





import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase     :			str     						=	logging.getLogger(__name__)


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    p * torch.log(lowerCAmelCase       )
       _UpperCamelCase														=    0
       return -plogp.sum(dim=-1       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       logger.info('''lv, h >\t''' + '''\t'''.join(f'''{x + 1}''' for x in range(len(lowerCAmelCase       )       )       )       )
       for row in range(len(lowerCAmelCase       )       ):
              if tensor.dtype != torch.long:
                     logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:.5f}''' for x in tensor[row].cpu().data       )       )
              else:
                     logger.info(f'''layer {row + 1}:\t''' + '''\t'''.join(f'''{x:d}''' for x in tensor[row].cpu().data       )       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=True     ,							lowerCAmelCase=True     ,							lowerCAmelCase=None     ,							lowerCAmelCase=False       ):
       _UpperCamelCase							,		_UpperCamelCase														=    model.config.num_hidden_layers, model.config.num_attention_heads
       _UpperCamelCase														=    torch.zeros(lowerCAmelCase     ,							lowerCAmelCase       ).to(args.device       )
       _UpperCamelCase														=    torch.zeros(lowerCAmelCase     ,							lowerCAmelCase       ).to(args.device       )

       if head_mask is None:
              _UpperCamelCase														=    torch.ones(lowerCAmelCase     ,							lowerCAmelCase       ).to(args.device       )

       head_mask.requires_grad_(requires_grad=lowerCAmelCase       )
       # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
       if actually_pruned:
              _UpperCamelCase														=    None

       _UpperCamelCase														=    None
       _UpperCamelCase														=    None
       _UpperCamelCase														=    0.0

       for step, inputs in enumerate(tqdm(lowerCAmelCase     ,							desc='''Iteration'''     ,							disable=args.local_rank not in [-1, 0]       )       ):
              for k, v in inputs.items():
                     _UpperCamelCase														=    v.to(args.device       )

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              _UpperCamelCase														=    model(**lowerCAmelCase     ,							head_mask=lowerCAmelCase       )
              _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    (
                  outputs[0],
                  outputs[1],
                  outputs[-1],
              )  # Loss and logits are the first, attention the last
              loss.backward()  # Backpropagate to populate the gradients in the head mask

              if compute_entropy:
                     for layer, attn in enumerate(lowerCAmelCase       ):
                            _UpperCamelCase														=    entropy(attn.detach()       ) * inputs['''attention_mask'''].float().unsqueeze(1       )
                            attn_entropy[layer] += masked_entropy.sum(-1       ).sum(0       ).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()

              # Also store our logits/labels if we want to compute metrics afterwards
              if preds is None:
                     _UpperCamelCase														=    logits.detach().cpu().numpy()
                     _UpperCamelCase														=    inputs['''labels'''].detach().cpu().numpy()
              else:
                     _UpperCamelCase														=    np.append(lowerCAmelCase     ,							logits.detach().cpu().numpy()     ,							axis=0       )
                     _UpperCamelCase														=    np.append(lowerCAmelCase     ,							inputs['''labels'''].detach().cpu().numpy()     ,							axis=0       )

              tot_tokens += inputs["attention_mask"].float().detach().sum().data

       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
              _UpperCamelCase														=    2
              _UpperCamelCase														=    torch.pow(torch.pow(lowerCAmelCase     ,							lowerCAmelCase       ).sum(-1       )     ,							1 / exponent       )
              head_importance /= norm_by_layer.unsqueeze(-1       ) + 1E-20

       if not args.dont_normalize_global_importance:
              _UpperCamelCase														=    (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

       # Print/save matrices
       np.save(os.path.join(args.output_dir     ,							'''attn_entropy.npy'''       )     ,							attn_entropy.detach().cpu().numpy()       )
       np.save(os.path.join(args.output_dir     ,							'''head_importance.npy'''       )     ,							head_importance.detach().cpu().numpy()       )

       logger.info('''Attention entropies'''       )
       print_ad_tensor(lowerCAmelCase       )
       logger.info('''Head importance scores'''       )
       print_ad_tensor(lowerCAmelCase       )
       logger.info('''Head ranked by importance scores'''       )
       _UpperCamelCase														=    torch.zeros(head_importance.numel()     ,							dtype=torch.long     ,							device=args.device       )
       _UpperCamelCase														=    torch.arange(
           head_importance.numel()     ,							device=args.device       )
       _UpperCamelCase														=    head_ranks.view_as(lowerCAmelCase       )
       print_ad_tensor(lowerCAmelCase       )

       return attn_entropy, head_importance, preds, labels


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    compute_heads_importance(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							compute_entropy=lowerCAmelCase       )
       _UpperCamelCase														=    np.argmax(lowerCAmelCase     ,							axis=1       ) if args.output_mode == '''classification''' else np.squeeze(lowerCAmelCase       )
       _UpperCamelCase														=    glue_compute_metrics(args.task_name     ,							lowerCAmelCase     ,							lowerCAmelCase       )[args.metric_name]
       logger.info('''Pruning: original score: %f, threshold: %f'''     ,							lowerCAmelCase     ,							original_score * args.masking_threshold       )

