




'''simple docstring'''






import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowercase	:      List[Any]				=       logging.getLogger(__name__)




def 		a_( lowerCamelCase      )				->Union[str, Any]:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      p * torch.log(__lowerCamelCase      )
       lowercase  :		Any						=      0
       return -plogp.sum(dim=-1      )




def 		a_( lowerCamelCase      )				->Optional[Any]:






       '''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   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase=True   ,				lowerCamelCase=True   ,				lowerCamelCase=None   ,				lowerCamelCase=False      )				->List[str]:






       '''simple docstring'''




       lowercase,     lowercase  :		int						=      model.config.num_hidden_layers, model.config.num_attention_heads
       lowercase  :		List[str]						=      torch.zeros(__lowerCamelCase   ,				__lowerCamelCase      ).to(args.device      )
       lowercase  :		List[str]						=      torch.zeros(__lowerCamelCase   ,				__lowerCamelCase      ).to(args.device      )

       if head_mask is None:
              lowercase  :		Optional[int]						=      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:
              lowercase  :		List[str]						=      None

       lowercase  :		Optional[Any]						=      None
       lowercase  :		Union[str, Any]						=      None
       lowercase  :		Union[str, Any]						=      0.0

       for step, inputs in enumerate(tqdm(__lowerCamelCase   ,				desc='''Iteration'''   ,				disable=args.local_rank not in [-1, 0]      )      ):
              for k, v in inputs.items():
                     lowercase  :		List[str]						=      v.to(args.device      )

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              lowercase  :		Optional[Any]						=      model(**__lowerCamelCase   ,				head_mask=__lowerCamelCase      )
              lowercase,     lowercase,     lowercase  :		Dict						=      (
                  outputs[0],
                  outputs[1],
                  outputs[-1],
              )  # Loss and logits are the first, attention the last
              loss.backward()  # Backpropagate to populate the gradients in the head mask

              if compute_entropy:
                     for layer, attn in enumerate(__lowerCamelCase      ):
                            lowercase  :		Union[str, Any]						=      entropy(attn.detach()      ) * inputs['''attention_mask'''].float().unsqueeze(1      )
                            attn_entropy[layer] += masked_entropy.sum(-1      ).sum(0      ).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()

              # Also store our logits/labels if we want to compute metrics afterwards
              if preds is None:
                     lowercase  :		List[str]						=      logits.detach().cpu().numpy()
                     lowercase  :		Tuple						=      inputs['''labels'''].detach().cpu().numpy()
              else:
                     lowercase  :		List[str]						=      np.append(__lowerCamelCase   ,				logits.detach().cpu().numpy()   ,				axis=0      )
                     lowercase  :		Union[str, Any]						=      np.append(__lowerCamelCase   ,				inputs['''labels'''].detach().cpu().numpy()   ,				axis=0      )

              tot_tokens += inputs["attention_mask"].float().detach().sum().data

       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
              lowercase  :		Tuple						=      2
              lowercase  :		Union[str, Any]						=      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:
              lowercase  :		Union[str, Any]						=      (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

       # Print/save matrices
       np.save(os.path.join(args.output_dir   ,				'''attn_entropy.npy'''      )   ,				attn_entropy.detach().cpu().numpy()      )
       np.save(os.path.join(args.output_dir   ,				'''head_importance.npy'''      )   ,				head_importance.detach().cpu().numpy()      )

       logger.info('''Attention entropies'''      )
       print_ad_tensor(__lowerCamelCase      )
       logger.info('''Head importance scores'''      )
       print_ad_tensor(__lowerCamelCase      )
       logger.info('''Head ranked by importance scores'''      )
       lowercase  :		Dict						=      torch.zeros(head_importance.numel()   ,				dtype=torch.long   ,				device=args.device      )
       lowercase  :		List[Any]						=      torch.arange(
           head_importance.numel()   ,				device=args.device      )
       lowercase  :		Dict						=      head_ranks.view_as(__lowerCamelCase      )
       print_ad_tensor(__lowerCamelCase      )

       return attn_entropy, head_importance, preds, labels




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->List[Any]:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      compute_heads_importance(__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				compute_entropy=__lowerCamelCase      )
       lowercase  :		Union[str, Any]						=      np.argmax(__lowerCamelCase   ,				axis=1      ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase      )
       lowercase  :		Optional[Any]						=      glue_compute_metrics(args.task_name   ,				__lowerCamelCase   ,				__lowerCamelCase      )[args.metric_name]
       logger.info('''Pruning: original score: %f, threshold: %f'''   ,				__lowerCamelCase   ,				original_score * args.masking_threshold      )

       lowercase  :		Optional[int]						=      torch.ones_like(__lowerCamelCase      )
       lowercase  :		List[Any]						=      max(1   ,				int(new_head_mask.numel() * args.masking_amount      )      )

