




'''simple docstring'''


from sklearn.metrics import mean_absolute_error

import datasets


lowerCAmelCase_			:     List[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'

lowerCAmelCase_			:     int							    =     '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


lowerCAmelCase_			:     str							    =     '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'







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






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(self._get_feature_types()					)	,	reference_urls=[
                   """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html"""
               ]	,	)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           if self.config_name == "multilist":
                return {
                    "predictions": datasets.Sequence(datasets.Value("""float"""					)					),
                    "references": datasets.Sequence(datasets.Value("""float"""					)					),
                }
           else:
                return {
                    "predictions": datasets.Value("""float"""					),
                    "references": datasets.Value("""float"""					),
                }






      def        lowercase_	(   self      :     List[str]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Any	,	lowercase__      :     Any=None	,	lowercase__      :     List[Any]="uniform_average"					):


           '''simple docstring'''

           a_    :       int    	=     mean_absolute_error(lowercase__	,	lowercase__	,	sample_weight=lowercase__	,	multioutput=lowercase__					)

           return {"mae": mae_score}






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_    :       Optional[Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

          if split_mlp_wi:
               a_    :       Tuple    	=     tax_mlp_wi_a
               a_    :       Dict    	=     tax_mlp_wi_a
          else:
               a_    :       Optional[Any]    	=     tax_mlp_wi

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

     # Only for layer 0:
     a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Union[str, Any]    	=     tax_encoder_rel_embedding

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_    :       int    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

          # Encoder-Decoder-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          a_    :       str    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_    :       List[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

          if split_mlp_wi:
               a_    :       int    	=     tax_mlp_wi_a
               a_    :       Optional[int]    	=     tax_mlp_wi_a
          else:
               a_    :       Tuple    	=     tax_mlp_wi

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

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

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

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

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


if __name__ == "__main__":
 lowerCAmelCase_			:     List[Any]							    =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 lowerCAmelCase_			:     str							    =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
