



"""simple docstring"""





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def    a__	(      lowerCAmelCase__	,			lowerCAmelCase__=0.999	,			lowerCAmelCase__="cosine"	,			):



      if alpha_transform_type == "cosine":

            def alpha_bar_fn(lowerCAmelCase__      ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(lowerCAmelCase__      ):
                  return math.exp(t * -12.0      )

      else:
            raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""      )

      UpperCAmelCase_			 =							[]
      for i in range(__lowercase      ):
            UpperCAmelCase_			 =							i / num_diffusion_timesteps
            UpperCAmelCase_			 =							(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__lowercase      ) / alpha_bar_fn(__lowercase      )	,			__lowercase      )      )
      return torch.tensor(__lowercase	,			dtype=torch.floataa      )


class       lowercase__   (    __snake_case				,					__snake_case      ):







       '''simple docstring'''


       UpperCamelCase						=     [e.name for e in KarrasDiffusionSchedulers]
       UpperCamelCase						=     2



       @register_to_config
       def __init__(							self			:  int		,				_UpperCAmelCase			:  List[Any] = 1000		,				_UpperCAmelCase			:  List[str] = 0.0_0085		,				_UpperCAmelCase			:  int = 0.012		,				_UpperCAmelCase			:  List[str] = "linear"		,				_UpperCAmelCase			:  List[Any] = None		,				_UpperCAmelCase			:  Tuple = "epsilon"		,				_UpperCAmelCase			:  List[str] = "linspace"		,				_UpperCAmelCase			:  Any = 0		,				) ->						Optional[Any]:


             '''simple docstring'''




             if trained_betas is not None:
                   UpperCAmelCase_			 =							torch.tensor(lowercase_		,				dtype=torch.floataa    )
             elif beta_schedule == "linear":
                   UpperCAmelCase_			 =							torch.linspace(lowercase_		,				lowercase_		,				lowercase_		,				dtype=torch.floataa    )
             elif beta_schedule == "scaled_linear":
                   # this schedule is very specific to the latent diffusion model.
                   UpperCAmelCase_			 =							(
                       torch.linspace(beta_start**0.5		,				beta_end**0.5		,				lowercase_		,				dtype=torch.floataa    ) ** 2
                   )
             elif beta_schedule == "squaredcos_cap_v2":
                   # Glide cosine schedule
                   UpperCAmelCase_			 =							betas_for_alpha_bar(lowercase_    )
             else:
                   raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

             UpperCAmelCase_			 =							1.0 - self.betas
             UpperCAmelCase_			 =							torch.cumprod(self.alphas		,				dim=0    )

             #  set all values
             self.set_timesteps(lowercase_		,				lowercase_		,				lowercase_    )



       def      lowercase__				(							self			:  int		,				_UpperCAmelCase			:  Optional[int]		,				_UpperCAmelCase			:  List[Any]=None    ) ->						List[str]:


             '''simple docstring'''




             if schedule_timesteps is None:
                   UpperCAmelCase_			 =							self.timesteps

             UpperCAmelCase_			 =							(schedule_timesteps == timestep).nonzero()

             # The sigma index that is taken for the **very** first `step`
             # is always the second index (or the last index if there is only 1)
             # This way we can ensure we don't accidentally skip a sigma in
             # case we start in the middle of the denoising schedule (e.g. for image-to-image)
             if len(self._index_counter    ) == 0:
                   UpperCAmelCase_			 =							1 if len(lowercase_    ) > 1 else 0
             else:
                   UpperCAmelCase_			 =							timestep.cpu().item() if torch.is_tensor(lowercase_    ) else timestep
                   UpperCAmelCase_			 =							self._index_counter[timestep_int]

             return indices[pos].item()



       @property
       def      lowercase__				(							self			:  Optional[int]    ) ->						Union[str, Any]:


