



'''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


A			:   Dict			=      logging.getLogger(__name__)
def 		_a   (  lowerCamelCase_							):
			snake_case   :							str						  =p * torch.log(_lowerCamelCase							)
			snake_case   :							int						  =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							):
			snake_case   :							int						  =model.config.num_hidden_layers, model.config.num_attention_heads
			snake_case   :							Optional[int]						  =torch.zeros(_lowerCamelCase					,						_lowerCamelCase							).to(args.device							)
			snake_case   :							Any						  =torch.zeros(_lowerCamelCase					,						_lowerCamelCase							).to(args.device							)

			if head_mask is None:
						snake_case   :							Optional[Any]						  =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:
						snake_case   :							str						  =None

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

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						snake_case   :							Tuple						  =model(**_lowerCamelCase					,						head_mask=_lowerCamelCase							)
						snake_case   :							Optional[int]						  =(
						    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							):
												snake_case   :							str						  =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:
									snake_case   :							Optional[int]						  =logits.detach().cpu().numpy()
									snake_case   :							Tuple						  =inputs["""labels"""].detach().cpu().numpy()
						else:
									snake_case   :							int						  =np.append(_lowerCamelCase					,						logits.detach().cpu().numpy()					,						axis=0							)
									snake_case   :							int						  =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:
						snake_case   :							int						  =2
						snake_case   :							Union[str, Any]						  =torch.pow(torch.pow(_lowerCamelCase					,						_lowerCamelCase							).sum(-1							)					,						1 / exponent							)
						head_importance /= norm_by_layer.unsqueeze(-1							) + 1e-20

			if not args.dont_normalize_global_importance:
						snake_case   :							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'''							)
			snake_case   :							List[Any]						  =torch.zeros(head_importance.numel()					,						dtype=torch.long					,						device=args.device							)
			snake_case   :							Union[str, Any]						  =torch.arange(
			    head_importance.numel()					,						device=args.device							)
			snake_case   :							Union[str, Any]						  =head_ranks.view_as(_lowerCamelCase							)
			print_ad_tensor(_lowerCamelCase							)

			return attn_entropy, head_importance, preds, labels
def 		_a   (  lowerCamelCase_					,						lowerCamelCase_					,						lowerCamelCase_							):
			snake_case   :							Optional[Any]						  =compute_heads_importance(_lowerCamelCase					,						_lowerCamelCase					,						_lowerCamelCase					,						compute_entropy=_lowerCamelCase							)
			snake_case   :							Optional[Any]						  =np.argmax(_lowerCamelCase					,						axis=1							) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase							)
			snake_case   :							List[Any]						  =glue_compute_metrics(args.task_name					,						_lowerCamelCase					,						_lowerCamelCase							)[args.metric_name]
			logger.info('''Pruning: original score: %f, threshold: %f'''					,						_lowerCamelCase					,						original_score * args.masking_threshold							)

			snake_case   :							Any						  =torch.ones_like(_lowerCamelCase							)
			snake_case   :							int						  =max(1					,						int(new_head_mask.numel() * args.masking_amount							)							)

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

						if len(_lowerCamelCase							) <= num_to_mask:
									break

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

						# Compute metric and head importance again
						snake_case   :							str						  =compute_heads_importance(
						    _lowerCamelCase					,						_lowerCamelCase					,						_lowerCamelCase					,						compute_entropy=_lowerCamelCase					,						head_mask=_lowerCamelCase							)
						snake_case   :							Any						  =np.argmax(_lowerCamelCase					,						axis=1							) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase							)
						snake_case   :							Optional[int]						  =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() * 1_00					,						)

			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_							):
			snake_case   :							Optional[int]						  =datetime.now()
			snake_case   :							int						  =compute_heads_importance(
			    _lowerCamelCase					,						_lowerCamelCase					,						_lowerCamelCase					,						compute_entropy=_lowerCamelCase					,						compute_importance=_lowerCamelCase					,						head_mask=_lowerCamelCase							)
			snake_case   :							str						  =np.argmax(_lowerCamelCase					,						axis=1							) if args.output_mode == """classification""" else np.squeeze(_lowerCamelCase							)
			snake_case   :							Any						  =glue_compute_metrics(args.task_name					,						_lowerCamelCase					,						_lowerCamelCase							)[args.metric_name]
			snake_case   :							List[str]						  =datetime.now() - before_time

			snake_case   :							Optional[Any]						  =sum(p.numel() for p in model.parameters()							)
			snake_case   :							List[str]						  ={
			    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							)
			snake_case   :							Union[str, Any]						  =sum(p.numel() for p in model.parameters()							)

