


from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class    _a				(							_lowercase):




	def __init__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						AutoencoderKL						,   _SCREAMING_SNAKE_CASE		:						CLIPTextModel						,   _SCREAMING_SNAKE_CASE		:						CLIPTokenizer						,   _SCREAMING_SNAKE_CASE		:						UNetaDConditionModel						,   _SCREAMING_SNAKE_CASE		:						DDIMScheduler						,   _SCREAMING_SNAKE_CASE		:						float = 0.93						,   _SCREAMING_SNAKE_CASE		:						bool = True						,   )->			List[str]:
			lowerCAmelCase__  :     str       	=   mixing_coeff
			lowerCAmelCase__  :     Optional[Any]       	=   leapfrog_steps

			super().__init__()
			self.register_modules(
			    vae=_SCREAMING_SNAKE_CASE						,   text_encoder=_SCREAMING_SNAKE_CASE						,   tokenizer=_SCREAMING_SNAKE_CASE						,   unet=_SCREAMING_SNAKE_CASE						,   scheduler=_SCREAMING_SNAKE_CASE						,   )

			lowerCAmelCase__  :     Optional[Any]       	=   2 ** (len(self.vae.config.block_out_channels						) - 1)
			lowerCAmelCase__  :     List[Any]       	=   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)




	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						Optional[str] = None						,   _SCREAMING_SNAKE_CASE		:						bool = False						)->			List[Any]:
			lowerCAmelCase__  :     int       	=   self.tokenizer(
			    _SCREAMING_SNAKE_CASE						,   padding='''max_length'''						,   max_length=self.tokenizer.model_max_length						,   truncation=_SCREAMING_SNAKE_CASE						,   return_tensors='''pt'''						,   )

			lowerCAmelCase__  :     List[str]       	=   self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

			lowerCAmelCase__  :     Optional[int]       	=   prompt_embeds.to(dtype=self.text_encoder.dtype						,   device=self.device						)

			if do_classifier_free_guidance:
					lowerCAmelCase__  :     int       	=   '''''' if negative_prompt is None else negative_prompt

					lowerCAmelCase__  :     Optional[Any]       	=   self.tokenizer(
					    _SCREAMING_SNAKE_CASE						,   padding='''max_length'''						,   max_length=self.tokenizer.model_max_length						,   truncation=_SCREAMING_SNAKE_CASE						,   return_tensors='''pt'''						,   )

					lowerCAmelCase__  :     List[Any]       	=   self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

					lowerCAmelCase__  :     Union[str, Any]       	=   torch.cat([negative_prompt_embeds, prompt_embeds]						)

			return prompt_embeds




	def 				UpperCAmelCase__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						)->			List[str]:
			lowerCAmelCase__  :     Any       	=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
			lowerCAmelCase__  :     Dict       	=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

			return [x, y]




	def 				UpperCAmelCase__(   self		:						Optional[int]						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						)->			Optional[Any]:
			lowerCAmelCase__  :     str       	=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
			lowerCAmelCase__  :     Tuple       	=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

			return [x, y]




	def 				UpperCAmelCase__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						)->			Dict:
			# as self.alphas_cumprod is always in cpu
			lowerCAmelCase__  :     int       	=   int(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     int       	=   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

			return alpha_prod, 1 - alpha_prod




	def 				UpperCAmelCase__(   self		:						Optional[Any]						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   )->			Any:
			lowerCAmelCase__  :     Union[str, Any]       	=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

			lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[Any]       	=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__	,      lowerCAmelCase__  :     Any       	=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Dict       	=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
			lowerCAmelCase__  :     Any       	=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

			lowerCAmelCase__  :     List[Any]       	=   (base - b_t * model_output) / a_t

			return model_input, next_model_input.to(base.dtype						)




	def 				UpperCAmelCase__(   self		:						str						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   )->			Dict:
			lowerCAmelCase__  :     Any       	=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

			lowerCAmelCase__	,      lowerCAmelCase__  :     str       	=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   self._get_alpha_and_beta(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     int       	=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
			lowerCAmelCase__  :     int       	=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
			lowerCAmelCase__  :     int       	=   a_t * base + b_t * model_output

			return model_input, next_model_input.to(base.dtype						)




