




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       a__   ( snake_case__				):
						_a							:   str =   ["""image_processor""", """tokenizer"""]
						_a							:   List[str] =   """Pix2StructImageProcessor"""
						_a							:   List[str] =   ("""T5Tokenizer""", """T5TokenizerFast""")






						def __init__(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	False
											super().__init__(_A		,      _A      )






						def __call__(       self		,      _A=None		,      _A = None		,      _A = True		,      _A = False		,      _A = None		,      _A = None		,      _A = 2_0_4_8		,      _A = 0		,      _A = None		,      _A = None		,      _A = False		,      _A = False		,      _A = False		,      _A = False		,      _A = False		,      _A = True		,      _A = None		,      **_A		,      ):




											"""simple docstring"""



											if images is None and text is None:
																raise ValueError("You have to specify either images or text."      )

											# Get only text
											if images is None and not self.image_processor.is_vqa:
																__lowerCAmelCase           =	self.tokenizer
																__lowerCAmelCase           =	self.tokenizer(
																    text=_A		,      add_special_tokens=_A		,      padding=_A		,      truncation=_A		,      max_length=_A		,      stride=_A		,      pad_to_multiple_of=_A		,      return_attention_mask=_A		,      return_overflowing_tokens=_A		,      return_special_tokens_mask=_A		,      return_offsets_mapping=_A		,      return_token_type_ids=_A		,      return_length=_A		,      verbose=_A		,      return_tensors=_A		,      **_A		,      )
																return text_encoding

											if not self.image_processor.is_vqa:
																# add pixel_values
																__lowerCAmelCase           =	self.image_processor(
																    _A		,      return_tensors=_A		,      max_patches=_A		,      **_A      )
											else:
																# add pixel_values and bbox
																__lowerCAmelCase           =	self.image_processor(
																    _A		,      return_tensors=_A		,      max_patches=_A		,      header_text=_A		,      **_A      )

											if text is not None and not self.image_processor.is_vqa:
																__lowerCAmelCase           =	self.tokenizer(
																    text=_A		,      add_special_tokens=_A		,      padding=_A		,      truncation=_A		,      max_length=_A		,      stride=_A		,      pad_to_multiple_of=_A		,      return_attention_mask=_A		,      return_overflowing_tokens=_A		,      return_special_tokens_mask=_A		,      return_offsets_mapping=_A		,      return_token_type_ids=_A		,      return_length=_A		,      verbose=_A		,      return_tensors=_A		,      **_A		,      )

																if "attention_mask" in text_encoding:
																					__lowerCAmelCase           =	text_encoding.pop("attention_mask"      )
																if "input_ids" in text_encoding:
																					__lowerCAmelCase           =	text_encoding.pop("input_ids"      )
											else:
																__lowerCAmelCase           =	None

											if text_encoding is not None:
																encoding_image_processor.update(_A      )

											return encoding_image_processor






						def     __SCREAMING_SNAKE_CASE(       self		,      *_A		,      **_A      ):




											"""simple docstring"""



											return self.tokenizer.batch_decode(*_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      *_A		,      **_A      ):




											"""simple docstring"""



											return self.tokenizer.decode(*_A		,      **_A      )






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer.model_input_names
											__lowerCAmelCase           =	self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )










import argparse
import os

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
# and perform gradient accumulation
#
# 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
#
########################################################################


UpperCamelCase__         =      16
UpperCamelCase__         =      32







def      _a				(						SCREAMING_SNAKE_CASE_							:    Accelerator				,				SCREAMING_SNAKE_CASE_							:    int = 16					):
					__lowerCAmelCase           =	AutoTokenizer.from_pretrained("bert-base-cased"					)
					__lowerCAmelCase           =	load_dataset("glue"				,				"mrpc"					)

					def tokenize_function(SCREAMING_SNAKE_CASE_							:    str					):
										# max_length=None => use the model max length (it's actually the default)
										__lowerCAmelCase           =	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           =	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           =	tokenized_datasets.rename_column("label"				,				"labels"					)

					def collate_fn(SCREAMING_SNAKE_CASE_							:    int					):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__lowerCAmelCase           =	1_28 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           =	16
										elif accelerator.mixed_precision != "no":
															__lowerCAmelCase           =	8
										else:
															__lowerCAmelCase           =	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           =	DataLoader(
					    tokenized_datasets["train"]				,				shuffle=SCREAMING_SNAKE_CASE_				,				collate_fn=SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	DataLoader(
					    tokenized_datasets["validation"]				,				shuffle=SCREAMING_SNAKE_CASE_				,				collate_fn=SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_					)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
					from accelerate.test_utils.training import mocked_dataloaders

					UpperCamelCase__         =      mocked_dataloaders  # noqa: F811







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str					):
					# For testing only
					if os.environ.get("TESTING_MOCKED_DATALOADERS"				,				SCREAMING_SNAKE_CASE_					) == "1":
										__lowerCAmelCase           =	2
					# New Code #
					__lowerCAmelCase           =	int(args.gradient_accumulation_steps					)
					# Initialize accelerator
					__lowerCAmelCase           =	Accelerator(
					    cpu=args.cpu				,				mixed_precision=args.mixed_precision				,				gradient_accumulation_steps=SCREAMING_SNAKE_CASE_					)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"					)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__lowerCAmelCase           =	config["lr"]
					__lowerCAmelCase           =	int(config["num_epochs"]					)
					__lowerCAmelCase           =	int(config["seed"]					)
					__lowerCAmelCase           =	int(config["batch_size"]					)

					__lowerCAmelCase           =	evaluate.load("glue"				,				"mrpc"					)

					set_seed(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase       ,							__lowerCAmelCase           =	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           =	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           =	model.to(accelerator.device					)

					# Instantiate optimizer
					__lowerCAmelCase           =	AdamW(params=model.parameters()				,				lr=SCREAMING_SNAKE_CASE_					)

					# Instantiate scheduler
					__lowerCAmelCase           =	get_linear_schedule_with_warmup(
					    optimizer=SCREAMING_SNAKE_CASE_				,				num_warmup_steps=1_00				,				num_training_steps=(len(SCREAMING_SNAKE_CASE_					) * num_epochs)				,				)

					# 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       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase       ,							__lowerCAmelCase           =	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					)
															# New code #
															# We use the new `accumulate` context manager to perform gradient accumulation
															# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
															with accelerator.accumulate(SCREAMING_SNAKE_CASE_					):
																				__lowerCAmelCase           =	model(**SCREAMING_SNAKE_CASE_					)
																				__lowerCAmelCase           =	output.loss
																				accelerator.backward(SCREAMING_SNAKE_CASE_					)
																				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           =	model(**SCREAMING_SNAKE_CASE_					)
															__lowerCAmelCase           =	outputs.logits.argmax(dim=-1					)
															__lowerCAmelCase       ,							__lowerCAmelCase           =	accelerator.gather_for_metrics((predictions, batch["labels"])					)
															metric.add_batch(
															    predictions=SCREAMING_SNAKE_CASE_				,				references=SCREAMING_SNAKE_CASE_				,				)

										__lowerCAmelCase           =	metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					__lowerCAmelCase           =	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."				,				)
					# New Code #
					parser.add_argument(
					    "--gradient_accumulation_steps"				,				type=SCREAMING_SNAKE_CASE_				,				default=1				,				help="The number of minibatches to be ran before gradients are accumulated."				,				)
					parser.add_argument("--cpu"				,				action="store_true"				,				help="If passed, will train on the CPU."					)
					__lowerCAmelCase           =	parser.parse_args()
					__lowerCAmelCase           =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					main()




