


'''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:		Optional[int]    						=  logging.getLogger(__name__)



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    p * torch.log(_UpperCamelCase )
   UpperCamelCase__			    =    0
   return -plogp.sum(dim=-1 )



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Tuple )		->       List[Any]:





   '''simple docstring'''







   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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Tuple=True     ,		_UpperCamelCase   :					Optional[int]=True     ,		_UpperCamelCase   :					int=None     ,		_UpperCamelCase   :					Dict=False )		->       int:





   '''simple docstring'''







   UpperCamelCase__    ,		UpperCamelCase__			    =    model.config.num_hidden_layers, model.config.num_attention_heads
   UpperCamelCase__			    =    torch.zeros(_UpperCamelCase     ,		_UpperCamelCase ).to(args.device )
   UpperCamelCase__			    =    torch.zeros(_UpperCamelCase     ,		_UpperCamelCase ).to(args.device )

   if head_mask is None:
      UpperCamelCase__			    =    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:
      UpperCamelCase__			    =    None

   UpperCamelCase__			    =    None
   UpperCamelCase__			    =    None
   UpperCamelCase__			    =    0.0

   for step, inputs in enumerate(tqdm(_UpperCamelCase     ,		desc="Iteration"     ,		disable=args.local_rank not in [-1, 0] ) ):
      for k, v in inputs.items():
         UpperCamelCase__			    =    v.to(args.device )

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

      if compute_entropy:
         for layer, attn in enumerate(_UpperCamelCase ):
            UpperCamelCase__			    =    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__			    =    logits.detach().cpu().numpy()
         UpperCamelCase__			    =    inputs["labels"].detach().cpu().numpy()
      else:
         UpperCamelCase__			    =    np.append(_UpperCamelCase     ,		logits.detach().cpu().numpy()     ,		axis=0 )
         UpperCamelCase__			    =    np.append(_UpperCamelCase     ,		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__			    =    2
      UpperCamelCase__			    =    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:
      UpperCamelCase__			    =    (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(_UpperCamelCase )
   logger.info("Head importance scores" )
   print_ad_tensor(_UpperCamelCase )
   logger.info("Head ranked by importance scores" )
   UpperCamelCase__			    =    torch.zeros(head_importance.numel()     ,		dtype=torch.long     ,		device=args.device )
   UpperCamelCase__			    =    torch.arange(
       head_importance.numel()     ,		device=args.device )
   UpperCamelCase__			    =    head_ranks.view_as(_UpperCamelCase )
   print_ad_tensor(_UpperCamelCase )

   return attn_entropy, head_importance, preds, labels



def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					Optional[int] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    compute_heads_importance(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		compute_entropy=_UpperCamelCase )
   UpperCamelCase__			    =    np.argmax(_UpperCamelCase     ,		axis=1 ) if args.output_mode == "classification" else np.squeeze(_UpperCamelCase )
   UpperCamelCase__			    =    glue_compute_metrics(args.task_name     ,		_UpperCamelCase     ,		_UpperCamelCase )[args.metric_name]
   logger.info("Pruning: original score: %f, threshold: %f"     ,		_UpperCamelCase     ,		original_score * args.masking_threshold )

   UpperCamelCase__			    =    torch.ones_like(_UpperCamelCase )
   UpperCamelCase__			    =    max(1     ,		int(new_head_mask.numel() * args.masking_amount ) )

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

      if len(_UpperCamelCase ) <= num_to_mask:
         break

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

      # Compute metric and head importance again
      UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    compute_heads_importance(
          _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		compute_entropy=_UpperCamelCase     ,		head_mask=_UpperCamelCase )
      UpperCamelCase__			    =    np.argmax(_UpperCamelCase     ,		axis=1 ) if args.output_mode == "classification" else np.squeeze(_UpperCamelCase )
      UpperCamelCase__			    =    glue_compute_metrics(args.task_name     ,		_UpperCamelCase     ,		_UpperCamelCase )[args.metric_name]
      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 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					int     ,		_UpperCamelCase   :					Dict     ,		_UpperCamelCase   :					List[str]     ,		_UpperCamelCase   :					str )		->       int:





