
from scipy.stats import pearsonr

import datasets


lowerCamelCase_						     =					'''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


lowerCamelCase_						     =					'''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


lowerCamelCase_						     =					'''
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
'''




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

		"""simple docstring"""







		def 	UpperCAmelCase_ (self						):
							return datasets.MetricInfo(
							    description=_DESCRIPTION	,      citation=_CITATION	,      inputs_description=_KWARGS_DESCRIPTION	,      features=datasets.Features(
							        {
							            """predictions""": datasets.Value("""float"""						),
							            """references""": datasets.Value("""float"""						),
							        }						)	,      reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]	,      )







		def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False						):
							if return_pvalue:
												UpperCamelCase__				=    pearsonr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
												return {"pearsonr": results[0], "p-value": results[1]}
							else:
												return {"pearsonr": float(pearsonr(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)[0]						)}







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Tuple	,		__a      :	int	,		__a      :	Dict="attention"						):



     '''simple docstring'''




     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/key/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/out/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/query/kernel"]
     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/{layer_name}/value/kernel"]
     return k, o, q, v






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Any	,		__a      :	Dict	,		__a      :	Optional[Any]=False						):



     '''simple docstring'''




     if split_mlp_wi:
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi_0/kernel"]
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi_1/kernel"]
          UpperCamelCase__				=    (wi_a, wi_a)
     else:
          UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wi/kernel"]

     UpperCamelCase__				=    params[f"{prefix}/layers_{i}/mlp/wo/kernel"]
     return wi, wo






def 						__magic_name__			(   __a      :	Tuple	,		__a      :	Tuple	,		__a      :	Optional[int]	,		__a      :	Dict						):



     '''simple docstring'''




     return params[f"{prefix}/layers_{i}/{layer_name}/scale"]






def 						__magic_name__			(   __a      :	dict	,		*, __a      :	int	,		__a      :	bool						):



     '''simple docstring'''




     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/layers_0/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

     UpperCamelCase__				=    old[
         """encoder/relpos_bias/rel_embedding"""
     ].T
     UpperCamelCase__				=    old["""encoder/encoder_norm/scale"""]

     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

          UpperCamelCase__				=    old["""decoder/decoder_norm/scale"""]
          UpperCamelCase__				=    old[
              """decoder/relpos_bias/rel_embedding"""
          ].T

          # 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 						__magic_name__			(   __a      :	List[Any]	,		__a      :	bool						):



     '''simple docstring'''




     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 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	int	,		__a      :	Dict						):



     '''simple docstring'''




     UpperCamelCase__				=    checkpoints.load_tax_checkpoint(__a						)
     UpperCamelCase__				=    convert_tax_to_pytorch(__a	,		num_layers=config.num_layers	,		is_encoder_only=__a						)
     UpperCamelCase__				=    make_state_dict(__a	,		__a						)
     model.load_state_dict(__a	,		strict=__a						)






def 						__magic_name__			(   __a      :	Optional[Any]	,		__a      :	Optional[Any]	,		__a      :	Any	,		__a      :	bool = False						):



     '''simple docstring'''




     UpperCamelCase__				=    TaConfig.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__				=    TaEncoderModel(__a						)
     else:
          UpperCamelCase__				=    TaForConditionalGeneration(__a						)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(__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__":
    lowerCamelCase_						     =					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
    )
    lowerCamelCase_						     =					parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





