'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_	:		Optional[Any]			=     logging.get_logger(__name__)


lowerCAmelCase_	:		str			=     {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase_	:		Any			=     {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

lowerCAmelCase_	:		Optional[int]			=     {
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}
@lru_cache()
def 						__A							(     )     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						(
						    list(range(ord("!"					)     ,ord("~"					) + 1					)					) + list(range(ord("¡"					)     ,ord("¬"					) + 1					)					) + list(range(ord("®"					)     ,ord("ÿ"					) + 1					)					)
						)
						_UpperCamelCase			:			int   			=						bs[:]
						_UpperCamelCase			:			Dict   			=						0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(UpperCAmelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						_UpperCamelCase			:			Optional[int]   			=						[chr(UpperCAmelCase					) for n in cs]
						return dict(zip(UpperCAmelCase     ,UpperCAmelCase					)					)
def 						__A							(     UpperCAmelCase					)     ->			Optional[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Tuple   			=						set()
						_UpperCamelCase			:			str   			=						word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												_UpperCamelCase			:			Any   			=						char
						return pairs






class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							VOCAB_FILES_NAMES
				UpperCAmelCase__							=							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__							=							['''input_ids''', '''attention_mask''']





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						List[str]       ,		lowercase__					:						Any="replace"       ,		lowercase__					:						List[Any]="<s>"       ,		lowercase__					:						int="</s>"       ,		lowercase__					:						Any="</s>"       ,		lowercase__					:						Dict="<s>"       ,		lowercase__					:						Optional[Any]="<unk>"       ,		lowercase__					:						str="<pad>"       ,		lowercase__					:						Optional[int]="<mask>"       ,		lowercase__					:						List[str]=False       ,		**lowercase__					:						List[str]       ,		)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else bos_token
										_UpperCamelCase			:			Tuple   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else eos_token
										_UpperCamelCase			:			Any   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else sep_token
										_UpperCamelCase			:			Any   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else cls_token
										_UpperCamelCase			:			Tuple   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else unk_token
										_UpperCamelCase			:			Dict   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										_UpperCamelCase			:			Any   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else mask_token

										super().__init__(
										    errors=lowercase__       ,		bos_token=lowercase__       ,		eos_token=lowercase__       ,		unk_token=lowercase__       ,		sep_token=lowercase__       ,		cls_token=lowercase__       ,		pad_token=lowercase__       ,		mask_token=lowercase__       ,		add_prefix_space=lowercase__       ,		**lowercase__       ,		)

										with open(lowercase__       ,		encoding="utf-8"					) as vocab_handle:
																_UpperCamelCase			:			Union[str, Any]   			=						json.load(lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						{v: k for k, v in self.encoder.items()}
										_UpperCamelCase			:			List[str]   			=						errors  # how to handle errors in decoding
										_UpperCamelCase			:			Optional[Any]   			=						bytes_to_unicode()
										_UpperCamelCase			:			Union[str, Any]   			=						{v: k for k, v in self.byte_encoder.items()}
										with open(lowercase__       ,		encoding="utf-8"					) as merges_handle:
																_UpperCamelCase			:			List[Any]   			=						merges_handle.read().split("\n"					)[1:-1]
										_UpperCamelCase			:			str   			=						[tuple(merge.split()					) for merge in bpe_merges]
										_UpperCamelCase			:			Optional[Any]   			=						dict(zip(lowercase__       ,		range(len(lowercase__					)					)					)					)
										_UpperCamelCase			:			Any   			=						{}
										_UpperCamelCase			:			Dict   			=						add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										_UpperCamelCase			:			Tuple   			=						re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"					)





				@property
				def  snake_case__				(						self					:						Dict					)							->Optional[int]:


										'''simple docstring'''




										return len(self.encoder					)





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


										'''simple docstring'''




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





				def  snake_case__				(						self					:						str       ,		lowercase__					:						List[str]					)							->Any:


										'''simple docstring'''




										if token in self.cache:
																return self.cache[token]
										_UpperCamelCase			:			str   			=						tuple(lowercase__					)
										_UpperCamelCase			:			str   			=						get_pairs(lowercase__					)

										if not pairs:
																return token

										while True:
																_UpperCamelCase			:			int   			=						min(lowercase__       ,		key=lambda lowercase__					: self.bpe_ranks.get(lowercase__       ,		float("inf"					)					)					)
																if bigram not in self.bpe_ranks:
																						break
																_UpperCamelCase			,       _UpperCamelCase			:			int   			=						bigram
																_UpperCamelCase			:			Tuple   			=						[]
																_UpperCamelCase			:			int   			=						0
																while i < len(lowercase__					):
																						try:
																												_UpperCamelCase			:			Union[str, Any]   			=						word.index(lowercase__       ,		lowercase__					)
																						except ValueError:
																												new_word.extend(word[i:]					)
																												break
																						else:
																												new_word.extend(word[i:j]					)
																												_UpperCamelCase			:			Any   			=						j

																						if word[i] == first and i < len(lowercase__					) - 1 and word[i + 1] == second:
																												new_word.append(first + second					)
																												i += 2
																						else:
																												new_word.append(word[i]					)
																												i += 1
																_UpperCamelCase			:			str   			=						tuple(lowercase__					)
																_UpperCamelCase			:			List[str]   			=						new_word
																if len(lowercase__					) == 1:
																						break
																else:
																						_UpperCamelCase			:			Optional[int]   			=						get_pairs(lowercase__					)
										_UpperCamelCase			:			str   			=						" ".join(lowercase__					)
										_UpperCamelCase			:			Any   			=						word
										return word





