


"""simple docstring"""



import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers






def       lowerCamelCase					(a_						:int)     ->							Optional[int]:
       random.seed(a_)
       np.random.seed(a_)
       torch.manual_seed(a_)
       torch.cuda.manual_seed_all(a_)
       # ^^ safe to call this function even if cuda is not available




class 					__magic_name__		:






       def __init__(   self  :    Union[str, Any]  ,     snake_case__  :    Iterable[torch.nn.Parameter]  ,     snake_case__  :    float = 0.99_99  ,     snake_case__  :    float = 0.0  ,     snake_case__  :    int = 0  ,     snake_case__  :    bool = False  ,     snake_case__  :    Union[float, int] = 1.0  ,     snake_case__  :    Union[float, int] = 2 / 3  ,     snake_case__  :    Optional[Any] = None  ,     snake_case__  :    Dict[str, Any] = None  ,     **snake_case__  :    Dict  ,     ):

              '''simple docstring'''






              if isinstance(snake_case__  ,     torch.nn.Module				):
                     lowercase		:List[Any]       							=				(
                         '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
                         '''Please pass the parameters of the module instead.'''
                     )
                     deprecate(
                         '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''  ,     '''1.0.0'''  ,     snake_case__  ,     standard_warn=snake_case__  ,     )
                     lowercase		:Optional[Any]       							=				parameters.parameters()

                     # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                     lowercase		:List[str]       							=				True

              if kwargs.get('''max_value'''  ,     snake_case__				) is not None:
                     lowercase		:Any       							=				'''The `max_value` argument is deprecated. Please use `decay` instead.'''
                     deprecate('''max_value'''  ,     '''1.0.0'''  ,     snake_case__  ,     standard_warn=snake_case__				)
                     lowercase		:int       							=				kwargs['''max_value''']

              if kwargs.get('''min_value'''  ,     snake_case__				) is not None:
                     lowercase		:str       							=				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
                     deprecate('''min_value'''  ,     '''1.0.0'''  ,     snake_case__  ,     standard_warn=snake_case__				)
                     lowercase		:Optional[Any]       							=				kwargs['''min_value''']

              lowercase		:List[str]       							=				list(snake_case__				)
              lowercase		:Any       							=				[p.clone().detach() for p in parameters]

              if kwargs.get('''device'''  ,     snake_case__				) is not None:
                     lowercase		:Tuple       							=				'''The `device` argument is deprecated. Please use `to` instead.'''
                     deprecate('''device'''  ,     '''1.0.0'''  ,     snake_case__  ,     standard_warn=snake_case__				)
                     self.to(device=kwargs['''device''']				)

              lowercase		:Optional[Any]       							=				None

              lowercase		:int       							=				decay
              lowercase		:Optional[int]       							=				min_decay
              lowercase		:List[Any]       							=				update_after_step
              lowercase		:List[str]       							=				use_ema_warmup
              lowercase		:Union[str, Any]       							=				inv_gamma
              lowercase		:List[str]       							=				power
              lowercase		:List[str]       							=				0
              lowercase		:str       							=				None  # set in `step()`

              lowercase		:Union[str, Any]       							=				model_cls
              lowercase		:Optional[Any]       							=				model_config






       @classmethod
       def  __snake_case						(   cls  :    Dict  ,     snake_case__  :    int  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Tuple       							=				model_cls.load_config(snake_case__  ,     return_unused_kwargs=snake_case__				)
              lowercase		:Dict       							=				model_cls.from_pretrained(snake_case__				)

              lowercase		:int       							=				cls(model.parameters()  ,     model_cls=snake_case__  ,     model_config=model.config				)

              ema_model.load_state_dict(snake_case__				)
              return ema_model






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Any				):

              '''simple docstring'''






              if self.model_cls is None:
                     raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''				)

              if self.model_config is None:
                     raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''				)

              lowercase		:str       							=				self.model_cls.from_config(self.model_config				)
              lowercase		:Optional[int]       							=				self.state_dict()
              state_dict.pop('''shadow_params'''  ,     snake_case__				)

              model.register_to_config(**snake_case__				)
              self.copy_to(model.parameters()				)
              model.save_pretrained(snake_case__				)






       def  __snake_case						(   self  :    str  ,     snake_case__  :    int				):

              '''simple docstring'''






              lowercase		:List[str]       							=				max(0  ,     optimization_step - self.update_after_step - 1				)

              if step <= 0:
                     return 0.0

              if self.use_ema_warmup:
                     lowercase		:str       							=				1 - (1 + step / self.inv_gamma) ** -self.power
              else:
                     lowercase		:int       							=				(1 + step) / (1_0 + step)

              lowercase		:List[str]       							=				min(snake_case__  ,     self.decay				)
              # make sure decay is not smaller than min_decay
              lowercase		:List[str]       							=				max(snake_case__  ,     self.min_decay				)
              return cur_decay_value






