





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:     List[str]			=							logging.get_logger(__name__)

lowercase__		:     int			=							{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	List[Any]									=       'gpt_neox'





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]=50432			,					lowerCAmelCase__  :						Optional[int]=6144			,					lowerCAmelCase__  :						Optional[Any]=44			,					lowerCAmelCase__  :						Optional[Any]=64			,					lowerCAmelCase__  :						List[str]=24576			,					lowerCAmelCase__  :						Any="gelu"			,					lowerCAmelCase__  :						Any=0.25			,					lowerCAmelCase__  :						str=10000			,					lowerCAmelCase__  :						Any=0.0			,					lowerCAmelCase__  :						Optional[Any]=0.0			,					lowerCAmelCase__  :						Tuple=0.1			,					lowerCAmelCase__  :						Dict=2048			,					lowerCAmelCase__  :						Optional[Any]=0.02			,					lowerCAmelCase__  :						Tuple=1e-5			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Dict=0			,					lowerCAmelCase__  :						Union[str, Any]=2			,					lowerCAmelCase__  :						int=False			,					lowerCAmelCase__  :						Union[str, Any]=True			,					lowerCAmelCase__  :						int=None			,					**lowerCAmelCase__  :						int			,					)       ->	Tuple:

            '''simple docstring'''




            super().__init__(bos_token_id=lowerCAmelCase__			,					eos_token_id=lowerCAmelCase__			,					**lowerCAmelCase__	)
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			max_position_embeddings
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			rotary_pct
            _UpperCamelCase							       =			rotary_emb_base
            _UpperCamelCase							       =			attention_dropout
            _UpperCamelCase							       =			hidden_dropout
            _UpperCamelCase							       =			classifier_dropout
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			layer_norm_eps
            _UpperCamelCase							       =			use_cache
            _UpperCamelCase							       =			tie_word_embeddings
            _UpperCamelCase							       =			use_parallel_residual
            _UpperCamelCase							       =			rope_scaling
            self._rope_scaling_validation()

            if self.hidden_size % self.num_attention_heads != 0:
                 raise ValueError(
                     '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''	)







       def snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            if self.rope_scaling is None:
                 return

            if not isinstance(self.rope_scaling			,					lowerCAmelCase__	) or len(self.rope_scaling	) != 2:
                 raise ValueError(
                     '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                     f"""got {self.rope_scaling}"""	)
            _UpperCamelCase							       =			self.rope_scaling.get('''type'''			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			self.rope_scaling.get('''factor'''			,					lowerCAmelCase__	)
            if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                 raise ValueError(
                     f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""	)
            if rope_scaling_factor is None or not isinstance(lowerCAmelCase__			,					lowerCAmelCase__	) or rope_scaling_factor <= 1.0:
                 raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""	)










'''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


lowercase__		:     Dict			=							[
    # 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__							(							lowercase   :	List[str] )						->						List[Any]:





     """simple docstring"""

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







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





     """simple docstring"""

     _UpperCamelCase							       =			DEFAULTS.copy()
     cfg_kwargs.update(lowercase )
     _UpperCamelCase							       =			PegasusConfig(**lowercase )
     _UpperCamelCase							       =			PegasusForConditionalGeneration(lowercase )
     _UpperCamelCase							       =			torch_model.model.state_dict()
     _UpperCamelCase							       =			{}
     for k, v in tf_weights.items():
          _UpperCamelCase							       =			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							       =			v.T
          _UpperCamelCase							       =			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							       =			torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			{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							       =			torch_model.model.load_state_dict(lowercase,   strict=lowercase )
     _UpperCamelCase							       =			[
         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__							(							lowercase   :	List[Any]="./ckpt/aeslc/model.ckpt-32000" )						->						Dict:





     """simple docstring"""

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







def    a__							(							lowercase   :	str,   lowercase   :	str )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			Path(lowercase ).parent.name
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
     _UpperCamelCase							       =			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							       =			get_tf_weights_as_numpy(lowercase )
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]
     if dataset == "large":
          _UpperCamelCase							       =			task_specific_params
     _UpperCamelCase							       =			convert_pegasus(lowercase,   lowercase )
     torch_model.save_pretrained(lowercase )
     _UpperCamelCase							       =			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__":
   lowercase__		:     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.')
   lowercase__		:     Optional[int]			=							parser.parse_args()
   if args.save_dir is None:
      lowercase__		:     Tuple			=							Path(args.tf_ckpt_path).parent.name
      lowercase__		:     Optional[Any]			=							os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



