


'''simple docstring'''
import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				ProphetNetTokenizer
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Optional[Any]:
					super().setUp()

					lowercase						:		str   					=		[
					    "[UNK]",
					    "[CLS]",
					    "[SEP]",
					    "[PAD]",
					    "[MASK]",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					lowercase						:		int   					=		os.path.join(self.tmpdirname							,      VOCAB_FILES_NAMES["vocab_file"]							)
					with open(self.vocab_file							,      "w"							,      encoding="utf-8"							) as vocab_writer:
						vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)

				def 	a__		(     self							,      a_							)    ->  int:
					lowercase						:		Any   					=		"UNwant\u00E9d,running"
					lowercase						:		List[Any]   					=		"unwanted, running"
					return input_text, output_text

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Optional[Any]   					=		self.tokenizer_class(self.vocab_file							)

					lowercase						:		List[Any]   					=		tokenizer.tokenize("UNwant\u00E9d,running"							)
					self.assertListEqual(a_							,      ["un", "##want", "##ed", ",", "runn", "##ing"]							)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a_							)							,      [9, 6, 7, 1_2, 1_0, 1_1]							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		BasicTokenizer()

					self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"							)							,      ["ah", "\u535A", "\u63A8", "zz"]							)

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		List[Any]   					=		BasicTokenizer(do_lower_case=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)							,      ["hello", "!", "how", "are", "you", "?"]							)
					self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,      ["hello"]							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		BasicTokenizer(do_lower_case=a_							,      strip_accents=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,      ["hällo", "!", "how", "are", "you", "?"]							)
					self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,      ["h\u00E9llo"]							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Optional[int]   					=		BasicTokenizer(do_lower_case=a_							,      strip_accents=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,      ["hallo", "!", "how", "are", "you", "?"]							)
					self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,      ["hello"]							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Optional[int]   					=		BasicTokenizer(do_lower_case=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,      ["hallo", "!", "how", "are", "you", "?"]							)
					self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)							,      ["hello"]							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Optional[int]   					=		BasicTokenizer(do_lower_case=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)							,      ["HeLLo", "!", "how", "Are", "yoU", "?"]							)

				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Dict   					=		BasicTokenizer(do_lower_case=a_							,      strip_accents=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,      ["HäLLo", "!", "how", "Are", "yoU", "?"]							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		List[str]   					=		BasicTokenizer(do_lower_case=a_							,      strip_accents=a_							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)							,      ["HaLLo", "!", "how", "Are", "yoU", "?"]							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Any   					=		BasicTokenizer(do_lower_case=a_							,      never_split=["[UNK]"]							)

					self.assertListEqual(
					    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"							)							,      ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

					lowercase						:		Any   					=		{}
					for i, token in enumerate(a_							):
						lowercase						:		List[str]   					=		i
					lowercase						:		Optional[int]   					=		WordpieceTokenizer(vocab=a_							,      unk_token="[UNK]"							)

					self.assertListEqual(tokenizer.tokenize(""							)							,      []							)

					self.assertListEqual(tokenizer.tokenize("unwanted running"							)							,      ["un", "##want", "##ed", "runn", "##ing"]							)

					self.assertListEqual(tokenizer.tokenize("unwantedX running"							)							,      ["[UNK]", "runn", "##ing"]							)

				@require_torch
				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Any   					=		self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"							)

					lowercase						:		Optional[int]   					=		["A long paragraph for summarization.", "Another paragraph for summarization."]
					lowercase						:		Union[str, Any]   					=		[1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
					lowercase						:		Any   					=		tokenizer(a_							,      padding=a_							,      return_tensors="pt"							)
					self.assertIsInstance(a_							,      a_							)
					lowercase						:		int   					=		list(batch.input_ids.numpy()[0]							)
					self.assertListEqual(a_							,      a_							)

					self.assertEqual((2, 9)							,      batch.input_ids.shape							)
					self.assertEqual((2, 9)							,      batch.attention_mask.shape							)

				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(_is_whitespace(" "							)							)
					self.assertTrue(_is_whitespace("\t"							)							)
					self.assertTrue(_is_whitespace("\r"							)							)
					self.assertTrue(_is_whitespace("\n"							)							)
					self.assertTrue(_is_whitespace("\u00A0"							)							)

					self.assertFalse(_is_whitespace("A"							)							)
					self.assertFalse(_is_whitespace("-"							)							)

				def 	a__		(     self							)    ->  Optional[int]:
					self.assertTrue(_is_control("\u0005"							)							)

					self.assertFalse(_is_control("A"							)							)
					self.assertFalse(_is_control(" "							)							)
					self.assertFalse(_is_control("\t"							)							)
					self.assertFalse(_is_control("\r"							)							)

				def 	a__		(     self							)    ->  str:
					self.assertTrue(_is_punctuation("-"							)							)
					self.assertTrue(_is_punctuation("$"							)							)
					self.assertTrue(_is_punctuation("`"							)							)
					self.assertTrue(_is_punctuation("."							)							)

					self.assertFalse(_is_punctuation("A"							)							)
					self.assertFalse(_is_punctuation(" "							)							)


