





"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__A											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , ):



         '''simple docstring'''

         super().__init__()
         self.register_modules(
             vae=__UpperCAmelCase    , text_encoder=__UpperCAmelCase    , tokenizer=__UpperCAmelCase    , unet=__UpperCAmelCase    , scheduler=__UpperCAmelCase    , safety_checker=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase    , )






       def      snake_case						(    self    , __UpperCAmelCase = "auto"      ):



         '''simple docstring'''

         if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           lowerCAmelCase__			:Tuple											=							self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         self.enable_attention_slicing(__UpperCAmelCase      )






       @torch.no_grad()
       def __call__(    self    , __UpperCAmelCase    , __UpperCAmelCase = 5_1_2    , __UpperCAmelCase = 5_1_2    , __UpperCAmelCase = 5_0    , __UpperCAmelCase = 7.5    , __UpperCAmelCase = None    , __UpperCAmelCase = 1    , __UpperCAmelCase = 0.0    , __UpperCAmelCase = None    , __UpperCAmelCase = None    , __UpperCAmelCase = "pil"    , __UpperCAmelCase = True    , __UpperCAmelCase = None    , __UpperCAmelCase = 1    , __UpperCAmelCase = None    , **__UpperCAmelCase    , ):



         '''simple docstring'''

         if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:List[str]											=							1
         elif isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:List[str]											=							len(__UpperCAmelCase      )
         else:
           raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__UpperCAmelCase      )}"      )

         if height % 8 != 0 or width % 8 != 0:
           raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."      )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) or callback_steps <= 0)
         ):
           raise ValueError(
               F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
               F" {type(__UpperCAmelCase      )}."      )

         # get prompt text embeddings
         lowerCAmelCase__			:str											=							self.tokenizer(
             __UpperCAmelCase    , padding='max_length'    , max_length=self.tokenizer.model_max_length    , return_tensors='pt'    , )
         lowerCAmelCase__			:Optional[int]											=							text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           lowerCAmelCase__			:List[str]											=							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
           logger.warning(
               'The following part of your input was truncated because CLIP can only handle sequences up to'
               F" {self.tokenizer.model_max_length} tokens: {removed_text}"      )
           lowerCAmelCase__			:Tuple											=							text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
           lowerCAmelCase__			:Any											=							self.text_encoder(text_input_ids.to(self.device      )      )[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							text_embeddings.shape
         lowerCAmelCase__			:int											=							text_embeddings.repeat(1    , __UpperCAmelCase    , 1      )
         lowerCAmelCase__			:List[Any]											=							text_embeddings.view(bs_embed * num_images_per_prompt    , __UpperCAmelCase    , -1      )

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         lowerCAmelCase__			:Tuple											=							guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
           lowerCAmelCase__			:List[str]
           if negative_prompt is None:
             lowerCAmelCase__			:int											=							['']
           elif type(__UpperCAmelCase      ) is not type(__UpperCAmelCase      ):
             raise TypeError(
                 F"`negative_prompt` should be the same type to `prompt`, but got {type(__UpperCAmelCase      )} !="
                 F" {type(__UpperCAmelCase      )}."      )
           elif isinstance(__UpperCAmelCase    , __UpperCAmelCase      ):
             lowerCAmelCase__			:str											=							[negative_prompt]
           elif batch_size != len(__UpperCAmelCase      ):
             raise ValueError(
                 F"`negative_prompt`: {negative_prompt} has batch size {len(__UpperCAmelCase      )}, but `prompt`:"
                 F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                 ' the batch size of `prompt`.'      )
           else:
             lowerCAmelCase__			:Union[str, Any]											=							negative_prompt

           lowerCAmelCase__			:Dict											=							text_input_ids.shape[-1]
           lowerCAmelCase__			:str											=							self.tokenizer(
               __UpperCAmelCase    , padding='max_length'    , max_length=__UpperCAmelCase    , truncation=__UpperCAmelCase    , return_tensors='pt'    , )
           lowerCAmelCase__			:Union[str, Any]											=							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           lowerCAmelCase__			:Optional[Any]											=							uncond_embeddings.shape[1]
           lowerCAmelCase__			:Optional[Any]											=							uncond_embeddings.repeat(__UpperCAmelCase    , __UpperCAmelCase    , 1      )
           lowerCAmelCase__			:Dict											=							uncond_embeddings.view(batch_size * num_images_per_prompt    , __UpperCAmelCase    , -1      )

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           lowerCAmelCase__			:List[Any]											=							torch.cat([uncond_embeddings, text_embeddings]      )

