



'''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


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






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






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						# save tokenizer first
						_UpperCamelCase					:		Optional[int] 				=   Path(UpperCAmelCase_							).parent.name
						_UpperCamelCase					:		Dict 				=   task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
						_UpperCamelCase					:		Tuple 				=   PegasusTokenizer.from_pretrained('sshleifer/pegasus'  ,  model_max_length=UpperCAmelCase_							)
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(UpperCAmelCase_							)

						# convert model
						_UpperCamelCase					:		Optional[Any] 				=   get_tf_weights_as_numpy(UpperCAmelCase_							)
						_UpperCamelCase					:		Optional[Any] 				=   task_specific_params[f'summarization_{dataset}']
						if dataset == "large":
												_UpperCamelCase					:		Optional[int] 				=   task_specific_params
						_UpperCamelCase					:		int 				=   convert_pegasus(UpperCAmelCase_  ,  UpperCAmelCase_							)
						torch_model.save_pretrained(UpperCAmelCase_							)
						_UpperCamelCase					:		List[str] 				=   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__":
					snake_case_ :	List[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_ :	Optional[int]         =  parser.parse_args()
					if args.save_dir is None:
										snake_case_ :	List[str]         =  Path(args.tf_ckpt_path).parent.name
										snake_case_ :	Optional[int]         =  os.path.join('pegasus', dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)









'''simple docstring'''




from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__SCREAMING_SNAKE_CASE     :int       =				logging.get_logger(__name__)







class   A_	:
 _lowerCamelCase  : str
 _lowerCamelCase  : str           =							None




 @staticmethod
 def      lowercase	(					):
      raise NotImplementedError




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[int]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[Any]    ):
      raise NotImplementedError




 def      lowercase	(					self       :    Any	,			snake_case_       :    int    ):
      raise NotImplementedError




 def      lowercase	(					self       :    List[str]    ):
      if not self.is_available():
           raise RuntimeError(
               f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'    )




 @classmethod
 def      lowercase	(					cls       :    List[Any]    ):
      return f'`pip install {cls.pip_package or cls.name}`'







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""optuna"""




 @staticmethod
 def      lowercase	(					):
      return is_optuna_available()




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Tuple    ):
      return run_hp_search_optuna(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    int	,			snake_case_       :    Optional[int]    ):
      return default_hp_space_optuna(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Any           =							"""ray"""
 _lowerCamelCase  : Tuple           =							"""'ray[tune]'"""




 @staticmethod
 def      lowercase	(					):
      return is_ray_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Any	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    List[str]    ):
      return run_hp_search_ray(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    str    ):
      return default_hp_space_ray(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""sigopt"""




 @staticmethod
 def      lowercase	(					):
      return is_sigopt_available()




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Dict    ):
      return run_hp_search_sigopt(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]    ):
      return default_hp_space_sigopt(snake_case_    )







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""wandb"""




 @staticmethod
 def      lowercase	(					):
      return is_wandb_available()




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    str	,			**snake_case_       :    Optional[Any]    ):
      return run_hp_search_wandb(snake_case_	,			snake_case_	,			snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Union[str, Any]    ):
      return default_hp_space_wandb(snake_case_    )





__SCREAMING_SNAKE_CASE     :Dict       =				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def 	UpperCAmelCase_							(					)		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(__lowercase       ) > 0:
          _UpperCAmelCase     							=   available_backends[0].name
          if len(__lowercase       ) > 1:
               logger.info(
                   f'{len(__lowercase       )} hyperparameter search backends available. Using {name} as the default.'       )
          return name
     raise RuntimeError(
         "No hyperparameter search backend available.\n"
         + "\n".join(
             f' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )




