

import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

lowerCAmelCase__		      =       {
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

lowerCAmelCase__		      =       {
    """abeja/gpt-neox-japanese-2.7b""": 2_0_4_8,
}



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							Tuple					)	->					Union[str, Any]:




				'''simple docstring'''





				with open(SCREAMING_SNAKE_CASE_		,				"r"		,				encoding="utf-8"					) as f:
								A__						=	json.loads(f.read()					)

				A__						=	collections.OrderedDict()
				A__						=	collections.OrderedDict()
				A__						=	collections.OrderedDict()
				with open(SCREAMING_SNAKE_CASE_		,				"r"		,				encoding="utf-8"					) as f:
								A__						=	f.readlines()
				A__						=	[[t.rstrip("\n"					)] if (t == "," or "," not in t) else t.rstrip("\n"					).split(","					) for t in token]
				for idx, b in enumerate(SCREAMING_SNAKE_CASE_					):
								A__						=	b
								A__						=	idx
								for wd in b:
												A__						=	idx

				return vocab, raw_vocab, ids_to_tokens, emoji

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				VOCAB_FILES_NAMES
			__lowerCamelCase       				=				PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase       				=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase       				=				['input_ids', 'attention_mask']


			def __init__(			self					,					lowercase					,					lowercase					,					lowercase="<|endoftext|>"					,					lowercase="<|endoftext|>"					,					lowercase="<|startoftext|>"					,					lowercase="<|endoftext|>"					,					lowercase=False					,					**lowercase					,					)     ->  str:
							'''simple docstring'''






							super().__init__(
							    unk_token=lowercase					,					pad_token=lowercase					,					bos_token=lowercase					,					eos_token=lowercase					,					do_clean_text=lowercase					,					**lowercase					,					)
							if not os.path.isfile(lowercase				):
											raise ValueError(
											    F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
											    " model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"				)
							if not os.path.isfile(lowercase				):
											raise ValueError(
											    F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
											    " pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"				)
							A__						=	do_clean_text
							A__			, A__			, A__			, A__						=	load_vocab_and_emoji(lowercase					,					lowercase				)
							A__						=	SubWordJapaneseTokenizer(
							    vocab=self.vocab					,					ids_to_tokens=self.ids_to_tokens					,					emoji=self.emoji				)


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return len(self.raw_vocab				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							return dict(self.raw_vocab					,					**self.added_tokens_encoder				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							return self.subword_tokenizer.tokenize(lowercase					,					clean=self.do_clean_text				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  List[str]:
							'''simple docstring'''






							return self.vocab.get(lowercase					,					self.vocab.get(self.unk_token				)				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  int:
							'''simple docstring'''






							return self.subword_tokenizer.convert_id_to_token(lowercase				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	"".join(lowercase				).strip()
							return out_string


			def    UpperCamelCase						(			self					,					lowercase				)     ->  List[int]:
							'''simple docstring'''






							A__						=	[]
							for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowercase					,					add_special_tokens=lowercase				) + [self.eos_token_id]				)

							if len(lowercase				) > self.model_max_length:
											A__						=	input_ids[-self.model_max_length :]
							return input_ids


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None				)     ->  Tuple[str]:
							'''simple docstring'''






							A__						=	0
							if os.path.isdir(lowercase				):
											A__						=	os.path.join(
											    lowercase					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
											A__						=	os.path.join(
											    lowercase					,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]				)
							else:
											A__						=	(
											    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
											)
											A__						=	(
											    (filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
											)
							with open(lowercase					,					"w"					,					encoding="utf-8"				) as writer:
											for token_index, token in self.ids_to_tokens.items():
															if index != token_index:
																			logger.warning(
																			    F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
																			    " Please check that the vocabulary is not corrupted!"				)
																			A__						=	token_index
															writer.write(",".join(lowercase				) + "\n"				)
															index += 1
							with open(lowercase					,					"w"					,					encoding="utf-8"				) as writer:
											json.dump(self.emoji					,					lowercase				)
							return vocab_file, emoji_file

