

"""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
#
########################################################################


__A						       =   16
__A						       =   32






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE = 1_6			)     ->     Tuple:
		__lowerCAmelCase:	str   				=      AutoTokenizer.from_pretrained("bert-base-cased"			)
		__lowerCAmelCase:	int   				=      load_dataset("glue" ,			"mrpc"			)

		def tokenize_function(__SCREAMING_SNAKE_CASE			):
				# max_length=None => use the model max length (it's actually the default)
				__lowerCAmelCase:	Optional[int]   				=      tokenizer(examples["sentence1"] ,			examples["sentence2"] ,			truncation=__snake_case ,			max_length=__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(
				    __snake_case ,			batched=__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			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__lowerCAmelCase:	List[str]   				=      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:	Optional[Any]   				=      1_6
				elif accelerator.mixed_precision != "no":
						__lowerCAmelCase:	Optional[int]   				=      8
				else:
						__lowerCAmelCase:	List[Any]   				=      None

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

		# Instantiate dataloaders.
		__lowerCAmelCase:	Optional[Any]   				=      DataLoader(
		    tokenized_datasets["train"] ,			shuffle=__snake_case ,			collate_fn=__snake_case ,			batch_size=__snake_case ,			drop_last=__snake_case			)
		__lowerCAmelCase:	Optional[int]   				=      DataLoader(
		    tokenized_datasets["validation"] ,			shuffle=__snake_case ,			collate_fn=__snake_case ,			batch_size=__snake_case ,			drop_last=(accelerator.mixed_precision == "fp8") ,			)

		return train_dataloader, eval_dataloader






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		# Initialize accelerator
		__lowerCAmelCase:	Tuple   				=      Accelerator(cpu=args.cpu ,			mixed_precision=args.mixed_precision			)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__lowerCAmelCase:	Any   				=      config["lr"]
		__lowerCAmelCase:	Any   				=      int(config["num_epochs"]			)
		__lowerCAmelCase:	str   				=      int(config["seed"]			)
		__lowerCAmelCase:	List[Any]   				=      int(config["batch_size"]			)

		__lowerCAmelCase:	List[Any]   				=      evaluate.load("glue" ,			"mrpc"			)

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

		set_seed(__snake_case			)
		__lowerCAmelCase:	List[Any]   				=      get_dataloaders(__snake_case ,			__snake_case			)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCAmelCase:	int   				=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,			return_dict=__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:	Union[str, Any]   				=      model.to(accelerator.device			)
		# Instantiate optimizer
		__lowerCAmelCase:	List[str]   				=      AdamW(params=model.parameters() ,			lr=__snake_case			)

		# Instantiate scheduler
		__lowerCAmelCase:	Optional[int]   				=      get_linear_schedule_with_warmup(
		    optimizer=__snake_case ,			num_warmup_steps=1_0_0 ,			num_training_steps=(len(__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:	Union[str, Any]   				=      accelerator.prepare(
		    __snake_case ,			__snake_case ,			__snake_case ,			__snake_case ,			__snake_case			)

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

				model.eval()
				for step, batch in enumerate(__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:	int   				=      model(**__snake_case			)
						__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits.argmax(dim=-1			)
						__lowerCAmelCase:	Dict   				=      accelerator.gather_for_metrics((predictions, batch["labels"])			)
						metric.add_batch(
						    predictions=__snake_case ,			references=__snake_case ,			)

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






def    a__   (   )     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      argparse.ArgumentParser(description="Simple example of training script."			)
		parser.add_argument(
		    "--mixed_precision" ,			type=__snake_case ,			default=__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:	Optional[int]   				=      parser.parse_args()
		__lowerCAmelCase:	Tuple   				=      {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
		training_function(__snake_case ,			__snake_case			)


if __name__ == "__main__":
			main()





