"""simple docstring"""






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


_A   					=    [
    # 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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Tuple:
				for pegasus_name, hf_name in PATTERNS:
								SCREAMING_SNAKE_CASE__		  =				k.replace(__UpperCAmelCase  ,					__UpperCAmelCase       )
				return k




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   PegasusForConditionalGeneration:
				SCREAMING_SNAKE_CASE__		  =				DEFAULTS.copy()
				cfg_kwargs.update(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				PegasusConfig(**__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				PegasusForConditionalGeneration(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				torch_model.model.state_dict()
				SCREAMING_SNAKE_CASE__		  =				{}
				for k, v in tf_weights.items():
								SCREAMING_SNAKE_CASE__		  =				rename_state_dict_key(__UpperCAmelCase       )
								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:
												SCREAMING_SNAKE_CASE__		  =				v.T
								SCREAMING_SNAKE_CASE__		  =				torch.tensor(__UpperCAmelCase  ,					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
				SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]       )
				SCREAMING_SNAKE_CASE__		  =				mapping["shared.weight"]
				SCREAMING_SNAKE_CASE__		  =				mapping["shared.weight"]
				SCREAMING_SNAKE_CASE__		  =				{k: torch.zeros_like(__UpperCAmelCase       ) for k, v in sd.items() if k.endswith("bias"       ) and k not in mapping}
				mapping.update(**__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch_model.model.load_state_dict(__UpperCAmelCase  ,					strict=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[
				    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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase="./ckpt/aeslc/model.ckpt-32000"       )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				tf.train.list_variables(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				["Adafactor", "global_step"]
				for name, shape in tqdm(__UpperCAmelCase  ,					desc="converting tf checkpoint to dict"       ):
								SCREAMING_SNAKE_CASE__		  =				any(pat in name for pat in ignore_name       )
								if skip_key:
												continue
								SCREAMING_SNAKE_CASE__		  =				tf.train.load_variable(__UpperCAmelCase  ,					__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__		  =				array
				return tf_weights




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				# save tokenizer first
				SCREAMING_SNAKE_CASE__		  =				Path(__UpperCAmelCase       ).parent.name
				SCREAMING_SNAKE_CASE__		  =				task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
				SCREAMING_SNAKE_CASE__		  =				PegasusTokenizer.from_pretrained("sshleifer/pegasus"  ,					model_max_length=__UpperCAmelCase       )
				assert tok.model_max_length == desired_max_model_length
				tok.save_pretrained(__UpperCAmelCase       )

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


if __name__ == "__main__":
						_A   					=    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.')
						_A   					=    parser.parse_args()
						if args.save_dir is None:
												_A   					=    Path(args.tf_ckpt_path).parent.name
												_A   					=    os.path.join('pegasus', dataset)
						convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




