
"""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, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


UpperCamelCase     :    str       =			logging.getLogger(__name__)






def 							A						(    snake_case				:int    , snake_case				:int		)				->     Any:
 # save results
 if os.path.exists(snake_case		):
  if os.path.exists(os.path.join(snake_case    , 'config.json'		)		) and os.path.isfile(
      os.path.join(snake_case    , 'config.json'		)		):
   os.remove(os.path.join(snake_case    , 'config.json'		)		)
  if os.path.exists(os.path.join(snake_case    , 'pytorch_model.bin'		)		) and os.path.isfile(
      os.path.join(snake_case    , 'pytorch_model.bin'		)		):
   os.remove(os.path.join(snake_case    , 'pytorch_model.bin'		)		)
 else:
  os.makedirs(snake_case		)
 model.save_pretrained(snake_case		)






def 							A						(    snake_case				:Dict    , snake_case				:int=False		)				->     Dict:
 __UpperCamelCase      =	2
 if unlogit:
  __UpperCamelCase      =	torch.pow(snake_case    , snake_case		)
 __UpperCamelCase      =	p * torch.log(snake_case		)
 __UpperCamelCase      =	0
 return -plogp.sum(dim=-1		)






def 							A						(    snake_case				:Union[str, Any]		)				->     Dict:
 logger.info('lv, h >\t' + '\t'.join(f'{x + 1}' for x in range(len(snake_case		)		)		)		)
 for row in range(len(snake_case		)		):
  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						(    snake_case				:Any    , snake_case				:List[str]    , snake_case				:Optional[int]    , snake_case				:List[Any]=True    , snake_case				:Optional[Any]=True    , snake_case				:int=None    , snake_case				:Optional[Any]=False		)				->     int:
 __UpperCamelCase  ,     __UpperCamelCase      =	model.config.num_hidden_layers, model.config.num_attention_heads
 __UpperCamelCase      =	torch.zeros(snake_case    , snake_case		).to(args.device		)
 __UpperCamelCase      =	torch.zeros(snake_case    , snake_case		).to(args.device		)

 if head_mask is None:
  __UpperCamelCase      =	torch.ones(snake_case    , snake_case		).to(args.device		)

 head_mask.requires_grad_(requires_grad=snake_case		)
 # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
 if actually_pruned:
  __UpperCamelCase      =	None

 __UpperCamelCase      =	0.0
 __UpperCamelCase      =	0.0
 for step, inputs in enumerate(tqdm(snake_case    , desc='Iteration'    , disable=args.local_rank not in [-1, 0]		)		):
  __UpperCamelCase      =	tuple(t.to(args.device		) for t in inputs		)
  ((__UpperCamelCase)  ,     )      =	inputs

  # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
  __UpperCamelCase      =	model(snake_case    , labels=snake_case    , head_mask=snake_case		)
  #  (loss), lm_logits, presents, (all hidden_states), (attentions)
  __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
  total_loss += loss.detach().cpu().numpy()
  if compute_entropy:
   for layer, attn in enumerate(snake_case		):
    __UpperCamelCase      =	entropy(attn.detach()    , snake_case		)
    attn_entropy[layer] += masked_entropy.sum(-1		).sum(0		).sum(0		).detach()

  if compute_importance:
   head_importance += head_mask.grad.abs().detach()
  tot_tokens += torch.ones_like(snake_case		).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(snake_case    , snake_case		).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 matrices
 if compute_entropy:
  logger.info('Attention entropies'		)
  print_ad_tensor(snake_case		)
 if compute_importance:
  logger.info('Head importance scores'		)
  print_ad_tensor(snake_case		)
 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(snake_case		)
 print_ad_tensor(snake_case		)
 return attn_entropy, head_importance, total_loss






def 							A						(    snake_case				:str    , snake_case				:str    , snake_case				:Tuple		)				->     Any:
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	compute_heads_importance(snake_case    , snake_case    , snake_case    , compute_entropy=snake_case		)
 __UpperCamelCase      =	1 / loss  # instead of downsteam score use the LM loss
 logger.info('Pruning: original score: %f, threshold: %f'    , snake_case    , original_score * args.masking_threshold		)

 __UpperCamelCase      =	torch.ones_like(snake_case		)
 __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().detach()  # 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(snake_case		) <= num_to_mask:
   print('BREAK BY 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(snake_case		)
  __UpperCamelCase      =	new_head_mask.clone().detach()
  print_ad_tensor(snake_case		)

  # Compute metric and head importance again
  __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	compute_heads_importance(
      snake_case    , snake_case    , snake_case    , compute_entropy=snake_case    , head_mask=snake_case		)
  __UpperCamelCase      =	1 / loss
  logger.info(
      'Masking: current score: %f, remaining heads %d (%.1f percents)'    , snake_case    , new_head_mask.sum()    , new_head_mask.sum() / new_head_mask.numel() * 1_0_0    , )