       _UpperCamelCase														=    torch.ones_like(lowerCAmelCase       )
       _UpperCamelCase														=    max(1     ,							int(new_head_mask.numel() * args.masking_amount       )       )

       _UpperCamelCase														=    original_score
       while current_score >= original_score * args.masking_threshold:
              _UpperCamelCase														=    new_head_mask.clone()  # save current head mask
              # heads from least important to most - keep only not-masked heads
              _UpperCamelCase														=    float('''Inf'''       )
              _UpperCamelCase														=    head_importance.view(-1       ).sort()[1]

              if len(lowerCAmelCase       ) <= num_to_mask:
                     break

              # mask heads
              _UpperCamelCase														=    current_heads_to_mask[:num_to_mask]
              logger.info('''Heads to mask: %s'''     ,							str(current_heads_to_mask.tolist()       )       )
              _UpperCamelCase														=    new_head_mask.view(-1       )
              _UpperCamelCase														=    0.0
              _UpperCamelCase														=    new_head_mask.view_as(lowerCAmelCase       )
              _UpperCamelCase														=    new_head_mask.clone().detach()
              print_ad_tensor(lowerCAmelCase       )

              # Compute metric and head importance again
              _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    compute_heads_importance(
                  lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							compute_entropy=lowerCAmelCase     ,							head_mask=lowerCAmelCase       )
              _UpperCamelCase														=    np.argmax(lowerCAmelCase     ,							axis=1       ) if args.output_mode == '''classification''' else np.squeeze(lowerCAmelCase       )
              _UpperCamelCase														=    glue_compute_metrics(args.task_name     ,							lowerCAmelCase     ,							lowerCAmelCase       )[args.metric_name]
              logger.info(
                  '''Masking: current score: %f, remaining heads %d (%.1f percents)'''     ,							lowerCAmelCase     ,							new_head_mask.sum()     ,							new_head_mask.sum() / new_head_mask.numel() * 100     ,							)

       logger.info('''Final head mask'''       )
       print_ad_tensor(lowerCAmelCase       )
       np.save(os.path.join(args.output_dir     ,							'''head_mask.npy'''       )     ,							head_mask.detach().cpu().numpy()       )

       return head_mask


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    datetime.now()
       _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    compute_heads_importance(
           lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							compute_entropy=lowerCAmelCase     ,							compute_importance=lowerCAmelCase     ,							head_mask=lowerCAmelCase       )
       _UpperCamelCase														=    np.argmax(lowerCAmelCase     ,							axis=1       ) if args.output_mode == '''classification''' else np.squeeze(lowerCAmelCase       )
       _UpperCamelCase														=    glue_compute_metrics(args.task_name     ,							lowerCAmelCase     ,							lowerCAmelCase       )[args.metric_name]
       _UpperCamelCase														=    datetime.now() - before_time

       _UpperCamelCase														=    sum(p.numel() for p in model.parameters()       )
       _UpperCamelCase														=    {
           layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase       )       )
       }

       assert sum(len(lowerCAmelCase       ) for h in heads_to_prune.values()       ) == (1 - head_mask.long()).sum().item()
       model.prune_heads(lowerCAmelCase       )
       _UpperCamelCase														=    sum(p.numel() for p in model.parameters()       )

