


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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
							for tf_name, hf_name in patterns:
														lowercase_  							=			k.replace(UpperCAmelCase__						,							UpperCAmelCase__  )
							return k


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
							lowercase_  							=			BigBirdPegasusConfig(**UpperCAmelCase__  )
							lowercase_  							=			BigBirdPegasusForConditionalGeneration(UpperCAmelCase__  )
							lowercase_  							=			torch_model.state_dict()
							lowercase_  							=			{}

							# separating decoder weights
							lowercase_  							=			{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""  )}
							lowercase_  							=			{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"""  ):
														lowercase_  							=			[k.endswith(UpperCAmelCase__  ) for ending in KEYS_TO_IGNORE]
														if any(UpperCAmelCase__  ):
																					continue
														lowercase_  							=			DECODER_PATTERNS
														lowercase_  							=			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"""]  ):
																					lowercase_  							=			v.T
														lowercase_  							=			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"""  ):
														lowercase_  							=			[k.endswith(UpperCAmelCase__  ) for ending in KEYS_TO_IGNORE]
														if any(UpperCAmelCase__  ):
																					continue
														lowercase_  							=			REMAINING_PATTERNS
														lowercase_  							=			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"""]  ):
																					lowercase_  							=			v.T
														lowercase_  							=			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}'''

							lowercase_  							=			mapping["""model.embed_positions.weight"""]
							lowercase_  							=			mapping.pop("""model.embed_positions.weight"""  )
							lowercase_							,					lowercase_  							=			torch_model.load_state_dict(UpperCAmelCase__						,							strict=UpperCAmelCase__  )
							lowercase_  							=			[
							    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 			UpperCAmelCase_				(      UpperCAmelCase__  ):
							lowercase_  							=			tf.train.list_variables(UpperCAmelCase__  )
							lowercase_  							=			{}
							lowercase_  							=			["""global_step"""]
							for name, shape in tqdm(UpperCAmelCase__						,							desc="""converting tf checkpoint to dict"""  ):
														lowercase_  							=			any(pat in name for pat in ignore_name  )
														if skip_key:
																					continue
														lowercase_  							=			tf.train.load_variable(UpperCAmelCase__						,							UpperCAmelCase__  )
														lowercase_  							=			array
							return tf_weights


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
							lowercase_  							=			get_tf_weights_as_numpy(UpperCAmelCase__  )
							lowercase_  							=			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)






def 			UpperCAmelCase_				(      UpperCAmelCase__=2_8_1_2_3  ):
					lowercase_  							=			[1] * (limit + 1)

					for i in range(2						,							int(limit**0.5  ) + 1  ):
										sum_divs[i * i] += i
										for k in range(i + 1						,							limit // i + 1  ):
															sum_divs[k * i] += k + i

					lowercase_  							=			set()
					lowercase_  							=			0

					for n in range(1						,							limit + 1  ):
										if sum_divs[n] > n:
															abundants.add(UpperCAmelCase__  )

										if not any((n - a in abundants) for a in abundants  ):
															res += n

					return res


if __name__ == "__main__":
							print(solution())