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

 return head_mask






def 							A						(    snake_case				:Optional[int]    , snake_case				:int    , snake_case				:List[Any]    , snake_case				:int		)				->     int:
 __UpperCamelCase      =	datetime.now()
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	compute_heads_importance(
     snake_case    , snake_case    , snake_case    , compute_entropy=snake_case    , compute_importance=snake_case    , head_mask=snake_case		)
 __UpperCamelCase      =	1 / loss
 __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(snake_case		)		)
 }

 for k, v in heads_to_prune.items():
  if isinstance(snake_case    , snake_case		):
   __UpperCamelCase      =	[
       v,
   ]

 assert sum(len(snake_case		) for h in heads_to_prune.values()		) == (1 - head_mask.long()).sum().item()
 model.prune_heads(snake_case		)
 __UpperCamelCase      =	sum(p.numel() for p in model.parameters()		)

 __UpperCamelCase      =	datetime.now()
 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	compute_heads_importance(
     snake_case    , snake_case    , snake_case    , compute_entropy=snake_case    , compute_importance=snake_case    , head_mask=snake_case    , actually_pruned=snake_case    , )

 __UpperCamelCase      =	1 / loss
 __UpperCamelCase      =	datetime.now() - before_time

 logger.info(
     'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'    , snake_case    , snake_case    , pruned_num_params / original_num_params * 1_0_0    , )
 logger.info('Pruning: score with masking: %f score with pruning: %f'    , snake_case    , snake_case		)
 logger.info('Pruning: speed ratio (original timing / new timing): %f percents'    , original_time / new_time * 1_0_0		)
 save_model(snake_case    , args.output_dir		)






def 							A						(    )				->     Any:
 __UpperCamelCase      =	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--data_dir'    , default=snake_case    , type=snake_case    , required=snake_case    , 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=snake_case    , type=snake_case    , required=snake_case    , help='Path to pretrained model or model identifier from huggingface.co/models'    , )
 parser.add_argument(
     '--output_dir'    , default=snake_case    , type=snake_case    , required=snake_case    , help='The output directory where the model predictions and checkpoints will be written.'    , )

 # Other parameters
 parser.add_argument(
     '--config_name'    , default=''    , type=snake_case    , help='Pretrained config name or path if not the same as model_name_or_path'    , )
 parser.add_argument(
     '--tokenizer_name'    , default=''    , type=snake_case    , help='Pretrained tokenizer name or path if not the same as model_name_or_path'    , )
 parser.add_argument(
     '--cache_dir'    , default=snake_case    , type=snake_case    , help='Where do you want to store the pre-trained models downloaded from s3'    , )
 parser.add_argument(
     '--data_subset'    , type=snake_case    , 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=snake_case    , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'    , )
 parser.add_argument(
     '--masking_amount'    , default=0.1    , type=snake_case    , help='Amount to heads to masking at each masking step.'		)
 parser.add_argument('--metric_name'    , default='acc'    , type=snake_case    , help='Metric to use for head masking.'		)

 parser.add_argument(
     '--max_seq_length'    , default=1_2_8    , type=snake_case    , 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=snake_case    , help='Batch size.'		)

 parser.add_argument('--seed'    , type=snake_case    , default=4_2		)
 parser.add_argument('--local_rank'    , type=snake_case    , 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=snake_case    , default=''    , help='Can be used for distant debugging.'		)
 parser.add_argument('--server_port'    , type=snake_case    , 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=snake_case		)
  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		)		)		)

 __UpperCamelCase      =	GPTaLMHeadModel.from_pretrained(args.model_name_or_path		)

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

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

 # Prepare dataset
 __UpperCamelCase      =	np.concatenate(
     [
         np.loadtxt(args.data_dir    , dtype=np.intaa		),
     ]		)
 __UpperCamelCase      =	(torch.from_numpy(snake_case		),)
 __UpperCamelCase      =	TensorDataset(*snake_case		)
 __UpperCamelCase      =	RandomSampler(snake_case		)
 __UpperCamelCase      =	DataLoader(snake_case    , sampler=snake_case    , batch_size=args.batch_size		)

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

 # 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(snake_case    , snake_case    , snake_case		)
  prune_heads(snake_case    , snake_case    , snake_case    , snake_case		)


if __name__ == "__main__":
      main()





"""simple docstring"""

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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,
          LevitForImageClassification,
          LevitForImageClassificationWithTeacher,
          LevitModel,
      )
      from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import LevitImageProcessor

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.config_class(**self.inputs_dict				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'hidden_sizes'				)				)
      self.parent.assertTrue(hasattr(__UpperCAmelCase     ,					'num_attention_heads'				)				)


class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=13     ,					__UpperCAmelCase=64     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=[128, 256, 384]     ,					__UpperCAmelCase=[4, 6, 8]     ,					__UpperCAmelCase=[2, 3, 4]     ,					__UpperCAmelCase=[16, 16, 16]     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=[2, 2, 2]     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=2     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	kernel_size
      __UpperCamelCase      =	stride
      __UpperCamelCase      =	padding
      __UpperCamelCase      =	hidden_sizes
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	depths
      __UpperCamelCase      =	key_dim
      __UpperCamelCase      =	drop_path_rate
      __UpperCamelCase      =	patch_size
      __UpperCamelCase      =	attention_ratio
      __UpperCamelCase      =	mlp_ratio
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	[
          ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
          ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
      ]
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	num_labels
      __UpperCamelCase      =	initializer_range