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	vocab  # same as swe
							A__						=	ids_to_tokens  # same as bpe
							A__						=	emoji
							A__						=	np.max([len(lowercase				) for w in self.vocab.keys()]				)
							A__						=	re.compile(R"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"				)
							A__						=	re.compile(R"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"				)
							A__						=	re.compile(R"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"				)
							A__						=	re.compile(
							    R"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"				)
							A__						=	re.compile(
							    R"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"				)
							A__						=	re.compile(
							    R"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"				)
							A__						=	"─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
							A__						=	"▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
							A__						=	str.maketrans({k: "<BLOCK>" for k in keisen + blocks}				)


			def __len__(			self				)     ->  Dict:
							'''simple docstring'''






							return len(self.ids_to_tokens				)


			def    UpperCamelCase						(			self					,					lowercase				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.content_repattera.sub("<URL>"					,					lowercase				)
							A__						=	self.content_repattera.sub("<EMAIL>"					,					lowercase				)
							A__						=	self.content_repattera.sub("<TEL>"					,					lowercase				)
							A__						=	self.content_repattera.sub("<DATE>"					,					lowercase				)
							A__						=	self.content_repattera.sub("<DATE>"					,					lowercase				)
							A__						=	self.content_repattera.sub("<PRICE>"					,					lowercase				)
							A__						=	content.translate(self.content_transa				)
							while "<BLOCK><BLOCK>" in content:
											A__						=	content.replace("<BLOCK><BLOCK>"					,					"<BLOCK>"				)
							return content


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase=False				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	text.replace(" "					,					"<SP>"				)
							A__						=	text.replace("　"					,					"<SP>"				)
							A__						=	text.replace("\r\n"					,					"<BR>"				)
							A__						=	text.replace("\n"					,					"<BR>"				)
							A__						=	text.replace("\r"					,					"<BR>"				)
							A__						=	text.replace("\t"					,					"<TAB>"				)
							A__						=	text.replace("—"					,					"ー"				)
							A__						=	text.replace("−"					,					"ー"				)
							for k, v in self.emoji["emoji"].items():
											if k in text:
															A__						=	text.replace(lowercase					,					lowercase				)
							if clean:
											A__						=	self.clean_text(lowercase				)

							def check_simbol(lowercase				):
											A__						=	x.encode()
											if len(lowercase				) == 1 and len(lowercase				) == 2:
															A__						=	(int(e[0]				) << 8) + int(e[1]				)
															if (
															    (c >= 0XC_2_A_1 and c <= 0XC_2_B_F)
															    or (c >= 0XC_7_8_0 and c <= 0XC_7_8_3)
															    or (c >= 0XC_A_B_9 and c <= 0XC_B_B_F)
															    or (c >= 0XC_C_8_0 and c <= 0XC_D_A_2)
															):
																			return True
											return False

							def checkuae(lowercase				):
											A__						=	x.encode()
											if len(lowercase				) == 1 and len(lowercase				) == 3:
															A__						=	(int(e[0]				) << 16) + (int(e[1]				) << 8) + int(e[2]				)
															if c >= 0XE_2_8_0_8_0 and c <= 0XE_2_B_0_7_F:
																			return True
											return False

							A__						=	0
							A__						=	[]
							while pos < len(lowercase				):
											A__						=	min(len(lowercase				)					,					pos + self.maxlen + 1				) if text[pos] == "<" else pos + 3
											A__						=	[]  # (token_id, token, pos)
											for e in range(lowercase					,					lowercase					,					-1				):
															A__						=	text[pos:e]
															if wd in self.vocab:
																			if wd[0] == "<" and len(lowercase				) > 2:
																							A__						=	[(self.vocab[wd], wd, e)]
																							break
																			else:
																							candidates.append((self.vocab[wd], wd, e)				)
											if len(lowercase				) > 0:
															# the smallest token_id is adopted
															A__			, A__			, A__						=	sorted(lowercase					,					key=lambda lowercase				: x[0]				)[0]
															result.append(lowercase				)
															A__						=	e
											else:
															A__						=	pos + 1
															A__						=	text[pos:end]
															if check_simbol(lowercase				):
																			result.append("<KIGOU>"				)
															elif checkuae(lowercase				):
																			result.append("<U2000U2BFF>"				)
															else:
																			for i in wd.encode("utf-8"				):
																							result.append("<|byte%d|>" % i				)
															A__						=	end
							return result


