




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


lowerCamelCase__			:       int         =							logging.getLogger(__name__)

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

def  UpperCAmelCase_							(				__UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     Optional[int]=False							) ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE_     						=      2
      if unlogit:
            SCREAMING_SNAKE_CASE_     						=      torch.pow(__UpperCAmelCase      ,      __UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      p * torch.log(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      0
      return -plogp.sum(dim=-1							)

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

      if head_mask is None:
            SCREAMING_SNAKE_CASE_     						=      torch.ones(__UpperCAmelCase      ,      __UpperCAmelCase							).to(args.device							)

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

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

            # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
            SCREAMING_SNAKE_CASE_     						=      model(__UpperCAmelCase      ,      labels=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase							)
            #  (loss), lm_logits, presents, (all hidden_states), (attentions)
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      (
                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(__UpperCAmelCase							):
                        SCREAMING_SNAKE_CASE_     						=      entropy(attn.detach()      ,      __UpperCAmelCase							)
                        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(__UpperCAmelCase							).float().detach().sum().data

      # Normalize
      attn_entropy /= tot_tokens
      head_importance /= tot_tokens
      # Layerwise importance normalization
      if not args.dont_normalize_importance_by_layer:
            SCREAMING_SNAKE_CASE_     						=      2
            SCREAMING_SNAKE_CASE_     						=      torch.pow(torch.pow(__UpperCAmelCase      ,      __UpperCAmelCase							).sum(-1							)      ,      1 / exponent							)
            head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-20

      if not args.dont_normalize_global_importance:
            SCREAMING_SNAKE_CASE_     						=      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

      # Print matrices
      if compute_entropy:
            logger.info('Attention entropies'							)
            print_ad_tensor(__UpperCAmelCase							)
      if compute_importance:
            logger.info('Head importance scores'							)
            print_ad_tensor(__UpperCAmelCase							)
      logger.info('Head ranked by importance scores'							)
      SCREAMING_SNAKE_CASE_     						=      torch.zeros(head_importance.numel()      ,      dtype=torch.long      ,      device=args.device							)
      SCREAMING_SNAKE_CASE_     						=      torch.arange(
          head_importance.numel()      ,      device=args.device							)
      SCREAMING_SNAKE_CASE_     						=      head_ranks.view_as(__UpperCAmelCase							)
      print_ad_tensor(__UpperCAmelCase							)
      return attn_entropy, head_importance, total_loss

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     str							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      compute_heads_importance(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      compute_entropy=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      1 / loss  # instead of downsteam score use the LM loss
      logger.info('Pruning: original score: %f, threshold: %f'      ,      __UpperCAmelCase      ,      original_score * args.masking_threshold							)

      SCREAMING_SNAKE_CASE_     						=      torch.ones_like(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      max(1      ,      int(new_head_mask.numel() * args.masking_amount							)							)

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

            if len(__UpperCAmelCase							) <= num_to_mask:
                  print('BREAK BY num_to_mask'							)
                  break

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

            # Compute metric and head importance again
            SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      compute_heads_importance(
                __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      compute_entropy=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_     						=      1 / loss
            logger.info(
                'Masking: current score: %f, remaining heads %d (%.1f percents)'      ,      __UpperCAmelCase      ,      new_head_mask.sum()      ,      new_head_mask.sum() / new_head_mask.numel() * 1_00      ,      )

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

      return head_mask

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict      ,      __UpperCAmelCase						:     Tuple      ,      __UpperCAmelCase						:     List[Any]      ,      __UpperCAmelCase						:     List[Any]							) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      datetime.now()
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      compute_heads_importance(
          __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      compute_entropy=__UpperCAmelCase      ,      compute_importance=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      1 / loss
      SCREAMING_SNAKE_CASE_     						=      datetime.now() - before_time

      SCREAMING_SNAKE_CASE_     						=      sum(p.numel() for p in model.parameters()							)
      SCREAMING_SNAKE_CASE_     						=      {
          layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__UpperCAmelCase							)							)
      }

