





import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  List[Any]				,						a					:  Tuple				,						a					:  Dict=False				,						a					:  Union[str, Any]=10_24				,						a					:  List[str]=10_24				,						a					:  Optional[Any]=3.6				,						a					:  List[Any]=False				,						)						->List[str]:
													SCREAMING_SNAKE_CASE__  :					str =       tokenizer
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tokenizer.bos_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataset
													SCREAMING_SNAKE_CASE__  :					List[Any] =       seq_length
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       infinite
													SCREAMING_SNAKE_CASE__  :					Tuple =       0
													SCREAMING_SNAKE_CASE__  :					Dict =       tokenized

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

							def __iter__(   self					:  Union[str, Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       iter(self.dataset     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
													while more_examples:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [], 0
																			while True:
																									if buffer_len >= self.max_buffer_size:
																															break
																									try:
																															buffer.append(next(a     )[self.content_field]     )
																															buffer_len += len(buffer[-1]     )
																									except StopIteration:
																															if self.infinite:
																																					SCREAMING_SNAKE_CASE__  :					Dict =       iter(self.dataset     )
																																					self.epoch += 1
																																					logger.info(f"""Dataset epoch: {self.epoch}"""     )
																															else:
																																					SCREAMING_SNAKE_CASE__  :					int =       False
																																					break
																			if self.tokenized:
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       buffer
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer(a				,						truncation=a     )["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Tuple =       []
																			for tokenized_input in tokenized_inputs:
																									all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																			for i in range(0				,						len(a     )				,						self.seq_length     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       all_token_ids[i : i + self.seq_length]
																									if len(a     ) == self.seq_length:
																															self.current_size += 1
																															yield torch.tensor(a     )






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       args.model_ckpt.split("/"  )[-1]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       logging.getLogger(__name__  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       Path(args.save_dir  ) / "log/"
						log_dir.mkdir(exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       f"""debug_{accelerator.process_index}.log"""
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      , datefmt="%m/%d/%Y %H:%M:%S"      , level=logging.INFO      , handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()]      , )
						if accelerator.is_main_process:  # we only want to setup logging once
												accelerator.init_trackers(_lowerCamelCase      , vars(_lowerCamelCase  )  )
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO  )
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ""
												logger.setLevel(logging.ERROR  )
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay  ):
																		params_without_wd.append(_lowerCamelCase  )
												else:
																		params_with_wd.append(_lowerCamelCase  )
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						logger.info(f"""Step {step}: {metrics}"""  )
						if accelerator.is_main_process:
												accelerator.log(_lowerCamelCase      , _lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						model.eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for step, batch in enumerate(_lowerCamelCase  ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE__  :					List[str] =       model(_lowerCamelCase      , labels=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       outputs.loss.repeat(args.valid_batch_size  )
												losses.append(accelerator.gather(_lowerCamelCase  )  )
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						SCREAMING_SNAKE_CASE__  :					str =       torch.cat(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       losses[: eval_dataloader.dataset.current_size].mean()
						try:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.exp(_lowerCamelCase  )
						except OverflowError:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       float("inf"  )
						return loss.item(), perplexity.item()


# Settings
__lowercase							:List[Any]							=	HfArgumentParser(TrainingArguments)
__lowercase							:Any							=	parser.parse_args()

# Accelerator
__lowercase							:List[Any]							=	ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
__lowercase							:Optional[int]							=	Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
__lowercase							:str							=	{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				__lowercase							:Dict							=	Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
				hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase							,  __lowercase							:Any							=	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)







def    UpperCAmelCase				(  ):





						'''simple docstring'''