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						Dict					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						[]
										for token in re.findall(self.pat       ,		lowercase__					):
																_UpperCamelCase			:			Tuple   			=						"".join(
																    self.byte_encoder[b] for b in token.encode("utf-8"					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__					).split(" "					)					)
										return bpe_tokens





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						Any					)							->Optional[int]:


										'''simple docstring'''




										return self.encoder.get(lowercase__       ,		self.encoder.get(self.unk_token					)					)





				def  snake_case__				(						self					:						Dict       ,		lowercase__					:						Any					)							->str:


										'''simple docstring'''




										return self.decoder.get(lowercase__					)





				def  snake_case__				(						self					:						Tuple       ,		lowercase__					:						str					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						"".join(lowercase__					)
										_UpperCamelCase			:			List[str]   			=						bytearray([self.byte_decoder[c] for c in text]					).decode("utf-8"       ,		errors=self.errors					)
										return text





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str       ,		lowercase__					:						Optional[str] = None					)							->Tuple[str]:


										'''simple docstring'''




										if not os.path.isdir(lowercase__					):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
																return
										_UpperCamelCase			:			Optional[int]   			=						os.path.join(
										    lowercase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
										_UpperCamelCase			:			Optional[Any]   			=						os.path.join(
										    lowercase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

										with open(lowercase__       ,		"w"       ,		encoding="utf-8"					) as f:
																f.write(json.dumps(self.encoder       ,		indent=2       ,		sort_keys=lowercase__       ,		ensure_ascii=lowercase__					) + "\n"					)

										_UpperCamelCase			:			Dict   			=						0
										with open(lowercase__       ,		"w"       ,		encoding="utf-8"					) as writer:
																writer.write("#version: 0.2\n"					)
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,		key=lambda lowercase__					: kv[1]					):
																						if index != token_index:
																												logger.warning(
																												    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																												    " Please check that the tokenizer is not corrupted!"					)
																												_UpperCamelCase			:			List[Any]   			=						token_index
																						writer.write(" ".join(lowercase__					) + "\n"					)
																						index += 1

										return vocab_file, merge_file





				def  snake_case__				(						self					:						int       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None					)							->List[int]:


										'''simple docstring'''




										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										_UpperCamelCase			:			Any   			=						[self.cls_token_id]
										_UpperCamelCase			:			List[str]   			=						[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						bool = False					)							->List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowercase__       ,		token_ids_a=lowercase__       ,		already_has_special_tokens=lowercase__					)

										if token_ids_a is None:
																return [1] + ([0] * len(lowercase__					)) + [1]
										return [1] + ([0] * len(lowercase__					)) + [1, 1] + ([0] * len(lowercase__					)) + [1]





				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None					)							->List[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						[self.sep_token_id]
										_UpperCamelCase			:			Tuple   			=						[self.cls_token_id]

										if token_ids_a is None:
																return len(cls + token_ids_a + sep					) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]







				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Any=False       ,		**lowercase__					:						List[str]					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						kwargs.pop("add_prefix_space"       ,		self.add_prefix_space					)
										if (is_split_into_words or add_prefix_space) and (len(lowercase__					) > 0 and not text[0].isspace()):
																_UpperCamelCase			:			Dict   			=						" " + text
										return (text, kwargs)



'''simple docstring'''





import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCAmelCase_	:		Tuple			=     """true"""
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=8_2     ,UpperCAmelCase=1_6					)     ->			Union[str, Any]:




						'''simple docstring'''
						set_seed(4_2					)
						_UpperCamelCase			:			List[Any]   			=						RegressionModel()
						_UpperCamelCase			:			Any   			=						deepcopy(UpperCAmelCase					)
						_UpperCamelCase			:			Tuple   			=						RegressionDataset(length=UpperCAmelCase					)
						_UpperCamelCase			:			Union[str, Any]   			=						DataLoader(UpperCAmelCase     ,batch_size=UpperCAmelCase					)
						model.to(accelerator.device					)
						_UpperCamelCase			,       _UpperCamelCase			:			Dict   			=						accelerator.prepare(UpperCAmelCase     ,UpperCAmelCase					)
						return model, ddp_model, dataloader
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=False					)     ->			List[str]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"					)
						_UpperCamelCase			:			Optional[Any]   			=						load_dataset("glue"     ,"mrpc"     ,split="validation"					)

						def tokenize_function(UpperCAmelCase					):
												_UpperCamelCase			:			Tuple   			=						tokenizer(examples["sentence1"]     ,examples["sentence2"]     ,truncation=UpperCAmelCase     ,max_length=UpperCAmelCase					)
												return outputs

						with accelerator.main_process_first():
												_UpperCamelCase			:			str   			=						dataset.map(
												    UpperCAmelCase     ,batched=UpperCAmelCase     ,remove_columns=["idx", "sentence1", "sentence2"]     ,)

