

'''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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


a   :			Dict         = 16
a   :			Dict         = 32





def       lowercase					(     __magic_name__ ,    __magic_name__ = 16							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]   					=   AutoTokenizer.from_pretrained("bert-base-cased"							)
					UpperCAmelCase		:							Dict   					=   load_dataset("glue" ,    "mrpc"							)

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

					def collate_fn(__magic_name__							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCAmelCase		:							str   					=   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":
															UpperCAmelCase		:							int   					=   16
										elif accelerator.mixed_precision != "no":
															UpperCAmelCase		:							List[Any]   					=   8
										else:
															UpperCAmelCase		:							int   					=   None

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

					# Instantiate dataloaders.
					UpperCAmelCase		:							List[str]   					=   DataLoader(
					    tokenized_datasets["train"] ,    shuffle=__A ,    collate_fn=__A ,    batch_size=__A							)
					UpperCAmelCase		:							int   					=   DataLoader(
					    tokenized_datasets["validation"] ,    shuffle=__A ,    collate_fn=__A ,    batch_size=__A							)

					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

				a   :			Any         = mocked_dataloaders  # noqa: F811





def       lowercase					(     __magic_name__ ,    __magic_name__							):



					'''simple docstring'''







					if os.environ.get("TESTING_MOCKED_DATALOADERS" ,    __A							) == "1":
										UpperCAmelCase		:							str   					=   2
					# Initialize accelerator
					UpperCAmelCase		:							str   					=   Accelerator(cpu=args.cpu ,    mixed_precision=args.mixed_precision							)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase		:							List[str]   					=   config["lr"]
					UpperCAmelCase		:							Any   					=   int(config["num_epochs"]							)
					UpperCAmelCase		:							Dict   					=   int(config["seed"]							)
					UpperCAmelCase		:							List[str]   					=   int(config["batch_size"]							)

					UpperCAmelCase		:							List[str]   					=   evaluate.load("glue" ,    "mrpc"							)

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

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

					# 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).
					UpperCAmelCase		:							Dict   					=   model.to(accelerator.device							)

					# Instantiate optimizer
					UpperCAmelCase		:							int   					=   AdamW(params=model.parameters() ,    lr=__A							)

					# Instantiate scheduler
					UpperCAmelCase		:							str   					=   get_linear_schedule_with_warmup(
					    optimizer=__A ,    num_warmup_steps=100 ,    num_training_steps=(len(__A							) * 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.
					UpperCAmelCase					, UpperCAmelCase					, UpperCAmelCase					, UpperCAmelCase					, UpperCAmelCase		:							str   					=   accelerator.prepare(
					    __A ,    __A ,    __A ,    __A ,    __A							)

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

										model.eval()
										UpperCAmelCase		:							Tuple   					=   0
										for step, batch in enumerate(__A							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															with torch.no_grad():
																				UpperCAmelCase		:							Optional[Any]   					=   model(**__A							)
															UpperCAmelCase		:							Any   					=   outputs.logits.argmax(dim=-1							)
															UpperCAmelCase					, UpperCAmelCase		:							Optional[int]   					=   accelerator.gather((predictions, batch["labels"])							)
															# New Code #
															# First we check if it's a distributed system
															if accelerator.use_distributed:
																				# Then see if we're on the last batch of our eval dataloader
																				if step == len(__A							) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									UpperCAmelCase		:							List[Any]   					=   predictions[: len(eval_dataloader.dataset							) - samples_seen]
																									UpperCAmelCase		:							List[Any]   					=   references[: len(eval_dataloader.dataset							) - samples_seen]
																				else:
																									# Otherwise we add the number of samples seen
																									samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=__A ,    references=__A ,    )

										UpperCAmelCase		:							Union[str, Any]   					=   metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"epoch {epoch}:" ,    __A							)





def       lowercase					(     ):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple   					=   argparse.ArgumentParser(description="Simple example of training script."							)
					parser.add_argument(
					    "--mixed_precision" ,    type=__A ,    default=__A ,    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."							)
					UpperCAmelCase		:							List[Any]   					=   parser.parse_args()
					UpperCAmelCase		:							List[str]   					=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(__A ,    __A							)


if __name__ == "__main__":
				main()


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase    :							Any      =					{
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[Any]      =					["ChineseCLIPFeatureExtractor"]
					lowercase    :							List[Any]      =					["ChineseCLIPImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ChineseCLIPModel",
					    "ChineseCLIPPreTrainedModel",
					    "ChineseCLIPTextModel",
					    "ChineseCLIPVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

					lowercase    :							List[Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