             '''simple docstring'''




             if self.config.timestep_spacing in ["linspace", "trailing"]:
                   return self.sigmas.max()

             return (self.sigmas.max() ** 2 + 1) ** 0.5



       def      lowercase__				(							self			:  int		,				_UpperCAmelCase			:  List[Any]		,				_UpperCAmelCase			:  str		,				) ->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_			 =							self.index_for_timestep(lowercase_    )

             if self.state_in_first_order:
                   UpperCAmelCase_			 =							self.sigmas[step_index]
             else:
                   UpperCAmelCase_			 =							self.sigmas_interpol[step_index - 1]

             UpperCAmelCase_			 =							sample / ((sigma**2 + 1) ** 0.5)
             return sample



       def      lowercase__				(							self			:  Optional[int]		,				_UpperCAmelCase			:  Tuple		,				_UpperCAmelCase			:  int = None		,				_UpperCAmelCase			:  List[str] = None		,				) ->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_			 =							num_inference_steps

             UpperCAmelCase_			 =							num_train_timesteps or self.config.num_train_timesteps

             # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
             if self.config.timestep_spacing == "linspace":
                   UpperCAmelCase_			 =							np.linspace(0		,				num_train_timesteps - 1		,				lowercase_		,				dtype=lowercase_    )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                   UpperCAmelCase_			 =							num_train_timesteps // self.num_inference_steps
                   # creates integer timesteps by multiplying by ratio
                   # casting to int to avoid issues when num_inference_step is power of 3
                   UpperCAmelCase_			 =							(np.arange(0		,				lowercase_    ) * step_ratio).round()[::-1].copy().astype(lowercase_    )
                   timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                   UpperCAmelCase_			 =							num_train_timesteps / self.num_inference_steps
                   # creates integer timesteps by multiplying by ratio
                   # casting to int to avoid issues when num_inference_step is power of 3
                   UpperCAmelCase_			 =							(np.arange(lowercase_		,				0		,				-step_ratio    )).round().copy().astype(lowercase_    )
                   timesteps -= 1
             else:
                   raise ValueError(
                       F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

             UpperCAmelCase_			 =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
             UpperCAmelCase_			 =							torch.from_numpy(np.log(lowercase_    )    ).to(lowercase_    )

             UpperCAmelCase_			 =							np.interp(lowercase_		,				np.arange(0		,				len(lowercase_    )    )		,				lowercase_    )
             UpperCAmelCase_			 =							np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
             UpperCAmelCase_			 =							torch.from_numpy(lowercase_    ).to(device=lowercase_    )

             # compute up and down sigmas
             UpperCAmelCase_			 =							sigmas.roll(-1    )
             UpperCAmelCase_			 =							0.0
             UpperCAmelCase_			 =							(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
             UpperCAmelCase_			 =							(sigmas_next**2 - sigmas_up**2) ** 0.5
             UpperCAmelCase_			 =							0.0

             # compute interpolated sigmas
             UpperCAmelCase_			 =							sigmas.log().lerp(sigmas_down.log()		,				0.5    ).exp()
             UpperCAmelCase_			 =							0.0

             # set sigmas
             UpperCAmelCase_			 =							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
             UpperCAmelCase_			 =							torch.cat(
                 [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )
             UpperCAmelCase_			 =							torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2    ), sigmas_up[-1:]]    )
             UpperCAmelCase_			 =							torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2    ), sigmas_down[-1:]]    )

             if str(lowercase_    ).startswith("mps"    ):
                   # mps does not support float64
                   UpperCAmelCase_			 =							torch.from_numpy(lowercase_    ).to(lowercase_		,				dtype=torch.floataa    )
             else:
                   UpperCAmelCase_			 =							torch.from_numpy(lowercase_    ).to(lowercase_    )

             UpperCAmelCase_			 =							self.sigma_to_t(lowercase_    ).to(lowercase_		,				dtype=timesteps.dtype    )
             UpperCAmelCase_			 =							torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])		,				dim=-1    ).flatten()