   '''simple docstring'''







   UpperCamelCase__			    =    datetime.now()
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    compute_heads_importance(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		compute_entropy=_UpperCamelCase     ,		compute_importance=_UpperCamelCase     ,		head_mask=_UpperCamelCase )
   UpperCamelCase__			    =    np.argmax(_UpperCamelCase     ,		axis=1 ) if args.output_mode == "classification" else np.squeeze(_UpperCamelCase )
   UpperCamelCase__			    =    glue_compute_metrics(args.task_name     ,		_UpperCamelCase     ,		_UpperCamelCase )[args.metric_name]
   UpperCamelCase__			    =    datetime.now() - before_time

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

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

   UpperCamelCase__			    =    datetime.now()
   UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    compute_heads_importance(
       _UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		compute_entropy=_UpperCamelCase     ,		compute_importance=_UpperCamelCase     ,		head_mask=_UpperCamelCase     ,		actually_pruned=_UpperCamelCase     ,		)
   UpperCamelCase__			    =    np.argmax(_UpperCamelCase     ,		axis=1 ) if args.output_mode == "classification" else np.squeeze(_UpperCamelCase )
   UpperCamelCase__			    =    glue_compute_metrics(args.task_name     ,		_UpperCamelCase     ,		_UpperCamelCase )[args.metric_name]
   UpperCamelCase__			    =    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 (new timing / original timing): %f percents"     ,		original_time / new_time * 1_00 )



def 							SCREAMING_SNAKE_CASE__(		)		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    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(
       "--task_name"     ,		default=_UpperCamelCase     ,		type=_UpperCamelCase     ,		required=_UpperCamelCase     ,		help="The name of the task to train selected in the list: " + ", ".join(glue_processors.keys() )     ,		)
   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 huggingface.co"     ,		)
   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." )
   UpperCamelCase__			    =    parser.parse_args()

   if args.server_ip and args.server_port:
      # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
      import ptvsd

      print("Waiting for debugger attach" )
      ptvsd.enable_attach(address=(args.server_ip, args.server_port)     ,		redirect_output=_UpperCamelCase )
      ptvsd.wait_for_attach()

   # Setup devices and distributed training
   if args.local_rank == -1 or args.no_cuda:
      UpperCamelCase__			    =    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
      UpperCamelCase__			    =    0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank )
      UpperCamelCase__			    =    torch.device("cuda"     ,		args.local_rank )
      UpperCamelCase__			    =    1
      torch.distributed.init_process_group(backend="nccl" )  # Initializes the distributed backend

   # Setup logging
   logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
   logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device     ,		args.n_gpu     ,		bool(args.local_rank != -1 ) ) )
   # 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__			    =    args.task_name.lower()
   if args.task_name not in glue_processors:
      raise ValueError("Task not found: %s" % (args.task_name) )
   UpperCamelCase__			    =    glue_processors[args.task_name]()
   UpperCamelCase__			    =    glue_output_modes[args.task_name]
   UpperCamelCase__			    =    processor.get_labels()
   UpperCamelCase__			    =    len(_UpperCamelCase )

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

   UpperCamelCase__			    =    AutoConfig.from_pretrained(
       args.config_name if args.config_name else args.model_name_or_path     ,		num_labels=_UpperCamelCase     ,		finetuning_task=args.task_name     ,		output_attentions=_UpperCamelCase     ,		cache_dir=args.cache_dir     ,		)
   UpperCamelCase__			    =    AutoTokenizer.from_pretrained(
       args.tokenizer_name if args.tokenizer_name else args.model_name_or_path     ,		cache_dir=args.cache_dir     ,		)
   UpperCamelCase__			    =    AutoModelForSequenceClassification.from_pretrained(
       args.model_name_or_path     ,		from_tf=bool(".ckpt" in args.model_name_or_path )     ,		config=_UpperCamelCase     ,		cache_dir=args.cache_dir     ,		)

