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    lowerCAmelCase_  (		lowercase:				Dict		)     ->					List[Any]:







 '''simple docstring'''
 for pegasus_name, hf_name in PATTERNS:
  _UpperCamelCase:  Tuple    						=			k.replace(lowercase	,       lowercase		)
 return k




def    lowerCAmelCase_  (		lowercase:				dict	,       lowercase:				dict		)     ->					PegasusForConditionalGeneration:







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







 '''simple docstring'''
 _UpperCamelCase:  str    						=			tf.train.list_variables(lowercase		)
 _UpperCamelCase:  Optional[int]    						=			{}
 _UpperCamelCase:  Dict    						=			['''Adafactor''', '''global_step''']
 for name, shape in tqdm(lowercase	,       desc='''converting tf checkpoint to dict'''		):
  _UpperCamelCase:  int    						=			any(pat in name for pat in ignore_name		)
  if skip_key:
   continue
  _UpperCamelCase:  Union[str, Any]    						=			tf.train.load_variable(lowercase	,       lowercase		)
  _UpperCamelCase:  Tuple    						=			array
 return tf_weights




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				str		)     ->					Union[str, Any]:







 '''simple docstring'''
 # save tokenizer first
 _UpperCamelCase:  Dict    						=			Path(lowercase		).parent.name
 _UpperCamelCase:  Tuple    						=			task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
 _UpperCamelCase:  Union[str, Any]    						=			PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''	,       model_max_length=lowercase		)
 assert tok.model_max_length == desired_max_model_length
 tok.save_pretrained(lowercase		)

 # convert model
 _UpperCamelCase:  List[Any]    						=			get_tf_weights_as_numpy(lowercase		)
 _UpperCamelCase:  int    						=			task_specific_params[F"""summarization_{dataset}"""]
 if dataset == "large":
  _UpperCamelCase:  str    						=			task_specific_params
 _UpperCamelCase:  Dict    						=			convert_pegasus(lowercase	,       lowercase		)
 torch_model.save_pretrained(lowercase		)
 _UpperCamelCase:  int    						=			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__":
  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 random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()