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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_SCREAMING_SNAKE_CASE										=						logging.getLogger(__name__)






def  snake_case  (       snake_case__			:List[str]   ,	snake_case__			:int)						->   Tuple:
       # save results
       if os.path.exists(snake_case__):
              if os.path.exists(os.path.join(snake_case__   ,	"""config.json""")) and os.path.isfile(
                  os.path.join(snake_case__   ,	"""config.json""")):
                     os.remove(os.path.join(snake_case__   ,	"""config.json"""))
              if os.path.exists(os.path.join(snake_case__   ,	"""pytorch_model.bin""")) and os.path.isfile(
                  os.path.join(snake_case__   ,	"""pytorch_model.bin""")):
                     os.remove(os.path.join(snake_case__   ,	"""pytorch_model.bin"""))
       else:
              os.makedirs(snake_case__)
       model.save_pretrained(snake_case__)






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:List[Any]=False)						->   List[str]:
       _A        =  2
       if unlogit:
              _A        =  torch.pow(snake_case__   ,	snake_case__)
       _A        =  p * torch.log(snake_case__)
       _A        =  0
       return -plogp.sum(dim=-1)






def  snake_case  (       snake_case__			:Union[str, Any])						->   Any:
       logger.info("""lv, h >\t""" + """\t""".join(F'''{x + 1}''' for x in range(len(snake_case__))))
       for row in range(len(snake_case__)):
              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  snake_case  (       snake_case__			:Any   ,	snake_case__			:Any   ,	snake_case__			:Optional[int]   ,	snake_case__			:Union[str, Any]=True   ,	snake_case__			:Tuple=True   ,	snake_case__			:Optional[int]=None   ,	snake_case__			:Union[str, Any]=False)						->   List[str]:
       _A   ,  _A        =  model.config.num_hidden_layers, model.config.num_attention_heads
       _A        =  torch.zeros(snake_case__   ,	snake_case__).to(args.device)
       _A        =  torch.zeros(snake_case__   ,	snake_case__).to(args.device)

       if head_mask is None:
              _A        =  torch.ones(snake_case__   ,	snake_case__).to(args.device)

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

       _A        =  0.0
       _A        =  0.0
       for step, inputs in enumerate(tqdm(snake_case__   ,	desc="""Iteration"""   ,	disable=args.local_rank not in [-1, 0])):
              _A        =  tuple(t.to(args.device) for t in inputs)
              ((_A)   ,  )        =  inputs

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              _A        =  model(snake_case__   ,	labels=snake_case__   ,	head_mask=snake_case__)
              #  (loss), lm_logits, presents, (all hidden_states), (attentions)
              _A   ,  _A   ,  _A        =  (
                  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
              total_loss += loss.detach().cpu().numpy()
              if compute_entropy:
                     for layer, attn in enumerate(snake_case__):
                            _A        =  entropy(attn.detach()   ,	snake_case__)
                            attn_entropy[layer] += masked_entropy.sum(-1).sum(0).sum(0).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()
              tot_tokens += torch.ones_like(snake_case__).float().detach().sum().data

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

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

       # Print matrices
       if compute_entropy:
              logger.info("""Attention entropies""")
              print_ad_tensor(snake_case__)
       if compute_importance:
              logger.info("""Head importance scores""")
              print_ad_tensor(snake_case__)
       logger.info("""Head ranked by importance scores""")
       _A        =  torch.zeros(head_importance.numel()   ,	dtype=torch.long   ,	device=args.device)
       _A        =  torch.arange(
           head_importance.numel()   ,	device=args.device)
       _A        =  head_ranks.view_as(snake_case__)
       print_ad_tensor(snake_case__)
       return attn_entropy, head_importance, total_loss






def  snake_case  (       snake_case__			:List[Any]   ,	snake_case__			:List[Any]   ,	snake_case__			:Union[str, Any])						->   Dict:
       _A   ,  _A   ,  _A        =  compute_heads_importance(snake_case__   ,	snake_case__   ,	snake_case__   ,	compute_entropy=snake_case__)
       _A        =  1 / loss  # instead of downsteam score use the LM loss
       logger.info("""Pruning: original score: %f, threshold: %f"""   ,	snake_case__   ,	original_score * args.masking_threshold)

       _A        =  torch.ones_like(snake_case__)
       _A        =  max(1   ,	int(new_head_mask.numel() * args.masking_amount))

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

              if len(snake_case__) <= num_to_mask:
                     print("""BREAK BY num_to_mask""")
                     break

