
'''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


lowerCAmelCase_			:			Union[str, Any]			   =     logging.getLogger(__name__)







def __A  (    lowerCAmelCase_       ):
     _UpperCAmelCase					:  int         =    p * torch.log(lowerCAmelCase_       )
     _UpperCAmelCase					:  Any         =    0
     return -plogp.sum(dim=-1       )







def __A  (    lowerCAmelCase_       ):
     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       ):
     _UpperCAmelCase  ,     _UpperCAmelCase					:  Optional[Any]         =    model.config.num_hidden_layers, model.config.num_attention_heads
     _UpperCAmelCase					:  Optional[Any]         =    torch.zeros(lowerCAmelCase_ ,       lowerCAmelCase_       ).to(args.device       )
     _UpperCAmelCase					:  Any         =    torch.zeros(lowerCAmelCase_ ,       lowerCAmelCase_       ).to(args.device       )

     if head_mask is None:
          _UpperCAmelCase					:  str         =    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					:  Tuple         =    None

     _UpperCAmelCase					:  List[str]         =    None
     _UpperCAmelCase					:  str         =    None
     _UpperCAmelCase					:  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():
               _UpperCAmelCase					:  Optional[Any]         =    v.to(args.device       )

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _UpperCAmelCase					:  Optional[int]         =    model(**lowerCAmelCase_ ,       head_mask=lowerCAmelCase_       )
          _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  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_       ):
                    _UpperCAmelCase					:  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:
               _UpperCAmelCase					:  List[str]         =    logits.detach().cpu().numpy()
               _UpperCAmelCase					:  Optional[Any]         =    inputs["""labels"""].detach().cpu().numpy()
          else:
               _UpperCAmelCase					:  Optional[int]         =    np.append(lowerCAmelCase_ ,       logits.detach().cpu().numpy() ,       axis=0       )
               _UpperCAmelCase					:  Dict         =    np.append(lowerCAmelCase_ ,       inputs["""labels"""].detach().cpu().numpy() ,       axis=0       )

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

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

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

     logger.info("""Attention entropies"""       )
     print_ad_tensor(lowerCAmelCase_       )
     logger.info("""Head importance scores"""       )
     print_ad_tensor(lowerCAmelCase_       )
     logger.info("""Head ranked by importance scores"""       )
     _UpperCAmelCase					:  List[str]         =    torch.zeros(head_importance.numel() ,       dtype=torch.long ,       device=args.device       )
     _UpperCAmelCase					:  Any         =    torch.arange(
         head_importance.numel() ,       device=args.device       )
     _UpperCAmelCase					:  Union[str, Any]         =    head_ranks.view_as(lowerCAmelCase_       )
     print_ad_tensor(lowerCAmelCase_       )

     return attn_entropy, head_importance, preds, labels







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    compute_heads_importance(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_       )
     _UpperCAmelCase					:  List[str]         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
     _UpperCAmelCase					:  Tuple         =    glue_compute_metrics(args.task_name ,       lowerCAmelCase_ ,       lowerCAmelCase_       )[args.metric_name]
     logger.info("""Pruning: original score: %f, threshold: %f""" ,       lowerCAmelCase_ ,       original_score * args.masking_threshold       )

     _UpperCAmelCase					:  List[Any]         =    torch.ones_like(lowerCAmelCase_       )
     _UpperCAmelCase					:  List[str]         =    max(1 ,       int(new_head_mask.numel() * args.masking_amount       )       )

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

          if len(lowerCAmelCase_       ) <= num_to_mask:
               break

          # mask heads
          _UpperCAmelCase					:  Dict         =    current_heads_to_mask[:num_to_mask]
          logger.info("""Heads to mask: %s""" ,       str(current_heads_to_mask.tolist()       )       )
          _UpperCAmelCase					:  Dict         =    new_head_mask.view(-1       )
          _UpperCAmelCase					:  str         =    0.0
          _UpperCAmelCase					:  int         =    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  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Any         =    compute_heads_importance(
              lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_ ,       head_mask=lowerCAmelCase_       )
          _UpperCAmelCase					:  str         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
          _UpperCAmelCase					:  Any         =    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_       ):
     _UpperCAmelCase					:  int         =    datetime.now()
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  Tuple         =    compute_heads_importance(
         lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_ ,       compute_importance=lowerCAmelCase_ ,       head_mask=lowerCAmelCase_       )
     _UpperCAmelCase					:  str         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
     _UpperCAmelCase					:  List[str]         =    glue_compute_metrics(args.task_name ,       lowerCAmelCase_ ,       lowerCAmelCase_       )[args.metric_name]
     _UpperCAmelCase					:  Any         =    datetime.now() - before_time

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

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

     _UpperCAmelCase					:  Optional[Any]         =    datetime.now()
     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase					:  int         =    compute_heads_importance(
         lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       compute_entropy=lowerCAmelCase_ ,       compute_importance=lowerCAmelCase_ ,       head_mask=lowerCAmelCase_ ,       actually_pruned=lowerCAmelCase_ ,       )
     _UpperCAmelCase					:  List[str]         =    np.argmax(lowerCAmelCase_ ,       axis=1       ) if args.output_mode == """classification""" else np.squeeze(lowerCAmelCase_       )
     _UpperCAmelCase					:  Tuple         =    glue_compute_metrics(args.task_name ,       lowerCAmelCase_ ,       lowerCAmelCase_       )[args.metric_name]
     _UpperCAmelCase					:  Union[str, Any]         =    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  (    ):
     _UpperCAmelCase					:  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."""       )
     _UpperCAmelCase					:  Dict         =    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					:  Optional[int]         =    torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""       )
          _UpperCAmelCase					:  Any         =    0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank       )
          _UpperCAmelCase					:  List[Any]         =    torch.device("""cuda""" ,       args.local_rank       )
          _UpperCAmelCase					:  Dict         =    1
          torch.distributed.init_process_group(backend="""nccl"""       )  # Initializes the distributed backend