       @torch.no_grad()
       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Iterable[torch.nn.Parameter]				):

              '''simple docstring'''






              if isinstance(snake_case__  ,     torch.nn.Module				):
                     lowercase		:Union[str, Any]       							=				(
                         '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
                         '''Please pass the parameters of the module instead.'''
                     )
                     deprecate(
                         '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''  ,     '''1.0.0'''  ,     snake_case__  ,     standard_warn=snake_case__  ,     )
                     lowercase		:Optional[int]       							=				parameters.parameters()

              lowercase		:List[Any]       							=				list(snake_case__				)

              self.optimization_step += 1

              # Compute the decay factor for the exponential moving average.
              lowercase		:int       							=				self.get_decay(self.optimization_step				)
              lowercase		:List[str]       							=				decay
              lowercase		:List[str]       							=				1 - decay

              lowercase		:int       							=				contextlib.nullcontext
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                     import deepspeed

              for s_param, param in zip(self.shadow_params  ,     snake_case__				):
                     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                            lowercase		:Any       							=				deepspeed.zero.GatheredParameters(snake_case__  ,     modifier_rank=snake_case__				)

                     with context_manager():
                            if param.requires_grad:
                                   s_param.sub_(one_minus_decay * (s_param - param)				)
                            else:
                                   s_param.copy_(snake_case__				)






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Iterable[torch.nn.Parameter]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				list(snake_case__				)
              for s_param, param in zip(self.shadow_params  ,     snake_case__				):
                     param.data.copy_(s_param.to(param.device				).data				)






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    List[str]=None				):

              '''simple docstring'''






              lowercase		:str       							=				[
                  p.to(device=snake_case__  ,     dtype=snake_case__				) if p.is_floating_point() else p.to(device=snake_case__				)
                  for p in self.shadow_params
              ]






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return {
                  "decay": self.decay,
                  "min_decay": self.min_decay,
                  "optimization_step": self.optimization_step,
                  "update_after_step": self.update_after_step,
                  "use_ema_warmup": self.use_ema_warmup,
                  "inv_gamma": self.inv_gamma,
                  "power": self.power,
                  "shadow_params": self.shadow_params,
              }






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Iterable[torch.nn.Parameter]				):

              '''simple docstring'''






              lowercase		:Dict       							=				[param.detach().cpu().clone() for param in parameters]






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    Iterable[torch.nn.Parameter]				):

              '''simple docstring'''






              if self.temp_stored_params is None:
                     raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''				)
              for c_param, param in zip(self.temp_stored_params  ,     snake_case__				):
                     param.data.copy_(c_param.data				)

              # Better memory-wise.
              lowercase		:int       							=				None







       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    dict				):

              '''simple docstring'''






              lowercase		:str       							=				copy.deepcopy(snake_case__				)

              lowercase		:int       							=				state_dict.get('''decay'''  ,     self.decay				)
              if self.decay < 0.0 or self.decay > 1.0:
                     raise ValueError('''Decay must be between 0 and 1'''				)

              lowercase		:int       							=				state_dict.get('''min_decay'''  ,     self.min_decay				)
              if not isinstance(self.min_decay  ,     snake_case__				):
                     raise ValueError('''Invalid min_decay'''				)

              lowercase		:List[Any]       							=				state_dict.get('''optimization_step'''  ,     self.optimization_step				)
              if not isinstance(self.optimization_step  ,     snake_case__				):
                     raise ValueError('''Invalid optimization_step'''				)

              lowercase		:int       							=				state_dict.get('''update_after_step'''  ,     self.update_after_step				)
              if not isinstance(self.update_after_step  ,     snake_case__				):
                     raise ValueError('''Invalid update_after_step'''				)

              lowercase		:Optional[int]       							=				state_dict.get('''use_ema_warmup'''  ,     self.use_ema_warmup				)
              if not isinstance(self.use_ema_warmup  ,     snake_case__				):
                     raise ValueError('''Invalid use_ema_warmup'''				)

              lowercase		:List[Any]       							=				state_dict.get('''inv_gamma'''  ,     self.inv_gamma				)
              if not isinstance(self.inv_gamma  ,     (float, int)				):
                     raise ValueError('''Invalid inv_gamma'''				)

              lowercase		:List[str]       							=				state_dict.get('''power'''  ,     self.power				)
              if not isinstance(self.power  ,     (float, int)				):
                     raise ValueError('''Invalid power'''				)

              lowercase		:Optional[int]       							=				state_dict.get('''shadow_params'''  ,     snake_case__				)
              if shadow_params is not None:
                     lowercase		:Dict       							=				shadow_params
                     if not isinstance(self.shadow_params  ,     snake_case__				):
                            raise ValueError('''shadow_params must be a list'''				)
                     if not all(isinstance(snake_case__  ,     torch.Tensor				) for p in self.shadow_params				):
                            raise ValueError('''shadow_params must all be Tensors'''				)





"""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					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






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






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''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)

