






"""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_       			=   '''true'''

def        snake_case   (     A__			,A__=82			,A__=16		):
   set_seed(42		)
   UpperCAmelCase_  :				Tuple      =      RegressionModel()
   UpperCAmelCase_  :				Union[str, Any]      =      deepcopy(snake_case__		)
   UpperCAmelCase_  :				Any      =      RegressionDataset(length=snake_case__		)
   UpperCAmelCase_  :				Optional[int]      =      DataLoader(snake_case__			,batch_size=snake_case__		)
   model.to(accelerator.device		)
   UpperCAmelCase_  :				str      =      accelerator.prepare(snake_case__			,snake_case__		)
   return model, ddp_model, dataloader

def        snake_case   (     A__			,A__=False		):
   UpperCAmelCase_  :				Optional[int]      =      AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"		)
   UpperCAmelCase_  :				Tuple      =      load_dataset("glue"			,"mrpc"			,split="validation"		)

   def tokenize_function(A__		):
      UpperCAmelCase_  :				Tuple      =      tokenizer(examples["sentence1"]			,examples["sentence2"]			,truncation=snake_case__			,max_length=snake_case__		)
      return outputs

   with accelerator.main_process_first():
      UpperCAmelCase_  :				List[Any]      =      dataset.map(
          snake_case__			,batched=snake_case__			,remove_columns=["idx", "sentence1", "sentence2"]			,)

   UpperCAmelCase_  :				List[str]      =      tokenized_datasets.rename_column("label"			,"labels"		)

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

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

def        snake_case   (     A__			,A__		):
   UpperCAmelCase_  :				Optional[int]      =      Accelerator(dispatch_batches=snake_case__			,split_batches=snake_case__		)
   UpperCAmelCase_  :				Dict      =      get_dataloader(snake_case__			,not dispatch_batches		)
   UpperCAmelCase_  :				Tuple      =      AutoModelForSequenceClassification.from_pretrained(
       "hf-internal-testing/mrpc-bert-base-cased"			,return_dict=snake_case__		)
   UpperCAmelCase_  :				Tuple      =      accelerator.prepare(snake_case__			,snake_case__		)
   return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

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

def        snake_case   (     A__			,A__=82			,A__=False			,A__=False			,A__=16		):
   UpperCAmelCase_  :				int      =      get_basic_setup(snake_case__			,snake_case__			,snake_case__		)
   UpperCAmelCase_  :				str      =      generate_predictions(snake_case__			,snake_case__			,snake_case__		)
   assert (
       len(snake_case__		) == num_samples
   ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case__		)}"""

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

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

def        snake_case   (     A__		):
   main()


if __name__ == "__main__":
   main()











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


if __name__ == "__main__":
			print(f'{solution() = }')



