






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					:		Any	  =						[
    # 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      a_ (      _A  )			->			List[Any]:







				"""simple docstring"""
				for pegasus_name, hf_name in PATTERNS:
								snake_case__									=     k.replace(_A      ,   _A  )
				return k
def      a_ (      _A      ,   _A  )			->			PegasusForConditionalGeneration:







				"""simple docstring"""
				snake_case__									=     DEFAULTS.copy()
				cfg_kwargs.update(_A  )
				snake_case__									=     PegasusConfig(**_A  )
				snake_case__									=     PegasusForConditionalGeneration(_A  )
				snake_case__									=     torch_model.model.state_dict()
				snake_case__									=     {}
				for k, v in tf_weights.items():
								snake_case__									=     rename_state_dict_key(_A  )
								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:
												snake_case__									=     v.T
								snake_case__									=     torch.tensor(_A      ,   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
				snake_case__									=     torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
				snake_case__									=     mapping['shared.weight']
				snake_case__									=     mapping['shared.weight']
				snake_case__									=     {k: torch.zeros_like(_A  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
				mapping.update(**_A  )
				snake_case__							,						snake_case__									=     torch_model.model.load_state_dict(_A      ,   strict=_A  )
				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      a_ (      _A="./ckpt/aeslc/model.ckpt-32000"  )			->			Dict:







				"""simple docstring"""
				snake_case__									=     tf.train.list_variables(_A  )
				snake_case__									=     {}
				snake_case__									=     ['Adafactor', '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      a_ (      _A      ,   _A  )			->			str:







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

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


if __name__ == "__main__":
							__UpperCamelCase					:		Tuple	  =						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					:		Dict	  =						parser.parse_args()
							if args.save_dir is None:
														__UpperCamelCase					:		Optional[Any]	  =						Path(args.tf_ckpt_path).parent.name
														__UpperCamelCase					:		List[Any]	  =						os.path.join("""pegasus""", dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












__UpperCamelCase					:		List[str]	  =						256
# Modulus to hash a string
__UpperCamelCase					:		int	  =						1000003
def      a_ (      _A      ,   _A  )			->			bool:







				"""simple docstring"""
				snake_case__									=     len(_A  )
				snake_case__									=     len(_A  )
				if p_len > t_len:
								return False

				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     1

				# Calculating the hash of pattern and substring of text
				for i in range(_A  ):
								snake_case__									=     (ord(pattern[i]  ) + p_hash * alphabet_size) % modulus
								snake_case__									=     (ord(text[i]  ) + text_hash * alphabet_size) % modulus
								if i == p_len - 1:
												continue
								snake_case__									=     (modulus_power * alphabet_size) % modulus

				for i in range(0      ,   t_len - p_len + 1  ):
								if text_hash == p_hash and text[i : i + p_len] == pattern:
												return True
								if i == t_len - p_len:
												continue
								# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
								snake_case__									=     (
								    (text_hash - ord(text[i]  ) * modulus_power) * alphabet_size
								    + ord(text[i + p_len]  )
								) % modulus
				return False
def      a_ (      )			->			None:







				"""simple docstring"""
				snake_case__									=     'abc1abc12'
				snake_case__									=     'alskfjaldsabc1abc1abc12k23adsfabcabc'
				snake_case__									=     'alskfjaldsk23adsfabcabc'
				assert rabin_karp(_A      ,   _A  ) and not rabin_karp(_A      ,   _A  )

				# Test 2)
				snake_case__									=     'ABABX'
				snake_case__									=     'ABABZABABYABABX'
				assert rabin_karp(_A      ,   _A  )

				# Test 3)
				snake_case__									=     'AAAB'
				snake_case__									=     'ABAAAAAB'
				assert rabin_karp(_A      ,   _A  )

				# Test 4)
				snake_case__									=     'abcdabcy'
				snake_case__									=     'abcxabcdabxabcdabcdabcy'
				assert rabin_karp(_A      ,   _A  )

				# Test 5)
				snake_case__									=     'Lü'
				snake_case__									=     'Lüsai'
				assert rabin_karp(_A      ,   _A  )
				snake_case__									=     'Lue'
				assert not rabin_karp(_A      ,   _A  )
				print('Success.'  )


if __name__ == "__main__":
							test_rabin_karp()