						_UpperCamelCase			:			Optional[int]   			=						tokenized_datasets.rename_column("label"     ,"labels"					)

						def collate_fn(UpperCAmelCase					):
												if use_longest:
																		return tokenizer.pad(UpperCAmelCase     ,padding="longest"     ,return_tensors="pt"					)
												return tokenizer.pad(UpperCAmelCase     ,padding="max_length"     ,max_length=1_2_8     ,return_tensors="pt"					)

						return DataLoader(UpperCAmelCase     ,shuffle=UpperCAmelCase     ,collate_fn=UpperCAmelCase     ,batch_size=1_6					)
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			Dict:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						Accelerator(dispatch_batches=UpperCAmelCase     ,split_batches=UpperCAmelCase					)
						_UpperCamelCase			:			Union[str, Any]   			=						get_dataloader(UpperCAmelCase     ,not dispatch_batches					)
						_UpperCamelCase			:			Optional[int]   			=						AutoModelForSequenceClassification.from_pretrained(
						    "hf-internal-testing/mrpc-bert-base-cased"     ,return_dict=UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						accelerator.prepare(UpperCAmelCase     ,UpperCAmelCase					)
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)     ->			int:




						'''simple docstring'''
						_UpperCamelCase			:			List[str]   			=						[]
						for batch in dataloader:
												_UpperCamelCase			,       _UpperCamelCase			:			int   			=						batch.values()
												with torch.no_grad():
																		_UpperCamelCase			:			Tuple   			=						model(UpperCAmelCase					)
																		_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						accelerator.gather_for_metrics((logit, target)					)
																		logits_and_targets.append((logit, target)					)
						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						[], []
						for logit, targ in logits_and_targets:
												logits.append(UpperCAmelCase					)
												targs.append(UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			int   			=						torch.cat(UpperCAmelCase					), torch.cat(UpperCAmelCase					)
						return logits, targs
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase=8_2     ,UpperCAmelCase=False     ,UpperCAmelCase=False     ,UpperCAmelCase=1_6					)     ->			Any:




						'''simple docstring'''
						_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						get_basic_setup(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)
						_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						generate_predictions(UpperCAmelCase     ,UpperCAmelCase     ,UpperCAmelCase					)
						assert (
						    len(UpperCAmelCase					) == num_samples
						), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase					)}'''
def 						__A							(     UpperCAmelCase = False     ,UpperCAmelCase = False					)     ->			Tuple:




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						evaluate.load("glue"     ,"mrpc"					)
						_UpperCamelCase			,       _UpperCamelCase			:			Any   			=						get_mrpc_setup(UpperCAmelCase     ,UpperCAmelCase					)
						# First do baseline
						_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						setup["no"]
						model.to(UpperCAmelCase					)
						model.eval()
						for batch in dataloader:
												batch.to(UpperCAmelCase					)
												with torch.inference_mode():
																		_UpperCamelCase			:			Optional[Any]   			=						model(**UpperCAmelCase					)
												_UpperCamelCase			:			Any   			=						outputs.logits.argmax(dim=-1					)
												metric.add_batch(predictions=UpperCAmelCase     ,references=batch["labels"]					)
						_UpperCamelCase			:			List[str]   			=						metric.compute()

						# Then do distributed
						_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						setup["ddp"]
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		_UpperCamelCase			:			int   			=						model(**UpperCAmelCase					)
												_UpperCamelCase			:			Tuple   			=						outputs.logits.argmax(dim=-1					)
												_UpperCamelCase			:			List[str]   			=						batch["labels"]
												_UpperCamelCase			,       _UpperCamelCase			:			List[str]   			=						accelerator.gather_for_metrics((preds, references)					)
												metric.add_batch(predictions=UpperCAmelCase     ,references=UpperCAmelCase					)
						_UpperCamelCase			:			int   			=						metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key]     ,distributed[key]					), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def 						__A							(     )     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						Accelerator(split_batches=UpperCAmelCase     ,dispatch_batches=UpperCAmelCase					)
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print("**Testing gather_for_metrics**"					)
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''					)
																								test_mrpc(UpperCAmelCase     ,UpperCAmelCase					)
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test torch metrics**"					)
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		_UpperCamelCase			:			int   			=						Accelerator(split_batches=UpperCAmelCase     ,dispatch_batches=UpperCAmelCase					)
																		if accelerator.is_local_main_process:
																								print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''					)
																		test_torch_metrics(UpperCAmelCase     ,9_9					)
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print("**Test last batch is not dropped when perfectly divisible**"					)
						_UpperCamelCase			:			int   			=						Accelerator()
						test_torch_metrics(UpperCAmelCase     ,5_1_2					)
						accelerator.state._reset_state()
def 						__A							(     UpperCAmelCase					)     ->			List[str]:




						'''simple docstring'''
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
			main()


