code: 
  """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
  
  
  __magic_name__		       =				logging.getLogger(__name__)
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						p * torch.log(__lowerCamelCase      )
  	_UpperCAmelCase =						0
  	return -plogp.sum(dim=-1      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	logger.info("lv, h >\t" + "\t".join(f"{x + 1}" for x in range(len(__lowerCamelCase      )      )      )      )
  	for row in range(len(__lowerCamelCase      )      ):
  		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  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__=True				,     UpperCamelCase__=True				,     UpperCamelCase__=None				,     UpperCamelCase__=False      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase ,      _UpperCAmelCase =						model.config.num_hidden_layers, model.config.num_attention_heads
  	_UpperCAmelCase =						torch.zeros(__lowerCamelCase				,     __lowerCamelCase      ).to(args.device      )
  	_UpperCAmelCase =						torch.zeros(__lowerCamelCase				,     __lowerCamelCase      ).to(args.device      )
  
  	if head_mask is None:
  		_UpperCAmelCase =						torch.ones(__lowerCamelCase				,     __lowerCamelCase      ).to(args.device      )
  
  	head_mask.requires_grad_(requires_grad=__lowerCamelCase      )
  	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
  	if actually_pruned:
  		_UpperCAmelCase =						None
  
  	_UpperCAmelCase =						None
  	_UpperCAmelCase =						None
  	_UpperCAmelCase =						0.0
  
  	for step, inputs in enumerate(tqdm(__lowerCamelCase				,     desc="Iteration"				,     disable=args.local_rank not in [-1, 0]      )      ):
  		for k, v in inputs.items():
  			_UpperCAmelCase =						v.to(args.device      )
  
  		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
  		_UpperCAmelCase =						model(**__lowerCamelCase				,     head_mask=__lowerCamelCase      )
  		_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						(
  		    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(__lowerCamelCase      ):
  				_UpperCAmelCase =						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:
  			_UpperCAmelCase =						logits.detach().cpu().numpy()
  			_UpperCAmelCase =						inputs["labels"].detach().cpu().numpy()
  		else:
  			_UpperCAmelCase =						np.append(__lowerCamelCase				,     logits.detach().cpu().numpy()				,     axis=0      )
  			_UpperCAmelCase =						np.append(__lowerCamelCase				,     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:
  		_UpperCAmelCase =						2
  		_UpperCAmelCase =						torch.pow(torch.pow(__lowerCamelCase				,     __lowerCamelCase      ).sum(-1      )				,     1 / exponent      )
  		head_importance /= norm_by_layer.unsqueeze(-1      ) + 1E-20
  
  	if not args.dont_normalize_global_importance:
  		_UpperCAmelCase =						(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(__lowerCamelCase      )
  	logger.info("Head importance scores"      )
  	print_ad_tensor(__lowerCamelCase      )
  	logger.info("Head ranked by importance scores"      )
  	_UpperCAmelCase =						torch.zeros(head_importance.numel()				,     dtype=torch.long				,     device=args.device      )
  	_UpperCAmelCase =						torch.arange(
  	    head_importance.numel()				,     device=args.device      )
  	_UpperCAmelCase =						head_ranks.view_as(__lowerCamelCase      )
  	print_ad_tensor(__lowerCamelCase      )
  
  	return attn_entropy, head_importance, preds, labels
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						compute_heads_importance(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     compute_entropy=__lowerCamelCase      )
  	_UpperCAmelCase =						np.argmax(__lowerCamelCase				,     axis=1      ) if args.output_mode == "classification" else np.squeeze(__lowerCamelCase      )
  	_UpperCAmelCase =						glue_compute_metrics(args.task_name				,     __lowerCamelCase				,     __lowerCamelCase      )[args.metric_name]
  	logger.info("Pruning: original score: %f, threshold: %f"				,     __lowerCamelCase				,     original_score * args.masking_threshold      )
  
  	_UpperCAmelCase =						torch.ones_like(__lowerCamelCase      )
  	_UpperCAmelCase =						max(1				,     int(new_head_mask.numel() * args.masking_amount      )      )
  
