
"""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


A__      :	Any          =    logging.getLogger(__name__)



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''
     # save results
     if os.path.exists(lowerCAmelCase		):
          if os.path.exists(os.path.join(lowerCAmelCase       ,    "config.json"		)		) and os.path.isfile(
              os.path.join(lowerCAmelCase       ,    "config.json"		)		):
               os.remove(os.path.join(lowerCAmelCase       ,    "config.json"		)		)
          if os.path.exists(os.path.join(lowerCAmelCase       ,    "pytorch_model.bin"		)		) and os.path.isfile(
              os.path.join(lowerCAmelCase       ,    "pytorch_model.bin"		)		):
               os.remove(os.path.join(lowerCAmelCase       ,    "pytorch_model.bin"		)		)
     else:
          os.makedirs(lowerCAmelCase		)
     model.save_pretrained(lowerCAmelCase		)



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Dict=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						2
     if unlogit:
          UpperCAmelCase__							: Union[str, Any] 							=						torch.pow(lowerCAmelCase       ,    lowerCAmelCase		)
     UpperCAmelCase__							: List[str] 							=						p * torch.log(lowerCAmelCase		)
     UpperCAmelCase__							: Any 							=						0
     return -plogp.sum(dim=-1		)



def 							a__ (				lowerCAmelCase	:       str		):






     '''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 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[int]       ,    lowerCAmelCase	:       Optional[int]=True       ,    lowerCAmelCase	:       List[str]=True       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       str=False		):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						model.config.num_hidden_layers, model.config.num_attention_heads
     UpperCAmelCase__							: List[str] 							=						torch.zeros(lowerCAmelCase       ,    lowerCAmelCase		).to(args.device		)
     UpperCAmelCase__							: Optional[Any] 							=						torch.zeros(lowerCAmelCase       ,    lowerCAmelCase		).to(args.device		)

     if head_mask is None:
          UpperCAmelCase__							: Optional[Any] 							=						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__							: Dict 							=						None

     UpperCAmelCase__							: List[str] 							=						0.0
     UpperCAmelCase__							: int 							=						0.0
     for step, inputs in enumerate(tqdm(lowerCAmelCase       ,    desc="Iteration"       ,    disable=args.local_rank not in [-1, 0]		)		):
          UpperCAmelCase__							: Tuple 							=						tuple(t.to(args.device		) for t in inputs		)
          (UpperCAmelCase__	)							: Optional[Any] 							=						inputs

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          UpperCAmelCase__							: Union[str, Any] 							=						model(lowerCAmelCase       ,    labels=lowerCAmelCase       ,    head_mask=lowerCAmelCase		)
          #  (loss), lm_logits, presents, (all hidden_states), (attentions)
          UpperCAmelCase__							: Optional[int] 							=						(
              outputs[0],
              outputs[1],
              outputs[-1],
          )  # Loss and logits are the first, attention the last
          loss.backward()  # Backpropagate to populate the gradients in the head mask
          total_loss += loss.detach().cpu().numpy()
          if compute_entropy:
               for layer, attn in enumerate(lowerCAmelCase		):
                    UpperCAmelCase__							: Union[str, Any] 							=						entropy(attn.detach()       ,    lowerCAmelCase		)
                    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(lowerCAmelCase		).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__							: int 							=						2
          UpperCAmelCase__							: Dict 							=						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__							: Tuple 							=						(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

     # Print matrices
     if compute_entropy:
          logger.info("Attention entropies"		)
          print_ad_tensor(lowerCAmelCase		)
     if compute_importance:
          logger.info("Head importance scores"		)
          print_ad_tensor(lowerCAmelCase		)
     logger.info("Head ranked by importance scores"		)
     UpperCAmelCase__							: Dict 							=						torch.zeros(head_importance.numel()       ,    dtype=torch.long       ,    device=args.device		)
     UpperCAmelCase__							: Optional[int] 							=						torch.arange(
         head_importance.numel()       ,    device=args.device		)
     UpperCAmelCase__							: Any 							=						head_ranks.view_as(lowerCAmelCase		)
     print_ad_tensor(lowerCAmelCase		)
     return attn_entropy, head_importance, total_loss



def 							a__ (				lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						compute_heads_importance(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    compute_entropy=lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						1 / loss  # instead of downsteam score use the LM loss
     logger.info("Pruning: original score: %f, threshold: %f"       ,    lowerCAmelCase       ,    original_score * args.masking_threshold		)

