'''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__     :	Optional[Any]				=			'true'







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				int=82  ,			_A					:				int=16    ):
   '''simple docstring'''
   set_seed(42    )
   UpperCAmelCase__							:				Tuple   				=  RegressionModel()
   UpperCAmelCase__							:				Optional[Any]   				=  deepcopy(_A    )
   UpperCAmelCase__							:				List[str]   				=  RegressionDataset(length=_A    )
   UpperCAmelCase__							:				str   				=  DataLoader(_A  ,			batch_size=_A    )
   model.to(accelerator.device    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  accelerator.prepare(_A  ,			_A    )
   return model, ddp_model, dataloader







def __UpperCamelCase(   _A					:				Accelerator  ,			_A					:				Union[str, Any]=False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''    )
   UpperCAmelCase__							:				List[Any]   				=  load_dataset('''glue'''  ,			'''mrpc'''  ,			split='''validation'''    )

   def tokenize_function(_A					:				Optional[int]    ):
      UpperCAmelCase__							:				List[str]   				=  tokenizer(examples['''sentence1''']  ,			examples['''sentence2''']  ,			truncation=_A  ,			max_length=_A    )
      return outputs

   with accelerator.main_process_first():
      UpperCAmelCase__							:				str   				=  dataset.map(
          _A  ,			batched=_A  ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,			)

   UpperCAmelCase__							:				Union[str, Any]   				=  tokenized_datasets.rename_column('''label'''  ,			'''labels'''    )

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







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				int  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  []
   for batch in dataloader:
      UpperCAmelCase__				,      UpperCAmelCase__							:				Dict   				=  batch.values()
      with torch.no_grad():
         UpperCAmelCase__							:				List[str]   				=  model(_A    )
         UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  accelerator.gather_for_metrics((logit, target)    )
         logits_and_targets.append((logit, target)    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  [], []
   for logit, targ in logits_and_targets:
      logits.append(_A    )
      targs.append(_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[Any]   				=  torch.cat(_A    ), torch.cat(_A    )
   return logits, targs







def __UpperCamelCase(   _A					:				Accelerator  ,			_A					:				str=82  ,			_A					:				Optional[Any]=False  ,			_A					:				Tuple=False  ,			_A					:				Tuple=16    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  get_basic_setup(_A  ,			_A  ,			_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  generate_predictions(_A  ,			_A  ,			_A    )
   assert (
       len(_A    ) == num_samples
   ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_A    )}'''







def __UpperCamelCase(   _A					:				bool = False  ,			_A					:				bool = False    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  evaluate.load('''glue'''  ,			'''mrpc'''    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  get_mrpc_setup(_A  ,			_A    )
   # First do baseline
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  setup['''no''']
   model.to(_A    )
   model.eval()
   for batch in dataloader:
      batch.to(_A    )
      with torch.inference_mode():
         UpperCAmelCase__							:				List[Any]   				=  model(**_A    )
      UpperCAmelCase__							:				Dict   				=  outputs.logits.argmax(dim=-1    )
      metric.add_batch(predictions=_A  ,			references=batch['''labels''']    )
   UpperCAmelCase__							:				Union[str, Any]   				=  metric.compute()

   # Then do distributed
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  setup['''ddp''']
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         UpperCAmelCase__							:				Optional[Any]   				=  model(**_A    )
      UpperCAmelCase__							:				int   				=  outputs.logits.argmax(dim=-1    )
      UpperCAmelCase__							:				Optional[int]   				=  batch['''labels''']
      UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  accelerator.gather_for_metrics((preds, references)    )
      metric.add_batch(predictions=_A  ,			references=_A    )
   UpperCAmelCase__							:				str   				=  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 __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  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]:
         UpperCAmelCase__							:				str   				=  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**'''    )
   UpperCAmelCase__							:				Optional[int]   				=  Accelerator()
   test_torch_metrics(_A  ,			5_12    )
   accelerator.state._reset_state()







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   # 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 				_lowercase	(      Generic[T]   ):
      '''simple docstring'''


      UpperCAmelCase_      :      deque[T]  # Cache store of keys
      UpperCAmelCase_      :      set[T]  # References of the keys in cache
      UpperCAmelCase_      :      int      							=   10  # Maximum capacity of cache




      def __init__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  deque()
         UpperCAmelCase__							:				Optional[int]   				=  set()
         if not n:
            UpperCAmelCase__							:				List[Any]   				=  sys.maxsize
         elif n < 0:
            raise ValueError('''n should be an integer greater than 0.'''							)
         else:
            UpperCAmelCase__							:				Any   				=  n




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         if x not in self.key_reference:
            if len(self.dq_store							) == LRUCache._MAX_CAPACITY:
               UpperCAmelCase__							:				int   				=  self.dq_store.pop()
               self.key_reference.remove(lowerCamelCase_							)
         else:
            self.dq_store.remove(lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         for k in self.dq_store:
            print(lowerCamelCase_							)




      def __repr__(				self							)			->			str:



         '''simple docstring'''



         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]"