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

			logger.info(
			    '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''					,						_lowerCamelCase					,						_lowerCamelCase					,						pruned_num_params / original_num_params * 1_00					,						)
			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 * 1_00							)
def 		_a   (  ):
			snake_case   :							List[str]						  =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=1_28					,						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.'''							)
			snake_case   :							Union[str, Any]						  =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:
						snake_case   :							List[Any]						  =torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
						snake_case   :							Union[str, Any]						  =0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank							)
						snake_case   :							Optional[int]						  =torch.device('''cuda'''					,						args.local_rank							)
						snake_case   :							str						  =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
			snake_case   :							List[str]						  =args.task_name.lower()
			if args.task_name not in glue_processors:
						raise ValueError('''Task not found: %s''' % (args.task_name)							)
			snake_case   :							Union[str, Any]						  =glue_processors[args.task_name]()
			snake_case   :							Optional[int]						  =glue_output_modes[args.task_name]
			snake_case   :							Optional[int]						  =processor.get_labels()
			snake_case   :							Tuple						  =len(_lowerCamelCase							)

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

			snake_case   :							List[str]						  =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					,						)
			snake_case   :							List[str]						  =AutoTokenizer.from_pretrained(
			    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path					,						cache_dir=args.cache_dir					,						)
			snake_case   :							Any						  =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:
						snake_case   :							Optional[Any]						  =nn.parallel.DistributedDataParallel(
						    _lowerCamelCase					,						device_ids=[args.local_rank]					,						output_device=args.local_rank					,						find_unused_parameters=_lowerCamelCase							)
			elif args.n_gpu > 1:
						snake_case   :							Union[str, Any]						  =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
			snake_case   :							List[str]						  =GlueDataset(_lowerCamelCase					,						tokenizer=_lowerCamelCase					,						mode='''dev'''							)
			if args.data_subset > 0:
						snake_case   :							Optional[int]						  =Subset(_lowerCamelCase					,						list(range(min(args.data_subset					,						len(_lowerCamelCase							)							)							)							)							)
			snake_case   :							Tuple						  =SequentialSampler(_lowerCamelCase							) if args.local_rank == -1 else DistributedSampler(_lowerCamelCase							)
			snake_case   :							Union[str, Any]						  =DataLoader(
			    _lowerCamelCase					,						sampler=_lowerCamelCase					,						batch_size=args.batch_size					,						collate_fn=_lowerCamelCase							)

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

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


if __name__ == "__main__":
						main()










'''simple docstring'''





import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import (
          MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      )

if is_tf_available() and is_tensorflow_probability_available():
      import tensorflow as tf
      import tensorflow_probability as tfp

      from ..models.auto.modeling_tf_auto import (
          TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
          TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      )





class 							lowerCAmelCase_		(	a_						):





    def __call__(    self    :    str,    _snake_case    :    Dict=None,    _snake_case    :    str=None,    **_snake_case    :    Any     ):



       '''simple docstring'''







       requires_backends(self,    '''pandas'''     )
       import pandas as pd

       if table is None:
          raise ValueError('''Keyword argument `table` cannot be None.'''     )
       elif query is None:
          if isinstance(_snake_case,    _snake_case     ) and table.get('''query'''     ) is not None and table.get('''table'''     ) is not None:
             snake_case   :							Union[str, Any]						  =[table]
          elif isinstance(_snake_case,    _snake_case     ) and len(_snake_case     ) > 0:
             if not all(isinstance(_snake_case,    _snake_case     ) for d in table     ):
                raise ValueError(
                    f'''Keyword argument `table` should be a list of dict, but is {(type(_snake_case     ) for d in table)}'''     )