     UpperCAmelCase__							: str 							=						torch.ones_like(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						max(1       ,    int(new_head_mask.numel() * args.masking_amount		)		)

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

          if len(lowerCAmelCase		) <= num_to_mask:
               print("BREAK BY num_to_mask"		)
               break

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

          # Compute metric and head importance again
          UpperCAmelCase__							: List[str] 							=						compute_heads_importance(
              lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    compute_entropy=lowerCAmelCase       ,    head_mask=lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						1 / loss
          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 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: Tuple 							=						datetime.now()
     UpperCAmelCase__							: Any 							=						compute_heads_importance(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    compute_entropy=lowerCAmelCase       ,    compute_importance=lowerCAmelCase       ,    head_mask=lowerCAmelCase		)
     UpperCAmelCase__							: Union[str, Any] 							=						1 / loss
     UpperCAmelCase__							: int 							=						datetime.now() - before_time

     UpperCAmelCase__							: Dict 							=						sum(p.numel() for p in model.parameters()		)
     UpperCAmelCase__							: Optional[int] 							=						{
         layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCAmelCase		)		)
     }

     for k, v in heads_to_prune.items():
          if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
               UpperCAmelCase__							: List[Any] 							=						[
                   v,
               ]

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

     UpperCAmelCase__							: Optional[int] 							=						datetime.now()
     UpperCAmelCase__							: int 							=						compute_heads_importance(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    compute_entropy=lowerCAmelCase       ,    compute_importance=lowerCAmelCase       ,    head_mask=lowerCAmelCase       ,    actually_pruned=lowerCAmelCase       ,    )

     UpperCAmelCase__							: List[str] 							=						1 / loss
     UpperCAmelCase__							: Optional[int] 							=						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 (original timing / new timing): %f percents"       ,    original_time / new_time * 100		)
     save_model(lowerCAmelCase       ,    args.output_dir		)



def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						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(
         "--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 s3"       ,    )
     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__							: Any 							=						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__							: List[str] 							=						torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
          UpperCAmelCase__							: List[str] 							=						0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank		)
          UpperCAmelCase__							: Any 							=						torch.device("cuda"       ,    args.local_rank		)
          UpperCAmelCase__							: int 							=						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__							: str 							=						GPTaLMHeadModel.from_pretrained(args.model_name_or_path		)

     # Distributed and parallel training
     model.to(args.device		)
     if args.local_rank != -1:
          UpperCAmelCase__							: List[str] 							=						nn.parallel.DistributedDataParallel(
              lowerCAmelCase       ,    device_ids=[args.local_rank]       ,    output_device=args.local_rank       ,    find_unused_parameters=lowerCAmelCase		)
     elif args.n_gpu > 1:
          UpperCAmelCase__							: Union[str, Any] 							=						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
     UpperCAmelCase__							: Dict 							=						np.concatenate(
         [
             np.loadtxt(args.data_dir       ,    dtype=np.intaa		),
         ]		)
     UpperCAmelCase__							: str 							=						(torch.from_numpy(lowerCAmelCase		),)
     UpperCAmelCase__							: int 							=						TensorDataset(*lowerCAmelCase		)
     UpperCAmelCase__							: List[Any] 							=						RandomSampler(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						DataLoader(lowerCAmelCase       ,    sampler=lowerCAmelCase       ,    batch_size=args.batch_size		)

     # 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__							: Optional[int] 							=						mask_heads(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)
          prune_heads(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)


if __name__ == "__main__":
    main()





"""simple docstring"""




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 torch import nn

    from transformers import ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor







class   _lowercase						:


