






'''simple docstring'''
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


a_					=	[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
a_					=	[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

a_					=	(
    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
)

a_					=	(
    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
)

a_					=	[
    '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(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      for tf_name, hf_name in patterns:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=k.replace(UpperCamelCase__,		UpperCamelCase__							)
      return k





def 	_a(					UpperCamelCase__			:  dict,		UpperCamelCase__			:  dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			int													=BigBirdPegasusConfig(**UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict													=BigBirdPegasusForConditionalGeneration(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			str													=torch_model.state_dict()
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={}

      # separating decoder weights
      SCREAMING_SNAKE_CASE__       :			List[Any]													={k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''							)}
      SCREAMING_SNAKE_CASE__       :			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'''							):
            SCREAMING_SNAKE_CASE__       :			int													=[k.endswith(UpperCamelCase__							) for ending in KEYS_TO_IGNORE]
            if any(UpperCamelCase__							):
                  continue
            SCREAMING_SNAKE_CASE__       :			int													=DECODER_PATTERNS
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=rename_state_dict_key(UpperCamelCase__,		UpperCamelCase__							)
            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''']							):
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v.T
            SCREAMING_SNAKE_CASE__       :			List[Any]													=torch.from_numpy(UpperCamelCase__							)
            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'''							):
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[k.endswith(UpperCamelCase__							) for ending in KEYS_TO_IGNORE]
            if any(UpperCamelCase__							):
                  continue
            SCREAMING_SNAKE_CASE__       :			Tuple													=REMAINING_PATTERNS
            SCREAMING_SNAKE_CASE__       :			Dict													=rename_state_dict_key(UpperCamelCase__,		UpperCamelCase__							)
            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''']							):
                  SCREAMING_SNAKE_CASE__       :			int													=v.T
            SCREAMING_SNAKE_CASE__       :			str													=torch.from_numpy(UpperCamelCase__							)
            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}"

      SCREAMING_SNAKE_CASE__       :			str													=mapping['''model.embed_positions.weight''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=mapping.pop('''model.embed_positions.weight'''							)
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch_model.load_state_dict(UpperCamelCase__,		strict=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=[
          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(					UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=tf.train.list_variables(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={}
      SCREAMING_SNAKE_CASE__       :			Dict													=['''global_step''']
      for name, shape in tqdm(UpperCamelCase__,		desc='''converting tf checkpoint to dict'''							):
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=any(pat in name for pat in ignore_name							)
            if skip_key:
                  continue
            SCREAMING_SNAKE_CASE__       :			Dict													=tf.train.load_variable(UpperCamelCase__,		UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			str													=array
      return tf_weights





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  str,		UpperCamelCase__			:  dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=get_tf_weights_as_numpy(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict													=convert_bigbird_pegasus(UpperCamelCase__,		UpperCamelCase__							)
      torch_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  a_					=	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.')
  a_					=	parser.parse_args()
  a_					=	{}
  convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







'''simple docstring'''
from scipy.stats import pearsonr

import datasets


a_					=	'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe 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.\nThe 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.\n'


a_					=	'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    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.\n    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.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


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






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




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       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        __magic_name__		( self		:							Optional[int]   , __lowercase		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=False )		->  int:
       if return_pvalue:
             SCREAMING_SNAKE_CASE__       :			List[str]													=pearsonr(__lowercase   , __lowercase )
             return {"pearsonr": results[0], "p-value": results[1]}
       else:
             return {"pearsonr": float(pearsonr(__lowercase   , __lowercase )[0] )}