






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

from accelerate.utils.dataclasses import DistributedType




class     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=6_4  ,      _lowerCamelCase=None							):
         UpperCamelCase_: Tuple   				=    np.random.default_rng(_lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    length
         UpperCamelCase_: Optional[int]   				=    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							):
         return self.length





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







class     _lowerCAmelCase(	torch.nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=False							):
         super().__init__()
         UpperCamelCase_: str   				=    torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         UpperCamelCase_: Union[str, Any]   				=    torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
         UpperCamelCase_: List[Any]   				=    True





       def       _a   ( self  ,      _lowerCamelCase=None							):
         if self.first_batch:
           print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
           UpperCamelCase_: Dict   				=    False
         return x * self.a[0] + self.b[0]







class     _lowerCAmelCase(	torch.nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase=0  ,      _lowerCamelCase=0  ,      _lowerCamelCase=False							):
         super().__init__()
         UpperCamelCase_: Any   				=    torch.nn.Parameter(torch.tensor(_lowerCamelCase							).float()							)
         UpperCamelCase_: Union[str, Any]   				=    torch.nn.Parameter(torch.tensor(_lowerCamelCase							).float()							)
         UpperCamelCase_: Any   				=    True





       def       _a   ( self  ,      _lowerCamelCase=None							):
         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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = 1_6  )	->    Optional[Any]:
  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  UpperCamelCase_: Any   				=    {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)
    UpperCamelCase_: Dict   				=    tokenizer(
        examples['sentence1']     ,   examples['sentence2']     ,   truncation=UpperCAmelCase__     ,   max_length=UpperCAmelCase__     ,   padding='max_length'  )
    if "label" in examples:
      UpperCamelCase_: Any   				=    [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_: int   				=    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=1_2_8     ,   return_tensors='pt'  )
    return tokenizer.pad(UpperCAmelCase__     ,   padding='longest'     ,   return_tensors='pt'  )

  # Instantiate dataloaders.
  UpperCamelCase_: int   				=    DataLoader(tokenized_datasets['train']     ,   shuffle=UpperCAmelCase__     ,   collate_fn=UpperCAmelCase__     ,   batch_size=2  )
  UpperCamelCase_: Optional[Any]   				=    DataLoader(tokenized_datasets['validation']     ,   shuffle=UpperCAmelCase__     ,   collate_fn=UpperCAmelCase__     ,   batch_size=1  )

  return train_dataloader, eval_dataloader









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Any   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: List[str]   				=    get_activation('gelu'							)
         self.assertTrue(torch.allclose(gelu_python(_lowerCamelCase							)  ,      torch_builtin(_lowerCamelCase							)							)							)
         self.assertFalse(torch.allclose(gelu_python(_lowerCamelCase							)  ,      gelu_new(_lowerCamelCase							)							)							)





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]							)
         UpperCamelCase_: Union[str, Any]   				=    get_activation('gelu'							)
         UpperCamelCase_: int   				=    get_activation('gelu_10'							)

         UpperCamelCase_: Union[str, Any]   				=    torch_builtin(_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    geluaa(_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    torch.where(y_gelu_aa < 1_0.0  ,      1  ,      0							)

         self.assertTrue(torch.max(_lowerCamelCase							).item() == 1_0.0							)
         self.assertTrue(torch.allclose(y_gelu * clipped_mask  ,      y_gelu_aa * clipped_mask							)							)





       def       _a   ( self							):
         get_activation('gelu'							)
         get_activation('gelu_10'							)
         get_activation('gelu_fast'							)
         get_activation('gelu_new'							)
         get_activation('gelu_python'							)
         get_activation('gelu_pytorch_tanh'							)
         get_activation('linear'							)
         get_activation('mish'							)
         get_activation('quick_gelu'							)
         get_activation('relu'							)
         get_activation('sigmoid'							)
         get_activation('silu'							)
         get_activation('swish'							)
         get_activation('tanh'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation('bogus'							)
         with self.assertRaises(_lowerCamelCase							):
           get_activation(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    get_activation('gelu'							)
         UpperCamelCase_: str   				=    1
         UpperCamelCase_: int   				=    get_activation('gelu'							)
         self.assertEqual(acta.a  ,      1							)
         with self.assertRaises(_lowerCamelCase							):
           UpperCamelCase_: Tuple   				=    acta.a