             UpperCAmelCase_			 =							torch.cat([timesteps[:1], interleaved_timesteps]    )

             UpperCAmelCase_			 =							None

             # for exp beta schedules, such as the one for `pipeline_shap_e.py`
             # we need an index counter
             UpperCAmelCase_			 =							defaultdict(lowercase_    )



       def      lowercase__				(							self			:  List[Any]		,				_UpperCAmelCase			:  int    ) ->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			 =							sigma.log()

             # get distribution
             UpperCAmelCase_			 =							log_sigma - self.log_sigmas[:, None]

             # get sigmas range
             UpperCAmelCase_			 =							dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
             UpperCAmelCase_			 =							low_idx + 1

             UpperCAmelCase_			 =							self.log_sigmas[low_idx]
             UpperCAmelCase_			 =							self.log_sigmas[high_idx]

             # interpolate sigmas
             UpperCAmelCase_			 =							(low - log_sigma) / (low - high)
             UpperCAmelCase_			 =							w.clamp(0		,				1    )

             # transform interpolation to time range
             UpperCAmelCase_			 =							(1 - w) * low_idx + w * high_idx
             UpperCAmelCase_			 =							t.view(sigma.shape    )
             return t



       @property
       def      lowercase__				(							self			:  Union[str, Any]    ) ->						Any:


             '''simple docstring'''




             return self.sample is None



       def      lowercase__				(							self			:  Tuple		,				_UpperCAmelCase			:  Dict		,				_UpperCAmelCase			:  List[str]		,				_UpperCAmelCase			:  Tuple		,				_UpperCAmelCase			:  Dict = None		,				_UpperCAmelCase			:  Tuple = True		,				) ->						List[str]:


             '''simple docstring'''




             UpperCAmelCase_			 =							self.index_for_timestep(lowercase_    )

             # advance index counter by 1
             UpperCAmelCase_			 =							timestep.cpu().item() if torch.is_tensor(lowercase_    ) else timestep
             self._index_counter[timestep_int] += 1

             if self.state_in_first_order:
                   UpperCAmelCase_			 =							self.sigmas[step_index]
                   UpperCAmelCase_			 =							self.sigmas_interpol[step_index]
                   UpperCAmelCase_			 =							self.sigmas_up[step_index]
                   UpperCAmelCase_			 =							self.sigmas_down[step_index - 1]
             else:
                   # 2nd order / KPDM2's method
                   UpperCAmelCase_			 =							self.sigmas[step_index - 1]
                   UpperCAmelCase_			 =							self.sigmas_interpol[step_index - 1]
                   UpperCAmelCase_			 =							self.sigmas_up[step_index - 1]
                   UpperCAmelCase_			 =							self.sigmas_down[step_index - 1]

             # currently only gamma=0 is supported. This usually works best anyways.
             # We can support gamma in the future but then need to scale the timestep before
             # passing it to the model which requires a change in API
             UpperCAmelCase_			 =							0
             UpperCAmelCase_			 =							sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

             UpperCAmelCase_			 =							model_output.device
             UpperCAmelCase_			 =							randn_tensor(model_output.shape		,				dtype=model_output.dtype		,				device=lowercase_		,				generator=lowercase_    )

             # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
             if self.config.prediction_type == "epsilon":
                   UpperCAmelCase_			 =							sigma_hat if self.state_in_first_order else sigma_interpol
                   UpperCAmelCase_			 =							sample - sigma_input * model_output
             elif self.config.prediction_type == "v_prediction":
                   UpperCAmelCase_			 =							sigma_hat if self.state_in_first_order else sigma_interpol
                   UpperCAmelCase_			 =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                       sample / (sigma_input**2 + 1)
                   )
             elif self.config.prediction_type == "sample":
                   raise NotImplementedError("prediction_type not implemented yet: sample"    )
             else:
                   raise ValueError(
                       F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

             if self.state_in_first_order:
                   # 2. Convert to an ODE derivative for 1st order
                   UpperCAmelCase_			 =							(sample - pred_original_sample) / sigma_hat
                   # 3. delta timestep
                   UpperCAmelCase_			 =							sigma_interpol - sigma_hat