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

     # Set seeds
     set_seed(args.seed       )

     # Prepare GLUE task
     _UpperCAmelCase					:  List[Any]         =    args.task_name.lower()
     if args.task_name not in glue_processors:
          raise ValueError("""Task not found: %s""" % (args.task_name)       )
     _UpperCAmelCase					:  Any         =    glue_processors[args.task_name]()
     _UpperCAmelCase					:  Optional[int]         =    glue_output_modes[args.task_name]
     _UpperCAmelCase					:  List[Any]         =    processor.get_labels()
     _UpperCAmelCase					:  List[str]         =    len(lowerCAmelCase_       )

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

     _UpperCAmelCase					:  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 ,       )
     _UpperCAmelCase					:  Union[str, Any]         =    AutoTokenizer.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path ,       cache_dir=args.cache_dir ,       )
     _UpperCAmelCase					:  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:
          _UpperCAmelCase					:  int         =    nn.parallel.DistributedDataParallel(
              lowerCAmelCase_ ,       device_ids=[args.local_rank] ,       output_device=args.local_rank ,       find_unused_parameters=lowerCAmelCase_       )
     elif args.n_gpu > 1:
          _UpperCAmelCase					:  List[str]         =    nn.DataParallel(lowerCAmelCase_       )

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

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

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

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


if __name__ == "__main__":
   main()










"""simple docstring"""







import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.electra.modeling_flax_electra import (
     FlaxElectraForCausalLM,
     FlaxElectraForMaskedLM,
     FlaxElectraForMultipleChoice,
     FlaxElectraForPreTraining,
     FlaxElectraForQuestionAnswering,
     FlaxElectraForSequenceClassification,
     FlaxElectraForTokenClassification,
     FlaxElectraModel,
 )





class      UpperCamelCase_       (				unittest.TestCase     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__=13       ,						snake_case__=7       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=True       ,						snake_case__=99       ,						snake_case__=24       ,						snake_case__=32       ,						snake_case__=5       ,						snake_case__=4       ,						snake_case__=37       ,						snake_case__="gelu"       ,						snake_case__=0.1       ,						snake_case__=0.1       ,						snake_case__=5_12       ,						snake_case__=16       ,						snake_case__=2       ,						snake_case__=0.02       ,						snake_case__=4       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	parent
         UpperCAmelCase					=	batch_size
         UpperCAmelCase					=	seq_length
         UpperCAmelCase					=	is_training
         UpperCAmelCase					=	use_attention_mask
         UpperCAmelCase					=	use_token_type_ids
         UpperCAmelCase					=	use_labels
         UpperCAmelCase					=	vocab_size
         UpperCAmelCase					=	hidden_size
         UpperCAmelCase					=	embedding_size
         UpperCAmelCase					=	num_hidden_layers
         UpperCAmelCase					=	num_attention_heads
         UpperCAmelCase					=	intermediate_size
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	attention_probs_dropout_prob
         UpperCAmelCase					=	max_position_embeddings
         UpperCAmelCase					=	type_vocab_size
         UpperCAmelCase					=	type_sequence_label_size
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	num_choices



  def 						UpperCamelCase_ (					self )	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size )

         UpperCAmelCase					=	None
         if self.use_attention_mask:
                UpperCAmelCase					=	random_attention_mask([self.batch_size, self.seq_length] )

         UpperCAmelCase					=	None
         if self.use_token_type_ids:
                UpperCAmelCase					=	ids_tensor([self.batch_size, self.seq_length]       ,						self.type_vocab_size )

         UpperCAmelCase					=	ElectraConfig(
             vocab_size=self.vocab_size       ,						hidden_size=self.hidden_size       ,						embedding_size=self.embedding_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       ,						max_position_embeddings=self.max_position_embeddings       ,						type_vocab_size=self.type_vocab_size       ,						initializer_range=self.initializer_range       ,						)

         return config, input_ids, token_type_ids, attention_mask



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	config_and_inputs
         UpperCAmelCase					=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
         return config, inputs_dict





@require_flax
class      UpperCamelCase_       (				a_      ,    unittest.TestCase     ):
  _A				:    str =		True

  _A				:    List[Any] =		(
      (
          FlaxElectraModel,
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForPreTraining,
          FlaxElectraForTokenClassification,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForMultipleChoice,
          FlaxElectraForSequenceClassification,
      )
      if is_flax_available()
      else ()
  )



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	FlaxElectraModelTester(self )



  @slow
  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         for model_class_name in self.all_model_classes:
                if model_class_name == FlaxElectraForMaskedLM:
                       UpperCAmelCase					=	model_class_name.from_pretrained("""google/electra-small-generator""" )
                else:
                       UpperCAmelCase					=	model_class_name.from_pretrained("""google/electra-small-discriminator""" )
                UpperCAmelCase					=	model(np.ones((1, 1) ) )
                self.assertIsNotNone(snake_case__ )