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

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

      SCREAMING_SNAKE_CASE_     						=      datetime.now()
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      compute_heads_importance(
          __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      compute_entropy=__UpperCAmelCase      ,      compute_importance=__UpperCAmelCase      ,      head_mask=__UpperCAmelCase      ,      actually_pruned=__UpperCAmelCase      ,      )

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

      logger.info(
          'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      pruned_num_params / original_num_params * 1_00      ,      )
      logger.info('Pruning: score with masking: %f score with pruning: %f'      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)
      logger.info('Pruning: speed ratio (original timing / new timing): %f percents'      ,      original_time / new_time * 1_00							)
      save_model(__UpperCAmelCase      ,      args.output_dir							)

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

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

      parser.add_argument(
          '--max_seq_length'      ,      default=1_28      ,      type=__UpperCAmelCase      ,      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=__UpperCAmelCase      ,      help='Batch size.'							)

      parser.add_argument('--seed'      ,      type=__UpperCAmelCase      ,      default=42							)
      parser.add_argument('--local_rank'      ,      type=__UpperCAmelCase      ,      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=__UpperCAmelCase      ,      default=''      ,      help='Can be used for distant debugging.'							)
      parser.add_argument('--server_port'      ,      type=__UpperCAmelCase      ,      default=''      ,      help='Can be used for distant debugging.'							)
      SCREAMING_SNAKE_CASE_     						=      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=__UpperCAmelCase							)
            ptvsd.wait_for_attach()

      # Setup devices and distributed training
      if args.local_rank == -1 or args.no_cuda:
            SCREAMING_SNAKE_CASE_     						=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
            SCREAMING_SNAKE_CASE_     						=      0 if args.no_cuda else torch.cuda.device_count()
      else:
            torch.cuda.set_device(args.local_rank							)
            SCREAMING_SNAKE_CASE_     						=      torch.device('cuda'      ,      args.local_rank							)
            SCREAMING_SNAKE_CASE_     						=      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							)							)							)

      SCREAMING_SNAKE_CASE_     						=      GPTaLMHeadModel.from_pretrained(args.model_name_or_path							)

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

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

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

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

      # 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:
            SCREAMING_SNAKE_CASE_     						=      mask_heads(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)
            prune_heads(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)


if __name__ == "__main__":
   main()





import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Any    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		List[Any]=2    ,    _lowerCAmelCase  :		List[str]=True    ,    _lowerCAmelCase  :		Any=False    ,    _lowerCAmelCase  :		Tuple=10    ,    _lowerCAmelCase  :		Optional[int]=3    ,    _lowerCAmelCase  :		Dict=32 * 8    ,    _lowerCAmelCase  :		List[str]=32 * 8    ,    _lowerCAmelCase  :		List[Any]=4    ,    _lowerCAmelCase  :		Optional[Any]=64    ,    ):
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      is_training
       SCREAMING_SNAKE_CASE_     						=      use_auxiliary_loss
       SCREAMING_SNAKE_CASE_     						=      num_queries
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      min_size
       SCREAMING_SNAKE_CASE_     						=      max_size
       SCREAMING_SNAKE_CASE_     						=      num_labels
       SCREAMING_SNAKE_CASE_     						=      hidden_dim
       SCREAMING_SNAKE_CASE_     						=      hidden_dim



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
           _lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.ones([self.batch_size, self.min_size, self.max_size]    ,    device=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]    ,    device=_lowerCAmelCase						) > 0.5
       ).float()
       SCREAMING_SNAKE_CASE_     						=      (torch.rand((self.batch_size, self.num_labels)    ,    device=_lowerCAmelCase						) > 0.5).long()

       SCREAMING_SNAKE_CASE_     						=      self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerConfig(
           hidden_size=self.hidden_dim    ,    )
       SCREAMING_SNAKE_CASE_     						=      self.num_queries
       SCREAMING_SNAKE_CASE_     						=      self.num_labels

       SCREAMING_SNAKE_CASE_     						=      [1, 1, 1, 1]
       SCREAMING_SNAKE_CASE_     						=      self.num_channels

       SCREAMING_SNAKE_CASE_     						=      64
       SCREAMING_SNAKE_CASE_     						=      128
       SCREAMING_SNAKE_CASE_     						=      self.hidden_dim
       SCREAMING_SNAKE_CASE_     						=      self.hidden_dim
       SCREAMING_SNAKE_CASE_     						=      self.hidden_dim
       return config