                   # store for 2nd order step
                   UpperCAmelCase_			 =							sample
                   UpperCAmelCase_			 =							dt
                   UpperCAmelCase_			 =							sample + derivative * dt
             else:
                   # DPM-Solver-2
                   # 2. Convert to an ODE derivative for 2nd order
                   UpperCAmelCase_			 =							(sample - pred_original_sample) / sigma_interpol
                   # 3. delta timestep
                   UpperCAmelCase_			 =							sigma_down - sigma_hat

                   UpperCAmelCase_			 =							self.sample
                   UpperCAmelCase_			 =							None

                   UpperCAmelCase_			 =							sample + derivative * dt
                   UpperCAmelCase_			 =							prev_sample + noise * sigma_up

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=lowercase_    )



       def      lowercase__				(							self			:  Optional[int]		,				_UpperCAmelCase			:  List[Any]		,				_UpperCAmelCase			:  List[str]		,				_UpperCAmelCase			:  Optional[int]		,				) ->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			 =							self.sigmas.to(device=original_samples.device		,				dtype=original_samples.dtype    )
             if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_    ):
                   # mps does not support float64
                   UpperCAmelCase_			 =							self.timesteps.to(original_samples.device		,				dtype=torch.floataa    )
                   UpperCAmelCase_			 =							timesteps.to(original_samples.device		,				dtype=torch.floataa    )
             else:
                   UpperCAmelCase_			 =							self.timesteps.to(original_samples.device    )
                   UpperCAmelCase_			 =							timesteps.to(original_samples.device    )

             UpperCAmelCase_			 =							[self.index_for_timestep(lowercase_		,				lowercase_    ) for t in timesteps]

             UpperCAmelCase_			 =							sigmas[step_indices].flatten()
             while len(sigma.shape    ) < len(original_samples.shape    ):
                   UpperCAmelCase_			 =							sigma.unsqueeze(-1    )

             UpperCAmelCase_			 =							original_samples + noise * sigma
             return noisy_samples







       def __len__(							self			:  int    ) ->						Optional[Any]:


             '''simple docstring'''




             return self.config.num_train_timesteps





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


__SCREAMING_SNAKE_CASE						:    str     = logging.getLogger(__name__)
def 					snake_case						(__lowercase						)       ->       str:

						'''simple docstring'''



						_snake_case						:     List[str]				=    p * torch.log(__lowercase						)
						_snake_case						:     List[Any]				=    0
						return -plogp.sum(dim=-1						)
def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(__lowercase						)						)						)						)
						for row in range(len(__lowercase						)						):
												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						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=True					,			__lowercase=True					,			__lowercase=None					,			__lowercase=False						)       ->       List[Any]:

						'''simple docstring'''



						_snake_case				,_snake_case						:     List[Any]				=    model.config.num_hidden_layers, model.config.num_attention_heads
						_snake_case						:     Tuple				=    torch.zeros(__lowercase					,			__lowercase						).to(args.device						)
						_snake_case						:     str				=    torch.zeros(__lowercase					,			__lowercase						).to(args.device						)

						if head_mask is None:
												_snake_case						:     Tuple				=    torch.ones(__lowercase					,			__lowercase						).to(args.device						)

						head_mask.requires_grad_(requires_grad=__lowercase						)
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												_snake_case						:     Optional[int]				=    None