	@torch.no_grad()
	def 				UpperCAmelCase__(   self		:						int						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						)->			Optional[Any]:
			lowerCAmelCase__  :     Dict       	=   1 / self.vae.config.scaling_factor * latents
			lowerCAmelCase__  :     Optional[Any]       	=   self.vae.decode(_SCREAMING_SNAKE_CASE						).sample
			lowerCAmelCase__  :     Optional[int]       	=   (image / 2 + 0.5).clamp(0						,   1						)
			return image




	@torch.no_grad()
	def 				UpperCAmelCase__(   self		:						List[Any]						,   _SCREAMING_SNAKE_CASE		:						Image.Image						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						torch.Tensor						,   _SCREAMING_SNAKE_CASE		:						float						,   _SCREAMING_SNAKE_CASE		:						Optional[torch.Generator] = None						,   )->			int:
			lowerCAmelCase__  :     Optional[Any]       	=   guidance_scale > 1.0

			lowerCAmelCase__  :     Optional[int]       	=   image.to(device=self.device						,   dtype=text_embeds.dtype						)
			lowerCAmelCase__  :     Union[str, Any]       	=   self.vae.encode(_SCREAMING_SNAKE_CASE						).latent_dist.sample(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Optional[Any]       	=   self.vae.config.scaling_factor * latent

			lowerCAmelCase__  :     List[str]       	=   [latent.clone(), latent.clone()]

			for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE						)						,   total=len(_SCREAMING_SNAKE_CASE						)						):
					lowerCAmelCase__  :     Any       	=   self.noise_mixing_layer(x=coupled_latents[0]						,   y=coupled_latents[1]						)

					# j - model_input index, k - base index
					for j in range(2						):
							lowerCAmelCase__  :     Any       	=   j ^ 1

							if self.leapfrog_steps:
									if i % 2 == 0:
											lowerCAmelCase__	,      lowerCAmelCase__  :     int       	=   j, k

							lowerCAmelCase__  :     int       	=   coupled_latents[j]
							lowerCAmelCase__  :     Optional[int]       	=   coupled_latents[k]

							lowerCAmelCase__  :     List[Any]       	=   torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

							lowerCAmelCase__  :     List[str]       	=   self.unet(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   encoder_hidden_states=_SCREAMING_SNAKE_CASE						).sample

							if do_classifier_free_guidance:
									lowerCAmelCase__	,      lowerCAmelCase__  :     Tuple       	=   noise_pred.chunk(2						)
									lowerCAmelCase__  :     List[str]       	=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   self.noise_step(
							    base=_SCREAMING_SNAKE_CASE						,   model_input=_SCREAMING_SNAKE_CASE						,   model_output=_SCREAMING_SNAKE_CASE						,   timestep=_SCREAMING_SNAKE_CASE						,   )

							lowerCAmelCase__  :     List[str]       	=   model_input

			return coupled_latents




	@torch.no_grad()
	def __call__(   self		:						Dict						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						str						,   _SCREAMING_SNAKE_CASE		:						Image.Image						,   _SCREAMING_SNAKE_CASE		:						float = 3.0						,   _SCREAMING_SNAKE_CASE		:						int = 50						,   _SCREAMING_SNAKE_CASE		:						float = 0.8						,   _SCREAMING_SNAKE_CASE		:						Optional[str] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[torch.Generator] = None						,   _SCREAMING_SNAKE_CASE		:						Optional[str] = "pil"						,   )->			str:
			lowerCAmelCase__  :     Any       	=   guidance_scale > 1.0

			lowerCAmelCase__  :     Union[str, Any]       	=   self.image_processor.preprocess(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Optional[int]       	=   self._encode_prompt(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Union[str, Any]       	=   self._encode_prompt(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

			self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE						,   self.device						)