             if table[0].get('''query'''     ) is not None and table[0].get('''table'''     ) is not None:
                snake_case   :							Dict						  =table
             else:
                raise ValueError(
                    '''If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'''
                    f''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''     )
          elif Dataset is not None and isinstance(_snake_case,    _snake_case     ) or isinstance(_snake_case,    types.GeneratorType     ):
             return table
          else:
             raise ValueError(
                 '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
                 f'''is {type(_snake_case     )})'''     )
       else:
          snake_case   :							Union[str, Any]						  =[{'''table''': table, '''query''': query}]

       for tqa_pipeline_input in tqa_pipeline_inputs:
          if not isinstance(tqa_pipeline_input['''table'''],    pd.DataFrame     ):
             if tqa_pipeline_input["table"] is None:
                raise ValueError('''Table cannot be None.'''     )

             snake_case   :							Any						  =pd.DataFrame(tqa_pipeline_input['''table''']     )

       return tqa_pipeline_inputs





@add_end_docstrings(a_						)
class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	'table,query'





    def __init__(    self    :    Any,    _snake_case    :    Any=TableQuestionAnsweringArgumentHandler(),    *_snake_case    :    List[Any],    **_snake_case    :    Dict     ):



       '''simple docstring'''







       super().__init__(*_snake_case,    **_snake_case     )
       snake_case   :							str						  =args_parser

       self.check_model_type(
           dict(
               TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
               + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )
           if self.framework == '''tf'''
           else dict(
               MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )     )

       snake_case   :							Dict						  =bool(getattr(self.model.config,    '''aggregation_labels''',    _snake_case     )     ) and bool(
           getattr(self.model.config,    '''num_aggregation_labels''',    _snake_case     )     )
       snake_case   :							List[str]						  ='''tapas''' if hasattr(self.model.config,    '''aggregation_labels'''     ) else None





    def 					__snake_case       (    self    :    Dict,    **_snake_case    :    Tuple     ):



       '''simple docstring'''







       return self.model(**_snake_case     )





    def 					__snake_case       (    self    :    List[Any],    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       if self.framework == "pt":
          snake_case   :							str						  =[]
          snake_case   :							str						  =[]
          snake_case   :							Optional[int]						  =None
          snake_case   :							List[str]						  =inputs['''input_ids'''].shape[0]

          snake_case   :							Union[str, Any]						  =inputs['''input_ids'''].to(self.device     )
          snake_case   :							List[str]						  =inputs['''attention_mask'''].to(self.device     )
          snake_case   :							Optional[int]						  =inputs['''token_type_ids'''].to(self.device     )
          snake_case   :							Any						  =None

          for index in range(_snake_case     ):
             # If sequences have already been processed, the token type IDs will be created according to the previous
             # answer.
             if prev_answers is not None:
                snake_case   :							Union[str, Any]						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							Any						  =np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

                snake_case   :							Any						  =token_type_ids[index]  # shape (seq_len, 7)
                for i in range(model_labels.shape[0]     ):
                   snake_case   :							Tuple						  =token_type_ids_example[:, 0].tolist()[i]
                   snake_case   :							Tuple						  =token_type_ids_example[:, 1].tolist()[i] - 1
                   snake_case   :							Optional[int]						  =token_type_ids_example[:, 2].tolist()[i] - 1

                   if row_id >= 0 and col_id >= 0 and segment_id == 1:
                      snake_case   :							List[Any]						  =int(prev_answers[(col_id, row_id)]     )

                snake_case   :							List[Any]						  =torch.from_numpy(_snake_case     ).type(torch.long     ).to(self.device     )

             snake_case   :							Tuple						  =input_ids[index]
             snake_case   :							Optional[int]						  =attention_mask[index]  # shape (seq_len,)
             snake_case   :							List[Any]						  =token_type_ids[index]  # shape (seq_len, 7)
             snake_case   :							Union[str, Any]						  =self.model(
                 input_ids=input_ids_example.unsqueeze(0     ),    attention_mask=attention_mask_example.unsqueeze(0     ),    token_type_ids=token_type_ids_example.unsqueeze(0     ),    )
             snake_case   :							int						  =outputs.logits

             if self.aggregate:
                all_aggregations.append(outputs.logits_aggregation     )

             all_logits.append(_snake_case     )

             snake_case   :							List[Any]						  =torch.distributions.Bernoulli(logits=_snake_case     )
             snake_case   :							Optional[Any]						  =dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
                 dist_per_token.probs.device     )