						_snake_case						:     Optional[int]				=    None
						_snake_case						:     List[str]				=    None
						_snake_case						:     List[Any]				=    0.0

						for step, inputs in enumerate(tqdm(__lowercase					,			desc="Iteration"					,			disable=args.local_rank not in [-1, 0]						)						):
												for k, v in inputs.items():
																		_snake_case						:     List[str]				=    v.to(args.device						)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												_snake_case						:     int				=    model(**__lowercase					,			head_mask=__lowercase						)
												_snake_case				,_snake_case				,_snake_case						:     Any				=    (
												    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(__lowercase						):
																								_snake_case						:     Union[str, Any]				=    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:
																		_snake_case						:     str				=    logits.detach().cpu().numpy()
																		_snake_case						:     Union[str, Any]				=    inputs["labels"].detach().cpu().numpy()
												else:
																		_snake_case						:     Optional[int]				=    np.append(__lowercase					,			logits.detach().cpu().numpy()					,			axis=0						)
																		_snake_case						:     Dict				=    np.append(__lowercase					,			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:
												_snake_case						:     str				=    2
												_snake_case						:     Any				=    torch.pow(torch.pow(__lowercase					,			__lowercase						).sum(-1						)					,			1 / exponent						)
												head_importance /= norm_by_layer.unsqueeze(-1						) + 1e-20

						if not args.dont_normalize_global_importance:
												_snake_case						:     Union[str, Any]				=    (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(__lowercase						)
						logger.info("Head importance scores"						)
						print_ad_tensor(__lowercase						)
						logger.info("Head ranked by importance scores"						)
						_snake_case						:     int				=    torch.zeros(head_importance.numel()					,			dtype=torch.long					,			device=args.device						)
						_snake_case						:     List[str]				=    torch.arange(
						    head_importance.numel()					,			device=args.device						)
						_snake_case						:     Tuple				=    head_ranks.view_as(__lowercase						)
						print_ad_tensor(__lowercase						)

						return attn_entropy, head_importance, preds, labels
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       int:

						'''simple docstring'''



						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     List[Any]				=    compute_heads_importance(__lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase						)
						_snake_case						:     List[Any]				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     Tuple				=    glue_compute_metrics(args.task_name					,			__lowercase					,			__lowercase						)[args.metric_name]
						logger.info("Pruning: original score: %f, threshold: %f"					,			__lowercase					,			original_score * args.masking_threshold						)

						_snake_case						:     List[str]				=    torch.ones_like(__lowercase						)
						_snake_case						:     Union[str, Any]				=    max(1					,			int(new_head_mask.numel() * args.masking_amount						)						)

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

												if len(__lowercase						) <= num_to_mask:
																		break

												# mask heads
												_snake_case						:     Optional[Any]				=    current_heads_to_mask[:num_to_mask]
												logger.info("Heads to mask: %s"					,			str(current_heads_to_mask.tolist()						)						)
												_snake_case						:     List[str]				=    new_head_mask.view(-1						)
												_snake_case						:     str				=    0.0
												_snake_case						:     str				=    new_head_mask.view_as(__lowercase						)
												_snake_case						:     Union[str, Any]				=    new_head_mask.clone().detach()
												print_ad_tensor(__lowercase						)

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

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

						return head_mask
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Optional[int]:

						'''simple docstring'''



						_snake_case						:     Dict				=    datetime.now()
						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     Any				=    compute_heads_importance(
						    __lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase					,			compute_importance=__lowercase					,			head_mask=__lowercase						)
						_snake_case						:     int				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     Dict				=    glue_compute_metrics(args.task_name					,			__lowercase					,			__lowercase						)[args.metric_name]
						_snake_case						:     Union[str, Any]				=    datetime.now() - before_time