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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.num_labels				)

      __UpperCamelCase      =	self.get_config()
      return config, pixel_values, labels





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return LevitConfig(
          image_size=self.image_size     ,					num_channels=self.num_channels     ,					kernel_size=self.kernel_size     ,					stride=self.stride     ,					padding=self.padding     ,					patch_size=self.patch_size     ,					hidden_sizes=self.hidden_sizes     ,					num_attention_heads=self.num_attention_heads     ,					depths=self.depths     ,					key_dim=self.key_dim     ,					drop_path_rate=self.drop_path_rate     ,					mlp_ratio=self.mlp_ratio     ,					attention_ratio=self.attention_ratio     ,					initializer_range=self.initializer_range     ,					down_ops=self.down_ops     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModel(config=__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase				)
      __UpperCamelCase      =	(self.image_size, self.image_size)
      __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
      for _ in range(4				):
       __UpperCamelCase      =	floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
       __UpperCamelCase      =	floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1				)
      self.parent.assertEqual(
          result.last_hidden_state.shape     ,					(self.batch_size, ceil(height / 4				) * ceil(width / 4				), self.hidden_sizes[-1])     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.num_labels
      __UpperCamelCase      =	LevitForImageClassification(__UpperCAmelCase				)
      model.to(__UpperCAmelCase				)
      model.eval()
      __UpperCamelCase      =	model(__UpperCAmelCase     ,					labels=__UpperCAmelCase				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						(
         (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
         if is_torch_available()
         else ()
     )
     lowercase						       =						(
         {
             "feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()
         else {}
     )

     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitModelTester(self				)
      __UpperCamelCase      =	ConfigTester(self     ,					config_class=__UpperCAmelCase     ,					has_text_modality=__UpperCAmelCase     ,					hidden_size=37				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return





     @unittest.skip(reason='Levit does not use inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Levit does not support input and output embeddings'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='Levit does not output attentions'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       __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]     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.eval()

       with torch.no_grad():
        __UpperCamelCase      =	model(**self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase				)				)

       __UpperCamelCase      =	outputs.hidden_states

       __UpperCamelCase      =	len(self.model_tester.depths				) + 1
       self.assertEqual(len(__UpperCAmelCase				)     ,					__UpperCAmelCase				)

       __UpperCamelCase      =	(self.model_tester.image_size, self.model_tester.image_size)
       __UpperCamelCase  ,     __UpperCamelCase      =	image_size[0], image_size[1]
       for _ in range(4				):
        __UpperCamelCase      =	floor(
            (
                (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1				)
        __UpperCamelCase      =	floor(
            (
                (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                / self.model_tester.stride
            )
            + 1				)
       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]				)     ,					[
               height * width,
               self.model_tester.hidden_sizes[0],
           ]     ,					)

      __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(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __UpperCamelCase      =	True

       check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




      __UpperCamelCase      =	super()._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

      if return_labels:
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        del inputs_dict["labels"]

      return inputs_dict





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      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:
       # LevitForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(__UpperCAmelCase				)
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      if not self.model_tester.is_training:
       return

      __UpperCamelCase      =	False
      __UpperCamelCase      =	True

      for model_class in self.all_model_classes:
       if model_class in get_values(__UpperCAmelCase				) or not model_class.supports_gradient_checkpointing:
        continue
       # LevitForImageClassificationWithTeacher supports inference-only
       if model_class.__name__ == "LevitForImageClassificationWithTeacher":
        continue
       __UpperCamelCase      =	model_class(__UpperCAmelCase				)
       model.gradient_checkpointing_enable()
       model.to(__UpperCAmelCase				)
       model.train()
       __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)
       __UpperCamelCase      =	model(**__UpperCAmelCase				).loss
       loss.backward()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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(__UpperCAmelCase				),
           ]
           or model_class.__name__ == "LevitForImageClassificationWithTeacher"
       ):
        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(__UpperCAmelCase				)
         model.to(__UpperCAmelCase				)
         model.train()

         __UpperCamelCase      =	self._prepare_for_class(__UpperCAmelCase     ,					__UpperCAmelCase     ,					return_labels=__UpperCAmelCase				)

         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=__UpperCAmelCase				) as warning_list:
          __UpperCamelCase      =	model(**__UpperCAmelCase				).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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	LevitModel.from_pretrained(__UpperCAmelCase				)
       self.assertIsNotNone(__UpperCAmelCase				)






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
 return image





@require_torch
@require_vision
class __lowerCAmelCase		(				unittest.TestCase      ):





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
          __UpperCAmelCase				)

      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=__UpperCAmelCase     ,					return_tensors='pt'				).to(__UpperCAmelCase				)

      # forward pass
      with torch.no_grad():
       __UpperCamelCase      =	model(**__UpperCAmelCase				)

      # verify the logits
      __UpperCamelCase      =	torch.Size((1, 1000)				)
      self.assertEqual(outputs.logits.shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]				).to(__UpperCAmelCase				)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,					__UpperCAmelCase     ,					atol=1E-4				)				)



