


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				:   str       ,   __snake_case				:   str=2       ,   __snake_case				:   Any=3       ,   __snake_case				:   Optional[Any]=64       ,   __snake_case				:   List[Any]=None	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   np.random.default_rng(__snake_case	)
       UpperCAmelCase_							:      str						=   length
       UpperCAmelCase_							:      Union[str, Any]						=   rng.normal(size=(length,)	).astype(np.floataa	)
       UpperCAmelCase_							:      int						=   a * self.x + b + rng.normal(scale=0.1       ,   size=(length,)	).astype(np.floataa	)





    def __len__(	self				:   Tuple	):





       '''simple docstring'''
       return self.length





    def __getitem__(	self				:   List[Any]       ,   __snake_case				:   List[str]	):





       '''simple docstring'''
       return {"x": self.x[i], "y": self.y[i]}





class  lowerCAmelCase__(    torch.nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   List[Any]       ,   __snake_case				:   List[Any]=0       ,   __snake_case				:   Union[str, Any]=0       ,   __snake_case				:   str=False	):





       '''simple docstring'''
       super().__init__()
       UpperCAmelCase_							:      Optional[Any]						=   torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       UpperCAmelCase_							:      Any						=   torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
       UpperCAmelCase_							:      List[Any]						=   True





    def 					_lowerCamelCase (	self				:   List[Any]       ,   __snake_case				:   Any=None	):





       '''simple docstring'''
       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[0] + self.b[0]





class  lowerCAmelCase__(    torch.nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   Dict       ,   __snake_case				:   List[str]=0       ,   __snake_case				:   Optional[int]=0       ,   __snake_case				:   Union[str, Any]=False	):





       '''simple docstring'''
       super().__init__()
       UpperCAmelCase_							:      Optional[int]						=   torch.nn.Parameter(torch.tensor(__snake_case	).float()	)
       UpperCAmelCase_							:      Dict						=   torch.nn.Parameter(torch.tensor(__snake_case	).float()	)
       UpperCAmelCase_							:      List[str]						=   True





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   str=None	):





       '''simple docstring'''
       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 + self.b





def      snake_case_						( __lowercase			,       __lowercase = 1_6       ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

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

   def tokenize_function(__lowercase       ):
      # max_length=None => use the model max length (it's actually the default)
      UpperCAmelCase_							:      Optional[Any]						=   tokenizer(
          examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=__lowercase			,       max_length=__lowercase			,       padding='''max_length'''       )
      if "label" in examples:
         UpperCAmelCase_							:      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
   UpperCAmelCase_							:      int						=   datasets.map(
       __lowercase			,       batched=__lowercase			,       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(__lowercase			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''       )
      return tokenizer.pad(__lowercase			,       padding='''longest'''			,       return_tensors='''pt'''       )

   # Instantiate dataloaders.
   UpperCAmelCase_							:      Tuple						=   DataLoader(tokenized_datasets['''train''']			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=2       )
   UpperCAmelCase_							:      Dict						=   DataLoader(tokenized_datasets['''validation''']			,       shuffle=__lowercase			,       collate_fn=__lowercase			,       batch_size=1       )

   return train_dataloader, eval_dataloader








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:  Tuple      					=							logging.get_logger(__name__)





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     Union[str, Any]			= 'encoder-decoder'
    A_  :     Optional[int]			= True





    def __init__(	self				:   Dict       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       super().__init__(**__snake_case	)
       assert (
           "encoder" in kwargs and "decoder" in kwargs
       ), "Config has to be initialized with encoder and decoder config"
       UpperCAmelCase_							:      int						=   kwargs.pop('''encoder'''	)
       UpperCAmelCase_							:      List[Any]						=   encoder_config.pop('''model_type'''	)
       UpperCAmelCase_							:      int						=   kwargs.pop('''decoder'''	)
       UpperCAmelCase_							:      Union[str, Any]						=   decoder_config.pop('''model_type'''	)

       from ..auto.configuration_auto import AutoConfig

       UpperCAmelCase_							:      Optional[int]						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      Any						=   AutoConfig.for_model(__snake_case       ,   **__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   True





    @classmethod
    def 					_lowerCamelCase (	cls				:   Optional[Any]       ,   __snake_case				:   PretrainedConfig       ,   __snake_case				:   PretrainedConfig       ,   **__snake_case				:   Union[str, Any]	):





       '''simple docstring'''
       logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
       UpperCAmelCase_							:      Dict						=   True
       UpperCAmelCase_							:      str						=   True

       return cls(encoder=encoder_config.to_dict()       ,   decoder=decoder_config.to_dict()       ,   **__snake_case	)





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   copy.deepcopy(self.__dict__	)
       UpperCAmelCase_							:      Tuple						=   self.encoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.decoder.to_dict()
       UpperCAmelCase_							:      Tuple						=   self.__class__.model_type
       return output