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

              # Compute metric and head importance again
              _A   ,  _A   ,  _A        =  compute_heads_importance(
                  snake_case__   ,	snake_case__   ,	snake_case__   ,	compute_entropy=snake_case__   ,	head_mask=snake_case__)
              _A        =  1 / loss
              logger.info(
                  """Masking: current score: %f, remaining heads %d (%.1f percents)"""   ,	snake_case__   ,	new_head_mask.sum()   ,	new_head_mask.sum() / new_head_mask.numel() * 100   ,	)

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

       return head_mask






def  snake_case  (       snake_case__			:Optional[Any]   ,	snake_case__			:str   ,	snake_case__			:Any   ,	snake_case__			:str)						->   Union[str, Any]:
       _A        =  datetime.now()
       _A   ,  _A   ,  _A        =  compute_heads_importance(
           snake_case__   ,	snake_case__   ,	snake_case__   ,	compute_entropy=snake_case__   ,	compute_importance=snake_case__   ,	head_mask=snake_case__)
       _A        =  1 / loss
       _A        =  datetime.now() - before_time

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

       for k, v in heads_to_prune.items():
              if isinstance(snake_case__   ,	snake_case__):
                     _A        =  [
                         v,
                     ]

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

       _A        =  datetime.now()
       _A   ,  _A   ,  _A        =  compute_heads_importance(
           snake_case__   ,	snake_case__   ,	snake_case__   ,	compute_entropy=snake_case__   ,	compute_importance=snake_case__   ,	head_mask=snake_case__   ,	actually_pruned=snake_case__   ,	)

       _A        =  1 / loss
       _A        =  datetime.now() - before_time

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






def  snake_case  (       )						->   List[Any]:
       _A        =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--data_dir"""   ,	default=snake_case__   ,	type=snake_case__   ,	required=snake_case__   ,	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=snake_case__   ,	type=snake_case__   ,	required=snake_case__   ,	help="""Path to pretrained model or model identifier from huggingface.co/models"""   ,	)
       parser.add_argument(
           """--output_dir"""   ,	default=snake_case__   ,	type=snake_case__   ,	required=snake_case__   ,	help="""The output directory where the model predictions and checkpoints will be written."""   ,	)

       # Other parameters
       parser.add_argument(
           """--config_name"""   ,	default=""""""   ,	type=snake_case__   ,	help="""Pretrained config name or path if not the same as model_name_or_path"""   ,	)
       parser.add_argument(
           """--tokenizer_name"""   ,	default=""""""   ,	type=snake_case__   ,	help="""Pretrained tokenizer name or path if not the same as model_name_or_path"""   ,	)
       parser.add_argument(
           """--cache_dir"""   ,	default=snake_case__   ,	type=snake_case__   ,	help="""Where do you want to store the pre-trained models downloaded from s3"""   ,	)
       parser.add_argument(
           """--data_subset"""   ,	type=snake_case__   ,	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=snake_case__   ,	help="""masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."""   ,	)
       parser.add_argument(
           """--masking_amount"""   ,	default=0.1   ,	type=snake_case__   ,	help="""Amount to heads to masking at each masking step.""")
       parser.add_argument("""--metric_name"""   ,	default="""acc"""   ,	type=snake_case__   ,	help="""Metric to use for head masking.""")

       parser.add_argument(
           """--max_seq_length"""   ,	default=128   ,	type=snake_case__   ,	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=snake_case__   ,	help="""Batch size.""")

       parser.add_argument("""--seed"""   ,	type=snake_case__   ,	default=42)
       parser.add_argument("""--local_rank"""   ,	type=snake_case__   ,	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=snake_case__   ,	default=""""""   ,	help="""Can be used for distant debugging.""")
       parser.add_argument("""--server_port"""   ,	type=snake_case__   ,	default=""""""   ,	help="""Can be used for distant debugging.""")
       _A        =  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=snake_case__)
              ptvsd.wait_for_attach()

       # Setup devices and distributed training
       if args.local_rank == -1 or args.no_cuda:
              _A        =  torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu""")
              _A        =  0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank)
              _A        =  torch.device("""cuda"""   ,	args.local_rank)
              _A        =  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)))

       _A        =  GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

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

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

       # Prepare dataset
       _A        =  np.concatenate(
           [
               np.loadtxt(args.data_dir   ,	dtype=np.intaa),
           ])
       _A        =  (torch.from_numpy(snake_case__),)
       _A        =  TensorDataset(*snake_case__)
       _A        =  RandomSampler(snake_case__)
       _A        =  DataLoader(snake_case__   ,	sampler=snake_case__   ,	batch_size=args.batch_size)

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

       # 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:
              _A        =  mask_heads(snake_case__   ,	snake_case__   ,	snake_case__)
              prune_heads(snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




