




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 lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     for tf_name, hf_name in patterns:
          UpperCAmelCase_     :				int  =     k.replace(_lowercase					,		_lowercase )
     return k







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusConfig(**_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusForConditionalGeneration(_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     torch_model.state_dict()
     UpperCAmelCase_     :				Dict  =     {}

     # separating decoder weights
     UpperCAmelCase_     :				Any  =     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
     UpperCAmelCase_     :				Optional[Any]  =     {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_     :				Optional[Any]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				Optional[int]  =     DECODER_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     rename_state_dict_key(_lowercase					,		_lowercase )
          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_     :				int  =     v.T
          UpperCAmelCase_     :				List[Any]  =     torch.from_numpy(_lowercase )
          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_     :				Optional[int]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				str  =     REMAINING_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     rename_state_dict_key(_lowercase					,		_lowercase )
          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_     :				int  =     v.T
          UpperCAmelCase_     :				Tuple  =     torch.from_numpy(_lowercase )
          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_     :				Tuple  =     mapping['''model.embed_positions.weight''']
     UpperCAmelCase_     :				str  =     mapping.pop('''model.embed_positions.weight''' )
     UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     torch_model.load_state_dict(_lowercase					,		strict=_lowercase )
     UpperCAmelCase_     :				str  =     [
         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 lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     get_tf_weights_as_numpy(_lowercase )
     UpperCAmelCase_     :				Any  =     convert_bigbird_pegasus(_lowercase					,		_lowercase )
     torch_model.save_pretrained(_lowercase )


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 __future__ import annotations



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[float]				):




     """simple docstring"""


     if len(UpperCamelCase__				) < 2:
          raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""				)
     if any(i <= 0 for i in nums				):
          raise ValueError("""All values must be greater than 0"""				)
     a_     :							int     						=  nums.copy()
     copy_nums.sort()
     return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