       _UpperCamelCase														=    datetime.now()
       _UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase							,		_UpperCamelCase														=    compute_heads_importance(
           lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							compute_entropy=lowerCAmelCase     ,							compute_importance=lowerCAmelCase     ,							head_mask=lowerCAmelCase     ,							actually_pruned=lowerCAmelCase     ,							)
       _UpperCamelCase														=    np.argmax(lowerCAmelCase     ,							axis=1       ) if args.output_mode == '''classification''' else np.squeeze(lowerCAmelCase       )
       _UpperCamelCase														=    glue_compute_metrics(args.task_name     ,							lowerCAmelCase     ,							lowerCAmelCase       )[args.metric_name]
       _UpperCamelCase														=    datetime.now() - before_time

       logger.info(
           '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							pruned_num_params / original_num_params * 100     ,							)
       logger.info('''Pruning: score with masking: %f score with pruning: %f'''     ,							lowerCAmelCase     ,							lowerCAmelCase       )
       logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''     ,							original_time / new_time * 100       )


def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--data_dir'''     ,							default=lowerCAmelCase     ,							type=lowerCAmelCase     ,							required=lowerCAmelCase     ,							help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''     ,							)
       parser.add_argument(
           '''--model_name_or_path'''     ,							default=lowerCAmelCase     ,							type=lowerCAmelCase     ,							required=lowerCAmelCase     ,							help='''Path to pretrained model or model identifier from huggingface.co/models'''     ,							)
       parser.add_argument(
           '''--task_name'''     ,							default=lowerCAmelCase     ,							type=lowerCAmelCase     ,							required=lowerCAmelCase     ,							help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()       )     ,							)
       parser.add_argument(
           '''--output_dir'''     ,							default=lowerCAmelCase     ,							type=lowerCAmelCase     ,							required=lowerCAmelCase     ,							help='''The output directory where the model predictions and checkpoints will be written.'''     ,							)

       # Other parameters
       parser.add_argument(
           '''--config_name'''     ,							default=''''''     ,							type=lowerCAmelCase     ,							help='''Pretrained config name or path if not the same as model_name_or_path'''     ,							)
       parser.add_argument(
           '''--tokenizer_name'''     ,							default=''''''     ,							type=lowerCAmelCase     ,							help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''     ,							)
       parser.add_argument(
           '''--cache_dir'''     ,							default=lowerCAmelCase     ,							type=lowerCAmelCase     ,							help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''     ,							)
       parser.add_argument(
           '''--data_subset'''     ,							type=lowerCAmelCase     ,							default=-1     ,							help='''If > 0: limit the data to a subset of data_subset instances.'''       )
       parser.add_argument(
           '''--overwrite_output_dir'''     ,							action='''store_true'''     ,							help='''Whether to overwrite data in output directory'''       )
       parser.add_argument(
           '''--overwrite_cache'''     ,							action='''store_true'''     ,							help='''Overwrite the cached training and evaluation sets'''       )

       parser.add_argument(
           '''--dont_normalize_importance_by_layer'''     ,							action='''store_true'''     ,							help='''Don\'t normalize importance score by layers'''       )
       parser.add_argument(
           '''--dont_normalize_global_importance'''     ,							action='''store_true'''     ,							help='''Don\'t normalize all importance scores between 0 and 1'''     ,							)

       parser.add_argument(
           '''--try_masking'''     ,							action='''store_true'''     ,							help='''Whether to try to mask head until a threshold of accuracy.'''       )
       parser.add_argument(
           '''--masking_threshold'''     ,							default=0.9     ,							type=lowerCAmelCase     ,							help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''     ,							)
       parser.add_argument(
           '''--masking_amount'''     ,							default=0.1     ,							type=lowerCAmelCase     ,							help='''Amount to heads to masking at each masking step.'''       )
       parser.add_argument('''--metric_name'''     ,							default='''acc'''     ,							type=lowerCAmelCase     ,							help='''Metric to use for head masking.'''       )

       parser.add_argument(
           '''--max_seq_length'''     ,							default=128     ,							type=lowerCAmelCase     ,							help=(
               '''The maximum total input sequence length after WordPiece tokenization. \n'''
               '''Sequences longer than this will be truncated, sequences shorter padded.'''
           )     ,							)
       parser.add_argument('''--batch_size'''     ,							default=1     ,							type=lowerCAmelCase     ,							help='''Batch size.'''       )

       parser.add_argument('''--seed'''     ,							type=lowerCAmelCase     ,							default=42       )
       parser.add_argument('''--local_rank'''     ,							type=lowerCAmelCase     ,							default=-1     ,							help='''local_rank for distributed training on gpus'''       )
       parser.add_argument('''--no_cuda'''     ,							action='''store_true'''     ,							help='''Whether not to use CUDA when available'''       )
       parser.add_argument('''--server_ip'''     ,							type=lowerCAmelCase     ,							default=''''''     ,							help='''Can be used for distant debugging.'''       )
       parser.add_argument('''--server_port'''     ,							type=lowerCAmelCase     ,							default=''''''     ,							help='''Can be used for distant debugging.'''       )
       _UpperCamelCase														=    parser.parse_args()

       if args.server_ip and args.server_port:
              # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
              import ptvsd

              print('''Waiting for debugger attach'''       )
              ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,							redirect_output=lowerCAmelCase       )
              ptvsd.wait_for_attach()

