


"""simple docstring"""







import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase__				    =       16
lowerCAmelCase__				    =       32
def  a__      (    SCREAMING_SNAKE_CASE   :   Accelerator    ,				SCREAMING_SNAKE_CASE   :   int = 1_6    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[Any]								=							AutoTokenizer.from_pretrained("bert-base-cased"    )
		lowerCAmelCase					:					Tuple								=							load_dataset("glue"    ,				"mrpc"    )

		def tokenize_function(SCREAMING_SNAKE_CASE   :   List[str]    ):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase					:					Union[str, Any]								=							tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=SCREAMING_SNAKE_CASE    ,				max_length=SCREAMING_SNAKE_CASE    )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowerCAmelCase					:					Any								=							datasets.map(
				    SCREAMING_SNAKE_CASE    ,				batched=SCREAMING_SNAKE_CASE    ,				remove_columns=["idx", "sentence1", "sentence2"]    ,				)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase					:					str								=							tokenized_datasets.rename_column("label"    ,				"labels"    )

		def collate_fn(SCREAMING_SNAKE_CASE   :   Optional[int]    ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase					:					Union[str, Any]								=							1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowerCAmelCase					:					int								=							1_6
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase					:					Optional[Any]								=							8
				else:
						lowerCAmelCase					:					str								=							None

				return tokenizer.pad(
				    SCREAMING_SNAKE_CASE    ,				padding="longest"    ,				max_length=SCREAMING_SNAKE_CASE    ,				pad_to_multiple_of=SCREAMING_SNAKE_CASE    ,				return_tensors="pt"    ,				)

		# Instantiate dataloaders.
		lowerCAmelCase					:					List[str]								=							DataLoader(
		    tokenized_datasets["train"]    ,				shuffle=SCREAMING_SNAKE_CASE    ,				collate_fn=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				drop_last=SCREAMING_SNAKE_CASE    )
		lowerCAmelCase					:					Dict								=							DataLoader(
		    tokenized_datasets["validation"]    ,				shuffle=SCREAMING_SNAKE_CASE    ,				collate_fn=SCREAMING_SNAKE_CASE    ,				batch_size=SCREAMING_SNAKE_CASE    ,				drop_last=(accelerator.mixed_precision == "fp8")    ,				)

		return train_dataloader, eval_dataloader
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   str    ):


		'''simple docstring'''







		lowerCAmelCase					:					int								=							Accelerator(cpu=args.cpu    ,				mixed_precision=args.mixed_precision    )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase					:					List[Any]								=							config['lr']
		lowerCAmelCase					:					Any								=							int(config["num_epochs"]    )
		lowerCAmelCase					:					Tuple								=							int(config["seed"]    )
		lowerCAmelCase					:					Optional[Any]								=							int(config["batch_size"]    )

		lowerCAmelCase					:					Union[str, Any]								=							evaluate.load("glue"    ,				"mrpc"    )

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase					:					Dict								=							1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase					:					str								=							batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase					:					List[str]								=							MAX_GPU_BATCH_SIZE

		set_seed(SCREAMING_SNAKE_CASE    )
		lowerCAmelCase					:					Optional[Any]								=							get_dataloaders(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase					:					Optional[Any]								=							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    ,				return_dict=SCREAMING_SNAKE_CASE    )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowerCAmelCase					:					Dict								=							model.to(accelerator.device    )
		# Instantiate optimizer
		lowerCAmelCase					:					Dict								=							AdamW(params=model.parameters()    ,				lr=SCREAMING_SNAKE_CASE    )

		# Instantiate scheduler
		lowerCAmelCase					:					Optional[int]								=							get_linear_schedule_with_warmup(
		    optimizer=SCREAMING_SNAKE_CASE    ,				num_warmup_steps=1_0_0    ,				num_training_steps=(len(SCREAMING_SNAKE_CASE    ) * num_epochs) // gradient_accumulation_steps    ,				)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.

		lowerCAmelCase					:					Optional[Any]								=							accelerator.prepare(
		    SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )

