





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


snake_case_     :       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        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict   ) ->	Optional[int]:






						for pegasus_name, hf_name in PATTERNS:
												UpperCAmelCase_			:      Optional[Any]     						=  k.replace(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )
						return k





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Dict   ) ->	List[Any]:
						UpperCAmelCase_			:      Union[str, Any]     						=  DEFAULTS.copy()
						cfg_kwargs.update(__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Union[str, Any]     						=  PegasusConfig(**__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Any     						=  PegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Dict     						=  torch_model.model.state_dict()
						UpperCAmelCase_			:      Union[str, Any]     						=  {}
						for k, v in tf_weights.items():
												UpperCAmelCase_			:      List[str]     						=  rename_state_dict_key(__SCREAMING_SNAKE_CASE   )
												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_			:      Dict     						=  v.T
												UpperCAmelCase_			:      Optional[Any]     						=  torch.tensor(__SCREAMING_SNAKE_CASE,			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_			:      Optional[int]     						=  {k: torch.zeros_like(__SCREAMING_SNAKE_CASE   ) for k, v in sd.items() if k.endswith('''bias'''   ) and k not in mapping}
						mapping.update(**__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Optional[int]     						=  torch_model.model.load_state_dict(__SCREAMING_SNAKE_CASE,			strict=__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Union[str, Any]     						=  [
						    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_      (  SCREAMING_SNAKE_CASE__      :			Optional[int]="./ckpt/aeslc/model.ckpt-32000"   ) ->	Optional[Any]:
						UpperCAmelCase_			:      Any     						=  tf.train.list_variables(__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      Tuple     						=  {}
						UpperCAmelCase_			:      Optional[int]     						=  ["Adafactor", "global_step"]
						for name, shape in tqdm(__SCREAMING_SNAKE_CASE,			desc='''converting tf checkpoint to dict'''   ):
												UpperCAmelCase_			:      Optional[int]     						=  any(pat in name for pat in ignore_name   )
												if skip_key:
																		continue
												UpperCAmelCase_			:      Dict     						=  tf.train.load_variable(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )
												UpperCAmelCase_			:      Optional[Any]     						=  array
						return tf_weights





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Optional[int]     						=  Path(__SCREAMING_SNAKE_CASE   ).parent.name
						UpperCAmelCase_			:      Optional[Any]     						=  task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
						UpperCAmelCase_			:      int     						=  PegasusTokenizer.from_pretrained('''sshleifer/pegasus''',			model_max_length=__SCREAMING_SNAKE_CASE   )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(__SCREAMING_SNAKE_CASE   )

						# convert model
						UpperCAmelCase_			:      Tuple     						=  get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      List[Any]     						=  task_specific_params[F"""summarization_{dataset}"""]
						if dataset == "large":
												UpperCAmelCase_			:      Optional[int]     						=  task_specific_params
						UpperCAmelCase_			:      Optional[int]     						=  convert_pegasus(__SCREAMING_SNAKE_CASE,			__SCREAMING_SNAKE_CASE   )
						torch_model.save_pretrained(__SCREAMING_SNAKE_CASE   )
						UpperCAmelCase_			:      int     						=  torch_model.state_dict()
						sd.pop('''model.decoder.embed_positions.weight'''   )
						sd.pop('''model.encoder.embed_positions.weight'''   )
						torch.save(__SCREAMING_SNAKE_CASE,			Path(__SCREAMING_SNAKE_CASE   ) / '''pytorch_model.bin'''   )


if __name__ == "__main__":
					snake_case_     :       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.")
					snake_case_     :       Tuple						=  parser.parse_args()
					if args.save_dir is None:
										snake_case_     :       Any						=  Path(args.tf_ckpt_path).parent.name
										snake_case_     :       str						=  os.path.join("pegasus", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			list[int]   ) ->	list[list[int]]:
						UpperCAmelCase_			:      int     						=  []
						if len(SCREAMING_SNAKE_CASE__   ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__   )   ):
												UpperCAmelCase_			:      List[Any]     						=  nums.pop(0   )
												UpperCAmelCase_			:      Optional[Any]     						=  permute(SCREAMING_SNAKE_CASE__   )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__   )
												result.extend(SCREAMING_SNAKE_CASE__   )
												nums.append(SCREAMING_SNAKE_CASE__   )
						return result





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Any:

						def backtrack(SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ):
												if start == len(SCREAMING_SNAKE_CASE__   ) - 1:
																		output.append(nums[:]   )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__,			len(SCREAMING_SNAKE_CASE__   )   ):
																								UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  nums[i], nums[start]
																								backtrack(start + 1   )
																								UpperCAmelCase_						,       UpperCAmelCase_			:      int     						=  nums[i], nums[start]  # backtrack

						UpperCAmelCase_			:      Optional[int]     						=  []
						backtrack(0   )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					snake_case_     :       Tuple						=  permutea([1, 2, 3])
					print(res)
					doctest.testmod()





