
'''simple docstring'''






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

from accelerate.utils.dataclasses import DistributedType

class 			UpperCAmelCase__    :



    def __init__(						self   :							Any,__A   :							int=2,__A   :							Any=3,__A   :							Optional[int]=6_4,__A   :							Tuple=None  ):
          _lowerCamelCase					: int		    = np.random.default_rng(__A  )
          _lowerCamelCase					: List[str]		    = length
          _lowerCamelCase					: Optional[Any]		    = rng.normal(size=(length,)  ).astype(np.floataa  )
          _lowerCamelCase					: Optional[int]		    = a * self.x + b + rng.normal(scale=0.1,size=(length,)  ).astype(np.floataa  )



    def __len__(						self   :							Dict  ):
          return self.length



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


class 			UpperCAmelCase__    (							torch.nn.Module			):



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



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							Tuple=None  ):
          if self.first_batch:
                print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
                _lowerCamelCase					: Optional[Any]		    = False
          return x * self.a[0] + self.b[0]


class 			UpperCAmelCase__    (							torch.nn.Module			):



    def __init__(						self   :							Union[str, Any],__A   :							List[str]=0,__A   :							List[str]=0,__A   :							int=False  ):
          super().__init__()
          _lowerCamelCase					: Optional[int]		    = torch.nn.Parameter(torch.tensor(__A  ).float()  )
          _lowerCamelCase					: Dict		    = torch.nn.Parameter(torch.tensor(__A  ).float()  )
          _lowerCamelCase					: Tuple		    = True



    def 	lowerCamelCase_	(						self   :							str,__A   :							List[Any]=None  ):
          if self.first_batch:
                print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
                _lowerCamelCase					: Optional[Any]		    = False
          return x * self.a + self.b







def 		A_							(						_lowerCAmelCase :     Any					,						_lowerCAmelCase :     int = 16				):



      """simple docstring"""




      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      _lowerCamelCase					: Optional[Any]		    = {v: i for i, v in enumerate(_lowerCAmelCase				)}

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

      def collate_fn(_lowerCAmelCase :     str				):
            # 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.
      _lowerCamelCase					: str		    = DataLoader(tokenized_datasets["train"]					,						shuffle=_lowerCAmelCase					,						collate_fn=_lowerCAmelCase					,						batch_size=2				)
      _lowerCamelCase					: Optional[int]		    = DataLoader(tokenized_datasets["validation"]					,						shuffle=_lowerCAmelCase					,						collate_fn=_lowerCAmelCase					,						batch_size=1				)

      return train_dataloader, eval_dataloader





'''simple docstring'''


from ..utils import DummyObject, requires_backends







class      SCREAMING_SNAKE_CASE    (						metaclass=snake_case_				):
      __magic_name__  :       Dict   							=					['''transformers''', '''torch''', '''note_seq''']






      def __init__( self   :  List[str]						,						*lowercase__   :  Dict						,						**lowercase__   :  int       ):


           '''simple docstring'''

           requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						*lowercase__   :  Optional[int]						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )
