


'''simple docstring'''
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,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__magic_name__ : Union[str, Any]					=16
__magic_name__ : Dict					=32




def   __snake_case							(							lowerCamelCase_			:					List[Any]						,	lowerCamelCase_			:					Optional[int] = 16    ):
				'''simple docstring'''




				__magic_name__							=			AutoTokenizer.from_pretrained("bert-base-cased"    )
				__magic_name__							=			load_dataset("glue"						,	"mrpc"    )

				def tokenize_function(lowerCamelCase_			:					Optional[Any]    ):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__							=			tokenizer(examples["sentence1"]						,	examples["sentence2"]						,	truncation=lowerCamelCase_						,	max_length=lowerCamelCase_    )
								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():
								__magic_name__							=			datasets.map(
								    lowerCamelCase_						,	batched=lowerCamelCase_						,	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
				__magic_name__							=			tokenized_datasets.rename_column("label"						,	"labels"    )

				def collate_fn(lowerCamelCase_			:					str    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__magic_name__							=			128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__magic_name__							=			16
								elif accelerator.mixed_precision != "no":
												__magic_name__							=			8
								else:
												__magic_name__							=			None

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

				# Instantiate dataloaders.
				__magic_name__							=			DataLoader(
				    tokenized_datasets["train"]						,	shuffle=lowerCamelCase_						,	collate_fn=lowerCamelCase_						,	batch_size=lowerCamelCase_    )
				__magic_name__							=			DataLoader(
				    tokenized_datasets["validation"]						,	shuffle=lowerCamelCase_						,	collate_fn=lowerCamelCase_						,	batch_size=lowerCamelCase_    )

				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

						__magic_name__ : str					=mocked_dataloaders  # noqa: F811




def   __snake_case							(							lowerCamelCase_			:					Any						,	lowerCamelCase_			:					Optional[int]    ):
				'''simple docstring'''




				if os.environ.get("TESTING_MOCKED_DATALOADERS"						,	lowerCamelCase_    ) == "1":
								__magic_name__							=			2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__magic_name__							=			Accelerator(
								    cpu=args.cpu						,	mixed_precision=args.mixed_precision						,	log_with="all"						,	project_dir=args.project_dir    )
				else:
								__magic_name__							=			Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__							=			config["lr"]
				__magic_name__							=			int(config["num_epochs"]    )
				__magic_name__							=			int(config["seed"]    )
				__magic_name__							=			int(config["batch_size"]    )
				set_seed(lowerCamelCase_    )

				__magic_name__	,   __magic_name__							=			get_dataloaders(lowerCamelCase_						,	lowerCamelCase_    )
				__magic_name__							=			evaluate.load("glue"						,	"mrpc"    )

				# If the batch size is too big we use gradient accumulation
				__magic_name__							=			1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__magic_name__							=			batch_size // MAX_GPU_BATCH_SIZE
								__magic_name__							=			MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__							=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,	return_dict=lowerCamelCase_    )

				# 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).
				__magic_name__							=			model.to(accelerator.device    )

				# Instantiate optimizer
				__magic_name__							=			AdamW(params=model.parameters()						,	lr=lowerCamelCase_    )

				# Instantiate scheduler
				__magic_name__							=			get_linear_schedule_with_warmup(
				    optimizer=lowerCamelCase_						,	num_warmup_steps=100						,	num_training_steps=(len(lowerCamelCase_    ) * 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.
				__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__							=			accelerator.prepare(
				    lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_						,	lowerCamelCase_    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__magic_name__							=			os.path.split(lowerCamelCase_    )[-1].split("."    )[0]
								accelerator.init_trackers(lowerCamelCase_						,	lowerCamelCase_    )

				# Now we train the model
				for epoch in range(lowerCamelCase_    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__magic_name__							=			0
								for step, batch in enumerate(lowerCamelCase_    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												__magic_name__							=			model(**lowerCamelCase_    )
												__magic_name__							=			outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__magic_name__							=			loss / gradient_accumulation_steps
												accelerator.backward(lowerCamelCase_    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								__magic_name__							=			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'						,	lowerCamelCase_    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(lowerCamelCase_    ),
												        "epoch": epoch,
												    }						,	step=lowerCamelCase_						,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()




def   __snake_case							(							):
				'''simple docstring'''




				__magic_name__							=			argparse.ArgumentParser(description="Simple example of training script."    )
				parser.add_argument(
				    "--mixed_precision"						,	type=lowerCamelCase_						,	default=lowerCamelCase_						,	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."    )
				parser.add_argument(
				    "--with_tracking"						,	action="store_true"						,	help="Whether to load in all available experiment trackers from the environment and use them for logging."						,	)
				parser.add_argument(
				    "--project_dir"						,	type=lowerCamelCase_						,	default="logs"						,	help="Location on where to store experiment tracking logs` and relevent project information"						,	)
				__magic_name__							=			parser.parse_args()
				__magic_name__							=			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowerCamelCase_						,	lowerCamelCase_    )


if __name__ == "__main__":
						main()





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__						=				logging.get_logger(__name__)

lowerCamelCase__						=				{
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Union[str, Any]						=		"pix2struct_text_model"
						UpperCamelCase_				:			str						=		["past_key_values"]
						UpperCamelCase_				:			str						=		{
						    "hidden_size": "hidden_size",
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}

						def __init__(      self							,				a=5_02_44							,				a=7_68							,				a=64							,				a=20_48							,				a=12							,				a=12							,				a=32							,				a=1_28							,				a=0.1							,				a=1e-6							,				a=1.0							,				a="gelu_new"							,				a=0							,				a=False							,				a=0							,				a=1							,				a=False							,				a=True							,				**a							,				)       ->   Optional[int]:


