
'''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__						:      Optional[int]   				=				[
    # 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							(						UpperCAmelCase		)->	Dict:







      """simple docstring"""
      for pegasus_name, hf_name in PATTERNS:
            __A						=	k.replace(_lowercase					,     _lowercase		)
      return k





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Tuple:







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







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





def 				snake_case							(						UpperCAmelCase					,     UpperCAmelCase		)->	Any:







      """simple docstring"""
      # save tokenizer first
      __A						=	Path(_lowercase		).parent.name
      __A						=	task_specific_params[f'summarization_{dataset}']['''max_position_embeddings''']
      __A						=	PegasusTokenizer.from_pretrained('sshleifer/pegasus'					,     model_max_length=_lowercase		)
      assert tok.model_max_length == desired_max_model_length
      tok.save_pretrained(_lowercase		)

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


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







import itertools
import string
from collections.abc import Generator, Iterable
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							iter(_lowercase						)
						while True:
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(itertools.islice(_lowercase		,  _lowercase						)						)
												if not chunk:
																		return
												yield chunk
def       A	(    _lowercase						):
						SCREAMING_SNAKE_CASE							:    int							=							''''''.join([c.upper() for c in dirty if c in string.ascii_letters]						)
						SCREAMING_SNAKE_CASE							:    List[str]							=							''''''

						if len(_lowercase						) < 2:
												return dirty

						for i in range(len(_lowercase						) - 1						):
												clean += dirty[i]

												if dirty[i] == dirty[i + 1]:
																		clean += "X"

						clean += dirty[-1]

						if len(_lowercase						) & 1:
												clean += "X"

						return clean
def       A	(    _lowercase						):
						# I and J are used interchangeably to allow
						# us to use a 5x5 table (25 letters)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
						# we're using a list instead of a '2d' array because it makes the math
						# for setting up the table and doing the actual encoding/decoding simpler
						SCREAMING_SNAKE_CASE							:    List[Any]							=							[]

						# copy key chars into the table if they are in `alphabet` ignoring duplicates
						for char in key.upper():
												if char not in table and char in alphabet:
																		table.append(_lowercase						)

    # fill the rest of the table in with the remaining alphabet chars
						for char in alphabet:
												if char not in table:
																		table.append(_lowercase						)

						return table
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							prepare_input(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
																		ciphertext += table[rowa * 5 + (cola + 1) % 5]
												elif cola == cola:
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
																		ciphertext += table[((rowa + 1) % 5) * 5 + cola]
												else:  # rectangle
																		ciphertext += table[rowa * 5 + cola]
																		ciphertext += table[rowa * 5 + cola]

						return ciphertext



def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							generate_table(_lowercase						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							''''''

						# https://en.wikipedia.org/wiki/Playfair_cipher#Description
						for chara, chara in chunker(_lowercase		,  2						):
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    List[str]							=							divmod(table.index(_lowercase						)		,  5						)
												SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Optional[Any]							=							divmod(table.index(_lowercase						)		,  5						)

												if rowa == rowa:
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
																		plaintext += table[rowa * 5 + (cola - 1) % 5]
												elif cola == cola:
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
																		plaintext += table[((rowa - 1) % 5) * 5 + cola]
												else:  # rectangle
																		plaintext += table[rowa * 5 + cola]
																		plaintext += table[rowa * 5 + cola]

						return plaintext