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         lowerCAmelCase__			:Optional[Any]											=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         lowerCAmelCase__			:Any											=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
         lowerCAmelCase__			:Any											=							text_embeddings.dtype
         if latents is None:
           if self.device.type == "mps":
             # randn does not exist on mps
             lowerCAmelCase__			:List[Any]											=							torch.randn(
                 __UpperCAmelCase    , generator=__UpperCAmelCase    , device='cpu'    , dtype=__UpperCAmelCase      ).to(self.device      )
             lowerCAmelCase__			:Optional[int]											=							torch.randn(__UpperCAmelCase    , generator=__UpperCAmelCase    , device='cpu'    , dtype=__UpperCAmelCase      ).to(
                 self.device      )
           else:
             lowerCAmelCase__			:List[Any]											=							torch.randn(
                 __UpperCAmelCase    , generator=__UpperCAmelCase    , device=self.device    , dtype=__UpperCAmelCase      )
             lowerCAmelCase__			:Optional[int]											=							torch.randn(__UpperCAmelCase    , generator=__UpperCAmelCase    , device=self.device    , dtype=__UpperCAmelCase      )
         else:
           if latents_reference.shape != latents_shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"      )
           lowerCAmelCase__			:Any											=							latents_reference.to(self.device      )
           lowerCAmelCase__			:List[Any]											=							latents.to(self.device      )

         # This is the key part of the pipeline where we
         # try to ensure that the generated images w/ the same seed
         # but different sizes actually result in similar images
         lowerCAmelCase__			:List[str]											=							(latents_shape[3] - latents_shape_reference[3]) // 2
         lowerCAmelCase__			:Dict											=							(latents_shape[2] - latents_shape_reference[2]) // 2
         lowerCAmelCase__			:Optional[Any]											=							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         lowerCAmelCase__			:str											=							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         lowerCAmelCase__			:Dict											=							0 if dx < 0 else dx
         lowerCAmelCase__			:Optional[Any]											=							0 if dy < 0 else dy
         lowerCAmelCase__			:int											=							max(-dx    , 0      )
         lowerCAmelCase__			:Optional[Any]											=							max(-dy    , 0      )
         # import pdb
         # pdb.set_trace()
         lowerCAmelCase__			:Optional[Any]											=							latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(__UpperCAmelCase      )

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         lowerCAmelCase__			:Any											=							self.scheduler.timesteps.to(self.device      )

         # scale the initial noise by the standard deviation required by the scheduler
         lowerCAmelCase__			:Optional[Any]											=							latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         lowerCAmelCase__			:List[Any]											=							'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         lowerCAmelCase__			:Optional[Any]											=							{}
         if accepts_eta:
           lowerCAmelCase__			:str											=							eta

         for i, t in enumerate(self.progress_bar(__UpperCAmelCase      )      ):
           # expand the latents if we are doing classifier free guidance
           lowerCAmelCase__			:Tuple											=							torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
           lowerCAmelCase__			:Dict											=							self.scheduler.scale_model_input(__UpperCAmelCase    , __UpperCAmelCase      )

           # predict the noise residual
           lowerCAmelCase__			:int											=							self.unet(__UpperCAmelCase    , __UpperCAmelCase    , encoder_hidden_states=__UpperCAmelCase      ).sample

           # perform guidance
           if do_classifier_free_guidance:
             lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							noise_pred.chunk(2      )
             lowerCAmelCase__			:Optional[int]											=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # compute the previous noisy sample x_t -> x_t-1
           lowerCAmelCase__			:Optional[Any]											=							self.scheduler.step(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , **__UpperCAmelCase      ).prev_sample

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
             callback(__UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							1 / 0.1_82_15 * latents
         lowerCAmelCase__			:Dict											=							self.vae.decode(__UpperCAmelCase      ).sample

         lowerCAmelCase__			:Optional[Any]											=							(image / 2 + 0.5).clamp(0    , 1      )

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         lowerCAmelCase__			:Optional[Any]											=							image.cpu().permute(0    , 2    , 3    , 1      ).float().numpy()

         if self.safety_checker is not None:
           lowerCAmelCase__			:Dict											=							self.feature_extractor(self.numpy_to_pil(__UpperCAmelCase      )    , return_tensors='pt'      ).to(
               self.device      )
           lowerCAmelCase__							,				lowerCAmelCase__			:Optional[int]											=							self.safety_checker(
               images=__UpperCAmelCase    , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
         else:
           lowerCAmelCase__			:int											=							None

         if output_type == "pil":
           lowerCAmelCase__			:Optional[int]											=							self.numpy_to_pil(__UpperCAmelCase      )

         if not return_dict:
           return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=__UpperCAmelCase    , nsfw_content_detected=__UpperCAmelCase      )







"""simple docstring"""