								'''simple docstring'''

								_UpperCamelCase         =						vocab_size
								_UpperCamelCase         =						hidden_size
								_UpperCamelCase         =						d_kv
								_UpperCamelCase         =						d_ff
								_UpperCamelCase         =						num_layers
								_UpperCamelCase         =						num_heads
								_UpperCamelCase         =						relative_attention_num_buckets
								_UpperCamelCase         =						relative_attention_max_distance
								_UpperCamelCase         =						dropout_rate
								_UpperCamelCase         =						layer_norm_epsilon
								_UpperCamelCase         =						initializer_factor
								_UpperCamelCase         =						use_cache

								_UpperCamelCase         =						eos_token_id
								_UpperCamelCase         =						decoder_start_token_id

								# for backwards compatibility
								_UpperCamelCase         =						dense_act_fn

								super().__init__(
								    pad_token_id=a							,				eos_token_id=a							,				decoder_start_token_id=a							,				tie_word_embeddings=a							,				is_decoder=a							,				**a							,				)







						@classmethod
						def 			A_					(      cls							,				a							,				**a					)       ->   "PretrainedConfig":


								'''simple docstring'''

								cls._set_token_in_kwargs(a					)

								_UpperCamelCase     ,						_UpperCamelCase         =						cls.get_config_dict(a							,				**a					)

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get("""model_type"""					) == "pix2struct":
										_UpperCamelCase         =						config_dict["""text_config"""]

								if "model_type" in config_dict and hasattr(cls							,				"""model_type"""					) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
										    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

								return cls.from_dict(a							,				**a					)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			int						=		"pix2struct_vision_model"

						def __init__(      self							,				a=7_68							,				a=7_68							,				a=20_48							,				a=64							,				a=12							,				a=12							,				a="gelu_new"							,				a=1e-6							,				a=0.0							,				a=0.0							,				a=1e-10							,				a=1.0							,				a=40_96							,				a=32							,				a=1_28							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								super().__init__(**a					)

								_UpperCamelCase         =						hidden_size
								_UpperCamelCase         =						patch_embed_hidden_size
								_UpperCamelCase         =						d_ff
								_UpperCamelCase         =						dropout_rate
								_UpperCamelCase         =						num_hidden_layers
								_UpperCamelCase         =						num_attention_heads
								_UpperCamelCase         =						initializer_range
								_UpperCamelCase         =						initializer_factor
								_UpperCamelCase         =						attention_dropout
								_UpperCamelCase         =						layer_norm_eps
								_UpperCamelCase         =						dense_act_fn
								_UpperCamelCase         =						seq_len
								_UpperCamelCase         =						relative_attention_num_buckets
								_UpperCamelCase         =						relative_attention_max_distance
								_UpperCamelCase         =						d_kv







						@classmethod
						def 			A_					(      cls							,				a							,				**a					)       ->   "PretrainedConfig":


								'''simple docstring'''

								cls._set_token_in_kwargs(a					)

								_UpperCamelCase     ,						_UpperCamelCase         =						cls.get_config_dict(a							,				**a					)

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get("""model_type"""					) == "pix2struct":
										_UpperCamelCase         =						config_dict["""vision_config"""]

								if "model_type" in config_dict and hasattr(cls							,				"""model_type"""					) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
										    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'					)

								return cls.from_dict(a							,				**a					)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Dict						=		"pix2struct"
						UpperCamelCase_				:			int						=		True

						def __init__(      self							,				a=None							,				a=None							,				a=1.0							,				a=0.02							,				a=False							,				a=False							,				a=True							,				**a							,				)       ->   Optional[Any]:


								'''simple docstring'''

								super().__init__(tie_word_embeddings=a							,				is_encoder_decoder=a							,				**a					)

								if text_config is None:
										_UpperCamelCase         =						{}
										logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""					)

								if vision_config is None:
										_UpperCamelCase         =						{}
										logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""					)

								_UpperCamelCase         =						PixaStructTextConfig(**a					)
								_UpperCamelCase         =						PixaStructVisionConfig(**a					)

								_UpperCamelCase         =						self.text_config.decoder_start_token_id
								_UpperCamelCase         =						self.text_config.pad_token_id
								_UpperCamelCase         =						self.text_config.eos_token_id

								_UpperCamelCase         =						initializer_factor
								_UpperCamelCase         =						initializer_range

								_UpperCamelCase         =						self.initializer_range
								_UpperCamelCase         =						self.initializer_range

								_UpperCamelCase         =						is_vqa

						@classmethod
						def 			A_					(      cls							,				a							,				a							,				**a					)       ->   str:


								'''simple docstring'''

								return cls(text_config=text_config.to_dict()							,				vision_config=vision_config.to_dict()							,				**a					)







						def 			A_					(      self					)       ->   Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase         =						copy.deepcopy(self.__dict__					)
								_UpperCamelCase         =						self.text_config.to_dict()
								_UpperCamelCase         =						self.vision_config.to_dict()
								_UpperCamelCase         =						self.__class__.model_type
								return output