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_     						=      {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      output.encoder_hidden_states
       SCREAMING_SNAKE_CASE_     						=      output.pixel_decoder_hidden_states
       SCREAMING_SNAKE_CASE_     						=      output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(_lowerCAmelCase						)    ,    len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(_lowerCAmelCase						)    ,    len(config.backbone_config.depths						)						)
       self.parent.assertTrue(len(_lowerCAmelCase						)    ,    config.decoder_layers						)



 def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Tuple    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any=False						):
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel(config=_lowerCAmelCase						)
             model.to(_lowerCAmelCase						)
             model.eval()

             SCREAMING_SNAKE_CASE_     						=      model(pixel_values=_lowerCAmelCase    ,    pixel_mask=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    output_hidden_states=_lowerCAmelCase						)

       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape    ,    (self.batch_size, self.num_queries, self.hidden_dim)    ,    )
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

       if output_hidden_states:
             self.check_output_hidden_state(_lowerCAmelCase    ,    _lowerCAmelCase						)




 def 				lowerCAmelCase_  (							self  :		str    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.eval()

       def comm_check_on_output(_lowerCAmelCase  :		List[str]						):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
             self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape    ,    (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)    ,    )
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape    ,    (self.batch_size, self.num_queries, self.num_labels + 1)						)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(pixel_values=_lowerCAmelCase    ,    pixel_mask=_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase						)

             comm_check_on_output(_lowerCAmelCase						)

             SCREAMING_SNAKE_CASE_     						=      model(
                 pixel_values=_lowerCAmelCase    ,    pixel_mask=_lowerCAmelCase    ,    mask_labels=_lowerCAmelCase    ,    class_labels=_lowerCAmelCase						)

       comm_check_on_output(_lowerCAmelCase						)

       self.parent.assertTrue(result.loss is not None						)
       self.parent.assertEqual(result.loss.shape    ,    torch.Size([1]						)						)





@require_torch
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 lowercase_       =   {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False
 lowercase_       =   False



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerModelTester(self						)
       SCREAMING_SNAKE_CASE_     						=      ConfigTester(self    ,    config_class=_lowerCAmelCase    ,    has_text_modality=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       self.config_tester.run_common_tests()



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase    ,    **_lowerCAmelCase    ,    output_hidden_states=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCAmelCase						)



 @unittest.skip(reason='Mask2Former does not use inputs_embeds'						)
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       pass



 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'						)
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       pass



 @unittest.skip(reason='Mask2Former is not a generative model'						)
 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       pass



 @unittest.skip(reason='Mask2Former does not use token embeddings'						)
 def 				lowerCAmelCase_  (							self  :		Tuple						):
       pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'						)
 def 				lowerCAmelCase_  (							self  :		Any						):
       pass



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



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE_     						=      ['pixel_values']
             self.assertListEqual(arg_names[:1]    ,    _lowerCAmelCase						)



 @slow
 def 				lowerCAmelCase_  (							self  :		Any						):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel.from_pretrained(_lowerCAmelCase						)
             self.assertIsNotNone(_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      (self.model_tester.min_size,) * 2
       SCREAMING_SNAKE_CASE_     						=      {
           'pixel_values': torch.randn((2, 3, *size)    ,    device=_lowerCAmelCase						),
           'mask_labels': torch.randn((2, 10, *size)    ,    device=_lowerCAmelCase						),
           'class_labels': torch.zeros(2    ,    10    ,    device=_lowerCAmelCase						).long(),
       }
       SCREAMING_SNAKE_CASE_     						=      self.model_tester.get_config()

       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation(_lowerCAmelCase						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)
       self.assertTrue(outputs.loss is not None						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase    ,    **_lowerCAmelCase    ,    output_hidden_states=_lowerCAmelCase						)



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						).to(_lowerCAmelCase						)
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase    ,    output_attentions=_lowerCAmelCase						)
             self.assertTrue(outputs.attentions is not None						)



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_     						=      self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						)
       model.to(_lowerCAmelCase						)
       model.train()