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

              if len(__lowerCamelCase      ) <= num_to_mask:
                     break

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

              # Compute metric and head importance again
              lowercase,     lowercase,     lowercase,     lowercase  :		Optional[Any]						=      compute_heads_importance(
                  __lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				compute_entropy=__lowerCamelCase   ,				head_mask=__lowerCamelCase      )
              lowercase  :		List[str]						=      np.argmax(__lowerCamelCase   ,				axis=1      ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase      )
              lowercase  :		Tuple						=      glue_compute_metrics(args.task_name   ,				__lowerCamelCase   ,				__lowerCamelCase      )[args.metric_name]
              logger.info(
                  '''Masking: current score: %f, remaining heads %d (%.1f percents)'''   ,				__lowerCamelCase   ,				new_head_mask.sum()   ,				new_head_mask.sum() / new_head_mask.numel() * 100   ,				)

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

       return head_mask




def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase      )				->str:






       '''simple docstring'''




       lowercase  :		str						=      datetime.now()
       lowercase,     lowercase,     lowercase,     lowercase  :		List[Any]						=      compute_heads_importance(
           __lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				compute_entropy=__lowerCamelCase   ,				compute_importance=__lowerCamelCase   ,				head_mask=__lowerCamelCase      )
       lowercase  :		List[str]						=      np.argmax(__lowerCamelCase   ,				axis=1      ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase      )
       lowercase  :		str						=      glue_compute_metrics(args.task_name   ,				__lowerCamelCase   ,				__lowerCamelCase      )[args.metric_name]
       lowercase  :		Any						=      datetime.now() - before_time

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

       assert sum(len(__lowerCamelCase      ) for h in heads_to_prune.values()      ) == (1 - head_mask.long()).sum().item()
       model.prune_heads(__lowerCamelCase      )
       lowercase  :		str						=      sum(p.numel() for p in model.parameters()      )

       lowercase  :		int						=      datetime.now()
       lowercase,     lowercase,     lowercase,     lowercase  :		Any						=      compute_heads_importance(
           __lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				compute_entropy=__lowerCamelCase   ,				compute_importance=__lowerCamelCase   ,				head_mask=__lowerCamelCase   ,				actually_pruned=__lowerCamelCase   ,				)
       lowercase  :		Optional[Any]						=      np.argmax(__lowerCamelCase   ,				axis=1      ) if args.output_mode == '''classification''' else np.squeeze(__lowerCamelCase      )
       lowercase  :		str						=      glue_compute_metrics(args.task_name   ,				__lowerCamelCase   ,				__lowerCamelCase      )[args.metric_name]
       lowercase  :		str						=      datetime.now() - before_time

       logger.info(
           '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				pruned_num_params / original_num_params * 100   ,				)
       logger.info('''Pruning: score with masking: %f score with pruning: %f'''   ,				__lowerCamelCase   ,				__lowerCamelCase      )
       logger.info('''Pruning: speed ratio (new timing / original timing): %f percents'''   ,				original_time / new_time * 100      )




def 		a_( )				->Any:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--data_dir'''   ,				default=__lowerCamelCase   ,				type=__lowerCamelCase   ,				required=__lowerCamelCase   ,				help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''   ,				)
       parser.add_argument(
           '''--model_name_or_path'''   ,				default=__lowerCamelCase   ,				type=__lowerCamelCase   ,				required=__lowerCamelCase   ,				help='''Path to pretrained model or model identifier from huggingface.co/models'''   ,				)
       parser.add_argument(
           '''--task_name'''   ,				default=__lowerCamelCase   ,				type=__lowerCamelCase   ,				required=__lowerCamelCase   ,				help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()      )   ,				)
       parser.add_argument(
           '''--output_dir'''   ,				default=__lowerCamelCase   ,				type=__lowerCamelCase   ,				required=__lowerCamelCase   ,				help='''The output directory where the model predictions and checkpoints will be written.'''   ,				)