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase="\n"				)     ->  Dict:
							'''simple docstring'''






							A__						=	[]
							A__						=	[]
							A__						=	self.ids_to_tokens[index][0]
							if word[:6] == "<|byte" and word[-2:] == "|>":
											byte_tokens.append(int(word[6:-2]				)				)
							else:
											if len(lowercase				) > 0:
															words.append(bytearray(lowercase				).decode("utf-8"					,					errors="replace"				)				)
															A__						=	[]
											if word[:7] == "<|emoji" and word[-2:] == "|>":
															words.append(self.emoji["emoji_inv"][word]				)
											elif word == "<SP>":
															words.append(" "				)
											elif word == "<BR>":
															words.append(lowercase				)
											elif word == "<TAB>":
															words.append("\t"				)
											elif word == "<BLOCK>":
															words.append("▀"				)
											elif word == "<KIGOU>":
															words.append("ǀ"				)
											elif word == "<U2000U2BFF>":
															words.append("‖"				)
											else:
															words.append(lowercase				)
							if len(lowercase				) > 0:
											words.append(bytearray(lowercase				).decode("utf-8"					,					errors="replace"				)				)
							A__						=	"".join(lowercase				)
							return text







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					:       Any    					=      16
__UpperCamelCase					:       Union[str, Any]    					=      32





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16    )      ->       List[str]:
	a										=							AutoTokenizer.from_pretrained("""bert-base-cased"""    )
	a										=							load_dataset("""glue"""							,			"""mrpc"""    )

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

	def collate_fn(__lowerCamelCase    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		a										=							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":
			a										=							16
		elif accelerator.mixed_precision != "no":
			a										=							8
		else:
			a										=							None

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

	# Instantiate dataloaders.
	a										=							DataLoader(
	    tokenized_datasets["""train"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )
	a										=							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

		__UpperCamelCase					:       Any    					=      mocked_dataloaders  # noqa: F811





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       List[str]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			__lowerCamelCase    ) == "1":
		a										=							2
	# New Code #
	a										=							int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	a										=							Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=__lowerCamelCase    )
	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
	a										=							config["""lr"""]
	a										=							int(config["""num_epochs"""]    )
	a										=							int(config["""seed"""]    )
	a										=							int(config["""batch_size"""]    )

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

	set_seed(__lowerCamelCase    )
	a				, a										=							get_dataloaders(__lowerCamelCase							,			__lowerCamelCase    )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	a										=							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).
	a										=							model.to(accelerator.device    )

	# Instantiate optimizer
	a										=							AdamW(params=model.parameters()							,			lr=__lowerCamelCase    )

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

	# Now we train the model
	for epoch in range(__lowerCamelCase    ):
		model.train()
		for step, batch in enumerate(__lowerCamelCase    ):
			# 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(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							output.loss
				accelerator.backward(__lowerCamelCase    )
				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`.
			batch.to(accelerator.device    )
			with torch.no_grad():
				a										=							model(**__lowerCamelCase    )
			a										=							outputs.logits.argmax(dim=-1    )
			a				, a										=							accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
			metric.add_batch(
			    predictions=__lowerCamelCase							,			references=__lowerCamelCase							,			)

		a										=							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,			__lowerCamelCase    )





def 		__A		(	)      ->       int:
	a										=							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."""							,			)
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""							,			type=__lowerCamelCase							,			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."""    )
	a										=							parser.parse_args()
	a										=							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()