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    Any=7					,  UpperCamelCase__							:    List[str]=3					,  UpperCamelCase__							:    List[Any]=1_8					,  UpperCamelCase__							:    List[Any]=3_0					,  UpperCamelCase__							:    List[str]=4_0_0					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Optional[Any]=None					,  UpperCamelCase__							:    List[str]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    int=[0.48145466, 0.4578275, 0.40821073]					,  UpperCamelCase__							:    str=[0.26862954, 0.26130258, 0.27577711]					,  UpperCamelCase__							:    List[str]=True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      size if size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:	Union[str, Any]   				=      crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	Optional[int]   				=      parent
						__lowerCAmelCase:	List[str]   				=      batch_size
						__lowerCAmelCase:	Union[str, Any]   				=      num_channels
						__lowerCAmelCase:	Optional[Any]   				=      image_size
						__lowerCAmelCase:	Tuple   				=      min_resolution
						__lowerCAmelCase:	List[str]   				=      max_resolution
						__lowerCAmelCase:	List[Any]   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	List[Any]   				=      do_center_crop
						__lowerCAmelCase:	Optional[int]   				=      crop_size
						__lowerCAmelCase:	Dict   				=      do_normalize
						__lowerCAmelCase:	List[str]   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std
						__lowerCAmelCase:	str   				=      do_convert_rgb

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}




				def 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    Tuple=False					,  UpperCamelCase__							:    List[str]=False					,  UpperCamelCase__							:    Dict=False)->       List[str]:



						'''simple docstring'''


						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
								__lowerCAmelCase:	Optional[int]   				=      []
								for i in range(self.batch_size):
										image_inputs.append(
										    np.random.randint(
										        2_5_5					,  size=(self.num_channels, self.max_resolution, self.max_resolution)					,  dtype=np.uinta))
						else:
								__lowerCAmelCase:	List[str]   				=      []
								for i in range(self.batch_size):
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      np.random.choice(np.arange(self.min_resolution					,  self.max_resolution)					,  2)
										image_inputs.append(np.random.randint(2_5_5					,  size=(self.num_channels, width, height)					,  dtype=np.uinta))

						if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__lowerCAmelCase:	Union[str, Any]   				=      [Image.fromarray(np.moveaxis(UpperCamelCase__					,  0					,  -1)) for x in image_inputs]

						if torchify:
								__lowerCAmelCase:	str   				=      [torch.from_numpy(UpperCamelCase__) for x in image_inputs]

						return image_inputs





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      ChineseCLIPImageProcessingTester(self					,  do_center_crop=UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Union[str, Any])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 2_2_4, "width": 2_2_4})
						self.assertEqual(image_processor.crop_size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	List[str]   				=      self.image_processing_class.from_dict(self.image_processor_dict					,  size=4_2					,  crop_size=8_4)
						self.assertEqual(image_processor.size					,  {"shortest_edge": 4_2})
						self.assertEqual(image_processor.crop_size					,  {"height": 8_4, "width": 8_4})

				def 							lowercase_					(     self							:    List[str])->       Optional[int]:



						'''simple docstring'''


						pass

				def 							lowercase_					(     self							:    Any)->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	Optional[int]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	int   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

				def 							lowercase_					(     self							:    int)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Any   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Dict   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ChineseCLIPImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      ChineseCLIPImageProcessingTester(self					,  num_channels=4					,  do_center_crop=UpperCamelCase__)
						__lowerCAmelCase:	Union[str, Any]   				=      3

				@property
				def 							lowercase_					(     self							:    Union[str, Any])->       Any:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "center_crop"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_convert_rgb"))

				def 							lowercase_					(     self							:    Tuple)->       Any:



						'''simple docstring'''


						pass




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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__lowerCAmelCase:	int   				=      self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  Image.Image)

						# Test not batched input
						__lowerCAmelCase:	List[Any]   				=      image_processing(image_inputs[0]					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	Optional[int]   				=      image_processing(UpperCamelCase__					,  return_tensors="pt").pixel_values
						self.assertEqual(
						    encoded_images.shape					,  (
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    )					,  )


