


'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_pt_objects import *  # noqa F403
else:
				from .consistency_models import ConsistencyModelPipeline
				from .dance_diffusion import DanceDiffusionPipeline
				from .ddim import DDIMPipeline
				from .ddpm import DDPMPipeline
				from .dit import DiTPipeline
				from .latent_diffusion import LDMSuperResolutionPipeline
				from .latent_diffusion_uncond import LDMPipeline
				from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
				from .pndm import PNDMPipeline
				from .repaint import RePaintPipeline
				from .score_sde_ve import ScoreSdeVePipeline
				from .stochastic_karras_ve import KarrasVePipeline

try:
				if not (is_torch_available() and is_librosa_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
				from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
				if not (is_torch_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
				from .audioldm import AudioLDMPipeline
				from .controlnet import (
				    StableDiffusionControlNetImgaImgPipeline,
				    StableDiffusionControlNetInpaintPipeline,
				    StableDiffusionControlNetPipeline,
				)
				from .deepfloyd_if import (
				    IFImgaImgPipeline,
				    IFImgaImgSuperResolutionPipeline,
				    IFInpaintingPipeline,
				    IFInpaintingSuperResolutionPipeline,
				    IFPipeline,
				    IFSuperResolutionPipeline,
				)
				from .kandinsky import (
				    KandinskyImgaImgPipeline,
				    KandinskyInpaintPipeline,
				    KandinskyPipeline,
				    KandinskyPriorPipeline,
				)
				from .kandinskya_a import (
				    KandinskyVaaControlnetImgaImgPipeline,
				    KandinskyVaaControlnetPipeline,
				    KandinskyVaaImgaImgPipeline,
				    KandinskyVaaInpaintPipeline,
				    KandinskyVaaPipeline,
				    KandinskyVaaPriorEmbaEmbPipeline,
				    KandinskyVaaPriorPipeline,
				)
				from .latent_diffusion import LDMTextToImagePipeline
				from .paint_by_example import PaintByExamplePipeline
				from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
				from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
				from .stable_diffusion import (
				    CycleDiffusionPipeline,
				    StableDiffusionAttendAndExcitePipeline,
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionImageVariationPipeline,
				    StableDiffusionImgaImgPipeline,
				    StableDiffusionInpaintPipeline,
				    StableDiffusionInpaintPipelineLegacy,
				    StableDiffusionInstructPixaPixPipeline,
				    StableDiffusionLatentUpscalePipeline,
				    StableDiffusionLDMaDPipeline,
				    StableDiffusionModelEditingPipeline,
				    StableDiffusionPanoramaPipeline,
				    StableDiffusionParadigmsPipeline,
				    StableDiffusionPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				    StableDiffusionSAGPipeline,
				    StableDiffusionUpscalePipeline,
				    StableUnCLIPImgaImgPipeline,
				    StableUnCLIPPipeline,
				)
				from .stable_diffusion_safe import StableDiffusionPipelineSafe
				from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
				from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
				from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
				from .versatile_diffusion import (
				    VersatileDiffusionDualGuidedPipeline,
				    VersatileDiffusionImageVariationPipeline,
				    VersatileDiffusionPipeline,
				    VersatileDiffusionTextToImagePipeline,
				)
				from .vq_diffusion import VQDiffusionPipeline


try:
				if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
				from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
				if not is_onnx_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_onnx_objects import *  # noqa F403
else:
				from .onnx_utils import OnnxRuntimeModel

try:
				if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
				from .stable_diffusion import (
				    OnnxStableDiffusionImgaImgPipeline,
				    OnnxStableDiffusionInpaintPipeline,
				    OnnxStableDiffusionInpaintPipelineLegacy,
				    OnnxStableDiffusionPipeline,
				    OnnxStableDiffusionUpscalePipeline,
				    StableDiffusionOnnxPipeline,
				)

try:
				if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_objects import *  # noqa F403
else:
				from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
				if not (is_flax_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
				from .controlnet import FlaxStableDiffusionControlNetPipeline
				from .stable_diffusion import (
				    FlaxStableDiffusionImgaImgPipeline,
				    FlaxStableDiffusionInpaintPipeline,
				    FlaxStableDiffusionPipeline,
				)
try:
				if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
				from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline









'''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, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

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


__lowercase  :      Union[str, Any]      		=				logging.getLogger(__name__)





def 							a_       (						lowercase    )			->List[str]:






				'''simple docstring'''

				__lowerCamelCase		:      Dict											=					p * torch.log(lowercase    )
				__lowerCamelCase		:      Dict											=					0
				return -plogp.sum(dim=-1    )





def 							a_       (						lowercase    )			->Optional[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 							a_       (						lowercase							,						lowercase							,						lowercase							,						lowercase=True							,						lowercase=True							,						lowercase=None							,						lowercase=False    )			->List[Any]:






				'''simple docstring'''

				__lowerCamelCase						,					__lowerCamelCase		:      str											=					model.config.num_hidden_layers, model.config.num_attention_heads
				__lowerCamelCase		:      List[str]											=					torch.zeros(lowercase							,						lowercase    ).to(args.device    )
				__lowerCamelCase		:      Union[str, Any]											=					torch.zeros(lowercase							,						lowercase    ).to(args.device    )

				if head_mask is None:
								__lowerCamelCase		:      str											=					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:
								__lowerCamelCase		:      str											=					None

