


'''simple docstring'''



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







    def __len__(							self	)				->     Union[str, Any]:
         return self.length







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







class    UpperCAmelCase_  (torch.nn.Module  ):





    """simple docstring"""






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







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
              __lowerCamelCase   :	List[Any]								=		False
         return x * self.a[0] + self.b[0]







class    UpperCAmelCase_  (torch.nn.Module  ):





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=False	)				->     Dict:
         super().__init__()
         __lowerCamelCase   :	Union[str, Any]								=		torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_	).float()	)
         __lowerCamelCase   :	Any								=		torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE_	).float()	)
         __lowerCamelCase   :	List[Any]								=		True







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'	)
              __lowerCamelCase   :	Any								=		False
         return x * self.a + self.b







def 			UpperCAmelCase__					(	UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				int = 16							)     ->		str:
     from datasets import load_dataset
     from transformers import AutoTokenizer

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

     __lowerCamelCase   :	List[str]								=		{v: i for i, v in enumerate(UpperCAmelCase_							)}

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

     def collate_fn(UpperCAmelCase_    :				int							):
          # 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.
     __lowerCamelCase   :	List[str]								=		DataLoader(tokenized_datasets['train']      ,    shuffle=UpperCAmelCase_      ,    collate_fn=UpperCAmelCase_      ,    batch_size=2							)
     __lowerCamelCase   :	Union[str, Any]								=		DataLoader(tokenized_datasets['validation']      ,    shuffle=UpperCAmelCase_      ,    collate_fn=UpperCAmelCase_      ,    batch_size=1							)

     return train_dataloader, eval_dataloader










'''simple docstring'''



from importlib import import_module

from .logging import get_logger


A__		:					str												=  get_logger(__name__)





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     List[Any]:
         __lowerCamelCase   :	List[str]								=		attrs or []
         if module is not None:
              for key in module.__dict__:
                   if key in attrs or not key.startswith('__'	):
                        setattr(self			,     SCREAMING_SNAKE_CASE_			,     getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)
         __lowerCamelCase   :	Optional[int]								=		module._original_module if isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) else module







class    UpperCAmelCase_  :





    """simple docstring"""






    lowerCamelCase  :					Tuple							     =			[]







    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_=None	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[int]								=		obj
         __lowerCamelCase   :	List[Any]								=		target
         __lowerCamelCase   :	Union[str, Any]								=		new
         __lowerCamelCase   :	Union[str, Any]								=		target.split('.'	)[0]
         __lowerCamelCase   :	Dict								=		{}
         __lowerCamelCase   :	Dict								=		attrs or []







    def __enter__(							self	)				->     Optional[int]:
         *__lowerCamelCase				,       __lowerCamelCase   :	int								=		self.target.split('.'	)

         # Patch modules:
         # it's used to patch attributes of submodules like "os.path.join";
         # in this case we need to patch "os" and "os.path"

         for i in range(len(SCREAMING_SNAKE_CASE_	)	):
              try:
                   __lowerCamelCase   :	Optional[int]								=		import_module('.'.join(submodules[: i + 1]	)	)
              except ModuleNotFoundError:
                   continue
              # We iterate over all the globals in self.obj in case we find "os" or "os.path"
              for attr in self.obj.__dir__():
                   __lowerCamelCase   :	List[Any]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                   # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                   # This allows to patch renamed modules like "from os import path as ospath".
                   if obj_attr is submodule or (
                       (isinstance(SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj	) and obj_attr._original_module is submodule)
                   ):
                        __lowerCamelCase   :	Optional[Any]								=		obj_attr
                        # patch at top level
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(SCREAMING_SNAKE_CASE_			,     attrs=self.attrs	)	)
                        __lowerCamelCase   :	str								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        # construct lower levels patches
                        for key in submodules[i + 1 :]:
                             setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     _PatchedModuleObj(getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     attrs=self.attrs	)	)
                             __lowerCamelCase   :	List[Any]								=		getattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                        # finally set the target attribute
                        setattr(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     self.new	)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

         if submodules:  # if it's an attribute of a submodule like "os.path.join"
              try:
                   __lowerCamelCase   :	Union[str, Any]								=		getattr(import_module('.'.join(SCREAMING_SNAKE_CASE_	)	)			,     SCREAMING_SNAKE_CASE_	)
              except (AttributeError, ModuleNotFoundError):
                   return
              # We iterate over all the globals in self.obj in case we find "os.path.join"
              for attr in self.obj.__dir__():
                   # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                   # This allows to patch renamed attributes like "from os.path import join as pjoin".
                   if getattr(self.obj			,     SCREAMING_SNAKE_CASE_	) is attr_value:
                        __lowerCamelCase   :	Optional[int]								=		getattr(self.obj			,     SCREAMING_SNAKE_CASE_	)
                        setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
              __lowerCamelCase   :	List[Any]								=		globals()['__builtins__'][target_attr]
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.new	)
         else:
              raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.'	)







    def __exit__(							self			,     *SCREAMING_SNAKE_CASE_	)				->     Optional[Any]:
         for attr in list(self.original	):
              setattr(self.obj			,     SCREAMING_SNAKE_CASE_			,     self.original.pop(SCREAMING_SNAKE_CASE_	)	)







    def       lowercase_				(							self	)				->     Optional[int]:
         self.__enter__()
         self._active_patches.append(self	)







    def       lowercase_				(							self	)				->     str:
         try:
              self._active_patches.remove(self	)
         except ValueError:
              # If the patch hasn't been started this will fail
              return None

         return self.__exit__()