  	_UpperCAmelCase =						original_score
  	while current_score >= original_score * args.masking_threshold:
  		_UpperCAmelCase =						new_head_mask.clone()  # save current head mask
  		# heads from least important to most - keep only not-masked heads
  		_UpperCAmelCase =						float("Inf"      )
  		_UpperCAmelCase =						head_importance.view(-1      ).sort()[1]
  
  		if len(__lowerCamelCase      ) <= num_to_mask:
  			break
  
  		# mask heads
  		_UpperCAmelCase =						current_heads_to_mask[:num_to_mask]
  		logger.info("Heads to mask: %s"				,     str(current_heads_to_mask.tolist()      )      )
  		_UpperCAmelCase =						new_head_mask.view(-1      )
  		_UpperCAmelCase =						0.0
  		_UpperCAmelCase =						new_head_mask.view_as(__lowerCamelCase      )
  		_UpperCAmelCase =						new_head_mask.clone().detach()
  		print_ad_tensor(__lowerCamelCase      )
  
  		# Compute metric and head importance again
  		_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						compute_heads_importance(
  		    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     compute_entropy=__lowerCamelCase				,     head_mask=__lowerCamelCase      )
  		_UpperCAmelCase =						np.argmax(__lowerCamelCase				,     axis=1      ) if args.output_mode == "classification" else np.squeeze(__lowerCamelCase      )
  		_UpperCAmelCase =						glue_compute_metrics(args.task_name				,     __lowerCamelCase				,     __lowerCamelCase      )[args.metric_name]
  		logger.info(
  		    "Masking: current score: %f, remaining heads %d (%.1f percents)"				,     __lowerCamelCase				,     new_head_mask.sum()				,     new_head_mask.sum() / new_head_mask.numel() * 100				,     )
  
  	logger.info("Final head mask"      )
  	print_ad_tensor(__lowerCamelCase      )
  	np.save(os.path.join(args.output_dir				,     "head_mask.npy"      )				,     head_mask.detach().cpu().numpy()      )
  
  	return head_mask
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						datetime.now()
  	_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						compute_heads_importance(
  	    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     compute_entropy=__lowerCamelCase				,     compute_importance=__lowerCamelCase				,     head_mask=__lowerCamelCase      )
  	_UpperCAmelCase =						np.argmax(__lowerCamelCase				,     axis=1      ) if args.output_mode == "classification" else np.squeeze(__lowerCamelCase      )
  	_UpperCAmelCase =						glue_compute_metrics(args.task_name				,     __lowerCamelCase				,     __lowerCamelCase      )[args.metric_name]
  	_UpperCAmelCase =						datetime.now() - before_time
  
  	_UpperCAmelCase =						sum(p.numel() for p in model.parameters()      )
  	_UpperCAmelCase =						{
  	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCamelCase      )      )
  	}
  
  	assert sum(len(__lowerCamelCase      ) for h in heads_to_prune.values()      ) == (1 - head_mask.long()).sum().item()
  	model.prune_heads(__lowerCamelCase      )
  	_UpperCAmelCase =						sum(p.numel() for p in model.parameters()      )
  
  	_UpperCAmelCase =						datetime.now()
  	_UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase ,      _UpperCAmelCase =						compute_heads_importance(
  	    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     compute_entropy=__lowerCamelCase				,     compute_importance=__lowerCamelCase				,     head_mask=__lowerCamelCase				,     actually_pruned=__lowerCamelCase				,     )
  	_UpperCAmelCase =						np.argmax(__lowerCamelCase				,     axis=1      ) if args.output_mode == "classification" else np.squeeze(__lowerCamelCase      )
  	_UpperCAmelCase =						glue_compute_metrics(args.task_name				,     __lowerCamelCase				,     __lowerCamelCase      )[args.metric_name]
  	_UpperCAmelCase =						datetime.now() - before_time
  
  	logger.info(
  	    "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"				,     __lowerCamelCase				,     __lowerCamelCase				,     pruned_num_params / original_num_params * 100				,     )
  	logger.info("Pruning: score with masking: %f score with pruning: %f"				,     __lowerCamelCase				,     __lowerCamelCase      )
  	logger.info("Pruning: speed ratio (new timing / original timing): %f percents"				,     original_time / new_time * 100      )
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument(
  	    "--data_dir"				,     default=__lowerCamelCase				,     type=__lowerCamelCase				,     required=__lowerCamelCase				,     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=__lowerCamelCase				,     type=__lowerCamelCase				,     required=__lowerCamelCase				,     help="Path to pretrained model or model identifier from huggingface.co/models"				,     )
  	parser.add_argument(
  	    "--task_name"				,     default=__lowerCamelCase				,     type=__lowerCamelCase				,     required=__lowerCamelCase				,     help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys()      )				,     )
  	parser.add_argument(
  	    "--output_dir"				,     default=__lowerCamelCase				,     type=__lowerCamelCase				,     required=__lowerCamelCase				,     help="The output directory where the model predictions and checkpoints will be written."				,     )
  