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=13 ,			__UpperCamelCase=30 ,			__UpperCamelCase=2 ,			__UpperCamelCase=3 ,			__UpperCamelCase=True ,			__UpperCamelCase=True ,			__UpperCamelCase=32 ,			__UpperCamelCase=5 ,			__UpperCamelCase=4 ,			__UpperCamelCase=37 ,			__UpperCamelCase="gelu" ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=0.1 ,			__UpperCamelCase=10 ,			__UpperCamelCase=0.02 ,			__UpperCamelCase=3 ,			__UpperCamelCase=0.6 ,			__UpperCamelCase=None ,			)->					List[Any]:
          UpperCAmelCase__							: str 							=						parent
          UpperCAmelCase__							: Optional[Any] 							=						batch_size
          UpperCAmelCase__							: Any 							=						image_size
          UpperCAmelCase__							: Dict 							=						patch_size
          UpperCAmelCase__							: Optional[Any] 							=						num_channels
          UpperCAmelCase__							: Union[str, Any] 							=						is_training
          UpperCAmelCase__							: Any 							=						use_labels
          UpperCAmelCase__							: List[Any] 							=						hidden_size
          UpperCAmelCase__							: Dict 							=						num_hidden_layers
          UpperCAmelCase__							: List[str] 							=						num_attention_heads
          UpperCAmelCase__							: List[Any] 							=						intermediate_size
          UpperCAmelCase__							: Dict 							=						hidden_act
          UpperCAmelCase__							: List[Any] 							=						hidden_dropout_prob
          UpperCAmelCase__							: Optional[int] 							=						attention_probs_dropout_prob
          UpperCAmelCase__							: Optional[Any] 							=						type_sequence_label_size
          UpperCAmelCase__							: Union[str, Any] 							=						initializer_range
          UpperCAmelCase__							: int 							=						mask_ratio
          UpperCAmelCase__							: Tuple 							=						scope

          # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
          # (we add 1 for the [CLS] token)
          UpperCAmelCase__							: int 							=						(image_size // patch_size) ** 2
          UpperCAmelCase__							: str 							=						int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: List[Any] 							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

          UpperCAmelCase__							: Optional[Any] 							=						None
          if self.use_labels:
               UpperCAmelCase__							: Dict 							=						ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )

          UpperCAmelCase__							: Optional[int] 							=						self.get_config()

          return config, pixel_values, labels



     def  lowerCAmelCase__       (      self  )->					int:
          return ViTMAEConfig(
              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=__UpperCamelCase ,			initializer_range=self.initializer_range ,			mask_ratio=self.mask_ratio ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						ViTMAEModel(config=__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[int]:
          UpperCAmelCase__							: List[Any] 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: Optional[int] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						(self.image_size // self.patch_size) ** 2
          UpperCAmelCase__							: List[str] 							=						self.patch_size**2 * self.num_channels
          self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )

          # test greyscale images
          UpperCAmelCase__							: Dict 							=						1
          UpperCAmelCase__							: str 							=						ViTMAEForPreTraining(__UpperCamelCase  )
          model.to(__UpperCamelCase  )
          model.eval()
          UpperCAmelCase__							: int 							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
          UpperCAmelCase__							: List[str] 							=						model(__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						self.patch_size**2
          self.parent.assertEqual(result.logits.shape ,			(self.batch_size, num_patches, expected_num_channels)  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: Tuple 							=						self.prepare_config_and_inputs()
          UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						config_and_inputs
          UpperCAmelCase__							: int 							=						{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
     _A								=							{'feature-extraction': ViTMAEModel} if is_torch_available() else {}

     _A								=							False
     _A								=							False
     _A								=							False
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						ViTMAEModelTester(self  )
          UpperCAmelCase__							: List[str] 							=						ConfigTester(self ,			config_class=__UpperCamelCase ,			has_text_modality=__UpperCamelCase ,			hidden_size=37  )



     def  lowerCAmelCase__       (      self  )->					int:
          self.config_tester.run_common_tests()



     @unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
     def  lowerCAmelCase__       (      self  )->					Dict:
          pass



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Tuple 							=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               UpperCAmelCase__							: int 							=						model_class(__UpperCamelCase  )
               self.assertIsInstance(model.get_input_embeddings() ,			(nn.Module)  )
               UpperCAmelCase__							: Tuple 							=						model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(__UpperCamelCase ,			nn.Linear  )  )