       # Setup devices and distributed training
       if args.local_rank == -1 or args.no_cuda:
              _UpperCamelCase														=    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''       )
              _UpperCamelCase														=    0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank       )
              _UpperCamelCase														=    torch.device('''cuda'''     ,							args.local_rank       )
              _UpperCamelCase														=    1
              torch.distributed.init_process_group(backend='''nccl'''       )  # Initializes the distributed backend

       # Setup logging
       logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN       )
       logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device     ,							args.n_gpu     ,							bool(args.local_rank != -1       )       )       )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(args.local_rank       ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()

       # Set seeds
       set_seed(args.seed       )

       # Prepare GLUE task
       _UpperCamelCase														=    args.task_name.lower()
       if args.task_name not in glue_processors:
              raise ValueError('''Task not found: %s''' % (args.task_name)       )
       _UpperCamelCase														=    glue_processors[args.task_name]()
       _UpperCamelCase														=    glue_output_modes[args.task_name]
       _UpperCamelCase														=    processor.get_labels()
       _UpperCamelCase														=    len(lowerCAmelCase       )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       _UpperCamelCase														=    AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path     ,							num_labels=lowerCAmelCase     ,							finetuning_task=args.task_name     ,							output_attentions=lowerCAmelCase     ,							cache_dir=args.cache_dir     ,							)
       _UpperCamelCase														=    AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path     ,							cache_dir=args.cache_dir     ,							)
       _UpperCamelCase														=    AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path     ,							from_tf=bool('''.ckpt''' in args.model_name_or_path       )     ,							config=lowerCAmelCase     ,							cache_dir=args.cache_dir     ,							)

       # Distributed and parallel training
       model.to(args.device       )
       if args.local_rank != -1:
              _UpperCamelCase														=    nn.parallel.DistributedDataParallel(
                  lowerCAmelCase     ,							device_ids=[args.local_rank]     ,							output_device=args.local_rank     ,							find_unused_parameters=lowerCAmelCase       )
       elif args.n_gpu > 1:
              _UpperCamelCase														=    nn.DataParallel(lowerCAmelCase       )

       # Print/save training arguments
       os.makedirs(args.output_dir     ,							exist_ok=lowerCAmelCase       )
       torch.save(lowerCAmelCase     ,							os.path.join(args.output_dir     ,							'''run_args.bin'''       )       )
       logger.info('''Training/evaluation parameters %s'''     ,							lowerCAmelCase       )

       # Prepare dataset for the GLUE task
       _UpperCamelCase														=    GlueDataset(lowerCAmelCase     ,							tokenizer=lowerCAmelCase     ,							mode='''dev'''       )
       if args.data_subset > 0:
              _UpperCamelCase														=    Subset(lowerCAmelCase     ,							list(range(min(args.data_subset     ,							len(lowerCAmelCase       )       )       )       )       )
       _UpperCamelCase														=    SequentialSampler(lowerCAmelCase       ) if args.local_rank == -1 else DistributedSampler(lowerCAmelCase       )
       _UpperCamelCase														=    DataLoader(
           lowerCAmelCase     ,							sampler=lowerCAmelCase     ,							batch_size=args.batch_size     ,							collate_fn=lowerCAmelCase       )

       # Compute head entropy and importance score
       compute_heads_importance(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )

       # Try head masking (set heads to zero until the score goes under a threshole)
       # and head pruning (remove masked heads and see the effect on the network)
       if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
              _UpperCamelCase														=    mask_heads(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
              prune_heads(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


if __name__ == "__main__":
       main()













import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=None       ):
       _UpperCamelCase														=    None
       # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
       if str(weight.dtype       ) == "torch.bfloat16":
              # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
              _UpperCamelCase														=    weight.view(torch.intaa       )
              _UpperCamelCase														=    '''bfloat16'''
       _UpperCamelCase														=    weight.cpu().numpy()
       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							f'''{weight_name}.dat'''       )
       if index is not None:
              if dtype is None:
                     _UpperCamelCase														=    str(array.dtype       )
              _UpperCamelCase														=    {'''dtype''': dtype, '''shape''': list(array.shape       )}
       if array.ndim == 0:
              _UpperCamelCase														=    array[None]
       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=array.dtype     ,							mode='''w+'''     ,							shape=array.shape       )
       _UpperCamelCase														=    array[:]
       file_array.flush()
       return index