             snake_case   :							Dict						  =collections.defaultdict(_snake_case     )
             for i, p in enumerate(probabilities.squeeze().tolist()     ):
                snake_case   :							int						  =token_type_ids_example[:, 0].tolist()[i]
                snake_case   :							Optional[int]						  =token_type_ids_example[:, 1].tolist()[i] - 1
                snake_case   :							Dict						  =token_type_ids_example[:, 2].tolist()[i] - 1
                if col >= 0 and row >= 0 and segment_id == 1:
                   coords_to_probs[(col, row)].append(_snake_case     )

             snake_case   :							Union[str, Any]						  ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

          snake_case   :							Tuple						  =torch.cat(tuple(_snake_case     ),    0     )

          return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_snake_case     ),    0     ))
       else:
          snake_case   :							Tuple						  =[]
          snake_case   :							Optional[int]						  =[]
          snake_case   :							str						  =None
          snake_case   :							Dict						  =inputs['''input_ids'''].shape[0]

          snake_case   :							Optional[Any]						  =inputs['''input_ids''']
          snake_case   :							Optional[int]						  =inputs['''attention_mask''']
          snake_case   :							Union[str, Any]						  =inputs['''token_type_ids'''].numpy()
          snake_case   :							Tuple						  =None

          for index in range(_snake_case     ):
             # If sequences have already been processed, the token type IDs will be created according to the previous
             # answer.
             if prev_answers is not None:
                snake_case   :							Any						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							str						  =np.zeros_like(_snake_case,    dtype=np.intaa     )  # shape (seq_len,)

                snake_case   :							Any						  =token_type_ids[index]  # shape (seq_len, 7)
                for i in range(model_labels.shape[0]     ):
                   snake_case   :							Union[str, Any]						  =token_type_ids_example[:, 0].tolist()[i]
                   snake_case   :							Dict						  =token_type_ids_example[:, 1].tolist()[i] - 1
                   snake_case   :							Any						  =token_type_ids_example[:, 2].tolist()[i] - 1

                   if row_id >= 0 and col_id >= 0 and segment_id == 1:
                      snake_case   :							Any						  =int(prev_answers[(col_id, row_id)]     )

                snake_case   :							Union[str, Any]						  =model_labels

             snake_case   :							List[Any]						  =input_ids[index]
             snake_case   :							Tuple						  =attention_mask[index]  # shape (seq_len,)
             snake_case   :							Optional[Any]						  =token_type_ids[index]  # shape (seq_len, 7)
             snake_case   :							Optional[Any]						  =self.model(
                 input_ids=np.expand_dims(_snake_case,    axis=0     ),    attention_mask=np.expand_dims(_snake_case,    axis=0     ),    token_type_ids=np.expand_dims(_snake_case,    axis=0     ),    )
             snake_case   :							List[str]						  =outputs.logits

             if self.aggregate:
                all_aggregations.append(outputs.logits_aggregation     )

             all_logits.append(_snake_case     )

             snake_case   :							int						  =tfp.distributions.Bernoulli(logits=_snake_case     )
             snake_case   :							Union[str, Any]						  =dist_per_token.probs_parameter() * tf.cast(_snake_case,    tf.floataa     )

             snake_case   :							List[Any]						  =collections.defaultdict(_snake_case     )
             snake_case   :							Dict						  =token_type_ids_example
             for i, p in enumerate(tf.squeeze(_snake_case     ).numpy().tolist()     ):
                snake_case   :							Optional[Any]						  =token_type_ids_example[:, 0].tolist()[i]
                snake_case   :							Optional[Any]						  =token_type_ids_example[:, 1].tolist()[i] - 1
                snake_case   :							str						  =token_type_ids_example[:, 2].tolist()[i] - 1
                if col >= 0 and row >= 0 and segment_id == 1:
                   coords_to_probs[(col, row)].append(_snake_case     )

             snake_case   :							Optional[int]						  ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

          snake_case   :							Dict						  =tf.concat(tuple(_snake_case     ),    0     )

          return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_snake_case     ),    0     ))





    def __call__(    self    :    List[str],    *_snake_case    :    Any,    **_snake_case    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Tuple						  =self._args_parser(*_snake_case,    **_snake_case     )

       snake_case   :							Dict						  =super().__call__(_snake_case,    **_snake_case     )
       if len(_snake_case     ) == 1:
          return results[0]
       return results





