





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__lowerCamelCase	:					Optional[Any]			  =					[
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
__lowerCamelCase	:					List[Any]			  =					[
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

__lowerCamelCase	:					Tuple			  =					(
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase	:					Union[str, Any]			  =					(
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

__lowerCamelCase	:					Optional[Any]			  =					[
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 for tf_name, hf_name in patterns:
  UpperCamelCase     : List[str]	     =       k.replace(_lowerCAmelCase							,       _lowerCAmelCase					)
 return k



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			BigBirdPegasusForConditionalGeneration:
 UpperCamelCase     : List[str]	     =       BigBirdPegasusConfig(**_lowerCAmelCase					)
 UpperCamelCase     : Dict	     =       BigBirdPegasusForConditionalGeneration(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       torch_model.state_dict()
 UpperCamelCase     : Optional[Any]	     =       {}

 # separating decoder weights
 UpperCamelCase     : Optional[Any]	     =       {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
 UpperCamelCase     : Dict	     =       {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder"					)}

 for k, v in tqdm(decoder_weights.items()							,       "tf -> hf conversion"					):
  UpperCamelCase     : List[str]	     =       [k.endswith(_lowerCAmelCase					) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase					):
   continue
  UpperCamelCase     : Optional[Any]	     =       DECODER_PATTERNS
  UpperCamelCase     : Union[str, Any]	     =       rename_state_dict_key(_lowerCAmelCase							,       _lowerCAmelCase					)
  if new_k not in state_dict:
   raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
  if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
   UpperCamelCase     : List[str]	     =       v.T
  UpperCamelCase     : Dict	     =       torch.from_numpy(_lowerCAmelCase					)
  assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

 for k, v in tqdm(remaining_weights.items()							,       "tf -> hf conversion"					):
  UpperCamelCase     : Union[str, Any]	     =       [k.endswith(_lowerCAmelCase					) for ending in KEYS_TO_IGNORE]
  if any(_lowerCAmelCase					):
   continue
  UpperCamelCase     : Tuple	     =       REMAINING_PATTERNS
  UpperCamelCase     : Any	     =       rename_state_dict_key(_lowerCAmelCase							,       _lowerCAmelCase					)
  if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
   raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""					)
  if any(True if i in k else False for i in ["dense", "query", "key", "value"]					):
   UpperCamelCase     : str	     =       v.T
  UpperCamelCase     : List[str]	     =       torch.from_numpy(_lowerCAmelCase					)
  if k != "pegasus/embeddings/position_embeddings":
   assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

 UpperCamelCase     : Dict	     =       mapping["model.embed_positions.weight"]
 UpperCamelCase     : Any	     =       mapping.pop("model.embed_positions.weight"					)
 UpperCamelCase     ,   UpperCamelCase     : List[Any]	     =       torch_model.load_state_dict(_lowerCAmelCase							,       strict=_lowerCAmelCase					)
 UpperCamelCase     : List[Any]	     =       [
     k
     for k in missing
     if k
     not in [
         "final_logits_bias",
         "model.encoder.embed_tokens.weight",
         "model.decoder.embed_tokens.weight",
         "lm_head.weight",
     ]
 ]
 assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
 assert extra == [], F"""no matches found for the following tf keys {extra}"""
 return torch_model



def 		A_    (					_lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Union[str, Any]	     =       tf.train.list_variables(_lowerCAmelCase					)
 UpperCamelCase     : Union[str, Any]	     =       {}
 UpperCamelCase     : Union[str, Any]	     =       ["global_step"]
 for name, shape in tqdm(_lowerCAmelCase							,       desc="converting tf checkpoint to dict"					):
  UpperCamelCase     : int	     =       any(pat in name for pat in ignore_name					)
  if skip_key:
   continue
  UpperCamelCase     : Optional[int]	     =       tf.train.load_variable(_lowerCAmelCase							,       _lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       array
 return tf_weights



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 UpperCamelCase     : Union[str, Any]	     =       get_tf_weights_as_numpy(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       convert_bigbird_pegasus(_lowerCAmelCase							,       _lowerCAmelCase					)
 torch_model.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Dict			  =					argparse.ArgumentParser()
    parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
    parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
    __lowerCamelCase	:					str			  =					parser.parse_args()
    __lowerCamelCase	:					Union[str, Any]			  =					{}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







from scipy.stats import spearmanr

import datasets


__lowerCamelCase	:					List[str]			  =					"""
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

__lowerCamelCase	:					List[Any]			  =					"""
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

__lowerCamelCase	:					Optional[int]			  =					r"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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      ):





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       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.spearmanr.html"]	,							)





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_=False       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       spearmanr(A_	,							A_       )
       if return_pvalue:
        return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
       else:
        return {"spearmanr": results[0]}
