




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowercase__				:     List[Any] 		=						[
    # tf -> hf
    ("/", "."),
    ("layer_", "layers."),
    ("kernel", "weight"),
    ("beta", "bias"),
    ("gamma", "weight"),
    ("pegasus", "model"),
]
lowercase__				:     Union[str, Any] 		=						[
    (".output.dense", ".fc2"),
    ("intermediate.LayerNorm", "final_layer_norm"),
    ("intermediate.dense", "fc1"),
]

lowercase__				:     int 		=						(
    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
)

lowercase__				:     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
)

lowercase__				:     Dict 		=						[
    "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__    (		_A     ,	_A							):







	'''simple docstring'''





	for tf_name, hf_name in patterns:
		snake_case_		    =     k.replace(_A     ,	_A							)
	return k


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     BigBirdPegasusConfig(**_A							)
	snake_case_		    =     BigBirdPegasusForConditionalGeneration(_A							)
	snake_case_		    =     torch_model.state_dict()
	snake_case_		    =     {}

	# separating decoder weights
	snake_case_		    =     {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"							)}
	snake_case_		    =     {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"							):
		snake_case_		    =     [k.endswith(_A							) for ending in KEYS_TO_IGNORE]
		if any(_A							):
			continue
		snake_case_		    =     DECODER_PATTERNS
		snake_case_		    =     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"]							):
			snake_case_		    =     v.T
		snake_case_		    =     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"							):
		snake_case_		    =     [k.endswith(_A							) for ending in KEYS_TO_IGNORE]
		if any(_A							):
			continue
		snake_case_		    =     REMAINING_PATTERNS
		snake_case_		    =     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"]							):
			snake_case_		    =     v.T
		snake_case_		    =     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}"

	snake_case_		    =     mapping["model.embed_positions.weight"]
	snake_case_		    =     mapping.pop("model.embed_positions.weight"							)
	snake_case_    ,      snake_case_		    =     torch_model.load_state_dict(_A     ,	strict=_A							)
	snake_case_		    =     [
	    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__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     tf.train.list_variables(_A							)
	snake_case_		    =     {}
	snake_case_		    =     ["global_step"]
	for name, shape in tqdm(_A     ,	desc="converting tf checkpoint to dict"							):
		snake_case_		    =     any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		snake_case_		    =     tf.train.load_variable(_A     ,	_A							)
		snake_case_		    =     array
	return tf_weights


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     get_tf_weights_as_numpy(_A							)
	snake_case_		    =     convert_bigbird_pegasus(_A     ,	_A							)
	torch_model.save_pretrained(_A							)


if __name__ == "__main__":
		lowercase__				:     Tuple 		=						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.")
		lowercase__				:     Optional[Any] 		=						parser.parse_args()
		lowercase__				:     Optional[int] 		=						{}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	if num <= 0:
		raise ValueError("Input must be a positive integer"							)

	snake_case_		    =     [True] * (num + 1)

	snake_case_		    =     2
	while p * p <= num:
		if primes[p]:
			for i in range(p * p     ,	num + 1     ,	_A							):
				snake_case_		    =     False
		p += 1

	return [prime for prime in range(2     ,	num + 1							) if primes[prime]]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowercase__				:     Tuple 		=						int(input("Enter a positive integer: ").strip())
		print(prime_sieve_eratosthenes(user_num))
