
'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







@slow
@require_torch_gpu
class 			__A					(			unittest.TestCase      ):
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  int       				=		"""runwayml/stable-diffusion-v1-5"""
   __A		:  Tuple       				=		TextToVideoZeroPipeline.from_pretrained(lowerCAmelCase_					, torch_dtype=torch.floataa							).to("""cuda"""							)
   __A		:  Tuple       				=		DDIMScheduler.from_config(pipe.scheduler.config							)
   __A		:  Optional[int]       				=		torch.Generator(device="""cuda"""							).manual_seed(0							)

   __A		:  Any       				=		"""A bear is playing a guitar on Times Square"""
   __A		:  List[Any]       				=		pipe(prompt=lowerCAmelCase_					, generator=lowerCAmelCase_							).images

   __A		:  List[Any]       				=		load_pt(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""							)

   assert_mean_pixel_difference(lowerCAmelCase_					, lowerCAmelCase_							)




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    : Optional[Any]		)      ->       Optional[int]:
	_lowercase   =p * torch.log(__a		)
	_lowercase   =0
	return -plogp.sum(dim=-1		)

def 	__lowerCamelCase							(				__a    : Optional[Any]		)      ->       Tuple:
	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    : Optional[Any]      ,							__a    : Dict      ,							__a    : Any      ,							__a    : Any=True      ,							__a    : Optional[Any]=True      ,							__a    : Union[str, Any]=None      ,							__a    : Union[str, Any]=False		)      ->       Union[str, Any]:
	_lowercase   ,  _lowercase   =model.config.num_hidden_layers, model.config.num_attention_heads
	_lowercase   =torch.zeros(__a      ,							__a		).to(args.device		)
	_lowercase   =torch.zeros(__a      ,							__a		).to(args.device		)

	if head_mask is None:
		_lowercase   =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:
		_lowercase   =None

	_lowercase   =None
	_lowercase   =None
	_lowercase   =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():
			_lowercase   =v.to(args.device		)

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

	if not args.dont_normalize_global_importance:
		_lowercase   =(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"		)
	_lowercase   =torch.zeros(head_importance.numel()      ,							dtype=torch.long      ,							device=args.device		)
	_lowercase   =torch.arange(
	    head_importance.numel()      ,							device=args.device		)
	_lowercase   =head_ranks.view_as(__a		)
	print_ad_tensor(__a		)

	return attn_entropy, head_importance, preds, labels

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

	_lowercase   =torch.ones_like(__a		)
	_lowercase   =max(1      ,							int(new_head_mask.numel() * args.masking_amount		)		)

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

		if len(__a		) <= num_to_mask:
			break

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

		# Compute metric and head importance again
		_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(
		    __a      ,							__a      ,							__a      ,							compute_entropy=__a      ,							head_mask=__a		)
		_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
		_lowercase   =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    : Tuple      ,							__a    : Tuple      ,							__a    : Optional[Any]      ,							__a    : Union[str, Any]		)      ->       List[str]:
	_lowercase   =datetime.now()
	_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(
	    __a      ,							__a      ,							__a      ,							compute_entropy=__a      ,							compute_importance=__a      ,							head_mask=__a		)
	_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
	_lowercase   =glue_compute_metrics(args.task_name      ,							__a      ,							__a		)[args.metric_name]
	_lowercase   =datetime.now() - before_time

	_lowercase   =sum(p.numel() for p in model.parameters()		)
	_lowercase   ={
	    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		)
	_lowercase   =sum(p.numel() for p in model.parameters()		)

	_lowercase   =datetime.now()
	_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(
	    __a      ,							__a      ,							__a      ,							compute_entropy=__a      ,							compute_importance=__a      ,							head_mask=__a      ,							actually_pruned=__a      ,							)
	_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
	_lowercase   =glue_compute_metrics(args.task_name      ,							__a      ,							__a		)[args.metric_name]
	_lowercase   =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							(				)      ->       Optional[Any]:
	_lowercase   =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."		)
	_lowercase   =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:
		_lowercase   =torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
		_lowercase   =0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank		)
		_lowercase   =torch.device("cuda"      ,							args.local_rank		)
		_lowercase   =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
	_lowercase   =args.task_name.lower()
	if args.task_name not in glue_processors:
		raise ValueError("Task not found: %s" % (args.task_name)		)
	_lowercase   =glue_processors[args.task_name]()
	_lowercase   =glue_output_modes[args.task_name]
	_lowercase   =processor.get_labels()
	_lowercase   =len(__a		)

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

	_lowercase   =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      ,							)
	_lowercase   =AutoTokenizer.from_pretrained(
	    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path      ,							cache_dir=args.cache_dir      ,							)
	_lowercase   =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:
		_lowercase   =nn.parallel.DistributedDataParallel(
		    __a      ,							device_ids=[args.local_rank]      ,							output_device=args.local_rank      ,							find_unused_parameters=__a		)
	elif args.n_gpu > 1:
		_lowercase   =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
	_lowercase   =GlueDataset(__a      ,							tokenizer=__a      ,							mode="dev"		)
	if args.data_subset > 0:
		_lowercase   =Subset(__a      ,							list(range(min(args.data_subset      ,							len(__a		)		)		)		)		)
	_lowercase   =SequentialSampler(__a		) if args.local_rank == -1 else DistributedSampler(__a		)
	_lowercase   =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:
		_lowercase   =mask_heads(__a      ,							__a      ,							__a		)
		prune_heads(__a      ,							__a      ,							__a      ,							__a		)


if __name__ == "__main__":
						main()