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

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

						_snake_case						:     List[str]				=    datetime.now()
						_snake_case				,_snake_case				,_snake_case				,_snake_case						:     Union[str, Any]				=    compute_heads_importance(
						    __lowercase					,			__lowercase					,			__lowercase					,			compute_entropy=__lowercase					,			compute_importance=__lowercase					,			head_mask=__lowercase					,			actually_pruned=__lowercase					,			)
						_snake_case						:     str				=    np.argmax(__lowercase					,			axis=1						) if args.output_mode == "classification" else np.squeeze(__lowercase						)
						_snake_case						:     List[str]				=    glue_compute_metrics(args.task_name					,			__lowercase					,			__lowercase						)[args.metric_name]
						_snake_case						:     str				=    datetime.now() - before_time

						logger.info(
						    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"					,			__lowercase					,			__lowercase					,			pruned_num_params / original_num_params * 100					,			)
						logger.info("Pruning: score with masking: %f score with pruning: %f"					,			__lowercase					,			__lowercase						)
						logger.info("Pruning: speed ratio (new timing / original timing): %f percents"					,			original_time / new_time * 100						)
def 					snake_case						()       ->       Any:

						'''simple docstring'''



						_snake_case						:     int				=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--data_dir"					,			default=__lowercase					,			type=__lowercase					,			required=__lowercase					,			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=__lowercase					,			type=__lowercase					,			required=__lowercase					,			help="Path to pretrained model or model identifier from huggingface.co/models"					,			)
						parser.add_argument(
						    "--task_name"					,			default=__lowercase					,			type=__lowercase					,			required=__lowercase					,			help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()						)					,			)
						parser.add_argument(
						    "--output_dir"					,			default=__lowercase					,			type=__lowercase					,			required=__lowercase					,			help="The output directory where the model predictions and checkpoints will be written."					,			)

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

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

						parser.add_argument("--seed"					,			type=__lowercase					,			default=42						)
						parser.add_argument("--local_rank"					,			type=__lowercase					,			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=__lowercase					,			default=""					,			help="Can be used for distant debugging."						)
						parser.add_argument("--server_port"					,			type=__lowercase					,			default=""					,			help="Can be used for distant debugging."						)
						_snake_case						:     List[str]				=    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=__lowercase						)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												_snake_case						:     Any				=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
												_snake_case						:     Tuple				=    0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank						)
												_snake_case						:     Tuple				=    torch.device("cuda"					,			args.local_rank						)
												_snake_case						:     Any				=    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
						_snake_case						:     List[str]				=    args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError("Task not found: %s" % (args.task_name)						)
						_snake_case						:     str				=    glue_processors[args.task_name]()
						_snake_case						:     Any				=    glue_output_modes[args.task_name]
						_snake_case						:     Any				=    processor.get_labels()
						_snake_case						:     Any				=    len(__lowercase						)

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

						_snake_case						:     Any				=    AutoConfig.from_pretrained(
						    args.config_name if args.config_name else args.model_name_or_path					,			num_labels=__lowercase					,			finetuning_task=args.task_name					,			output_attentions=__lowercase					,			cache_dir=args.cache_dir					,			)
						_snake_case						:     Any				=    AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path					,			cache_dir=args.cache_dir					,			)
						_snake_case						:     List[str]				=    AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path					,			from_tf=bool(".ckpt" in args.model_name_or_path						)					,			config=__lowercase					,			cache_dir=args.cache_dir					,			)

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

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

						# Prepare dataset for the GLUE task
						_snake_case						:     str				=    GlueDataset(__lowercase					,			tokenizer=__lowercase					,			mode="dev"						)
						if args.data_subset > 0:
												_snake_case						:     str				=    Subset(__lowercase					,			list(range(min(args.data_subset					,			len(__lowercase						)						)						)						)						)
						_snake_case						:     Optional[Any]				=    SequentialSampler(__lowercase						) if args.local_rank == -1 else DistributedSampler(__lowercase						)
						_snake_case						:     str				=    DataLoader(
						    __lowercase					,			sampler=__lowercase					,			batch_size=args.batch_size					,			collate_fn=__lowercase						)

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

						# 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:
												_snake_case						:     int				=    mask_heads(__lowercase					,			__lowercase					,			__lowercase						)
												prune_heads(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)


if __name__ == "__main__":
				main()