



'''simple docstring'''




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCamelCase__       :	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
)

UpperCamelCase__       :	int    = (
    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
)

UpperCamelCase__       :	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 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   List[str]					):
		for tf_name, hf_name in patterns:
				__SCREAMING_SNAKE_CASE       :		Dict           =		k.replace(_lowerCamelCase       ,      _lowerCamelCase					)
		return k


def 				lowerCAmelCase_   (	_lowerCamelCase:   dict       ,      _lowerCamelCase:   dict					):
		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		BigBirdPegasusConfig(**_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		BigBirdPegasusForConditionalGeneration(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		torch_model.state_dict()
		__SCREAMING_SNAKE_CASE       :		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       :		Tuple           =		{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       :		Optional[int]           =		[k.endswith(_lowerCamelCase					) for ending in KEYS_TO_IGNORE]
				if any(_lowerCamelCase					):
						continue
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		DECODER_PATTERNS
				__SCREAMING_SNAKE_CASE       :		List[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"""]					):
						__SCREAMING_SNAKE_CASE       :		Dict           =		v.T
				__SCREAMING_SNAKE_CASE       :		int           =		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"""					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		[k.endswith(_lowerCamelCase					) for ending in KEYS_TO_IGNORE]
				if any(_lowerCamelCase					):
						continue
				__SCREAMING_SNAKE_CASE       :		List[str]           =		REMAINING_PATTERNS
				__SCREAMING_SNAKE_CASE       :		Optional[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"""]					):
						__SCREAMING_SNAKE_CASE       :		Dict           =		v.T
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		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}"

		__SCREAMING_SNAKE_CASE       :		Tuple           =		mapping["""model.embed_positions.weight"""]
		__SCREAMING_SNAKE_CASE       :		int           =		mapping.pop("""model.embed_positions.weight"""					)
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		List[Any]           =		torch_model.load_state_dict(_lowerCamelCase       ,      strict=_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		[
		    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_   (	_lowerCamelCase:   Union[str, Any]					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		tf.train.list_variables(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Any           =		{}
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		["""global_step"""]
		for name, shape in tqdm(_lowerCamelCase       ,      desc="""converting tf checkpoint to dict"""					):
				__SCREAMING_SNAKE_CASE       :		Dict           =		any(pat in name for pat in ignore_name					)
				if skip_key:
						continue
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tf.train.load_variable(_lowerCamelCase       ,      _lowerCamelCase					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		array
		return tf_weights


def 				lowerCAmelCase_   (	_lowerCamelCase:   str       ,      _lowerCamelCase:   str       ,      _lowerCamelCase:   dict					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		get_tf_weights_as_numpy(_lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		convert_bigbird_pegasus(_lowerCamelCase       ,      _lowerCamelCase					)
		torch_model.save_pretrained(_lowerCamelCase					)


if __name__ == "__main__":
						UpperCamelCase__       :	Union[str, Any]    = 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.''')
						UpperCamelCase__       :	List[str]    = parser.parse_args()
						UpperCamelCase__       :	int    = {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   float       ,      _lowerCamelCase:   list[float]					):
		if discount_rate < 0:
				raise ValueError("""Discount rate cannot be negative"""					)
		if not cash_flows:
				raise ValueError("""Cash flows list cannot be empty"""					)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		sum(
		    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_lowerCamelCase					)					)
		return round(_lowerCamelCase       ,      ndigits=2					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()