       # Other parameters
       parser.add_argument(
           '''--config_name'''   ,				default=''''''   ,				type=__lowerCamelCase   ,				help='''Pretrained config name or path if not the same as model_name_or_path'''   ,				)
       parser.add_argument(
           '''--tokenizer_name'''   ,				default=''''''   ,				type=__lowerCamelCase   ,				help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''   ,				)
       parser.add_argument(
           '''--cache_dir'''   ,				default=__lowerCamelCase   ,				type=__lowerCamelCase   ,				help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''   ,				)
       parser.add_argument(
           '''--data_subset'''   ,				type=__lowerCamelCase   ,				default=-1   ,				help='''If > 0: limit the data to a subset of data_subset instances.'''      )
       parser.add_argument(
           '''--overwrite_output_dir'''   ,				action='''store_true'''   ,				help='''Whether to overwrite data in output directory'''      )
       parser.add_argument(
           '''--overwrite_cache'''   ,				action='''store_true'''   ,				help='''Overwrite the cached training and evaluation sets'''      )

       parser.add_argument(
           '''--dont_normalize_importance_by_layer'''   ,				action='''store_true'''   ,				help='''Don\'t normalize importance score by layers'''      )
       parser.add_argument(
           '''--dont_normalize_global_importance'''   ,				action='''store_true'''   ,				help='''Don\'t normalize all importance scores between 0 and 1'''   ,				)

       parser.add_argument(
           '''--try_masking'''   ,				action='''store_true'''   ,				help='''Whether to try to mask head until a threshold of accuracy.'''      )
       parser.add_argument(
           '''--masking_threshold'''   ,				default=0.9   ,				type=__lowerCamelCase   ,				help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''   ,				)
       parser.add_argument(
           '''--masking_amount'''   ,				default=0.1   ,				type=__lowerCamelCase   ,				help='''Amount to heads to masking at each masking step.'''      )
       parser.add_argument('''--metric_name'''   ,				default='''acc'''   ,				type=__lowerCamelCase   ,				help='''Metric to use for head masking.'''      )

       parser.add_argument(
           '''--max_seq_length'''   ,				default=128   ,				type=__lowerCamelCase   ,				help=(
               '''The maximum total input sequence length after WordPiece tokenization. \n'''
               '''Sequences longer than this will be truncated, sequences shorter padded.'''
           )   ,				)
       parser.add_argument('''--batch_size'''   ,				default=1   ,				type=__lowerCamelCase   ,				help='''Batch size.'''      )

       parser.add_argument('''--seed'''   ,				type=__lowerCamelCase   ,				default=42      )
       parser.add_argument('''--local_rank'''   ,				type=__lowerCamelCase   ,				default=-1   ,				help='''local_rank for distributed training on gpus'''      )
       parser.add_argument('''--no_cuda'''   ,				action='''store_true'''   ,				help='''Whether not to use CUDA when available'''      )
       parser.add_argument('''--server_ip'''   ,				type=__lowerCamelCase   ,				default=''''''   ,				help='''Can be used for distant debugging.'''      )
       parser.add_argument('''--server_port'''   ,				type=__lowerCamelCase   ,				default=''''''   ,				help='''Can be used for distant debugging.'''      )
       lowercase  :		Tuple						=      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:
              lowercase  :		Any						=      torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''      )
              lowercase  :		int						=      0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank      )
              lowercase  :		Any						=      torch.device('''cuda'''   ,				args.local_rank      )
              lowercase  :		Optional[Any]						=      1
              torch.distributed.init_process_group(backend='''nccl'''      )  # Initializes the distributed backend

       # Setup logging
       logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN      )
       logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device   ,				args.n_gpu   ,				bool(args.local_rank != -1      )      )      )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(args.local_rank      ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()

       # Set seeds
       set_seed(args.seed      )

       # Prepare GLUE task
       lowercase  :		Tuple						=      args.task_name.lower()
       if args.task_name not in glue_processors:
              raise ValueError('''Task not found: %s''' % (args.task_name)      )
       lowercase  :		str						=      glue_processors[args.task_name]()
       lowercase  :		Optional[int]						=      glue_output_modes[args.task_name]
       lowercase  :		Tuple						=      processor.get_labels()
       lowercase  :		int						=      len(__lowerCamelCase      )