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               UpperCAmelCase__							: List[str] 							=						model_class(__UpperCamelCase  )
               UpperCAmelCase__							: Dict 							=						inspect.signature(model.forward  )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase__							: Dict 							=						[*signature.parameters.keys()]

               UpperCAmelCase__							: Tuple 							=						["pixel_values"]
               self.assertListEqual(arg_names[:1] ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					Dict:
          UpperCAmelCase__							: int 							=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Dict:
          # make masks reproducible
          np.random.seed(2  )

          UpperCAmelCase__							: Tuple 							=						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
          UpperCAmelCase__							: Union[str, Any] 							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
          UpperCAmelCase__							: str 							=						torch.from_numpy(__UpperCamelCase  )

          # Add `noise` argument.
          # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
          UpperCAmelCase__							: Optional[Any] 							=						pt_noise

          super().check_pt_tf_models(__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               UpperCAmelCase__							: List[Any] 							=						model_class(__UpperCamelCase  )
               model.to(__UpperCamelCase  )
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2  )
               with torch.no_grad():
                    UpperCAmelCase__							: Optional[int] 							=						model(**self._prepare_for_class(__UpperCamelCase ,			__UpperCamelCase  )  )

               UpperCAmelCase__							: Optional[Any] 							=						outputs[0].cpu().numpy()
               UpperCAmelCase__							: Union[str, Any] 							=						0

               with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(__UpperCamelCase  )
                    UpperCAmelCase__							: Union[str, Any] 							=						model_class.from_pretrained(__UpperCamelCase  )
                    model.to(__UpperCamelCase  )
                    # make random mask reproducible
                    torch.manual_seed(2  )
                    with torch.no_grad():
                         UpperCAmelCase__							: List[Any] 							=						model(**self._prepare_for_class(__UpperCamelCase ,			__UpperCamelCase  )  )

                    # Make sure we don't have nans
                    UpperCAmelCase__							: Tuple 							=						after_outputs[0].cpu().numpy()
                    UpperCAmelCase__							: int 							=						0
                    UpperCAmelCase__							: str 							=						np.amax(np.abs(out_a - out_a  )  )
                    self.assertLessEqual(__UpperCamelCase ,			1E-5  )



     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
     def  lowerCAmelCase__       (      self  )->					List[str]:
          pass



     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
     def  lowerCAmelCase__       (      self  )->					Any:
          pass



     @unittest.skip(
         reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          pass



     @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          pass



     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          pass



     @slow
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__							: Tuple 							=						ViTMAEModel.from_pretrained(__UpperCamelCase  )
               self.assertIsNotNone(__UpperCamelCase  )





def 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: Union[str, Any] 							=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
     return image







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


     '''simple docstring'''





     @cached_property
     def  lowerCAmelCase__       (      self  )->					List[Any]:
          return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          # make random mask reproducible across the PT and TF model
          np.random.seed(2  )

          UpperCAmelCase__							: Any 							=						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(__UpperCamelCase  )

          UpperCAmelCase__							: Tuple 							=						self.default_image_processor
          UpperCAmelCase__							: List[Any] 							=						prepare_img()
          UpperCAmelCase__							: Optional[Any] 							=						image_processor(images=__UpperCamelCase ,			return_tensors="pt"  ).to(__UpperCamelCase  )

          # prepare a noise vector that will be also used for testing the TF model
          # (this way we can ensure that the PT and TF models operate on the same inputs)
          UpperCAmelCase__							: List[Any] 							=						ViTMAEConfig()
          UpperCAmelCase__							: str 							=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
          UpperCAmelCase__							: Optional[int] 							=						np.random.uniform(size=(1, num_patches)  )

          # forward pass
          with torch.no_grad():
               UpperCAmelCase__							: List[str] 							=						model(**__UpperCamelCase ,			noise=torch.from_numpy(__UpperCamelCase  ).to(device=__UpperCamelCase  )  )

          # verify the logits
          UpperCAmelCase__							: str 							=						torch.Size((1, 1_96, 7_68)  )
          self.assertEqual(outputs.logits.shape ,			__UpperCamelCase  )

          UpperCAmelCase__							: List[Any] 							=						torch.tensor(
              [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

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