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


__A											=		logging.getLogger(__name__)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  if os.path.exists(_SCREAMING_SNAKE_CASE			):
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'config.json'			)			)
    if os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			) and os.path.isfile(
        os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			):
      os.remove(os.path.join(_SCREAMING_SNAKE_CASE     ,   'pytorch_model.bin'			)			)
  else:
    os.makedirs(_SCREAMING_SNAKE_CASE			)
  model.save_pretrained(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=False			)					->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__			:Dict											=							2
  if unlogit:
    lowerCAmelCase__			:List[str]											=							torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:str											=							p * torch.log(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[str]											=							0
  return -plogp.sum(dim=-1			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  logger.info('lv, h >\t' + '\t'.join(F"{x + 1}" for x in range(len(_SCREAMING_SNAKE_CASE			)			)			)			)
  for row in range(len(_SCREAMING_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  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=True     ,   _SCREAMING_SNAKE_CASE=None     ,   _SCREAMING_SNAKE_CASE=False			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							model.config.num_hidden_layers, model.config.num_attention_heads
  lowerCAmelCase__			:Any											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)
  lowerCAmelCase__			:Tuple											=							torch.zeros(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  if head_mask is None:
    lowerCAmelCase__			:Optional[int]											=							torch.ones(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).to(args.device			)

  head_mask.requires_grad_(requires_grad=_SCREAMING_SNAKE_CASE			)
  # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  if actually_pruned:
    lowerCAmelCase__			:List[str]											=							None

  lowerCAmelCase__			:Any											=							0.0
  lowerCAmelCase__			:Any											=							0.0
  for step, inputs in enumerate(tqdm(_SCREAMING_SNAKE_CASE     ,   desc='Iteration'     ,   disable=args.local_rank not in [-1, 0]			)			):
    lowerCAmelCase__			:str											=							tuple(t.to(args.device			) for t in inputs			)
    ((lowerCAmelCase__)							,				)			:Dict											=							inputs

    # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
    lowerCAmelCase__			:str											=							model(_SCREAMING_SNAKE_CASE     ,   labels=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    #  (loss), lm_logits, presents, (all hidden_states), (attentions)
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							(
        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(_SCREAMING_SNAKE_CASE			):
        lowerCAmelCase__			:Optional[Any]											=							entropy(attn.detach()     ,   _SCREAMING_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(_SCREAMING_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:
    lowerCAmelCase__			:Union[str, Any]											=							2
    lowerCAmelCase__			:Tuple											=							torch.pow(torch.pow(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			).sum(-1			)     ,   1 / exponent			)
    head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-20

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

  # Print matrices
  if compute_entropy:
    logger.info('Attention entropies'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  if compute_importance:
    logger.info('Head importance scores'			)
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  logger.info('Head ranked by importance scores'			)
  lowerCAmelCase__			:List[Any]											=							torch.zeros(head_importance.numel()     ,   dtype=torch.long     ,   device=args.device			)
  lowerCAmelCase__			:List[Any]											=							torch.arange(
      head_importance.numel()     ,   device=args.device			)
  lowerCAmelCase__			:int											=							head_ranks.view_as(_SCREAMING_SNAKE_CASE			)
  print_ad_tensor(_SCREAMING_SNAKE_CASE			)
  return attn_entropy, head_importance, total_loss
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							1 / loss  # instead of downsteam score use the LM loss
  logger.info('Pruning: original score: %f, threshold: %f'     ,   _SCREAMING_SNAKE_CASE     ,   original_score * args.masking_threshold			)

  lowerCAmelCase__			:Optional[int]											=							torch.ones_like(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							max(1     ,   int(new_head_mask.numel() * args.masking_amount			)			)

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

    if len(_SCREAMING_SNAKE_CASE			) <= num_to_mask:
      print('BREAK BY num_to_mask'			)
      break

    # mask heads
    lowerCAmelCase__			:int											=							current_heads_to_mask[:num_to_mask]
    logger.info('Heads to mask: %s'     ,   str(current_heads_to_mask.tolist()			)			)
    lowerCAmelCase__			:Dict											=							new_head_mask.view(-1			)
    lowerCAmelCase__			:Any											=							0.0
    lowerCAmelCase__			:Tuple											=							new_head_mask.view_as(_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Optional[int]											=							new_head_mask.clone().detach()
    print_ad_tensor(_SCREAMING_SNAKE_CASE			)

    # Compute metric and head importance again
    lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							compute_heads_importance(
        _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
    lowerCAmelCase__			:Any											=							1 / loss
    logger.info(
        'Masking: current score: %f, remaining heads %d (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   new_head_mask.sum()     ,   new_head_mask.sum() / new_head_mask.numel() * 100     ,   )