  	# Other parameters
  	parser.add_argument(
  	    "--config_name"				,     default=""				,     type=__lowerCamelCase				,     help="Pretrained config name or path if not the same as model_name_or_path"				,     )
  	parser.add_argument(
  	    "--tokenizer_name"				,     default=""				,     type=__lowerCamelCase				,     help="Pretrained tokenizer name or path if not the same as model_name_or_path"				,     )
  	parser.add_argument(
  	    "--cache_dir"				,     default=__lowerCamelCase				,     type=__lowerCamelCase				,     help="Where do you want to store the pre-trained models downloaded from huggingface.co"				,     )
  	parser.add_argument(
  	    "--data_subset"				,     type=__lowerCamelCase				,     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=__lowerCamelCase				,     help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)."				,     )
  	parser.add_argument(
  	    "--masking_amount"				,     default=0.1				,     type=__lowerCamelCase				,     help="Amount to heads to masking at each masking step."      )
  	parser.add_argument("--metric_name"				,     default="acc"				,     type=__lowerCamelCase				,     help="Metric to use for head masking."      )
  
  	parser.add_argument(
  	    "--max_seq_length"				,     default=128				,     type=__lowerCamelCase				,     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=__lowerCamelCase				,     help="Batch size."      )
  
  	parser.add_argument("--seed"				,     type=__lowerCamelCase				,     default=42      )
  	parser.add_argument("--local_rank"				,     type=__lowerCamelCase				,     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=__lowerCamelCase				,     default=""				,     help="Can be used for distant debugging."      )
  	parser.add_argument("--server_port"				,     type=__lowerCamelCase				,     default=""				,     help="Can be used for distant debugging."      )
  	_UpperCAmelCase =						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=__lowerCamelCase      )
  		ptvsd.wait_for_attach()
  
  	# Setup devices and distributed training
  	if args.local_rank == -1 or args.no_cuda:
  		_UpperCAmelCase =						torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )
  		_UpperCAmelCase =						0 if args.no_cuda else torch.cuda.device_count()
  	else:
  		torch.cuda.set_device(args.local_rank      )
  		_UpperCAmelCase =						torch.device("cuda"				,     args.local_rank      )
  		_UpperCAmelCase =						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
  	_UpperCAmelCase =						args.task_name.lower()
  	if args.task_name not in glue_processors:
  		raise ValueError("Task not found: %s" % (args.task_name)      )
  	_UpperCAmelCase =						glue_processors[args.task_name]()
  	_UpperCAmelCase =						glue_output_modes[args.task_name]
  	_UpperCAmelCase =						processor.get_labels()
  	_UpperCAmelCase =						len(__lowerCamelCase      )
  
  	# Load pretrained model and tokenizer
  	#
  	# Distributed training:
  	# The .from_pretrained methods guarantee that only one local process can concurrently
  	# download model & vocab.
  
  	_UpperCAmelCase =						AutoConfig.from_pretrained(
  	    args.config_name if args.config_name else args.model_name_or_path				,     num_labels=__lowerCamelCase				,     finetuning_task=args.task_name				,     output_attentions=__lowerCamelCase				,     cache_dir=args.cache_dir				,     )
  	_UpperCAmelCase =						AutoTokenizer.from_pretrained(
  	    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path				,     cache_dir=args.cache_dir				,     )
  	_UpperCAmelCase =						AutoModelForSequenceClassification.from_pretrained(
  	    args.model_name_or_path				,     from_tf=bool(".ckpt" in args.model_name_or_path      )				,     config=__lowerCamelCase				,     cache_dir=args.cache_dir				,     )
  
