




'''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_    	=				"true"




def   lowercase__(			__UpperCamelCase:					Dict      ,__UpperCamelCase:					Any=82      ,__UpperCamelCase:					Union[str, Any]=16    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					Tuple=False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						load_dataset('glue'      ,'mrpc'      ,split='validation'    )

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

      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						dataset.map(
                __UpperCamelCase      ,batched=__UpperCamelCase      ,remove_columns=['idx', 'sentence1', 'sentence2']      ,)

      SCREAMING_SNAKE_CASE   :      Any    					=						tokenized_datasets.rename_column('label'      ,'labels'    )

      def collate_fn(__UpperCamelCase:					List[Any]    ):
            if use_longest:
                  return tokenizer.pad(__UpperCamelCase      ,padding='longest'      ,return_tensors='pt'    )
            return tokenizer.pad(__UpperCamelCase      ,padding='max_length'      ,max_length=1_28      ,return_tensors='pt'    )

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




def   lowercase__(			__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Tuple      ,__UpperCamelCase:					Optional[Any]      ,__UpperCamelCase:					int    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Accelerator      ,__UpperCamelCase:					int=82      ,__UpperCamelCase:					List[Any]=False      ,__UpperCamelCase:					Optional[Any]=False      ,__UpperCamelCase:					str=16    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						get_basic_setup(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						generate_predictions(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    )
      assert (
          len(__UpperCamelCase    ) == num_samples
      ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__UpperCamelCase    )}"




def   lowercase__(			__UpperCamelCase:					bool = False      ,__UpperCamelCase:					bool = False    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						evaluate.load('glue'      ,'mrpc'    )
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						get_mrpc_setup(__UpperCamelCase      ,__UpperCamelCase    )
      # First do baseline
      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						setup['no']
      model.to(__UpperCamelCase    )
      model.eval()
      for batch in dataloader:
            batch.to(__UpperCamelCase    )
            with torch.inference_mode():
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						outputs.logits.argmax(dim=-1    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=batch['labels']    )
      SCREAMING_SNAKE_CASE   :      List[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[str]    					=						model(**__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      str    					=						outputs.logits.argmax(dim=-1    )
            SCREAMING_SNAKE_CASE   :      Tuple    					=						batch['labels']
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						accelerator.gather_for_metrics((preds, references)    )
            metric.add_batch(predictions=__UpperCamelCase      ,references=__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[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   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
      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(__UpperCamelCase      ,__UpperCamelCase    )
                        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   :      Dict    					=						Accelerator(split_batches=__UpperCamelCase      ,dispatch_batches=__UpperCamelCase    )
                  if accelerator.is_local_main_process:
                        print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"    )
                  test_torch_metrics(__UpperCamelCase      ,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(__UpperCamelCase      ,5_12    )
      accelerator.state._reset_state()




def   lowercase__(			__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      main()


if __name__ == "__main__":
      main()









"""simple docstring"""






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			list[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			[0 for i in range(len(lowerCAmelCase__			)			)]

			# initialize interval's left pointer and right pointer
			a__ ,	a__  :	int		 =			0, 0

			for i in range(1							,			len(lowerCAmelCase__			)			):
						# case when current index is inside the interval
						if i <= right_pointer:
									a__  :	List[Any]		 =			min(right_pointer - i + 1							,			z_result[i - left_pointer]			)
									a__  :	List[str]		 =			min_edge

						while go_next(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									a__ ,	a__  :	str		 =			i, i + z_result[i] - 1

			return z_result






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			list[int]							,			lowerCAmelCase__   :			str			)    ->			bool:



			'''simple docstring'''
			return i + z_result[i] < len(lowerCAmelCase__			) and s[z_result[i]] == s[i + z_result[i]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			a__  :	List[str]		 =			z_function(pattern + input_str			)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(lowerCAmelCase__			):
									answer += 1

			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()