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

       lowercase  :		Union[str, Any]						=      AutoConfig.from_pretrained(
           args.config_name if args.config_name else args.model_name_or_path   ,				num_labels=__lowerCamelCase   ,				finetuning_task=args.task_name   ,				output_attentions=__lowerCamelCase   ,				cache_dir=args.cache_dir   ,				)
       lowercase  :		Any						=      AutoTokenizer.from_pretrained(
           args.tokenizer_name if args.tokenizer_name else args.model_name_or_path   ,				cache_dir=args.cache_dir   ,				)
       lowercase  :		List[str]						=      AutoModelForSequenceClassification.from_pretrained(
           args.model_name_or_path   ,				from_tf=bool('''.ckpt''' in args.model_name_or_path      )   ,				config=__lowerCamelCase   ,				cache_dir=args.cache_dir   ,				)

       # Distributed and parallel training
       model.to(args.device      )
       if args.local_rank != -1:
              lowercase  :		List[Any]						=      nn.parallel.DistributedDataParallel(
                  __lowerCamelCase   ,				device_ids=[args.local_rank]   ,				output_device=args.local_rank   ,				find_unused_parameters=__lowerCamelCase      )
       elif args.n_gpu > 1:
              lowercase  :		Optional[int]						=      nn.DataParallel(__lowerCamelCase      )

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

       # Prepare dataset for the GLUE task
       lowercase  :		Optional[int]						=      GlueDataset(__lowerCamelCase   ,				tokenizer=__lowerCamelCase   ,				mode='''dev'''      )
       if args.data_subset > 0:
              lowercase  :		Any						=      Subset(__lowerCamelCase   ,				list(range(min(args.data_subset   ,				len(__lowerCamelCase      )      )      )      )      )
       lowercase  :		Optional[Any]						=      SequentialSampler(__lowerCamelCase      ) if args.local_rank == -1 else DistributedSampler(__lowerCamelCase      )
       lowercase  :		List[Any]						=      DataLoader(
           __lowerCamelCase   ,				sampler=__lowerCamelCase   ,				batch_size=args.batch_size   ,				collate_fn=__lowerCamelCase      )

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

       # Try head masking (set heads to zero until the score goes under a threshole)
       # and head pruning (remove masked heads and see the effect on the network)
       if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
              lowercase  :		List[Any]						=      mask_heads(__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase      )
              prune_heads(__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase      )


if __name__ == "__main__":
      main()











'''simple docstring'''






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          DataaVecVisionForImageClassification,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
      )
      from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=100   ,		A=13   ,		A=30   ,		A=2   ,		A=3   ,		A=True   ,		A=True   ,		A=32   ,		A=4   ,		A=4   ,		A=37   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=10   ,		A=0.0_2   ,		A=3   ,		A=None   ,		A=[0, 1, 2, 3]   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      parent
        lowercase  :		str						=      100
        lowercase  :		Dict						=      batch_size
        lowercase  :		Tuple						=      image_size
        lowercase  :		int						=      patch_size
        lowercase  :		Optional[int]						=      num_channels
        lowercase  :		Any						=      is_training
        lowercase  :		Dict						=      use_labels
        lowercase  :		Any						=      hidden_size
        lowercase  :		List[Any]						=      num_hidden_layers
        lowercase  :		int						=      num_attention_heads
        lowercase  :		Dict						=      intermediate_size
        lowercase  :		Optional[int]						=      hidden_act
        lowercase  :		Union[str, Any]						=      hidden_dropout_prob
        lowercase  :		str						=      attention_probs_dropout_prob
        lowercase  :		List[str]						=      type_sequence_label_size
        lowercase  :		Dict						=      initializer_range
        lowercase  :		str						=      scope
        lowercase  :		Tuple						=      out_indices
        lowercase  :		Union[str, Any]						=      num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (image_size // patch_size) ** 2
        lowercase  :		List[Any]						=      num_patches + 1



