




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 		_A(       UpperCamelCase__							:			Any				,       UpperCamelCase__							:			Optional[int]=82				,       UpperCamelCase__							:			int=16				)    -> List[Any]:
       '''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Accelerator				,       UpperCamelCase__							:			List[str]=False				)    -> Optional[int]:
       '''simple docstring'''


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

       def tokenize_function(UpperCamelCase__							:			int				):
              __lowercase      =    tokenizer(examples['''sentence1''']				,       examples['''sentence2''']				,       truncation=UpperCamelCase__				,       max_length=UpperCamelCase__				)
              return outputs

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

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

       def collate_fn(UpperCamelCase__							:			Union[str, Any]				):
              if use_longest:
                     return tokenizer.pad(UpperCamelCase__				,       padding='''longest'''				,       return_tensors='''pt'''				)
              return tokenizer.pad(UpperCamelCase__				,       padding='''max_length'''				,       max_length=128				,       return_tensors='''pt'''				)

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







def 		_A(       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			List[str]				)    -> Any:
       '''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			List[Any]				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Any				)    -> Union[str, Any]:
       '''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			Accelerator				,       UpperCamelCase__							:			Union[str, Any]=82				,       UpperCamelCase__							:			Optional[int]=False				,       UpperCamelCase__							:			Optional[int]=False				,       UpperCamelCase__							:			Tuple=16				)    -> Dict:
       '''simple docstring'''


       __lowercase     ,     __lowercase     ,     __lowercase      =    get_basic_setup(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)
       __lowercase     ,     __lowercase      =    generate_predictions(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)
       assert (
           len(UpperCamelCase__				) == num_samples
       ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__				)}'







def 		_A(       UpperCamelCase__							:			bool = False				,       UpperCamelCase__							:			bool = False				)    -> int:
       '''simple docstring'''


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

       # Then do distributed
       __lowercase     ,     __lowercase     ,     __lowercase      =    setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     __lowercase      =    model(**UpperCamelCase__				)
              __lowercase      =    outputs.logits.argmax(dim=-1				)
              __lowercase      =    batch['''labels''']
              __lowercase     ,     __lowercase      =    accelerator.gather_for_metrics((preds, references)				)
              metric.add_batch(predictions=UpperCamelCase__				,       references=UpperCamelCase__				)
       __lowercase      =    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 		_A(       )    -> str:
       '''simple docstring'''


       __lowercase      =    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]:
                     __lowercase      =    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**'''				)
       __lowercase      =    Accelerator()
       test_torch_metrics(UpperCamelCase__				,       512				)
       accelerator.state._reset_state()







def 		_A(       UpperCamelCase__							:			Optional[Any]				)    -> Tuple:
       '''simple docstring'''


       main()


if __name__ == "__main__":
   main()












import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

							__lowercase      =    [[int(UpperCamelCase__				) for cell in row.split(''','''				)] for row in data.strip().splitlines()]
							__lowercase      =    [[0 for cell in row] for row in grid]
							__lowercase      =    len(grid[0]				)

							__lowercase      =    [[0 for i in range(UpperCamelCase__				)] for j in range(UpperCamelCase__				)]
							__lowercase      =    grid[0][0]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[0][i] + dp[0][i - 1]
							for i in range(1				,       UpperCamelCase__				):
														__lowercase      =    grid[i][0] + dp[i - 1][0]

							for i in range(1				,       UpperCamelCase__				):
														for j in range(1				,       UpperCamelCase__				):
																					__lowercase      =    grid[i][j] + min(dp[i - 1][j]				,       dp[i][j - 1]				)

							return dp[-1][-1]


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