						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Dict							=	accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
								accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
								accelerator.load_state(args.resume_from_checkpoint)
								__lowercase							:List[str]							=	os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								__lowercase							:Union[str, Any]							=	[f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
								dirs.sort(key=os.path.getctime)
								__lowercase							:Dict							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				__lowercase							:Any							=	os.path.splitext(path)[0]
				__lowercase							:str							=	int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowercase							:int							=	0
__lowercase							:List[Any]							=	time.time()
__lowercase							:Any							=	0
for step, batch in enumerate(train_dataloader, start=1):
				if args.resume_from_checkpoint and step < resume_step:
								continue  # we need to skip steps until we reach the resumed step
				__lowercase							:List[str]							=	model(batch, labels=batch, use_cache=False).loss
				__lowercase							:Union[str, Any]							=	accelerator.gather(loss.repeat(args.train_batch_size)).mean()
				loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
				log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
				__lowercase							:List[Any]							=	loss / args.gradient_accumulation_steps
				if step % args.gradient_accumulation_steps != 0:
								# Prevent backward from doing gradient all_reduce in every step
								if accelerator.distributed_type == DistributedType.MULTI_GPU:
												with model.no_sync():
																accelerator.backward(loss)
								else:
												accelerator.backward(loss)
				else:
								__lowercase							:List[Any]							=	get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								__lowercase							:str							=	time.time() - t_start
								__lowercase							:List[str]							=	compute_tflops(elapsed_time, accelerator, args)
								log_metrics(
								    step,
								    {
								        "steps": completed_steps,
								        "loss/train": loss_tracking,
								        "lr": lr,
								        "tflops": tflops,
								        "time_per_iteration": elapsed_time,
								    },
								)
								__lowercase							:str							=	time.time()
								__lowercase							:Optional[int]							=	0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								__lowercase							:int							=	os.path.join(args.save_dir, f"step_{step}")
								accelerator.save_state(save_dir)
								if accelerator.is_main_process:
												hf_repo.push_to_hub(commit_message=f"step {step}")
								model.train()
				if completed_steps >= args.max_train_steps:
								break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowercase							:List[str]							=	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowercase							:List[Any]							=	os.path.join(args.save_dir, f"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
				hf_repo.push_to_hub(commit_message="final model")






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  _a  (					lowercase__				):






							"""simple docstring"""

							def __call__(   self					:  List[Any]				,						a					:  str=None				,						a					:  Dict=None				,						**a					:  Tuple     )						->Dict:
													# Returns tqa_pipeline_inputs of shape:
													# [
													#   {"table": pd.DataFrame, "query": List[str]},
													#   ...,
													#   {"table": pd.DataFrame, "query" : List[str]}
													# ]
													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(a				,						a     ) and table.get("query"     ) is not None and table.get("table"     ) is not None:
																									SCREAMING_SNAKE_CASE__  :					int =       [table]
																			elif isinstance(a				,						a     ) and len(a     ) > 0:
																									if not all(isinstance(a				,						a     ) for d in table     ):
																															raise ValueError(
																															    f"""Keyword argument `table` should be a list of dict, but is {(type(a     ) for d in table)}"""     )

																									if table[0].get("query"     ) is not None and table[0].get("table"     ) is not None:
																															SCREAMING_SNAKE_CASE__  :					List[str] =       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(a				,						a     ) or isinstance(a				,						types.GeneratorType     ):
																									return table
																			else:
																									raise ValueError(
																									    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
																									    f"""is {type(a     )})"""     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[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."     )

																									SCREAMING_SNAKE_CASE__  :					Dict =       pd.DataFrame(tqa_pipeline_input["table"]     )

													return tqa_pipeline_inputs







@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "table,query"

							def __init__(   self					:  List[str]				,						a					:  Optional[int]=TableQuestionAnsweringArgumentHandler()				,						*a					:  List[str]				,						**a					:  Dict     )						->Any:
													super().__init__(*a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       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()     )     )

													SCREAMING_SNAKE_CASE__  :					str =       bool(getattr(self.model.config				,						"aggregation_labels"				,						a     )     ) and bool(
													    getattr(self.model.config				,						"num_aggregation_labels"				,						a     )     )
													SCREAMING_SNAKE_CASE__  :					int =       "tapas" if hasattr(self.model.config				,						"aggregation_labels"     ) else None

							def   A_	(   self					:  List[str]				,						**a					:  Any     )						->Optional[Any]:
													return self.model(**a     )

							def   A_	(   self					:  Any				,						**a					:  Any     )						->Dict:
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					int =       []
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

