



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 	_lowerCAmelCase						(				__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     int=82		,	__lowerCamelCase   :     Any=16     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Accelerator		,	__lowerCamelCase   :     List[Any]=False     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"     )
 __SCREAMING_SNAKE_CASE       :    Any       =	load_dataset("glue"		,	"mrpc"		,	split="validation"     )

 def tokenize_function(__lowerCamelCase   :     str     ):
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	tokenizer(examples["sentence1"]		,	examples["sentence2"]		,	truncation=__lowerCamelCase		,	max_length=__lowerCamelCase     )
  return outputs

 with accelerator.main_process_first():
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	dataset.map(
      __lowerCamelCase		,	batched=__lowerCamelCase		,	remove_columns=["idx", "sentence1", "sentence2"]		,	)

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	tokenized_datasets.rename_column("label"		,	"labels"     )

 def collate_fn(__lowerCamelCase   :     str     ):
  if use_longest:
   return tokenizer.pad(__lowerCamelCase		,	padding="longest"		,	return_tensors="pt"     )
  return tokenizer.pad(__lowerCamelCase		,	padding="max_length"		,	max_length=128		,	return_tensors="pt"     )

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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Tuple     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Accelerator		,	__lowerCamelCase   :     List[str]=82		,	__lowerCamelCase   :     List[Any]=False		,	__lowerCamelCase   :     List[Any]=False		,	__lowerCamelCase   :     Tuple=16     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[str]       =	get_basic_setup(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	generate_predictions(__lowerCamelCase		,	__lowerCamelCase		,	__lowerCamelCase     )
 assert (
     len(__lowerCamelCase     ) == num_samples
 ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCamelCase     )}"""






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bool = False		,	__lowerCamelCase   :     bool = False     ):



 """simple docstring"""





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

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



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	Accelerator(split_batches=__lowerCamelCase		,	dispatch_batches=__lowerCamelCase     )
 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(__lowerCamelCase		,	__lowerCamelCase     )
    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]:
   __SCREAMING_SNAKE_CASE       :    Any       =	Accelerator(split_batches=__lowerCamelCase		,	dispatch_batches=__lowerCamelCase     )
   if accelerator.is_local_main_process:
    print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
   test_torch_metrics(__lowerCamelCase		,	99     )
   accelerator.state._reset_state()
 if accelerator.is_local_main_process:
  print("**Test last batch is not dropped when perfectly divisible**"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	Accelerator()
 test_torch_metrics(__lowerCamelCase		,	512     )
 accelerator.state._reset_state()






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]     ):



 """simple docstring"""





 main()


if __name__ == "__main__":
      main()











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 1000000     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    int

 while num_cuboids <= limit:
  max_cuboid_size += 1
  for sum_shortest_sides in range(2		,	2 * max_cuboid_size + 1     ):
   if sqrt(sum_shortest_sides**2 + max_cuboid_size**2     ).is_integer():
    num_cuboids += (
        min(__lowerCamelCase		,	sum_shortest_sides // 2     )
        - max(1		,	sum_shortest_sides - max_cuboid_size     )
        + 1
    )

 return max_cuboid_size


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