   # Distributed and parallel training
   model.to(args.device )
   if args.local_rank != -1:
      UpperCamelCase__			    =    nn.parallel.DistributedDataParallel(
          _UpperCamelCase     ,		device_ids=[args.local_rank]     ,		output_device=args.local_rank     ,		find_unused_parameters=_UpperCamelCase )
   elif args.n_gpu > 1:
      UpperCamelCase__			    =    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 for the GLUE task
   UpperCamelCase__			    =    GlueDataset(_UpperCamelCase     ,		tokenizer=_UpperCamelCase     ,		mode="dev" )
   if args.data_subset > 0:
      UpperCamelCase__			    =    Subset(_UpperCamelCase     ,		list(range(min(args.data_subset     ,		len(_UpperCamelCase ) ) ) ) )
   UpperCamelCase__			    =    SequentialSampler(_UpperCamelCase ) if args.local_rank == -1 else DistributedSampler(_UpperCamelCase )
   UpperCamelCase__			    =    DataLoader(
       _UpperCamelCase     ,		sampler=_UpperCamelCase     ,		batch_size=args.batch_size     ,		collate_fn=_UpperCamelCase )

   # 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:
      UpperCamelCase__			    =    mask_heads(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      prune_heads(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )


if __name__ == "__main__":
       main()



'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowercase:		int    						=  logging.get_logger(__name__)

__lowercase:		Tuple    						=  {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       List[str]            =       'mbart'
 _lowerCamelCase			:       str            =       ['past_key_values']
 _lowerCamelCase			:       List[str]            =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(			self			:						Tuple,       a_			:						Union[str, Any]=5_0265,       a_			:						Union[str, Any]=1024,       a_			:						Optional[int]=12,       a_			:						str=4096,       a_			:						Optional[Any]=16,       a_			:						str=12,       a_			:						Union[str, Any]=4096,       a_			:						List[Any]=16,       a_			:						Optional[Any]=0.0,       a_			:						int=0.0,       a_			:						Any=True,       a_			:						Optional[Any]=True,       a_			:						Any="gelu",       a_			:						int=1024,       a_			:						Any=0.1,       a_			:						Optional[int]=0.0,       a_			:						Tuple=0.0,       a_			:						Optional[Any]=0.02,       a_			:						Any=0.0,       a_			:						Optional[Any]=False,       a_			:						Any=1,       a_			:						Optional[Any]=0,       a_			:						List[str]=2,       a_			:						Any=2,       **a_			:						int,       ):





    """simple docstring"""



    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    d_model
    UpperCamelCase__			    =    encoder_ffn_dim
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    encoder_attention_heads
    UpperCamelCase__			    =    decoder_ffn_dim
    UpperCamelCase__			    =    decoder_layers
    UpperCamelCase__			    =    decoder_attention_heads
    UpperCamelCase__			    =    dropout
    UpperCamelCase__			    =    attention_dropout
    UpperCamelCase__			    =    activation_dropout
    UpperCamelCase__			    =    activation_function
    UpperCamelCase__			    =    init_std
    UpperCamelCase__			    =    encoder_layerdrop
    UpperCamelCase__			    =    decoder_layerdrop
    UpperCamelCase__			    =    classifier_dropout
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=a_,       bos_token_id=a_,       eos_token_id=a_,       is_encoder_decoder=a_,       forced_eos_token_id=a_,       **a_,       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]       )

       if self.use_past:
          UpperCamelCase__			    =    {0: "batch"}
          UpperCamelCase__			    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}
          UpperCamelCase__			    =    {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(a_,       direction="inputs"       )
    elif self.task == "causal-lm":
       # TODO: figure this case out.
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]       )
       if self.use_past:
          UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
          for i in range(a_       ):
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
    else:
       UpperCamelCase__			    =    OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
               ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
           ]       )

    return common_inputs

 @property
 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    super().outputs
    else:
       UpperCamelCase__			    =    super(a_,       self       ).outputs
       if self.use_past:
          UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
          for i in range(a_       ):
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
             UpperCamelCase__			    =    {0: "batch", 2: "past_sequence + sequence"}
    return common_outputs

 def 		lowercase_    (			self			:						str,       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )

