


'''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


__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       _snake_case     (  A )		->					Tuple:
       for pegasus_name, hf_name in PATTERNS:
              lowerCAmelCase__      =			k.replace(A				,					A )
       return k


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


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

       # convert model
       lowerCAmelCase__      =			get_tf_weights_as_numpy(A )
       lowerCAmelCase__      =			task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowerCAmelCase__      =			task_specific_params
       lowerCAmelCase__      =			convert_pegasus(A				,					A )
       torch_model.save_pretrained(A )
       lowerCAmelCase__      =			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					       =							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)



'''simple docstring'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			0

							for character_count in character_freq_dict.values():
														if character_count % 2:
																					odd_char += 1
							if odd_char > 1:
														return False
							return True


def       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)
							print(
							    '''> can_string_be_rearranged_as_palindrome()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


if __name__ == "__main__":
		__UpperCAmelCase					       =							input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__UpperCAmelCase					       =							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")