  	# Distributed and parallel training
  	model.to(args.device      )
  	if args.local_rank != -1:
  		_UpperCAmelCase =						nn.parallel.DistributedDataParallel(
  		    __lowerCamelCase				,     device_ids=[args.local_rank]				,     output_device=args.local_rank				,     find_unused_parameters=__lowerCamelCase      )
  	elif args.n_gpu > 1:
  		_UpperCAmelCase =						nn.DataParallel(__lowerCamelCase      )
  
  	# Print/save training arguments
  	os.makedirs(args.output_dir				,     exist_ok=__lowerCamelCase      )
  	torch.save(__lowerCamelCase				,     os.path.join(args.output_dir				,     "run_args.bin"      )      )
  	logger.info("Training/evaluation parameters %s"				,     __lowerCamelCase      )
  
  	# Prepare dataset for the GLUE task
  	_UpperCAmelCase =						GlueDataset(__lowerCamelCase				,     tokenizer=__lowerCamelCase				,     mode="dev"      )
  	if args.data_subset > 0:
  		_UpperCAmelCase =						Subset(__lowerCamelCase				,     list(range(min(args.data_subset				,     len(__lowerCamelCase      )      )      )      )      )
  	_UpperCAmelCase =						SequentialSampler(__lowerCamelCase      ) if args.local_rank == -1 else DistributedSampler(__lowerCamelCase      )
  	_UpperCAmelCase =						DataLoader(
  	    __lowerCamelCase				,     sampler=__lowerCamelCase				,     batch_size=args.batch_size				,     collate_fn=__lowerCamelCase      )
  
  	# Compute head entropy and importance score
  	compute_heads_importance(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase      )
  
  	# 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:
  		_UpperCAmelCase =						mask_heads(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase      )
  		prune_heads(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase      )
  
  
  if __name__ == "__main__":
  			main()
  
  
  
  
  
style_context: 
  """simple docstring"""
  import inspect
  import unittest
  import warnings
  from typing import List
  
  from transformers import EfficientFormerConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property, is_torch_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  			import torch
  
  			from transformers import (
  			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
  			    MODEL_MAPPING,
  			    EfficientFormerForImageClassification,
  			    EfficientFormerForImageClassificationWithTeacher,
  			    EfficientFormerModel,
  			)
  			from transformers.models.efficientformer.modeling_efficientformer import (
  			    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  			)
  
  
  if is_vision_available():
  			from PIL import Image
  
  			from transformers import EfficientFormerImageProcessor
  
  
  class    _lowerCAmelCase						:
  
  
  
  							def __init__(      self ,						a_ ,						a_ = 13 ,						a_ = 64 ,						a_ = 2 ,						a_ = 3 ,						a_ = 3 ,						a_ = True ,						a_ = True ,						a_ = 128 ,						a_=[16, 32, 64, 128] ,						a_ = 7 ,						a_ = 4 ,						a_ = 37 ,						a_ = "gelu" ,						a_ = 0.1 ,						a_ = 0.1 ,						a_ = 10 ,						a_ = 0.02 ,						a_ = 2 ,						a_ = 1 ,						a_ = 128 ,						a_ = [2, 2, 2, 2] ,						a_ = 2 ,						a_ = 2 ,						)							->							Tuple:
  								_UpperCAmelCase =						parent
  								_UpperCAmelCase =						batch_size
  								_UpperCAmelCase =						image_size
  								_UpperCAmelCase =						patch_size
  								_UpperCAmelCase =						num_channels
  								_UpperCAmelCase =						is_training
  								_UpperCAmelCase =						use_labels
  								_UpperCAmelCase =						hidden_size
  								_UpperCAmelCase =						num_hidden_layers
  								_UpperCAmelCase =						num_attention_heads
  								_UpperCAmelCase =						intermediate_size
  								_UpperCAmelCase =						hidden_act
  								_UpperCAmelCase =						hidden_dropout_prob
  								_UpperCAmelCase =						attention_probs_dropout_prob
  								_UpperCAmelCase =						type_sequence_label_size
  								_UpperCAmelCase =						initializer_range
  								_UpperCAmelCase =						encoder_stride
  								_UpperCAmelCase =						num_attention_outputs
  								_UpperCAmelCase =						embed_dim
  								_UpperCAmelCase =						embed_dim + 1
  								_UpperCAmelCase =						resolution
  								_UpperCAmelCase =						depths
  								_UpperCAmelCase =						hidden_sizes
  								_UpperCAmelCase =						dim
  								_UpperCAmelCase =						mlp_expansion_ratio
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
  								_UpperCAmelCase =						None
  								if self.use_labels:
  									_UpperCAmelCase =						ids_tensor([self.batch_size] ,						self.type_sequence_label_size   )
  