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

  return head_mask
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:Union[str, Any]											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[Any]											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  lowerCAmelCase__			:List[str]											=							sum(p.numel() for p in model.parameters()			)
  lowerCAmelCase__			:List[Any]											=							{
      layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(_SCREAMING_SNAKE_CASE			)			)
  }

  for k, v in heads_to_prune.items():
    if isinstance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			):
      lowerCAmelCase__			:Union[str, Any]											=							[
          v,
      ]

  assert sum(len(_SCREAMING_SNAKE_CASE			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
  model.prune_heads(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Any											=							sum(p.numel() for p in model.parameters()			)

  lowerCAmelCase__			:int											=							datetime.now()
  lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Dict											=							compute_heads_importance(
      _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   compute_entropy=_SCREAMING_SNAKE_CASE     ,   compute_importance=_SCREAMING_SNAKE_CASE     ,   head_mask=_SCREAMING_SNAKE_CASE     ,   actually_pruned=_SCREAMING_SNAKE_CASE     ,   )

  lowerCAmelCase__			:int											=							1 / loss
  lowerCAmelCase__			:Tuple											=							datetime.now() - before_time

  logger.info(
      'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   pruned_num_params / original_num_params * 100     ,   )
  logger.info('Pruning: score with masking: %f score with pruning: %f'     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
  logger.info('Pruning: speed ratio (original timing / new timing): %f percents'     ,   original_time / new_time * 100			)
  save_model(_SCREAMING_SNAKE_CASE     ,   args.output_dir			)
def  __A   ()					->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__			:List[str]											=							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--data_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
  parser.add_argument(
      '--output_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   required=_SCREAMING_SNAKE_CASE     ,   help='The output directory where the model predictions and checkpoints will be written.'     ,   )

  # Other parameters
  parser.add_argument(
      '--config_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained config name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--tokenizer_name'     ,   default=''     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Pretrained tokenizer name or path if not the same as model_name_or_path'     ,   )
  parser.add_argument(
      '--cache_dir'     ,   default=_SCREAMING_SNAKE_CASE     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Where do you want to store the pre-trained models downloaded from s3'     ,   )
  parser.add_argument(
      '--data_subset'     ,   type=_SCREAMING_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=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,   help='Amount to heads to masking at each masking step.'			)
  parser.add_argument('--metric_name'     ,   default='acc'     ,   type=_SCREAMING_SNAKE_CASE     ,   help='Metric to use for head masking.'			)

  parser.add_argument(
      '--max_seq_length'     ,   default=128     ,   type=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,   help='Batch size.'			)

  parser.add_argument('--seed'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=42			)
  parser.add_argument('--local_rank'     ,   type=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  parser.add_argument('--server_port'     ,   type=_SCREAMING_SNAKE_CASE     ,   default=''     ,   help='Can be used for distant debugging.'			)
  lowerCAmelCase__			:Any											=							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=_SCREAMING_SNAKE_CASE			)
    ptvsd.wait_for_attach()

  # Setup devices and distributed training
  if args.local_rank == -1 or args.no_cuda:
    lowerCAmelCase__			:List[Any]											=							torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'			)
    lowerCAmelCase__			:Optional[int]											=							0 if args.no_cuda else torch.cuda.device_count()
  else:
    torch.cuda.set_device(args.local_rank			)
    lowerCAmelCase__			:Dict											=							torch.device('cuda'     ,   args.local_rank			)
    lowerCAmelCase__			:Tuple											=							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			)			)			)

  lowerCAmelCase__			:int											=							GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

  # Distributed and parallel training
  model.to(args.device			)
  if args.local_rank != -1:
    lowerCAmelCase__			:Optional[Any]											=							nn.parallel.DistributedDataParallel(
        _SCREAMING_SNAKE_CASE     ,   device_ids=[args.local_rank]     ,   output_device=args.local_rank     ,   find_unused_parameters=_SCREAMING_SNAKE_CASE			)
  elif args.n_gpu > 1:
    lowerCAmelCase__			:Union[str, Any]											=							nn.DataParallel(_SCREAMING_SNAKE_CASE			)

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

  # Prepare dataset
  lowerCAmelCase__			:Optional[int]											=							np.concatenate(
      [
          np.loadtxt(args.data_dir     ,   dtype=np.intaa			),
      ]			)
  lowerCAmelCase__			:Union[str, Any]											=							(torch.from_numpy(_SCREAMING_SNAKE_CASE			),)
  lowerCAmelCase__			:Optional[int]											=							TensorDataset(*_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:List[Any]											=							RandomSampler(_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:Dict											=							DataLoader(_SCREAMING_SNAKE_CASE     ,   sampler=_SCREAMING_SNAKE_CASE     ,   batch_size=args.batch_size			)

  # Compute head entropy and importance score
  compute_heads_importance(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_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:
    lowerCAmelCase__			:Optional[Any]											=							mask_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
    prune_heads(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  main()
