

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

from accelerate.utils.dataclasses import DistributedType







class 					lowercase							:




				def __init__(   self ,  snake_case=2 ,  snake_case=3 ,  snake_case=64 ,  snake_case=None			):
											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			):
											return self.length




				def __getitem__(   self ,  snake_case			):
											return {"x": self.x[i], "y": self.y[i]}







class 					lowercase							(			torch.nn.Module     ):




				def __init__(   self ,  snake_case=0 ,  snake_case=0 ,  snake_case=False			):
											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        a				(   self ,  snake_case=None			):
											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 					lowercase							(			torch.nn.Module     ):




				def __init__(   self ,  snake_case=0 ,  snake_case=0 ,  snake_case=False			):
											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        a				(   self ,  snake_case=None			):
											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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ = 16 ):







							'''simple docstring'''






							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=UpperCamelCase__ )
							snake_case_     							=       datasets['train'].unique('label' )

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

							def tokenize_function(UpperCamelCase__ ):
														# max_length=None => use the model max length (it's actually the default)
														snake_case_     							=       tokenizer(
														    examples['sentence1']  ,				examples['sentence2']  ,				truncation=UpperCamelCase__  ,				max_length=UpperCamelCase__  ,				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(
							    UpperCamelCase__  ,				batched=UpperCamelCase__  ,				remove_columns=['sentence1', 'sentence2', 'label']  ,				)

							def collate_fn(UpperCamelCase__ ):
														# 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(UpperCamelCase__  ,				padding='max_length'  ,				max_length=128  ,				return_tensors='pt' )
														return tokenizer.pad(UpperCamelCase__  ,				padding='longest'  ,				return_tensors='pt' )

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

							return train_dataloader, eval_dataloader







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

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







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