  								_UpperCAmelCase =						self.get_config()
  								return config, pixel_values, labels
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								return EfficientFormerConfig(
  								    image_size=self.image_size ,						patch_size=self.patch_size ,						num_channels=self.num_channels ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						is_decoder=a_ ,						initializer_range=self.initializer_range ,						encoder_stride=self.encoder_stride ,						resolution=self.resolution ,						depths=self.depths ,						hidden_sizes=self.hidden_sizes ,						dim=self.dim ,						mlp_expansion_ratio=self.mlp_expansion_ratio ,						)
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							int:
  								_UpperCAmelCase =						EfficientFormerModel(config=a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_   )
  								self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							str:
  								_UpperCAmelCase =						self.type_sequence_label_size
  								_UpperCAmelCase =						EfficientFormerForImageClassification(a_   )
  								model.to(a_   )
  								model.eval()
  								_UpperCAmelCase =						model(a_ ,						labels=a_   )
  								self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)   )
  
  								# test greyscale images
  								_UpperCAmelCase =						1
  								_UpperCAmelCase =						EfficientFormerForImageClassification(a_   )
  								model.to(a_   )
  								model.eval()
  
  								_UpperCAmelCase =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
  								_UpperCAmelCase =						model(a_   )
  								self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.type_sequence_label_size)   )
  
  
  
  							def       _a   (      self   )							->							Optional[int]:
  								_UpperCAmelCase =						self.prepare_config_and_inputs()
  								(
  								    (
  								    _UpperCAmelCase
  								) ,      (
  								    _UpperCAmelCase
  								) ,      (
  								    _UpperCAmelCase
  								) ,      
  								) =						config_and_inputs
  								_UpperCAmelCase =						{"pixel_values": pixel_values}
  								return config, inputs_dict
  
  
  
  
  
  @require_torch
  class    _lowerCAmelCase						(						lowerCamelCase    ,       lowerCamelCase    ,       unittest.TestCase						):
  							lowercase_     :     List[Any]       =							(
  							    (
  							        EfficientFormerModel,
  							        EfficientFormerForImageClassificationWithTeacher,
  							        EfficientFormerForImageClassification,
  							    )
  							    if is_torch_available()
  							    else ()
  							)
  							lowercase_     :     Optional[Any]       =							(
  							    {
  							        '''feature-extraction''': EfficientFormerModel,
  							        '''image-classification''': (
  							            EfficientFormerForImageClassification,
  							            EfficientFormerForImageClassificationWithTeacher,
  							        ),
  							    }
  							    if is_torch_available()
  							    else {}
  							)
  							lowercase_     :     List[str]       =							False
  
  							lowercase_     :     Dict       =							False
  							lowercase_     :     List[str]       =							False
  							lowercase_     :     List[str]       =							False
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						EfficientFormerModelTester(self   )
  								_UpperCAmelCase =						ConfigTester(
  								    self ,						config_class=a_ ,						has_text_modality=a_ ,						hidden_size=37   )
  
  
  
  							def       _a   (      self   )							->							List[Any]:
  								self.config_tester.run_common_tests()
  
  
  
  							@unittest.skip(reason="EfficientFormer does not use inputs_embeds"   )
  							def       _a   (      self   )							->							Tuple:
  								pass
  
  
  
  							@unittest.skip(reason="EfficientFormer does not support input and output embeddings"   )
  							def       _a   (      self   )							->							Optional[int]:
  								pass
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						model_class(a_   )
  									_UpperCAmelCase =						inspect.signature(model.forward   )
  									# signature.parameters is an OrderedDict => so arg_names order is deterministic
  									_UpperCAmelCase =						[*signature.parameters.keys()]
  
  									_UpperCAmelCase =						["pixel_values"]
  									self.assertListEqual(arg_names[:1] ,						a_   )
  
  
  
  							def       _a   (      self   )							->							int:
  								def check_hidden_states_output(a_ ,						a_ ,						a_   ):
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.eval()
  
  									with torch.no_grad():
  										_UpperCAmelCase =						model(**self._prepare_for_class(a_ ,						a_   )   )
  