    # Generate decoder inputs
    UpperCamelCase__			    =    seq_length if not self.use_past else 1
    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )
    UpperCamelCase__			    =    {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
    UpperCamelCase__			    =    dict(**a_,       **a_       )

    if self.use_past:
       if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."       )
       else:
          import torch
       UpperCamelCase__    ,		UpperCamelCase__			    =    common_inputs["input_ids"].shape
       UpperCamelCase__			    =    common_inputs["decoder_input_ids"].shape[1]
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_attention_heads
       UpperCamelCase__			    =    (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       UpperCamelCase__			    =    decoder_seq_length + 3
       UpperCamelCase__			    =    (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       UpperCamelCase__			    =    torch.cat(
           [common_inputs["decoder_attention_mask"], torch.ones(a_,       a_       )],       dim=1       )

       UpperCamelCase__			    =    []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
       UpperCamelCase__			    =    min(a_,       a_       )
       UpperCamelCase__			    =    max(a_,       a_       ) - min_num_layers
       UpperCamelCase__			    =    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

       for _ in range(a_       ):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(a_       ),
                  torch.zeros(a_       ),
                  torch.zeros(a_       ),
                  torch.zeros(a_       ),
              )       )
       # TODO: test this.
       UpperCamelCase__			    =    encoder_shape if remaining_side_name == "encoder" else decoder_shape
       for _ in range(a_,       a_       ):
          common_inputs["past_key_values"].append((torch.zeros(a_       ), torch.zeros(a_       ))       )
    return common_inputs

 def 		lowercase_    (			self			:						Optional[int],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        a_,       a_,       a_,       a_,       a_       )

    if self.use_past:
       if not is_torch_available():
          raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."       )
       else:
          import torch
       UpperCamelCase__    ,		UpperCamelCase__			    =    common_inputs["input_ids"].shape
       # Not using the same length for past_key_values
       UpperCamelCase__			    =    seqlen + 2
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_layers
       UpperCamelCase__    ,		UpperCamelCase__			    =    self.num_attention_heads
       UpperCamelCase__			    =    (
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       UpperCamelCase__			    =    common_inputs["attention_mask"].dtype
       UpperCamelCase__			    =    torch.cat(
           [common_inputs["attention_mask"], torch.ones(a_,       a_,       dtype=a_       )],       dim=1       )
       UpperCamelCase__			    =    [
           (torch.zeros(a_       ), torch.zeros(a_       )) for _ in range(a_       )
       ]
    return common_inputs

 def 		lowercase_    (			self			:						List[Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    compute_effective_axis_dimension(
        a_,       fixed_dimension=OnnxConfig.default_fixed_batch,       num_token_to_add=0       )

    # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
    UpperCamelCase__			    =    tokenizer.num_special_tokens_to_add(a_       )
    UpperCamelCase__			    =    compute_effective_axis_dimension(
        a_,       fixed_dimension=OnnxConfig.default_fixed_sequence,       num_token_to_add=a_       )

    # Generate dummy inputs according to compute batch and sequence
    UpperCamelCase__			    =    [" ".join([tokenizer.unk_token]       ) * seq_length] * batch_size
    UpperCamelCase__			    =    dict(tokenizer(a_,       return_tensors=a_       )       )
    return common_inputs

 def 		lowercase_    (			self			:						Union[str, Any],       a_			:						PreTrainedTokenizer,       a_			:						int = -1,       a_			:						int = -1,       a_			:						bool = False,       a_			:						Optional[TensorType] = None,       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )

    elif self.task == "causal-lm":
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_causal_lm(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )
    else:
       UpperCamelCase__			    =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a_,       batch_size=a_,       seq_length=a_,       is_pair=a_,       framework=a_       )

    return common_inputs

 def 		lowercase_    (			self			:						Dict,       a_			:						int,       a_			:						str,       a_			:						List[Any],       a_			:						int       ):





    """simple docstring"""



    if self.task in ["default", "seq2seq-lm"]:
       UpperCamelCase__			    =    super()._flatten_past_key_values_(a_,       a_,       a_,       a_       )
    else:
       UpperCamelCase__			    =    super(a_,       self       )._flatten_past_key_values_(
           a_,       a_,       a_,       a_       )