


"""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 lowerCAmelCase_      (				UpperCamelCase__	: List[str] ):






 """simple docstring"""







 for pegasus_name, hf_name in PATTERNS:
  __lowercase									=	k.replace(UpperCamelCase__				,   UpperCamelCase__ )
 return k


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







 __lowercase									=	DEFAULTS.copy()
 cfg_kwargs.update(UpperCamelCase__ )
 __lowercase									=	PegasusConfig(**UpperCamelCase__ )
 __lowercase									=	PegasusForConditionalGeneration(UpperCamelCase__ )
 __lowercase									=	torch_model.model.state_dict()
 __lowercase									=	{}
 for k, v in tf_weights.items():
  __lowercase									=	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:
   __lowercase									=	v.T
  __lowercase									=	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
 __lowercase									=	torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1] )
 __lowercase									=	mapping["""shared.weight"""]
 __lowercase									=	mapping["""shared.weight"""]
 __lowercase									=	{k: torch.zeros_like(UpperCamelCase__ ) for k, v in sd.items() if k.endswith("""bias""" ) and k not in mapping}
 mapping.update(**UpperCamelCase__ )
 __lowercase							,		__lowercase									=	torch_model.model.load_state_dict(UpperCamelCase__				,   strict=UpperCamelCase__ )
 __lowercase									=	[
     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_      (				UpperCamelCase__	: List[str]="./ckpt/aeslc/model.ckpt-32000" ):






 """simple docstring"""







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


def lowerCAmelCase_      (				UpperCamelCase__	: int				,   UpperCamelCase__	: int ):






 """simple docstring"""







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

 # convert model
 __lowercase									=	get_tf_weights_as_numpy(UpperCamelCase__ )
 __lowercase									=	task_specific_params[f'''summarization_{dataset}''']
 if dataset == "large":
  __lowercase									=	task_specific_params
 __lowercase									=	convert_pegasus(UpperCamelCase__				,   UpperCamelCase__ )
 torch_model.save_pretrained(UpperCamelCase__ )
 __lowercase									=	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__":
       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"""

def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: int				,   UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[False] * len(UpperCamelCase__ )
 __lowercase									=	[]
 queue.append(UpperCamelCase__ )
 __lowercase									=	True

 while queue:
  __lowercase									=	queue.pop(0 )
  for ind in range(len(graph[u] ) ):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(UpperCamelCase__ )
    __lowercase									=	True
    __lowercase									=	u

 return visited[t]


def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 __lowercase									=	[-1] * (len(UpperCamelCase__ ))
 __lowercase									=	0
 while bfs(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ ):
  __lowercase									=	float("""Inf""" )
  __lowercase									=	sink

  while s != source:
   # Find the minimum value in select path
   __lowercase									=	min(UpperCamelCase__				,   graph[parent[s]][s] )
   __lowercase									=	parent[s]

  max_flow += path_flow
  __lowercase									=	sink

  while v != source:
   __lowercase									=	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __lowercase									=	parent[v]
 return max_flow


UpperCAmelCase__  							=[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__							,      UpperCAmelCase__  							=0, 5

print(ford_fulkerson(graph, source, sink))