				__lowerCamelCase		:      Union[str, Any]											=					None
				__lowerCamelCase		:      Any											=					None
				__lowerCamelCase		:      Tuple											=					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():
												__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(**lowercase							,						head_mask=lowercase    )
								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[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    ):
																__lowerCamelCase		:      Optional[int]											=					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		:      Any											=					logits.detach().cpu().numpy()
												__lowerCamelCase		:      int											=					inputs["labels"].detach().cpu().numpy()
								else:
												__lowerCamelCase		:      Dict											=					np.append(lowercase							,						logits.detach().cpu().numpy()							,						axis=0    )
												__lowerCamelCase		:      List[str]											=					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:
								__lowerCamelCase		:      List[Any]											=					2
								__lowerCamelCase		:      List[str]											=					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:
								__lowerCamelCase		:      int											=					(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"    )
				__lowerCamelCase		:      Any											=					torch.zeros(head_importance.numel()							,						dtype=torch.long							,						device=args.device    )
				__lowerCamelCase		:      Optional[int]											=					torch.arange(
				    head_importance.numel()							,						device=args.device    )
				__lowerCamelCase		:      Optional[int]											=					head_ranks.view_as(lowercase    )
				print_ad_tensor(lowercase    )

				return attn_entropy, head_importance, preds, labels





def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					compute_heads_importance(lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase    )
				__lowerCamelCase		:      Union[str, Any]											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
				__lowerCamelCase		:      Optional[int]											=					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    )

				__lowerCamelCase		:      Tuple											=					torch.ones_like(lowercase    )
				__lowerCamelCase		:      str											=					max(1							,						int(new_head_mask.numel() * args.masking_amount    )    )

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

								if len(lowercase    ) <= num_to_mask:
												break

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

								# Compute metric and head importance again
								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      str											=					compute_heads_importance(
								    lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase							,						head_mask=lowercase    )
								__lowerCamelCase		:      Optional[Any]											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
								__lowerCamelCase		:      Tuple											=					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 							a_       (						lowercase							,						lowercase							,						lowercase							,						lowercase    )			->int:






				'''simple docstring'''

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

				__lowerCamelCase		:      Optional[int]											=					sum(p.numel() for p in model.parameters()    )
				__lowerCamelCase		:      Any											=					{
				    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    )
				__lowerCamelCase		:      Union[str, Any]											=					sum(p.numel() for p in model.parameters()    )

				__lowerCamelCase		:      Optional[int]											=					datetime.now()
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					compute_heads_importance(
				    lowercase							,						lowercase							,						lowercase							,						compute_entropy=lowercase							,						compute_importance=lowercase							,						head_mask=lowercase							,						actually_pruned=lowercase							,						)
				__lowerCamelCase		:      Dict											=					np.argmax(lowercase							,						axis=1    ) if args.output_mode == "classification" else np.squeeze(lowercase    )
				__lowerCamelCase		:      Tuple											=					glue_compute_metrics(args.task_name							,						lowercase							,						lowercase    )[args.metric_name]
				__lowerCamelCase		:      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 							a_       (						)			->Dict:






				'''simple docstring'''

				__lowerCamelCase		:      List[Any]											=					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."    )
				__lowerCamelCase		:      Tuple											=					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:
								__lowerCamelCase		:      int											=					torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"    )
								__lowerCamelCase		:      List[str]											=					0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank    )
								__lowerCamelCase		:      Union[str, Any]											=					torch.device("cuda"							,						args.local_rank    )
								__lowerCamelCase		:      Optional[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
				__lowerCamelCase		:      Tuple											=					args.task_name.lower()
				if args.task_name not in glue_processors:
								raise ValueError("Task not found: %s" % (args.task_name)    )
				__lowerCamelCase		:      int											=					glue_processors[args.task_name]()
				__lowerCamelCase		:      Union[str, Any]											=					glue_output_modes[args.task_name]
				__lowerCamelCase		:      Dict											=					processor.get_labels()
				__lowerCamelCase		:      str											=					len(lowercase    )

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

				__lowerCamelCase		:      Union[str, 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							,						)
				__lowerCamelCase		:      Dict											=					AutoTokenizer.from_pretrained(
				    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path							,						cache_dir=args.cache_dir							,						)
				__lowerCamelCase		:      Optional[int]											=					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:
								__lowerCamelCase		:      Optional[int]											=					nn.parallel.DistributedDataParallel(
								    lowercase							,						device_ids=[args.local_rank]							,						output_device=args.local_rank							,						find_unused_parameters=lowercase    )
				elif args.n_gpu > 1:
								__lowerCamelCase		:      Union[str, Any]											=					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
				__lowerCamelCase		:      Tuple											=					GlueDataset(lowercase							,						tokenizer=lowercase							,						mode="dev"    )
				if args.data_subset > 0:
								__lowerCamelCase		:      str											=					Subset(lowercase							,						list(range(min(args.data_subset							,						len(lowercase    )    )    )    )    )
				__lowerCamelCase		:      Optional[Any]											=					SequentialSampler(lowercase    ) if args.local_rank == -1 else DistributedSampler(lowercase    )
				__lowerCamelCase		:      Dict											=					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:
								__lowerCamelCase		:      List[str]											=					mask_heads(lowercase							,						lowercase							,						lowercase    )
								prune_heads(lowercase							,						lowercase							,						lowercase							,						lowercase    )


if __name__ == "__main__":
				main()





