



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_ 							=       """true"""





def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Dict=82    ,      UpperCamelCase__     :    List[str]=16  )     ->	int:



					'''simple docstring'''




					set_seed(42  )
					_snake_case													=			RegressionModel()
					_snake_case													=			deepcopy(_lowerCAmelCase  )
					_snake_case													=			RegressionDataset(length=_lowerCAmelCase  )
					_snake_case													=			DataLoader(_lowerCAmelCase    ,      batch_size=_lowerCAmelCase  )
					model.to(accelerator.device  )
					_snake_case													=			accelerator.prepare(_lowerCAmelCase    ,      _lowerCAmelCase  )
					return model, ddp_model, dataloader





def   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    List[Any]=False  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'  )
					_snake_case													=			load_dataset('glue'    ,      'mrpc'    ,      split='validation'  )

					def tokenize_function(UpperCamelCase__     :    Any  ):
										_snake_case													=			tokenizer(examples['sentence1']    ,      examples['sentence2']    ,      truncation=_lowerCAmelCase    ,      max_length=_lowerCAmelCase  )
										return outputs

					with accelerator.main_process_first():
										_snake_case													=			dataset.map(
										    _lowerCAmelCase    ,      batched=_lowerCAmelCase    ,      remove_columns=['idx', 'sentence1', 'sentence2']    ,      )

					_snake_case													=			tokenized_datasets.rename_column('label'    ,      'labels'  )

					def collate_fn(UpperCamelCase__     :    Any  ):
										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__       (       UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    Tuple  )     ->	List[str]:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    Any  )     ->	int:



					'''simple docstring'''




					_snake_case													=			[]
					for batch in dataloader:
										_snake_case													=			batch.values()
										with torch.no_grad():
															_snake_case													=			model(_lowerCAmelCase  )
															_snake_case													=			accelerator.gather_for_metrics((logit, target)  )
															logits_and_targets.append((logit, target)  )
					_snake_case													=			[], []
					for logit, targ in logits_and_targets:
										logits.append(_lowerCAmelCase  )
										targs.append(_lowerCAmelCase  )
					_snake_case													=			torch.cat(_lowerCAmelCase  ), torch.cat(_lowerCAmelCase  )
					return logits, targs





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    Tuple=82    ,      UpperCamelCase__     :    List[Any]=False    ,      UpperCamelCase__     :    Union[str, Any]=False    ,      UpperCamelCase__     :    Any=16  )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			get_basic_setup(_lowerCAmelCase    ,      _lowerCAmelCase    ,      _lowerCAmelCase  )
					_snake_case													=			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__       (       UpperCamelCase__     :    Dict = False    ,      UpperCamelCase__     :    int = False  )     ->	List[str]:



					'''simple docstring'''




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

					# Then do distributed
					_snake_case													=			setup["""ddp"""]
					model.eval()
					for batch in dataloader:
										with torch.inference_mode():
															_snake_case													=			model(**_lowerCAmelCase  )
										_snake_case													=			outputs.logits.argmax(dim=-1  )
										_snake_case													=			batch["""labels"""]
										_snake_case													=			accelerator.gather_for_metrics((preds, references)  )
										metric.add_batch(predictions=_lowerCAmelCase    ,      references=_lowerCAmelCase  )
					_snake_case													=			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__       (       )     ->	Any:



					'''simple docstring'''




					_snake_case													=			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]:
															_snake_case													=			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**'  )
					_snake_case													=			Accelerator()
					test_torch_metrics(_lowerCAmelCase    ,      512  )
					accelerator.state._reset_state()





def   lowerCamelCase__       (       UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					main()


if __name__ == "__main__":
					main()










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