       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    mask_labels=_lowerCAmelCase    ,    class_labels=_lowerCAmelCase						).loss
       loss.backward()




 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_     						=      True
       SCREAMING_SNAKE_CASE_     						=      True

       SCREAMING_SNAKE_CASE_     						=      model_class(_lowerCAmelCase						).to(_lowerCAmelCase						)
       model.train()

       SCREAMING_SNAKE_CASE_     						=      model(_lowerCAmelCase    ,    mask_labels=_lowerCAmelCase    ,    class_labels=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_     						=      outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_     						=      outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_     						=      outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=_lowerCAmelCase						)

       self.assertIsNotNone(encoder_hidden_states.grad						)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
       self.assertIsNotNone(attentions.grad						)


lowerCamelCase__			:       Tuple         =							1E-4

def  UpperCAmelCase_							(				) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image





@require_vision
@slow
class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 @cached_property
 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def 				lowerCAmelCase_  (							self  :		int						):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.default_image_processor
       SCREAMING_SNAKE_CASE_     						=      prepare_img()
       SCREAMING_SNAKE_CASE_     						=      image_processor(_lowerCAmelCase    ,    return_tensors='pt'						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
       # check size
       self.assertEqual(_lowerCAmelCase    ,    (1, 3, 384, 384)						)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor(
           [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(_lowerCAmelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]    ,    _lowerCAmelCase    ,    atol=_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor(
           [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(_lowerCAmelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]    ,    _lowerCAmelCase    ,    atol=_lowerCAmelCase						)						)

       SCREAMING_SNAKE_CASE_     						=      torch.tensor(
           [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(_lowerCAmelCase						)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]    ,    _lowerCAmelCase    ,    atol=_lowerCAmelCase						)						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_lowerCAmelCase						).eval()
       SCREAMING_SNAKE_CASE_     						=      self.default_image_processor
       SCREAMING_SNAKE_CASE_     						=      prepare_img()
       SCREAMING_SNAKE_CASE_     						=      image_processor(_lowerCAmelCase    ,    return_tensors='pt'						).to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
       # check size
       self.assertEqual(_lowerCAmelCase    ,    (1, 3, 384, 384)						)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)
       # masks_queries_logits
       SCREAMING_SNAKE_CASE_     						=      outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape    ,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
       SCREAMING_SNAKE_CASE_     						=      [
           [-8.7839, -9.0056, -8.8121],
           [-7.4104, -7.0313, -6.5401],
           [-6.6105, -6.3427, -6.4675],
       ]
       SCREAMING_SNAKE_CASE_     						=      torch.tensor(_lowerCAmelCase						).to(_lowerCAmelCase						)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]    ,    _lowerCAmelCase    ,    atol=_lowerCAmelCase						)						)
       # class_queries_logits
       SCREAMING_SNAKE_CASE_     						=      outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape    ,    (1, model.config.num_queries, model.config.num_labels + 1)						)
       SCREAMING_SNAKE_CASE_     						=      torch.tensor(
           [
               [1.8324, -8.0835, -4.1922],
               [0.8450, -9.0050, -3.6053],
               [0.3045, -7.7293, -3.0275],
           ]						).to(_lowerCAmelCase						)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,    _lowerCAmelCase    ,    atol=_lowerCAmelCase						)						)




 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(_lowerCAmelCase						).eval()
       SCREAMING_SNAKE_CASE_     						=      self.default_image_processor

       SCREAMING_SNAKE_CASE_     						=      image_processor(
           [np.zeros((3, 800, 1_333)						), np.zeros((3, 800, 1_333)						)]    ,    segmentation_maps=[np.zeros((384, 384)						).astype(np.floataa						), np.zeros((384, 384)						).astype(np.floataa						)]    ,    return_tensors='pt'    ,    )

       SCREAMING_SNAKE_CASE_     						=      inputs['pixel_values'].to(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      [el.to(_lowerCAmelCase						) for el in inputs['mask_labels']]
       SCREAMING_SNAKE_CASE_     						=      [el.to(_lowerCAmelCase						) for el in inputs['class_labels']]

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_     						=      model(**_lowerCAmelCase						)

       self.assertTrue(outputs.loss is not None						)