  									_UpperCAmelCase =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
  									_UpperCAmelCase =						getattr(
  									    self.model_tester ,						"expected_num_hidden_layers" ,						self.model_tester.num_hidden_layers + 1   )
  									self.assertEqual(len(a_   ) ,						a_   )
  
  									if hasattr(self.model_tester ,						"encoder_seq_length"   ):
  										_UpperCAmelCase =						self.model_tester.encoder_seq_length
  										if hasattr(self.model_tester ,						"chunk_length"   ) and self.model_tester.chunk_length > 1:
  											_UpperCAmelCase =						seq_length * self.model_tester.chunk_length
  									else:
  										_UpperCAmelCase =						self.model_tester.seq_length
  
  									self.assertListEqual(
  									    list(hidden_states[-1].shape[-2:]   ) ,						[seq_length, self.model_tester.hidden_size] ,						)
  
  									if config.is_encoder_decoder:
  										_UpperCAmelCase =						outputs.decoder_hidden_states
  
  										self.assertIsInstance(a_ ,						(list, tuple)   )
  										self.assertEqual(len(a_   ) ,						a_   )
  										_UpperCAmelCase =						getattr(self.model_tester ,						"seq_length" ,						a_   )
  										_UpperCAmelCase =						getattr(self.model_tester ,						"decoder_seq_length" ,						a_   )
  
  										self.assertListEqual(
  										    list(hidden_states[-1].shape[-2:]   ) ,						[decoder_seq_length, self.model_tester.hidden_size] ,						)
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						True
  									check_hidden_states_output(a_ ,						a_ ,						a_   )
  
  									# check that output_hidden_states also work using config
  									del inputs_dict["output_hidden_states"]
  									_UpperCAmelCase =						True
  
  									check_hidden_states_output(a_ ,						a_ ,						a_   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_=False   )							->							Optional[Any]:
  								_UpperCAmelCase =						super()._prepare_for_class(a_ ,						a_ ,						return_labels=a_   )
  
  								if return_labels:
  									if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
  										del inputs_dict["labels"]
  
  								return inputs_dict
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_model(*a_   )
  
  
  
  							@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"   )
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_masked_image_modeling(*a_   )
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_for_image_classification(*a_   )
  
  
  
  							def       _a   (      self   )							->							str:
  								if not self.model_tester.is_training:
  									return
  
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  								_UpperCAmelCase =						True
  
  								for model_class in self.all_model_classes:
  									# EfficientFormerForImageClassificationWithTeacher supports inference-only
  									if (
  									    model_class in get_values(a_   )
  									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  									):
  										continue
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.train()
  									_UpperCAmelCase =						self._prepare_for_class(a_ ,						a_ ,						return_labels=a_   )
  									_UpperCAmelCase =						model(**a_   ).loss
  									loss.backward()
  
  
  
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  
  								_UpperCAmelCase =						[
  								    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
  								    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
  								    {"title": "regression", "num_labels": 1, "dtype": torch.float},
  								]
  
  								for model_class in self.all_model_classes:
  									if (
  									    model_class
  									    not in [
  									        *get_values(a_   ),
  									    ]
  									    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
  									):
  										continue
  
  									for problem_type in problem_types:
  										with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"   ):
  											_UpperCAmelCase =						problem_type["title"]
  											_UpperCAmelCase =						problem_type["num_labels"]
  
  											_UpperCAmelCase =						model_class(a_   )
  											model.to(a_   )
  											model.train()
  
  											_UpperCAmelCase =						self._prepare_for_class(a_ ,						a_ ,						return_labels=a_   )
  
  											if problem_type["num_labels"] > 1:
  												_UpperCAmelCase =						inputs["labels"].unsqueeze(1   ).repeat(1 ,						problem_type["num_labels"]   )
  
  											_UpperCAmelCase =						inputs["labels"].to(problem_type["dtype"]   )
  
  											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
  											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
  											# they have the same size." which is a symptom something in wrong for the regression problem.
  											# See https://github.com/huggingface/transformers/issues/11780
  											with warnings.catch_warnings(record=a_   ) as warning_list:
  												_UpperCAmelCase =						model(**a_   ).loss
  											for w in warning_list:
  												if "Using a target size that is different to the input size" in str(w.message   ):
  													raise ValueError(
  													    f"Something is going wrong in the regression problem: intercepted {w.message}"   )
  
  											loss.backward()
  
