




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() = }""")






