




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

from accelerate.utils.dataclasses import DistributedType

class     __a:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=64  ,_SCREAMING_SNAKE_CASE=None    )					->						int:
         UpperCAmelCase_     :				List[Any]  =     np.random.default_rng(_lowerCAmelCase    )
         UpperCAmelCase_     :				str  =     length
         UpperCAmelCase_     :				Tuple  =     rng.normal(size=(length,)    ).astype(np.floataa    )
         UpperCAmelCase_     :				Optional[int]  =     a * self.x + b + rng.normal(scale=0.1  ,size=(length,)    ).astype(np.floataa    )





    def __len__( self    )					->						List[str]:
         return self.length





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


class     __a(	torch.nn.Module  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=False    )					->						Optional[int]:
         super().__init__()
         UpperCAmelCase_     :				List[Any]  =     torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
         UpperCAmelCase_     :				Optional[Any]  =     torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
         UpperCAmelCase_     :				List[Any]  =     True





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=None    )					->						int:
         if self.first_batch:
              print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
              UpperCAmelCase_     :				int  =     False
         return x * self.a[0] + self.b[0]


class     __a(	torch.nn.Module  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=False    )					->						List[Any]:
         super().__init__()
         UpperCAmelCase_     :				Tuple  =     torch.nn.Parameter(torch.tensor(_lowerCAmelCase    ).float()    )
         UpperCAmelCase_     :				str  =     torch.nn.Parameter(torch.tensor(_lowerCAmelCase    ).float()    )
         UpperCAmelCase_     :				Optional[int]  =     True





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE=None    )					->						str:
         if self.first_batch:
              print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
              UpperCAmelCase_     :				Any  =     False
         return x * self.a + self.b







def lowerCamelCase__  (     _lowercase					,		_lowercase = 16 ):

     '''simple docstring'''


     from datasets import load_dataset
     from transformers import AutoTokenizer

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

     UpperCAmelCase_     :				str  =     {v: i for i, v in enumerate(__UpperCAmelCase )}

     def tokenize_function(_lowercase ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase_     :				Tuple  =     tokenizer(
              examples['''sentence1''']					,		examples['''sentence2''']					,		truncation=__UpperCAmelCase					,		max_length=__UpperCAmelCase					,		padding='''max_length''' )
          if "label" in examples:
               UpperCAmelCase_     :				Dict  =     [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_     :				List[Any]  =     datasets.map(
         __UpperCAmelCase					,		batched=__UpperCAmelCase					,		remove_columns=['''sentence1''', '''sentence2''', '''label''']					,		)

     def collate_fn(_lowercase ):
          # 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.
     UpperCAmelCase_     :				List[Any]  =     DataLoader(tokenized_datasets['''train''']					,		shuffle=__UpperCAmelCase					,		collate_fn=__UpperCAmelCase					,		batch_size=2 )
     UpperCAmelCase_     :				Any  =     DataLoader(tokenized_datasets['''validation''']					,		shuffle=__UpperCAmelCase					,		collate_fn=__UpperCAmelCase					,		batch_size=1 )

     return train_dataloader, eval_dataloader





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    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




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





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

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

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # 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_28     ,     return_tensors='pt'				)
            return tokenizer.pad(__UpperCAmelCase     ,     padding='longest'     ,     return_tensors='pt'				)

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

      return train_dataloader, eval_dataloader