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    tuple(weight_info['''shape''']       )
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              _UpperCamelCase														=    (1,)

       _UpperCamelCase														=    weight_info['''dtype''']
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              _UpperCamelCase														=    '''int16'''

       _UpperCamelCase														=    np.memmap(lowerCAmelCase     ,							dtype=lowerCAmelCase     ,							shape=lowerCAmelCase     ,							mode='''r'''       )

       if len(weight_info['''shape''']       ) == 0:
              _UpperCamelCase														=    weight[0]
       _UpperCamelCase														=    torch.tensor(lowerCAmelCase       )
       if weight_info["dtype"] == "bfloat16":
              _UpperCamelCase														=    weight.view(torch.bfloataa       )

       return weight


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       if index is None or len(lowerCAmelCase       ) == 0:
              # Nothing to save
              return

       _UpperCamelCase														=    os.path.join(lowerCAmelCase     ,							'''index.json'''       )
       if os.path.isfile(lowerCAmelCase       ):
              with open(lowerCAmelCase     ,							'''r'''     ,							encoding='''utf-8'''       ) as f:
                     _UpperCamelCase														=    json.load(lowerCAmelCase       )
       else:
              _UpperCamelCase														=    {}
       current_index.update(lowerCAmelCase       )

       with open(lowerCAmelCase     ,							'''w'''     ,							encoding='''utf-8'''       ) as f:
              json.dump(lowerCAmelCase     ,							lowerCAmelCase     ,							indent=2       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       os.makedirs(lowerCAmelCase     ,							exist_ok=lowerCAmelCase       )
       _UpperCamelCase														=    {}
       for name, parameter in state_dict.items():
              _UpperCamelCase														=    offload_weight(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							index=lowerCAmelCase       )

       # Update index
       save_offload_index(lowerCAmelCase     ,							lowerCAmelCase       )

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    dataset
            _UpperCamelCase														=    prefix

     def __getitem__(   self,							A   ):





            """simple docstring"""

            return self.dataset[F'''{self.prefix}{key}''']

     def __iter__(   self   ):





            """simple docstring"""

            return iter([key for key in self.dataset if key.startswith(self.prefix   )]   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.dataset   )






class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							A = None,							A = None,							A = None,							A=None,							):





            """simple docstring"""

            if state_dict is None and save_folder is None:
                   raise ValueError('''Need either a `state_dict` or a `save_folder` containing offloaded weights.'''   )

            _UpperCamelCase														=    {} if state_dict is None else state_dict
            _UpperCamelCase														=    save_folder
            if index is None and save_folder is not None:
                   with open(os.path.join(A,							'''index.json'''   )   ) as f:
                          _UpperCamelCase														=    json.load(A   )
            _UpperCamelCase														=    {} if index is None else index
            _UpperCamelCase														=    list(self.state_dict.keys()   )
            self.all_keys.extend([key for key in self.index if key not in self.all_keys]   )
            _UpperCamelCase														=    device

     def __getitem__(   self,							A   ):





            """simple docstring"""

            if key in self.state_dict:
                   return self.state_dict[key]
            _UpperCamelCase														=    self.index[key]
            if weight_info.get('''safetensors_file'''   ) is not None:
                   if not is_safetensors_available():
                          raise ImportError('''These offloaded weights require the use of safetensors: `pip install safetensors`.'''   )

                   from safetensors import safe_open

                   _UpperCamelCase														=    '''cpu''' if self.device is None else self.device
                   with safe_open(weight_info['''safetensors_file'''],							framework='''pt''',							device=A   ) as f:
                          _UpperCamelCase														=    f.get_tensor(weight_info.get('''weight_name''',							A   )   )

                   if "dtype" in weight_info:
                          return tensor.to(getattr(A,							weight_info['''dtype''']   )   )
                   else:
                          return tensor

            _UpperCamelCase														=    os.path.join(self.save_folder,							F'''{key}.dat'''   )
            return load_offloaded_weight(A,							A   )

     def __iter__(   self   ):





            """simple docstring"""

            return iter(self.all_keys   )



     def __len__(   self   ):





            """simple docstring"""

            return len(self.all_keys   )






def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    {}
       for module_name in submodule_names:
              # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
              # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
              result.update(
                  {
                      key: param
                      for key, param in state_dict.items()
                      if key == module_name or key.startswith(module_name + '''.'''       )
                  }       )
       return result






