

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class 				a  :


						def __init__(       self						,					A_=2						,					A_=3						,					A_=64						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    np.random.default_rng(A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    length
										_UpperCAmelCase       :		List[str] 		=    rng.normal(size=(length,)	).astype(np.floataa	)
										_UpperCAmelCase       :		List[str] 		=    a * self.x + b + rng.normal(scale=0.1						,					size=(length,)	).astype(np.floataa	)


						def __len__(       self	):







										'''simple docstring'''


										return self.length





						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										return {"x": self.x[i], "y": self.y[i]}







class 				a  (	torch.nn.Module				):


						def __init__(       self						,					A_=0						,					A_=0						,					A_=False	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Union[str, Any] 		=    torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
										_UpperCAmelCase       :		str 		=    torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
										_UpperCAmelCase       :		Dict 		=    True





						def        _UpperCAmelCase				(       self						,					A_=None	):







										'''simple docstring'''


										if self.first_batch:
														print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
														_UpperCAmelCase       :		Tuple 		=    False
										return x * self.a[0] + self.b[0]







class 				a  (	torch.nn.Module				):


						def __init__(       self						,					A_=0						,					A_=0						,					A_=False	):







										'''simple docstring'''


										super().__init__()
										_UpperCAmelCase       :		Any 		=    torch.nn.Parameter(torch.tensor(A_	).float()	)
										_UpperCAmelCase       :		Optional[Any] 		=    torch.nn.Parameter(torch.tensor(A_	).float()	)
										_UpperCAmelCase       :		Tuple 		=    True





						def        _UpperCAmelCase				(       self						,					A_=None	):







										'''simple docstring'''


										if self.first_batch:
														print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
														_UpperCAmelCase       :		List[Any] 		=    False
										return x * self.a + self.b







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, Any]  ,      lowerCAmelCase:  int = 16 )   ->       List[str]:
				from datasets import load_dataset
				from transformers import AutoTokenizer

				_UpperCAmelCase       :		Tuple 		=    AutoTokenizer.from_pretrained("bert-base-cased" )
				_UpperCAmelCase       :		str 		=    {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
				_UpperCAmelCase       :		Optional[Any] 		=    load_dataset("csv"  ,      data_files=lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    datasets["train"].unique("label" )

				_UpperCAmelCase       :		List[Any] 		=    {v: i for i, v in enumerate(lowerCAmelCase )}

				def tokenize_function(lowerCAmelCase:  Optional[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								_UpperCAmelCase       :		Dict 		=    tokenizer(
								    examples["sentence1"]  ,      examples["sentence2"]  ,      truncation=lowerCAmelCase  ,      max_length=lowerCAmelCase  ,      padding="max_length" )
								if "label" in examples:
												_UpperCAmelCase       :		List[str] 		=    [label_to_id[l] for l in examples["label"]]
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_UpperCAmelCase       :		Optional[Any] 		=    datasets.map(
				    lowerCAmelCase  ,      batched=lowerCAmelCase  ,      remove_columns=["sentence1", "sentence2", "label"]  ,      )

				def collate_fn(lowerCAmelCase:  Tuple ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCAmelCase  ,      padding="max_length"  ,      max_length=128  ,      return_tensors="pt" )
								return tokenizer.pad(lowerCAmelCase  ,      padding="longest"  ,      return_tensors="pt" )

				# Instantiate dataloaders.
				_UpperCAmelCase       :		List[str] 		=    DataLoader(tokenized_datasets["train"]  ,      shuffle=lowerCAmelCase  ,      collate_fn=lowerCAmelCase  ,      batch_size=2 )
				_UpperCAmelCase       :		List[str] 		=    DataLoader(tokenized_datasets["validation"]  ,      shuffle=lowerCAmelCase  ,      collate_fn=lowerCAmelCase  ,      batch_size=1 )

				return train_dataloader, eval_dataloader





import fire

from utils import calculate_rouge, save_json

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  int  ,      lowerCAmelCase:  Tuple  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  Any )   ->       Dict:
				_UpperCAmelCase       :		Optional[Any] 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()]
				_UpperCAmelCase       :		str 		=    [x.strip() for x in open(lowerCAmelCase ).readlines()][: len(lowerCAmelCase )]
				_UpperCAmelCase       :		str 		=    calculate_rouge(lowerCAmelCase  ,      lowerCAmelCase  ,      **lowerCAmelCase )
				if save_path is not None:
								save_json(lowerCAmelCase  ,      lowerCAmelCase  ,      indent=lowerCAmelCase )
				return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)


