





import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCamelCase__											= [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["memory_attention", "encoder_attn"],
    ["attention", "attn"],
    ["/", "."],
    [".LayerNorm.gamma", "_layer_norm.weight"],
    [".LayerNorm.beta", "_layer_norm.bias"],
    ["r.layer_", "r.layers."],
    ["output_proj", "out_proj"],
    ["ffn.dense_1.", "fc2."],
    ["ffn.dense.", "fc1."],
    ["ffn_layer_norm", "final_layer_norm"],
    ["kernel", "weight"],
    ["encoder_layer_norm.", "encoder.layer_norm."],
    ["decoder_layer_norm.", "decoder.layer_norm."],
    ["embeddings.weights", "shared.weight"],
]





def     _UpperCamelCase     (a__   :str       ):






							"""simple docstring"""





							for pegasus_name, hf_name in PATTERNS:
														UpperCamelCase__				     =			k.replace(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE       )
							return k





def     _UpperCamelCase     (a__   :str   ,			a__   :Optional[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			DEFAULTS.copy()
							cfg_kwargs.update(__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			PegasusConfig(**__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			torch_model.model.state_dict()
							UpperCamelCase__				     =			{}
							for k, v in tf_weights.items():
														UpperCamelCase__				     =			rename_state_dict_key(__SCREAMING_SNAKE_CASE       )
														if new_k not in sd:
																					raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

														if "dense" in k or "proj" in new_k:
																					UpperCamelCase__				     =			v.T
														UpperCamelCase__				     =			torch.tensor(__SCREAMING_SNAKE_CASE   ,			dtype=sd[new_k].dtype       )
														assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
							# make sure embedding.padding_idx is respected
							UpperCamelCase__				     =			torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
							UpperCamelCase__				     =			mapping["shared.weight"]
							UpperCamelCase__				     =			mapping["shared.weight"]
							UpperCamelCase__				     =			{k: torch.zeros_like(__SCREAMING_SNAKE_CASE       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
							mapping.update(**__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE   ,			strict=__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			[
							    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.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     (a__   :Tuple="./ckpt/aeslc/model.ckpt-32000"       ):






							"""simple docstring"""





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





def     _UpperCamelCase     (a__   :int   ,			a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			Path(__SCREAMING_SNAKE_CASE       ).parent.name
							UpperCamelCase__				     =			task_specific_params[f"""summarization_{dataset}"""]["max_position_embeddings"]
							UpperCamelCase__				     =			PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""   ,			model_max_length=__SCREAMING_SNAKE_CASE       )
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(__SCREAMING_SNAKE_CASE       )

							# convert model
							UpperCamelCase__				     =			get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			task_specific_params[f"""summarization_{dataset}"""]
							if dataset == "large":
														UpperCamelCase__				     =			task_specific_params
							UpperCamelCase__				     =			convert_pegasus(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE       )
							torch_model.save_pretrained(__SCREAMING_SNAKE_CASE       )
							UpperCamelCase__				     =			torch_model.state_dict()
							sd.pop("""model.decoder.embed_positions.weight"""       )
							sd.pop("""model.encoder.embed_positions.weight"""       )
							torch.save(__SCREAMING_SNAKE_CASE   ,			Path(__SCREAMING_SNAKE_CASE       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
	UpperCamelCase__											= argparse.ArgumentParser()
	# Required parameters
	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__											= parser.parse_args()
	if args.save_dir is None:
		UpperCamelCase__											= Path(args.tf_ckpt_path).parent.name
		UpperCamelCase__											= os.path.join("pegasus", dataset)
	convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)











import math





def     _UpperCamelCase     (a__   :int       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[True] * n
							UpperCamelCase__				     =			False
							UpperCamelCase__				     =			False
							UpperCamelCase__				     =			True

							for i in range(3   ,			int(n**0.5 + 1       )   ,			2       ):
														UpperCamelCase__				     =			i * 2
														while index < n:
																					UpperCamelCase__				     =			False
																					UpperCamelCase__				     =			index + i

							UpperCamelCase__				     =			[2]

							for i in range(3   ,			a__   ,			2       ):
														if is_prime[i]:
																					primes.append(a__       )

							return primes





def     _UpperCamelCase     (a__   :int = 9999_6666_3333       ):






							"""simple docstring"""





							UpperCamelCase__				     =			math.floor(math.sqrt(a__       )       ) + 100
							UpperCamelCase__				     =			prime_sieve(a__       )

							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			primes[prime_index]

							while (last_prime**2) <= limit:
														UpperCamelCase__				     =			primes[prime_index + 1]

														UpperCamelCase__				     =			last_prime**2
														UpperCamelCase__				     =			next_prime**2

														# Get numbers divisible by lps(current)
														UpperCamelCase__				     =			lower_bound + last_prime
														while upper_bound > current <= limit:
																					matches_sum += current
																					current += last_prime

														# Reset the upper_bound
														while (upper_bound - next_prime) > limit:
																					upper_bound -= next_prime

														# Add the numbers divisible by ups(current)
														UpperCamelCase__				     =			upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														UpperCamelCase__				     =			0
														while upper_bound > current <= limit:
																					if current <= lower_bound:
																												# Increment the current number
																												current += last_prime * next_prime
																												continue

																					if current > limit:
																												break

																					# Remove twice since it was added by both ups and lps
																					matches_sum -= current * 2

																					# Increment the current number
																					current += last_prime * next_prime

														# Setup for next pair
														UpperCamelCase__				     =			next_prime
														prime_index += 1

							return matches_sum


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