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        lowercase  :		Union[str, Any]						=      None
        lowercase  :		int						=      None
        if self.use_labels:
               lowercase  :		Dict						=      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size )
               lowercase  :		Tuple						=      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,		self.num_labels )

        lowercase  :		int						=      self.get_config()

        return config, pixel_values, labels, pixel_labels



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return DataaVecVisionConfig(
            vocab_size=self.vocab_size   ,		image_size=self.image_size   ,		patch_size=self.patch_size   ,		num_channels=self.num_channels   ,		hidden_size=self.hidden_size   ,		num_hidden_layers=self.num_hidden_layers   ,		num_attention_heads=self.num_attention_heads   ,		intermediate_size=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		is_decoder=A   ,		initializer_range=self.initializer_range   ,		out_indices=self.out_indices   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      DataaVecVisionModel(config=A )
        model.to(A )
        model.eval()
        lowercase  :		Tuple						=      model(A )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, num_patches + 1, self.hidden_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Any						=      self.type_sequence_label_size
        lowercase  :		Any						=      DataaVecVisionForImageClassification(A )
        model.to(A )
        model.eval()
        lowercase  :		Dict						=      model(A   ,		labels=A )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.num_labels
        lowercase  :		List[Any]						=      DataaVecVisionForSemanticSegmentation(A )
        model.to(A )
        model.eval()
        lowercase  :		int						=      model(A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
        lowercase  :		List[str]						=      model(A   ,		labels=A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.prepare_config_and_inputs()
        lowercase,     lowercase,     lowercase,     lowercase  :		Any						=      config_and_inputs
        lowercase  :		Union[str, Any]						=      {'''pixel_values''': pixel_values}
        return config, inputs_dict






@require_torch
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      (
     (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __snake_case	:     Optional[Any]			   =      (
     {
         "feature-extraction": DataaVecVisionModel,
         "image-classification": DataaVecVisionForImageClassification,
         "image-segmentation": DataaVecVisionForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __snake_case	:     str			   =      False
 __snake_case	:     str			   =      False
 __snake_case	:     int			   =      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      DataaVecVisionModelTester(self )
        lowercase  :		List[str]						=      ConfigTester(
            self   ,		config_class=A   ,		has_text_modality=A   ,		hidden_size=37 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.config_tester.run_common_tests()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase  :		int						=      model_class(A )
               self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module) )
               lowercase  :		Optional[int]						=      model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(A   ,		nn.Linear ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Dict						=      self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase  :		List[Any]						=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if not self.model_tester.is_training:
               return

        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        lowercase  :		Union[str, Any]						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )]:
                      continue

               lowercase  :		Optional[int]						=      model_class(A )
               model.to(A )
               model.train()
               lowercase  :		int						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		List[str]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		List[str]						=      False
        lowercase  :		str						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )] or not model_class.supports_gradient_checkpointing:
                      continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                      lowercase,     lowercase,     lowercase,     lowercase  :		Tuple						=      inputs_dict['''pixel_values'''].shape
                      lowercase  :		int						=      torch.zeros(
                          [self.model_tester.batch_size, height, width]   ,		device=A ).long()
               lowercase  :		Optional[Any]						=      model_class(A )
               model.gradient_checkpointing_enable()
               model.to(A )
               model.train()
               lowercase  :		Any						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		Optional[int]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

        lowercase  :		Union[str, Any]						=      _config_zero_init(A )
        for model_class in self.all_model_classes:
               lowercase  :		Dict						=      model_class(config=A )
               for name, param in model.named_parameters():
                      # we skip lambda parameters as these require special initial values
                      # determined by config.layer_scale_init_value
                      if "lambda" in name:
                             continue
                      if param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1E9).round() / 1E9).item()   ,		[0.0, 1.0]   ,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A=2E-4   ,		A="outputs"   ,		A=None ):






        '''simple docstring'''




        super().check_pt_tf_outputs(A   ,		A   ,		A   ,		A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase  :		List[Any]						=      DataaVecVisionModel.from_pretrained(A )
               self.assertIsNotNone(A )






def 		a_( )				->Any:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image





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



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (
            BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ) if is_vision_available() else None
        )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ).to(
            A )

        lowercase  :		List[str]						=      self.default_image_processor
        lowercase  :		List[Any]						=      prepare_img()
        lowercase  :		List[Any]						=      image_processor(images=A   ,		return_tensors='''pt''' ).to(A )

        # forward pass
        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A )
        lowercase  :		Any						=      outputs.logits

        # verify the logits
        lowercase  :		List[Any]						=      torch.Size((1, 1000) )
        self.assertEqual(logits.shape   ,		A )

        lowercase  :		str						=      torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1] ).to(A )

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

        lowercase  :		Dict						=      [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
        self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()   ,		A )