				@slow
				def 	a__		(     self							)    ->  Tuple:
					lowercase						:		Union[str, Any]   					=		self.tokenizer_class.from_pretrained("microsoft/prophetnet-large-uncased"							)

					lowercase						:		List[str]   					=		tokenizer.encode("sequence builders"							,      add_special_tokens=a_							)
					lowercase						:		str   					=		tokenizer.encode("multi-sequence build"							,      add_special_tokens=a_							)

					lowercase						:		Optional[int]   					=		tokenizer.build_inputs_with_special_tokens(a_							)
					lowercase						:		Optional[Any]   					=		tokenizer.build_inputs_with_special_tokens(a_							,      a_							)

					assert encoded_sentence == text + [1_0_2]
					assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]






'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


lowerCAmelCase   : Tuple             =				1_6
lowerCAmelCase   : Optional[int]             =				3_2




def 	_A  (       A							,A = 1_6							)  ->				Union[str, Any]:
	lowercase						:		List[Any]   					=		AutoTokenizer.from_pretrained("bert-base-cased"							)
	lowercase						:		Any   					=		load_dataset("glue"							,"mrpc"							)

	def tokenize_function(A							):
		# max_length=None => use the model max length (it's actually the default)
		lowercase						:		List[str]   					=		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():
		lowercase						:		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
	lowercase						:		Optional[int]   					=		tokenized_datasets.rename_column("label"							,"labels"							)

	def collate_fn(A							):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		lowercase						:		Tuple   					=		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":
			lowercase						:		Any   					=		1_6
		elif accelerator.mixed_precision != "no":
			lowercase						:		List[Any]   					=		8
		else:
			lowercase						:		List[Any]   					=		None

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

	# Instantiate dataloaders.
	lowercase						:		Any   					=		DataLoader(
	    tokenized_datasets["train"]							,shuffle=A							,collate_fn=A							,batch_size=A							)
	lowercase						:		List[str]   					=		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

					lowerCAmelCase   : List[Any]             =				mocked_dataloaders  # noqa: F811




def 	_A  (       A							,A							)  ->				str:
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"							,A							) == "1":
		lowercase						:		Optional[Any]   					=		2
	# New Code #
	lowercase						:		List[str]   					=		int(args.gradient_accumulation_steps							)
	lowercase						:		Union[str, Any]   					=		int(args.local_sgd_steps							)
	# Initialize accelerator
	lowercase						:		Union[str, Any]   					=		Accelerator(
	    cpu=args.cpu							,mixed_precision=args.mixed_precision							,gradient_accumulation_steps=A							)
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	lowercase						:		Dict   					=		config["lr"]
	lowercase						:		List[Any]   					=		int(config["num_epochs"]							)
	lowercase						:		int   					=		int(config["seed"]							)
	lowercase						:		Dict   					=		int(config["batch_size"]							)

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

	set_seed(A							)
	lowercase					,		lowercase						:		Any   					=		get_dataloaders(A							,A							)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	lowercase						:		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).
	lowercase						:		Optional[int]   					=		model.to(accelerator.device							)

	# Instantiate optimizer
	lowercase						:		Tuple   					=		AdamW(params=model.parameters()							,lr=A							)

	# Instantiate scheduler
	lowercase						:		Union[str, Any]   					=		get_linear_schedule_with_warmup(
	    optimizer=A							,num_warmup_steps=1_0_0							,num_training_steps=(len(A							) * 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.
	lowercase					,		lowercase					,		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		accelerator.prepare(
	    A							,A							,A							,A							,A							)

	# Now we train the model
	for epoch in range(A							):
		model.train()
		with LocalSGD(
		    accelerator=A							,model=A							,local_sgd_steps=A							,enabled=local_sgd_steps is not None							) as local_sgd:
			for step, batch in enumerate(A							):
				# 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(A							):
					lowercase						:		Any   					=		model(**A							)
					lowercase						:		Dict   					=		output.loss
					accelerator.backward(A							)
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		model.eval()
		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():
				lowercase						:		Dict   					=		model(**A							)
			lowercase						:		Tuple   					=		outputs.logits.argmax(dim=-1							)
			lowercase					,		lowercase						:		str   					=		accelerator.gather_for_metrics((predictions, batch["labels"])							)
			metric.add_batch(
			    predictions=A							,references=A							,)

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




def 	_A  (       )  ->				Dict:
	lowercase						:		Optional[int]   					=		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."							,)
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"							,type=A							,default=1							,help="The number of minibatches to be ran before gradients are accumulated."							,)
	parser.add_argument(
	    "--local_sgd_steps"							,type=A							,default=8							,help="Number of local SGD steps or None to disable local SGD"							)
	parser.add_argument("--cpu"							,action="store_true"							,help="If passed, will train on the CPU."							)
	lowercase						:		Optional[Any]   					=		parser.parse_args()
	lowercase						:		Union[str, Any]   					=		{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
	training_function(A							,A							)


if __name__ == "__main__":
					main()


