
import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


A		: Optional[Any]								=     logging.getLogger(__name__)




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[Any]:







						if os.path.exists(SCREAMING_SNAKE_CASE_       ):
												if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_		,   """config.json"""       )       ) and os.path.isfile(
												    os.path.join(SCREAMING_SNAKE_CASE_		,   """config.json"""       )       ):
																		os.remove(os.path.join(SCREAMING_SNAKE_CASE_		,   """config.json"""       )       )
												if os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_		,   """pytorch_model.bin"""       )       ) and os.path.isfile(
												    os.path.join(SCREAMING_SNAKE_CASE_		,   """pytorch_model.bin"""       )       ):
																		os.remove(os.path.join(SCREAMING_SNAKE_CASE_		,   """pytorch_model.bin"""       )       )
						else:
												os.makedirs(SCREAMING_SNAKE_CASE_       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )




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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Dict:
						logger.info("""lv, h >\t""" + """\t""".join(f"""{x + 1}""" for x in range(len(SCREAMING_SNAKE_CASE_       )       )       )       )
						for row in range(len(SCREAMING_SNAKE_CASE_       )       ):
												if tensor.dtype != torch.long:
																		logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:.5f}""" for x in tensor[row].cpu().data       )       )
												else:
																		logger.info(f"""layer {row + 1}:\t""" + """\t""".join(f"""{x:d}""" for x in tensor[row].cpu().data       )       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	int=True		,   SCREAMING_SNAKE_CASE_	:	int=True		,   SCREAMING_SNAKE_CASE_	:	int=None		,   SCREAMING_SNAKE_CASE_	:	str=False       )  ->     List[str]:
						_lowercase       ,						_lowercase					       =					model.config.num_hidden_layers, model.config.num_attention_heads
						_lowercase					       =					torch.zeros(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).to(args.device       )
						_lowercase					       =					torch.zeros(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).to(args.device       )

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

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

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

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_		,   labels=SCREAMING_SNAKE_CASE_		,   head_mask=SCREAMING_SNAKE_CASE_       )
												#  (loss), lm_logits, presents, (all hidden_states), (attentions)
												_lowercase       ,						_lowercase       ,						_lowercase					       =					(
												    outputs[0],
												    outputs[1],
												    outputs[-1],
												)  # Loss and logits are the first, attention the last
												loss.backward()  # Backpropagate to populate the gradients in the head mask
												total_loss += loss.detach().cpu().numpy()
												if compute_entropy:
																		for layer, attn in enumerate(SCREAMING_SNAKE_CASE_       ):
																								_lowercase					       =					entropy(attn.detach()		,   SCREAMING_SNAKE_CASE_       )
																								attn_entropy[layer] += masked_entropy.sum(-1       ).sum(0       ).sum(0       ).detach()

												if compute_importance:
																		head_importance += head_mask.grad.abs().detach()
												tot_tokens += torch.ones_like(SCREAMING_SNAKE_CASE_       ).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					       =					2
												_lowercase					       =					torch.pow(torch.pow(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).sum(-1       )		,   1 / exponent       )
												head_importance /= norm_by_layer.unsqueeze(-1       ) + 1E-20

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

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




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

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

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

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

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

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

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

						return head_mask




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Dict       )  ->     List[Any]:
						_lowercase					       =					datetime.now()
						_lowercase       ,						_lowercase       ,						_lowercase					       =					compute_heads_importance(
						    SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   compute_entropy=SCREAMING_SNAKE_CASE_		,   compute_importance=SCREAMING_SNAKE_CASE_		,   head_mask=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					1 / loss
						_lowercase					       =					datetime.now() - before_time

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

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

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

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

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

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




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

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

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

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

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

						_lowercase					       =					GPTaLMHeadModel.from_pretrained(args.model_name_or_path       )

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

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

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

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

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


if __name__ == "__main__":
		main()


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
      )

class    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]=1_3			,						lowerCamelCase__	:    Dict=7			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Any=9_9			,						lowerCamelCase__	:    Union[str, Any]=3_2			,						lowerCamelCase__	:    Dict=2			,						lowerCamelCase__	:    Tuple=4			,						lowerCamelCase__	:    int=3_7			,						lowerCamelCase__	:    List[str]="gelu"			,						lowerCamelCase__	:    str=0.1			,						lowerCamelCase__	:    Any=0.1			,						lowerCamelCase__	:    int=5_1_2			,						lowerCamelCase__	:    Optional[Any]=1_6			,						lowerCamelCase__	:    Optional[Any]=2			,						lowerCamelCase__	:    List[str]=0.02			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    List[Any]="None"			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    List[Any]=4			,						lowerCamelCase__	:    List[str]=None			,						) ->			List[str]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_labels
         A_							       =		num_choices
         A_							       =		relative_attention
         A_							       =		position_biased_input
         A_							       =		pos_att_type
         A_							       =		scope

   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





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

         A_							       =		None
         if self.use_input_mask:
               A_							       =		random_attention_mask([self.batch_size, self.seq_length]       )

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

         A_							       =		None
         A_							       =		None
         A_							       =		None
         if self.use_labels:
               A_							       =		ids_tensor([self.batch_size]			,						self.type_sequence_label_size       )
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels       )

         A_							       =		DebertaVaConfig(
             vocab_size=self.vocab_size			,						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			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						relative_attention=self.relative_attention			,						position_biased_input=self.position_biased_input			,						initializer_range=self.initializer_range			,						return_dict=lowerCamelCase__			,						)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel(config=lowerCamelCase__       )
         A_							       =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A_							       =		[input_ids, input_mask]
         A_							       =		model(lowerCamelCase__       )

         A_							       =		model(lowerCamelCase__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





         A_							       =		TFDebertaVaForMaskedLM(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)       )

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForSequenceClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForTokenClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaForQuestionAnswering(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)       )





   def     UpperCamelCase					(				self	:    int       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         (
             (
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,
         )							       =		config_and_inputs
         A_							       =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict



@require_tf
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Union[str, Any]  					=    (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _lowercase     :       Optional[Any]  					=    (
       {
           'feature-extraction': TFDebertaVaModel,
           'fill-mask': TFDebertaVaForMaskedLM,
           'question-answering': TFDebertaVaForQuestionAnswering,
           'text-classification': TFDebertaVaForSequenceClassification,
           'token-classification': TFDebertaVaForTokenClassification,
           'zero-shot': TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _lowercase     :       List[str]  					=    False
   _lowercase     :       Any  					=    False

   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_							       =		TFDebertaVaModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         self.assertIsNotNone(lowerCamelCase__       )



@require_tf
class    _lowercase				(					unittest.TestCase			):

   @unittest.skip(reason='''Model not available yet'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         pass





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         A_							       =		tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       )
         A_							       =		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
         A_							       =		model(lowerCamelCase__			,						attention_mask=lowerCamelCase__       )[0]

         A_							       =		tf.constant(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4       )





