






"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase_							      = (
    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_							      = (
    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_							      = [
    '''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 							snake_case (  A__      ,A__		):
			for tf_name, hf_name in patterns:
						UpperCAmelCase_	:					Tuple     	=   k.replace(A__      ,A__		)
			return k

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   BigBirdPegasusConfig(**A__		)
			UpperCAmelCase_	:					List[Any]     	=   BigBirdPegasusForConditionalGeneration(A__		)
			UpperCAmelCase_	:					List[str]     	=   torch_model.state_dict()
			UpperCAmelCase_	:					Any     	=   {}

			# separating decoder weights
			UpperCAmelCase_	:					Union[str, 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[Any]     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Any     	=   DECODER_PATTERNS
						UpperCAmelCase_	:					int     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					Optional[Any]     	=   v.T
						UpperCAmelCase_	:					str     	=   torch.from_numpy(A__		)
						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_	:					str     	=   [k.endswith(A__		) for ending in KEYS_TO_IGNORE]
						if any(A__		):
									continue
						UpperCAmelCase_	:					Optional[int]     	=   REMAINING_PATTERNS
						UpperCAmelCase_	:					Tuple     	=   rename_state_dict_key(A__      ,A__		)
						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_	:					int     	=   torch.from_numpy(A__		)
						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_	:					Any     	=   mapping["model.embed_positions.weight"]
			UpperCAmelCase_	:					List[Any]     	=   mapping.pop("model.embed_positions.weight"		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   torch_model.load_state_dict(A__      ,strict=A__		)
			UpperCAmelCase_	:					Optional[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 							snake_case (  A__		):
			UpperCAmelCase_	:					List[str]     	=   tf.train.list_variables(A__		)
			UpperCAmelCase_	:					List[Any]     	=   {}
			UpperCAmelCase_	:					Optional[int]     	=   ["global_step"]
			for name, shape in tqdm(A__      ,desc="converting tf checkpoint to dict"		):
						UpperCAmelCase_	:					Union[str, Any]     	=   any(pat in name for pat in ignore_name		)
						if skip_key:
									continue
						UpperCAmelCase_	:					Dict     	=   tf.train.load_variable(A__      ,A__		)
						UpperCAmelCase_	:					int     	=   array
			return tf_weights

def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   get_tf_weights_as_numpy(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   convert_bigbird_pegasus(A__      ,A__		)
			torch_model.save_pretrained(A__		)


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







'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


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






