






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

from accelerate.utils.dataclasses import DistributedType




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a=2       ,			a=3       ,			a=64       ,			a=None					)       ->    Union[str, Any]:
								snake_case_				=							np.random.default_rng(_snake_case					)
								snake_case_				=							length
								snake_case_				=							rng.normal(size=(length,)					).astype(np.floataa					)
								snake_case_				=							a * self.x + b + rng.normal(scale=0.1       ,			size=(length,)					).astype(np.floataa					)




	def __len__(       self					)       ->    Tuple:
								return self.length






	def __getitem__(       self       ,			a					)       ->    Union[str, Any]:
								return {"x": self.x[i], "y": self.y[i]}






class  UpperCamelCase_      (			torch.nn.Module				):



	'''simple docstring'''




	def __init__(       self       ,			a=0       ,			a=0       ,			a=False					)       ->    Any:
								super().__init__()
								snake_case_				=							torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
								snake_case_				=							torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
								snake_case_				=							True






	def 							_UpperCamelCase			(       self       ,			a=None					)       ->    Tuple:




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






class  UpperCamelCase_      (			torch.nn.Module				):



	'''simple docstring'''




	def __init__(       self       ,			a=0       ,			a=0       ,			a=False					)       ->    Dict:
								super().__init__()
								snake_case_				=							torch.nn.Parameter(torch.tensor(_snake_case					).float()					)
								snake_case_				=							torch.nn.Parameter(torch.tensor(_snake_case					).float()					)
								snake_case_				=							True






	def 							_UpperCamelCase			(       self       ,			a=None					)       ->    List[Any]:




								if self.first_batch:
															print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''					)
															snake_case_				=							False
								return x * self.a + self.b






def 						__UpperCAmelCase			(     a_							,     a_ = 16):
							from datasets import load_dataset
							from transformers import AutoTokenizer

							snake_case_				=							AutoTokenizer.from_pretrained('bert-base-cased')
							snake_case_				=							{'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
							snake_case_				=							load_dataset('csv'							,     data_files=a__)
							snake_case_				=							datasets['train'].unique('label')

							snake_case_				=							{v: i for i, v in enumerate(a__)}

							def tokenize_function(a_):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_				=							tokenizer(
														    examples['sentence1']							,     examples['sentence2']							,     truncation=a__							,     max_length=a__							,     padding='max_length')
														if "label" in examples:
																					snake_case_				=							[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
							snake_case_				=							datasets.map(
							    a__							,     batched=a__							,     remove_columns=['sentence1', 'sentence2', 'label']							,     )

							def collate_fn(a_):
														# 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(a__							,     padding='max_length'							,     max_length=1_28							,     return_tensors='pt')
														return tokenizer.pad(a__							,     padding='longest'							,     return_tensors='pt')

							# Instantiate dataloaders.
							snake_case_				=							DataLoader(tokenized_datasets['train']							,     shuffle=a__							,     collate_fn=a__							,     batch_size=2)
							snake_case_				=							DataLoader(tokenized_datasets['validation']							,     shuffle=a__							,     collate_fn=a__							,     batch_size=1)

							return train_dataloader, eval_dataloader







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=a			)
class 			__A( a			):
				snake_case_						   =    field(default='''language-modeling'''  ,   metadata={'''include_in_asdict_even_if_is_default''': True}			)

				snake_case_						   =    Features({'''text''': Value('''string'''			)}			)
				snake_case_						   =    Features({}			)
				snake_case_						   =    "text"




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict[str, str]:


								'''simple docstring'''




								return {self.text_column: "text"}