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

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(a     ).type(torch.long     ).to(self.device     )

																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       input_ids[index]
																									SCREAMING_SNAKE_CASE__  :					Any =       attention_mask[index]  # shape (seq_len,)
																									SCREAMING_SNAKE_CASE__  :					List[str] =       token_type_ids[index]  # shape (seq_len, 7)
																									SCREAMING_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     )				,						)
																									SCREAMING_SNAKE_CASE__  :					Any =       outputs.logits

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

																									all_logits.append(a     )

																									SCREAMING_SNAKE_CASE__  :					Any =       torch.distributions.Bernoulli(logits=a     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
																									    dist_per_token.probs.device     )

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

																									SCREAMING_SNAKE_CASE__  :					Tuple =       {key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

																			SCREAMING_SNAKE_CASE__  :					List[str] =       torch.cat(tuple(a     )				,						0     )

																			return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a     )				,						0     ))
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

																			SCREAMING_SNAKE_CASE__  :					Tuple =       inputs["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["attention_mask"]
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       inputs["token_type_ids"].numpy()
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Any =       model_labels

																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       input_ids[index]
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       attention_mask[index]  # shape (seq_len,)
																									SCREAMING_SNAKE_CASE__  :					Any =       token_type_ids[index]  # shape (seq_len, 7)
																									SCREAMING_SNAKE_CASE__  :					Any =       self.model(
																									    input_ids=np.expand_dims(a				,						axis=0     )				,						attention_mask=np.expand_dims(a				,						axis=0     )				,						token_type_ids=np.expand_dims(a				,						axis=0     )				,						)
																									SCREAMING_SNAKE_CASE__  :					str =       outputs.logits

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

																									all_logits.append(a     )

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

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

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

																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.concat(tuple(a     )				,						0     )

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

							def __call__(   self					:  Optional[Any]				,						*a					:  Any				,						**a					:  Optional[int]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       self._args_parser(*a				,						**a     )

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

							def   A_	(   self					:  Any				,						a					:  Dict=None				,						a					:  Dict=None				,						a					:  Union[str, Any]=None				,						**a					:  int     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       {}
													if padding is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padding
													if truncation is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       truncation

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {}
													if sequential is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       sequential
													return preprocess_params, forward_params, {}

							def   A_	(   self					:  Any				,						a					:  Any				,						a					:  Union[str, Any]=None				,						a					:  Union[str, Any]=True				,						a					:  Dict=None     )						->int:
													if truncation is None:
																			if self.type == "tapas":
																									SCREAMING_SNAKE_CASE__  :					Any =       "drop_rows_to_fit"
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       "do_not_truncate"

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       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"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.tokenizer(a				,						a				,						return_tensors=self.framework				,						truncation=a				,						padding=a     )
													SCREAMING_SNAKE_CASE__  :					str =       table
													return inputs

							def   A_	(   self					:  Any				,						a					:  Tuple				,						a					:  Union[str, Any]=False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_inputs.pop("table"     )

													if self.type == "tapas":
																			if sequential:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       self.sequential_inference(**a     )
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[Any] =       self.batch_inference(**a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.model.generate(**a     )
													SCREAMING_SNAKE_CASE__  :					str =       {"model_inputs": model_inputs, "table": table, "outputs": outputs}
													return model_outputs






							def   A_	(   self					:  str				,						a					:  Any     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["model_inputs"]
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model_outputs["table"]
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["outputs"]
													if self.type == "tapas":
																			if self.aggregate:
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       outputs[:2]
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer.convert_logits_to_predictions(a				,						a				,						a     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       predictions
																									SCREAMING_SNAKE_CASE__  :					Tuple =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(a     )}

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

																									answers.append(a     )
																			if len(a     ) == 0:
																									raise PipelineException("Empty answer"     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [{"answer": answer} for answer in self.tokenizer.batch_decode(a				,						skip_special_tokens=a     )]

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