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    List[Any]=None,    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  ={}
       if padding is not None:
          snake_case   :							List[Any]						  =padding
       if truncation is not None:
          snake_case   :							int						  =truncation

       snake_case   :							Any						  ={}
       if sequential is not None:
          snake_case   :							int						  =sequential
       return preprocess_params, forward_params, {}





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Dict,    _snake_case    :    List[Any]=None,    _snake_case    :    Optional[int]=True,    _snake_case    :    Dict=None     ):



       '''simple docstring'''







       if truncation is None:
          if self.type == "tapas":
             snake_case   :							Union[str, Any]						  ='''drop_rows_to_fit'''
          else:
             snake_case   :							Dict						  ='''do_not_truncate'''

       snake_case  ,    snake_case   :							Optional[int]						  =pipeline_input['''table'''], pipeline_input['''query''']
       if table.empty:
          raise ValueError('''table is empty'''     )
       if query is None or query == "":
          raise ValueError('''query is empty'''     )
       snake_case   :							Dict						  =self.tokenizer(_snake_case,    _snake_case,    return_tensors=self.framework,    truncation=_snake_case,    padding=_snake_case     )
       snake_case   :							List[Any]						  =table
       return inputs





    def 					__snake_case       (    self    :    List[str],    _snake_case    :    List[Any],    _snake_case    :    Any=False     ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  =model_inputs.pop('''table'''     )

       if self.type == "tapas":
          if sequential:
             snake_case   :							Dict						  =self.sequential_inference(**_snake_case     )
          else:
             snake_case   :							Optional[int]						  =self.batch_inference(**_snake_case     )
       else:
          snake_case   :							str						  =self.model.generate(**_snake_case     )
       snake_case   :							int						  ={'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
       return model_outputs






    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							str						  =model_outputs['''model_inputs''']
       snake_case   :							Tuple						  =model_outputs['''table''']
       snake_case   :							Union[str, Any]						  =model_outputs['''outputs''']
       if self.type == "tapas":
          if self.aggregate:
             snake_case  ,    snake_case   :							Union[str, Any]						  =outputs[:2]
             snake_case   :							Tuple						  =self.tokenizer.convert_logits_to_predictions(_snake_case,    _snake_case,    _snake_case     )
             snake_case  ,    snake_case   :							Any						  =predictions
             snake_case   :							int						  ={i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_snake_case     )}

             snake_case   :							List[str]						  =self.model.config.no_aggregation_label_index
             snake_case   :							Dict						  ={
                 i: aggregators[i] + ''' > ''' for i, pred in enumerate(_snake_case     ) if pred != no_agg_label_index
             }
          else:
             snake_case   :							List[str]						  =outputs[0]
             snake_case   :							List[Any]						  =self.tokenizer.convert_logits_to_predictions(_snake_case,    _snake_case     )
             snake_case   :							str						  =predictions[0]
             snake_case   :							str						  ={}
             snake_case   :							str						  ={}
          snake_case   :							Optional[int]						  =[]
          for index, coordinates in enumerate(_snake_case     ):
             snake_case   :							int						  =[table.iat[coordinate] for coordinate in coordinates]
             snake_case   :							Dict						  =aggregators.get(_snake_case,    ''''''     )
             snake_case   :							Any						  =aggregators_prefix.get(_snake_case,    ''''''     )
             snake_case   :							int						  ={
                 '''answer''': aggregator_prefix + ''', '''.join(_snake_case     ),
                 '''coordinates''': coordinates,
                 '''cells''': [table.iat[coordinate] for coordinate in coordinates],
             }
             if aggregator:
                snake_case   :							Tuple						  =aggregator

             answers.append(_snake_case     )
          if len(_snake_case     ) == 0:
             raise PipelineException('''Empty answer'''     )
       else:
          snake_case   :							Optional[int]						  =[{'''answer''': answer} for answer in self.tokenizer.batch_decode(_snake_case,    skip_special_tokens=_snake_case     )]

       return answers if len(_snake_case     ) > 1 else answers[0]





