

"""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


__UpperCamelCase  :      List[str]							      =		'''true'''

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=16 ):
	set_seed(42 )
	lowerCAmelCase__	:						Union[str, Any]  				=	RegressionModel()
	lowerCAmelCase__	:						Optional[int]  				=	deepcopy(A_ )
	lowerCAmelCase__	:						Any  				=	RegressionDataset(length=A_ )
	lowerCAmelCase__	:						List[str]  				=	DataLoader(A_						,     batch_size=A_ )
	model.to(accelerator.device )
	lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(A_						,     A_ )
	return model, ddp_model, dataloader

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=False ):
	lowerCAmelCase__	:						List[str]  				=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
	lowerCAmelCase__	:						List[str]  				=	load_dataset('''glue'''						,     '''mrpc'''						,     split='''validation''' )

	def tokenize_function(A_ ):
		lowerCAmelCase__	:						Optional[Any]  				=	tokenizer(examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=A_						,     max_length=A_ )
		return outputs

	with accelerator.main_process_first():
		lowerCAmelCase__	:						Dict  				=	dataset.map(
		    A_						,     batched=A_						,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']						,     )

	lowerCAmelCase__	:						int  				=	tokenized_datasets.rename_column('''label'''						,     '''labels''' )

	def collate_fn(A_ ):
		if use_longest:
			return tokenizer.pad(A_						,     padding='''longest'''						,     return_tensors='''pt''' )
		return tokenizer.pad(A_						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt''' )

	return DataLoader(A_						,     shuffle=A_						,     collate_fn=A_						,     batch_size=16 )

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	[]
	for batch in dataloader:
		lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	batch.values()
		with torch.no_grad():
			lowerCAmelCase__	:						List[str]  				=	model(A_ )
			lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	accelerator.gather_for_metrics((logit, target) )
			logits_and_targets.append((logit, target) )
	lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	[], []
	for logit, targ in logits_and_targets:
		logits.append(A_ )
		targs.append(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[str]  				=	torch.cat(A_ ), torch.cat(A_ )
	return logits, targs

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_=82						,     A_=False						,     A_=False						,     A_=16 ):
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Union[str, Any]  				=	get_basic_setup(A_						,     A_						,     A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	generate_predictions(A_						,     A_						,     A_ )
	assert (
	    len(A_ ) == num_samples
	), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A_ )}'

def   __SCREAMING_SNAKE_CASE  (      A_ = False						,     A_ = False ):
	lowerCAmelCase__	:						Optional[Any]  				=	evaluate.load('''glue'''						,     '''mrpc''' )
	lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	get_mrpc_setup(A_						,     A_ )
	# First do baseline
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	setup['''no''']
	model.to(A_ )
	model.eval()
	for batch in dataloader:
		batch.to(A_ )
		with torch.inference_mode():
			lowerCAmelCase__	:						Optional[int]  				=	model(**A_ )
		lowerCAmelCase__	:						Dict  				=	outputs.logits.argmax(dim=-1 )
		metric.add_batch(predictions=A_						,     references=batch['''labels'''] )
	lowerCAmelCase__	:						Dict  				=	metric.compute()

	# Then do distributed
	lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			lowerCAmelCase__	:						Union[str, Any]  				=	model(**A_ )
		lowerCAmelCase__	:						int  				=	outputs.logits.argmax(dim=-1 )
		lowerCAmelCase__	:						int  				=	batch['''labels''']
		lowerCAmelCase__				,lowerCAmelCase__	:						int  				=	accelerator.gather_for_metrics((preds, references) )
		metric.add_batch(predictions=A_						,     references=A_ )
	lowerCAmelCase__	:						List[Any]  				=	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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
	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(A_						,     A_ )
				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]:
			lowerCAmelCase__	:						Optional[Any]  				=	Accelerator(split_batches=A_						,     dispatch_batches=A_ )
			if accelerator.is_local_main_process:
				print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99' )
			test_torch_metrics(A_						,     99 )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**''' )
	lowerCAmelCase__	:						List[str]  				=	Accelerator()
	test_torch_metrics(A_						,     5_12 )
	accelerator.state._reset_state()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()







"""simple docstring"""





from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

__UpperCamelCase  :      Tuple							      =		TypeVar('''T''')







class 				SCREAMING_SNAKE_CASE					(		Generic[T]       ):






					"""simple docstring"""



					lowercase__				 =       42  # Cache store of keys
					lowercase__				 =       42  # References of the keys in cache
					lowercase__				 =       10  # Maximum capacity of cache







					def __init__(       self			:				Dict   ,lowercase_			:				int		):
						lowerCAmelCase__	:						str  				=	deque()
						lowerCAmelCase__	:						Any  				=	set()
						if not n:
							lowerCAmelCase__	:						Optional[Any]  				=	sys.maxsize
						elif n < 0:
							raise ValueError('''n should be an integer greater than 0.'''		)
						else:
							lowerCAmelCase__	:						int  				=	n







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				T		):
						if x not in self.key_reference:
							if len(self.dq_store		) == LRUCache._MAX_CAPACITY:
								lowerCAmelCase__	:						Any  				=	self.dq_store.pop()
								self.key_reference.remove(lowercase_		)
						else:
							self.dq_store.remove(lowercase_		)

						self.dq_store.appendleft(lowercase_		)
						self.key_reference.add(lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						for k in self.dq_store:
							print(lowercase_		)







					def __repr__(       self			:				Tuple		):
						return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store		)}'


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__UpperCamelCase  :      LRUCache[str | int]							      =		LRUCache(4)
							lru_cache.refer('''A''')
							lru_cache.refer(2)
							lru_cache.refer(3)
							lru_cache.refer('''A''')
							lru_cache.refer(4)
							lru_cache.refer(5)
							lru_cache.display()

							print(lru_cache)
							assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"




