    ->  Optional[int]:
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       k_tmp.reshape(k_tmp.shape[0]						,	k_tmp.shape[1] * k_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,	o_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       q_tmp.reshape(q_tmp.shape[0]						,	q_tmp.shape[1] * q_tmp.shape[2]					)
  __UpperCAmelCase						    =       np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]					)
  __UpperCAmelCase						    =       v_tmp.reshape(v_tmp.shape[0]						,	v_tmp.shape[1] * v_tmp.shape[2]					)
  return k, o, q, v




def       __lowerCAmelCase      (						A_   : Dict						,	A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : int=False					)    ->  List[Any]:
  if split_mlp_wi:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    __UpperCAmelCase						    =       (wi_a, wi_a)
  else:
    __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  __UpperCAmelCase						    =       params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo




def       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : int						,	A_   : Optional[Any]						,	A_   : Optional[Any]					)    ->  Tuple:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def       __lowerCAmelCase      (						A_   : dict						,	*, A_   : int						,	A_   : bool						,	A_   : bool = False					)    ->  List[str]:
  __UpperCAmelCase						    =       traverse_util.flatten_dict(variables["target"]					)
  __UpperCAmelCase						    =       {"/".join(A_					): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __UpperCAmelCase						    =       "encoder/encoder/mlp/wi_0/kernel" in old
  print("Split MLP:"						,	A_					)

  __UpperCAmelCase						    =       collections.OrderedDict()

  # Shared embeddings.
  __UpperCAmelCase						    =       old["token_embedder/embedding"]

  # Encoder.
  for i in range(A_					):
    # Block i, layer 0 (Self Attention).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_attention_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"encoder"						,	"attention"					)
    __UpperCAmelCase						    =       layer_norm
    __UpperCAmelCase						    =       k.T
    __UpperCAmelCase						    =       o.T
    __UpperCAmelCase						    =       q.T
    __UpperCAmelCase						    =       v.T

    # Block i, layer 1 (MLP).
    __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"encoder"						,	"pre_mlp_layer_norm"					)
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"encoder"						,	A_					)
    __UpperCAmelCase						    =       layer_norm
    if split_mlp_wi:
      __UpperCAmelCase						    =       wi[0].T
      __UpperCAmelCase						    =       wi[1].T
    else:
      __UpperCAmelCase						    =       wi.T
    __UpperCAmelCase						    =       wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      __UpperCAmelCase						    =       tax_relpos_bias_lookup(
          A_						,	A_						,	"encoder"					).T

  __UpperCAmelCase						    =       old["encoder/encoder_norm/scale"]

  if not scalable_attention:
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"encoder"					).T
    __UpperCAmelCase						    =       tax_relpos_bias_lookup(
        A_						,	0						,	"decoder"					).T

  if not is_encoder_only:
    # Decoder.
    for i in range(A_					):
      # Block i, layer 0 (Self Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_self_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"self_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 1 (Cross Attention).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_cross_attention_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_attention_lookup(A_						,	A_						,	"decoder"						,	"encoder_decoder_attention"					)
      __UpperCAmelCase						    =       layer_norm
      __UpperCAmelCase						    =       k.T
      __UpperCAmelCase						    =       o.T
      __UpperCAmelCase						    =       q.T
      __UpperCAmelCase						    =       v.T

      # Block i, layer 2 (MLP).
      __UpperCAmelCase						    =       tax_layer_norm_lookup(A_						,	A_						,	"decoder"						,	"pre_mlp_layer_norm"					)
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       tax_mlp_lookup(A_						,	A_						,	"decoder"						,	A_					)
      __UpperCAmelCase						    =       layer_norm
      if split_mlp_wi:
        __UpperCAmelCase						    =       wi[0].T
        __UpperCAmelCase						    =       wi[1].T
      else:
        __UpperCAmelCase						    =       wi.T
      __UpperCAmelCase						    =       wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        __UpperCAmelCase						    =       tax_relpos_bias_lookup(A_						,	A_						,	"decoder"					).T

    __UpperCAmelCase						    =       old["decoder/decoder_norm/scale"]

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __UpperCAmelCase						    =       old["decoder/logits_dense/kernel"].T

  return new




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : bool					)    ->  List[Any]:
  __UpperCAmelCase						    =       collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __UpperCAmelCase						    =       state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __UpperCAmelCase						    =       state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."					)
      __UpperCAmelCase						    =       state_dict["shared.weight"]

  return state_dict




def       __lowerCAmelCase      (						A_   : Optional[Any]						,	A_   : int						,	A_   : List[Any]						,	A_   : List[Any]						,	A_   : List[Any]					)    ->  Optional[int]:
  __UpperCAmelCase						    =       checkpoints.load_tax_checkpoint(A_					)
  __UpperCAmelCase						    =       convert_tax_to_pytorch(
      A_						,	num_layers=config.num_layers						,	is_encoder_only=A_						,	scalable_attention=A_					)
  __UpperCAmelCase						    =       make_state_dict(A_						,	A_					)
  model.load_state_dict(A_						,	strict=A_					)




def       __lowerCAmelCase      (						A_   : List[str]						,	A_   : List[Any]						,	A_   : List[str]						,	A_   : bool = False						,	A_   : bool = False						,	)    ->  Optional[int]:
  __UpperCAmelCase						    =       MTaConfig.from_json_file(A_					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __UpperCAmelCase						    =       UMTaEncoderModel(A_					)
  else:
    __UpperCAmelCase						    =       UMTaForConditionalGeneration(A_					)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(A_						,	A_						,	A_						,	A_						,	A_					)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
  model.save_pretrained(A_					)

  # Verify that we can load the checkpoint.
  model.from_pretrained(A_					)
  print("Done"					)


if __name__ == "__main__":
       a_							=      argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
       # Required parameters
       parser.add_argument(
           """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       parser.add_argument(
           """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
       )
       parser.add_argument(
           """--scalable_attention""",
           action="""store_true""",
           help="""Whether the model uses scaled attention (umt5 model)""",
           default=False,
       )
       a_							=      parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path,
           args.config_file,
           args.pytorch_dump_path,
           args.is_encoder_only,
           args.scalable_attention,
       )




from sklearn.metrics import fa_score

import datasets


a_							=      """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


a_							=      """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - '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 (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


a_							=      """
@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 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
            {
                "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                "references": datasets.Sequence(datasets.Value("int32"   )   ),
            }
            if self.config_name == "multilabel"
            else {
                "predictions": datasets.Value("int32"   ),
                "references": datasets.Value("int32"   ),
            }   )				,				reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       fa_score(
        __lowerCAmelCase				,				__lowerCAmelCase				,				labels=__lowerCAmelCase				,				pos_label=__lowerCAmelCase				,				average=__lowerCAmelCase				,				sample_weight=__lowerCAmelCase   )
    return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