		# Now we train the model
		for epoch in range(SCREAMING_SNAKE_CASE    ):
				model.train()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						lowerCAmelCase					:					Optional[int]								=							model(**SCREAMING_SNAKE_CASE    )
						lowerCAmelCase					:					Optional[int]								=							outputs.loss
						lowerCAmelCase					:					Optional[int]								=							loss / gradient_accumulation_steps
						accelerator.backward(SCREAMING_SNAKE_CASE    )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(SCREAMING_SNAKE_CASE    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						with torch.no_grad():
								lowerCAmelCase					:					List[str]								=							model(**SCREAMING_SNAKE_CASE    )
						lowerCAmelCase					:					Optional[Any]								=							outputs.logits.argmax(dim=-1    )
						lowerCAmelCase					:					int								=							accelerator.gather_for_metrics((predictions, batch["labels"])    )
						metric.add_batch(
						    predictions=SCREAMING_SNAKE_CASE    ,				references=SCREAMING_SNAKE_CASE    ,				)

				lowerCAmelCase					:					List[str]								=							metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f"""epoch {epoch}:"""    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    ):


		'''simple docstring'''







		lowerCAmelCase					:					Any								=							argparse.ArgumentParser(description="Simple example of training script."    )
		parser.add_argument(
		    "--mixed_precision"    ,				type=SCREAMING_SNAKE_CASE    ,				default=SCREAMING_SNAKE_CASE    ,				choices=["no", "fp16", "bf16", "fp8"]    ,				help="Whether to use mixed precision. Choose"
		    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
		    "and an Nvidia Ampere GPU."    ,				)
		parser.add_argument("--cpu"    ,				action="store_true"    ,				help="If passed, will train on the CPU."    )
		lowerCAmelCase					:					List[Any]								=							parser.parse_args()
		lowerCAmelCase					:					Any								=							{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
		training_function(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
					main()









"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase__				    =       {
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,					**snake_case__						)

         # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
         lowerCAmelCase					:					List[Any]								=							vocab_size
         lowerCAmelCase					:					List[str]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					List[str]								=							num_attention_heads
         lowerCAmelCase					:					Optional[Any]								=							hidden_act
         lowerCAmelCase					:					List[Any]								=							intermediate_size
         lowerCAmelCase					:					Optional[Any]								=							hidden_dropout_prob
         lowerCAmelCase					:					str								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							max_position_embeddings
         lowerCAmelCase					:					Dict								=							type_vocab_sizes
         lowerCAmelCase					:					Union[str, Any]								=							initializer_range
         lowerCAmelCase					:					str								=							layer_norm_eps

         # Fine-tuning task hyperparameters
         lowerCAmelCase					:					Dict								=							positive_label_weight
         lowerCAmelCase					:					Union[str, Any]								=							num_aggregation_labels
         lowerCAmelCase					:					Optional[Any]								=							aggregation_loss_weight
         lowerCAmelCase					:					List[Any]								=							use_answer_as_supervision
         lowerCAmelCase					:					Dict								=							answer_loss_importance
         lowerCAmelCase					:					List[Any]								=							use_normalized_answer_loss
         lowerCAmelCase					:					List[str]								=							huber_loss_delta
         lowerCAmelCase					:					Optional[int]								=							temperature
         lowerCAmelCase					:					Optional[int]								=							aggregation_temperature
         lowerCAmelCase					:					Any								=							use_gumbel_for_cells
         lowerCAmelCase					:					Union[str, Any]								=							use_gumbel_for_aggregation
         lowerCAmelCase					:					Union[str, Any]								=							average_approximation_function
         lowerCAmelCase					:					int								=							cell_selection_preference
         lowerCAmelCase					:					Dict								=							answer_loss_cutoff
         lowerCAmelCase					:					Optional[int]								=							max_num_rows
         lowerCAmelCase					:					Union[str, Any]								=							max_num_columns
         lowerCAmelCase					:					Any								=							average_logits_per_cell
         lowerCAmelCase					:					List[Any]								=							select_one_column
         lowerCAmelCase					:					Tuple								=							allow_empty_column_selection
         lowerCAmelCase					:					str								=							init_cell_selection_weights_to_zero
         lowerCAmelCase					:					List[Any]								=							reset_position_index_per_cell
         lowerCAmelCase					:					Optional[Any]								=							disable_per_token_loss

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