  
  
  							@slow
  							def       _a   (      self   )							->							Tuple:
  								for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  									_UpperCAmelCase =						EfficientFormerModel.from_pretrained(a_   )
  									self.assertIsNotNone(a_   )
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								_UpperCAmelCase ,      _UpperCAmelCase =						self.model_tester.prepare_config_and_inputs_for_common()
  								_UpperCAmelCase =						True
  
  								_UpperCAmelCase =						getattr(self.model_tester ,						"seq_length" ,						a_   )
  								_UpperCAmelCase =						getattr(self.model_tester ,						"encoder_seq_length" ,						a_   )
  								_UpperCAmelCase =						getattr(self.model_tester ,						"key_length" ,						a_   )
  								_UpperCAmelCase =						getattr(self.model_tester ,						"chunk_length" ,						a_   )
  
  								if chunk_length is not None and hasattr(self.model_tester ,						"num_hashes"   ):
  									_UpperCAmelCase =						encoder_seq_length * self.model_tester.num_hashes
  
  								for model_class in self.all_model_classes:
  									_UpperCAmelCase =						True
  									_UpperCAmelCase =						False
  									_UpperCAmelCase =						True
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.eval()
  									with torch.no_grad():
  										_UpperCAmelCase =						model(**self._prepare_for_class(a_ ,						a_   )   )
  									_UpperCAmelCase =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  									self.assertEqual(len(a_   ) ,						self.model_tester.num_attention_outputs   )
  
  									# check that output_attentions also work using config
  									del inputs_dict["output_attentions"]
  									_UpperCAmelCase =						True
  									_UpperCAmelCase =						model_class(a_   )
  									model.to(a_   )
  									model.eval()
  									with torch.no_grad():
  										_UpperCAmelCase =						model(**self._prepare_for_class(a_ ,						a_   )   )
  									_UpperCAmelCase =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
  									self.assertEqual(len(a_   ) ,						self.model_tester.num_attention_outputs   )
  
  									if chunk_length is not None:
  										self.assertListEqual(
  										    list(attentions[0].shape[-4:]   ) ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] ,						)
  									else:
  										self.assertListEqual(
  										    list(attentions[0].shape[-3:]   ) ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] ,						)
  
  
  
  
  
  def     __lowerCamelCase  (		):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
  	return image
  
  
  
  @require_torch
  @require_vision
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
  							@cached_property
  							def       _a   (      self   )							->							Optional[Any]:
  								return (
  								    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"   )
  								    if is_vision_available()
  								    else None
  								)
  
  
  
  							@slow
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"   ).to(
  								    a_   )
  
  								_UpperCAmelCase =						self.default_image_processor
  								_UpperCAmelCase =						prepare_img()
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  
  								# verify the logits
  								_UpperCAmelCase =						(1, 1000)
  								self.assertEqual(outputs.logits.shape ,						a_   )
  
  								_UpperCAmelCase =						torch.tensor([-0.0555, 0.4825, -0.0852]   ).to(a_   )
  								self.assertTrue(torch.allclose(outputs.logits[0][:3] ,						a_ ,						atol=1e-4   )   )
  
  
  
  							@slow
  							def       _a   (      self   )							->							Tuple:
  								_UpperCAmelCase =						EfficientFormerForImageClassificationWithTeacher.from_pretrained(
  								    "snap-research/efficientformer-l1-300"   ).to(a_   )
  
  								_UpperCAmelCase =						self.default_image_processor
  								_UpperCAmelCase =						prepare_img()
  								_UpperCAmelCase =						image_processor(images=a_ ,						return_tensors="pt"   ).to(a_   )
  
  								# forward pass
  								with torch.no_grad():
  									_UpperCAmelCase =						model(**a_   )
  
  								# verify the logits
  								_UpperCAmelCase =						(1, 1000)
  								self.assertEqual(outputs.logits.shape ,						a_   )
  
  								_UpperCAmelCase =						torch.tensor([-0.1312, 0.4353, -1.0499]   ).to(a_   )
  								self.assertTrue(torch.allclose(outputs.logits[0][:3] ,						a_ ,						atol=1e-4   )   )
  
  
  
  
  