			lowerCAmelCase__  :     Dict       	=   num_inference_steps - int(num_inference_steps * strength						)
			lowerCAmelCase__  :     List[str]       	=   self.scheduler.timesteps[t_limit:]
			lowerCAmelCase__  :     str       	=   fwd_timesteps.flip(0						)

			lowerCAmelCase__  :     Optional[int]       	=   self.prepare_latents(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

			for i, t in tqdm(enumerate(_SCREAMING_SNAKE_CASE						)						,   total=len(_SCREAMING_SNAKE_CASE						)						):
					# j - model_input index, k - base index
					for k in range(2						):
							lowerCAmelCase__  :     Dict       	=   k ^ 1

							if self.leapfrog_steps:
									if i % 2 == 1:
											lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   j, k

							lowerCAmelCase__  :     Optional[Any]       	=   coupled_latents[j]
							lowerCAmelCase__  :     Optional[int]       	=   coupled_latents[k]

							lowerCAmelCase__  :     Optional[Any]       	=   torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

							lowerCAmelCase__  :     Dict       	=   self.unet(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						,   encoder_hidden_states=_SCREAMING_SNAKE_CASE						).sample

							if do_classifier_free_guidance:
									lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[Any]       	=   noise_pred.chunk(2						)
									lowerCAmelCase__  :     Union[str, Any]       	=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   self.denoise_step(
							    base=_SCREAMING_SNAKE_CASE						,   model_input=_SCREAMING_SNAKE_CASE						,   model_output=_SCREAMING_SNAKE_CASE						,   timestep=_SCREAMING_SNAKE_CASE						,   )

							lowerCAmelCase__  :     str       	=   model_input

					lowerCAmelCase__  :     int       	=   self.denoise_mixing_layer(x=coupled_latents[0]						,   y=coupled_latents[1]						)

			# either one is fine
			lowerCAmelCase__  :     Dict       	=   coupled_latents[0]

			if output_type not in ["latent", "pt", "np", "pil"]:
					lowerCAmelCase__  :     List[Any]       	=   (
					    F'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
					    '''`pil`, `np`, `pt`, `latent`'''
					)
					deprecate('''Unsupported output_type'''						,   '''1.0.0'''						,   _SCREAMING_SNAKE_CASE						,   standard_warn=_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     List[Any]       	=   '''np'''

			if output_type == "latent":
					lowerCAmelCase__  :     Optional[Any]       	=   final_latent
			else:
					lowerCAmelCase__  :     Tuple       	=   self.decode_latents(_SCREAMING_SNAKE_CASE						)
					lowerCAmelCase__  :     Dict       	=   self.image_processor.postprocess(_SCREAMING_SNAKE_CASE						,   output_type=_SCREAMING_SNAKE_CASE						)

			return image








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


lowerCamelCase		=						logging.getLogger(__name__)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     str       	=   p * torch.log(_a			)
		lowerCAmelCase__  :     List[str]       	=   0
		return -plogp.sum(dim=-1			)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		logger.info('''lv, h >\t''' + '''\t'''.join(f'{x + 1}' for x in range(len(_a			)			)			)			)
		for row in range(len(_a			)			):
				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 	lowerCamelCase_       (			_a      ,  _a      ,  _a      ,  _a=True      ,  _a=True      ,  _a=None      ,  _a=False			):




		"""simple docstring"""






		lowerCAmelCase__	,      lowerCAmelCase__  :     int       	=   model.config.num_hidden_layers, model.config.num_attention_heads
		lowerCAmelCase__  :     Optional[int]       	=   torch.zeros(_a      ,  _a			).to(args.device			)
		lowerCAmelCase__  :     Optional[int]       	=   torch.zeros(_a      ,  _a			).to(args.device			)

		if head_mask is None:
				lowerCAmelCase__  :     Union[str, Any]       	=   torch.ones(_a      ,  _a			).to(args.device			)

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

		lowerCAmelCase__  :     List[str]       	=   None
		lowerCAmelCase__  :     Any       	=   None
		lowerCAmelCase__  :     Optional[Any]       	=   0.0

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

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				lowerCAmelCase__  :     Optional[int]       	=   model(**_a      ,  head_mask=_a			)
				lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   (
				    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(_a			):
								lowerCAmelCase__  :     List[str]       	=   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:
						lowerCAmelCase__  :     str       	=   logits.detach().cpu().numpy()
						lowerCAmelCase__  :     Any       	=   inputs['''labels'''].detach().cpu().numpy()
				else:
						lowerCAmelCase__  :     Tuple       	=   np.append(_a      ,  logits.detach().cpu().numpy()      ,  axis=0			)
						lowerCAmelCase__  :     Any       	=   np.append(_a      ,  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:
				lowerCAmelCase__  :     str       	=   2
				lowerCAmelCase__  :     List[str]       	=   torch.pow(torch.pow(_a      ,  _a			).sum(-1			)      ,  1 / exponent			)
				head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-2_0

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

		return attn_entropy, head_importance, preds, labels






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   compute_heads_importance(_a      ,  _a      ,  _a      ,  compute_entropy=_a			)
		lowerCAmelCase__  :     Dict       	=   np.argmax(_a      ,  axis=1			) if args.output_mode == '''classification''' else np.squeeze(_a			)
		lowerCAmelCase__  :     int       	=   glue_compute_metrics(args.task_name      ,  _a      ,  _a			)[args.metric_name]
		logger.info('''Pruning: original score: %f, threshold: %f'''      ,  _a      ,  original_score * args.masking_threshold			)

		lowerCAmelCase__  :     int       	=   torch.ones_like(_a			)
		lowerCAmelCase__  :     List[Any]       	=   max(1      ,  int(new_head_mask.numel() * args.masking_amount			)			)

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

				if len(_a			) <= num_to_mask:
						break

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

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

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

		return head_mask






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     int       	=   datetime.now()
		lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__  :     Optional[int]       	=   compute_heads_importance(
		    _a      ,  _a      ,  _a      ,  compute_entropy=_a      ,  compute_importance=_a      ,  head_mask=_a			)
		lowerCAmelCase__  :     str       	=   np.argmax(_a      ,  axis=1			) if args.output_mode == '''classification''' else np.squeeze(_a			)
		lowerCAmelCase__  :     Union[str, Any]       	=   glue_compute_metrics(args.task_name      ,  _a      ,  _a			)[args.metric_name]
		lowerCAmelCase__  :     Tuple       	=   datetime.now() - before_time

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

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

		lowerCAmelCase__  :     Tuple       	=   datetime.now()
		lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   compute_heads_importance(
		    _a      ,  _a      ,  _a      ,  compute_entropy=_a      ,  compute_importance=_a      ,  head_mask=_a      ,  actually_pruned=_a      ,  )
		lowerCAmelCase__  :     Optional[int]       	=   np.argmax(_a      ,  axis=1			) if args.output_mode == '''classification''' else np.squeeze(_a			)
		lowerCAmelCase__  :     Any       	=   glue_compute_metrics(args.task_name      ,  _a      ,  _a			)[args.metric_name]
		lowerCAmelCase__  :     Dict       	=   datetime.now() - before_time

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






def 	lowerCamelCase_       (			):




		"""simple docstring"""






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

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

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

		parser.add_argument('''--seed'''      ,  type=_a      ,  default=42			)
		parser.add_argument('''--local_rank'''      ,  type=_a      ,  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=_a      ,  default=''''''      ,  help='''Can be used for distant debugging.'''			)
		parser.add_argument('''--server_port'''      ,  type=_a      ,  default=''''''      ,  help='''Can be used for distant debugging.'''			)
		lowerCAmelCase__  :     Optional[int]       	=   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=_a			)
				ptvsd.wait_for_attach()

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

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

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

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

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

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

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

		# 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__  :     List[str]       	=   mask_heads(_a      ,  _a      ,  _a			)
				prune_heads(_a      ,  _a      ,  _a      ,  _a			)


if __name__ == "__main__":
		main()




