



'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType



class  snake_case__						:
 def __init__(   self						:							Dict      ,     __a						:							Union[str, Any]=2      ,     __a						:							str=3      ,     __a						:							Optional[int]=64      ,     __a						:							Union[str, Any]=None  )      ->   str:







       '''simple docstring'''
       __snake_case :   Optional[Any]													=    np.random.default_rng(UpperCAmelCase__  )
       __snake_case :   Union[str, Any]													=    length
       __snake_case :   List[Any]													=    rng.normal(size=(length,)  ).astype(np.floataa  )
       __snake_case :   Dict													=    a * self.x + b + rng.normal(scale=0.1      ,     size=(length,)  ).astype(np.floataa  )
 def __len__(   self						:							Tuple  )      ->   Union[str, Any]:







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





 def __getitem__(   self						:							Union[str, Any]      ,     __a						:							Any  )      ->   Dict:







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



class  snake_case__						(    torch.nn.Module				):
 def __init__(   self						:							Union[str, Any]      ,     __a						:							List[Any]=0      ,     __a						:							List[Any]=0      ,     __a						:							str=False  )      ->   int:







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





 def A_   (   self						:							str      ,     __a						:							Any=None  )      ->   Tuple:







       '''simple docstring'''
       if self.first_batch:
             print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
             __snake_case :   Any													=    False
       return x * self.a[0] + self.b[0]



class  snake_case__						(    torch.nn.Module				):
 def __init__(   self						:							List[Any]      ,     __a						:							Optional[Any]=0      ,     __a						:							int=0      ,     __a						:							Optional[Any]=False  )      ->   Optional[int]:







       '''simple docstring'''
       super().__init__()
       __snake_case :   Optional[int]													=    torch.nn.Parameter(torch.tensor(UpperCAmelCase__  ).float()  )
       __snake_case :   int													=    torch.nn.Parameter(torch.tensor(UpperCAmelCase__  ).float()  )
       __snake_case :   Tuple													=    True





 def A_   (   self						:							List[Any]      ,     __a						:							Optional[Any]=None  )      ->   Optional[Any]:







       '''simple docstring'''
       if self.first_batch:
             print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
             __snake_case :   List[Any]													=    False
       return x * self.a + self.b





def   a_   (		_UpperCAmelCase    :      Tuple ,_UpperCAmelCase    :      int = 16				)    ->  Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

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

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

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

      def collate_fn(_UpperCAmelCase    :      Any				):
            # 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.
      __snake_case :   Union[str, Any]													=    DataLoader(tokenized_datasets['train'] ,shuffle=_UpperCAmelCase ,collate_fn=_UpperCAmelCase ,batch_size=2				)
      __snake_case :   Tuple													=    DataLoader(tokenized_datasets['validation'] ,shuffle=_UpperCAmelCase ,collate_fn=_UpperCAmelCase ,batch_size=1				)

      return train_dataloader, eval_dataloader






'''simple docstring'''





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


UpperCamelCase_    						=       logging.get_logger(__name__)

UpperCamelCase_    						=       {
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):
				lowerCamelCase_		       =    'speech_to_text_2'
				lowerCamelCase_		       =    ['past_key_values']
				lowerCamelCase_		       =    {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Dict=10000		,					UpperCAmelCase__     :		Optional[int]=6		,					UpperCAmelCase__     :		str=2048		,					UpperCAmelCase__     :		Optional[Any]=4		,					UpperCAmelCase__     :		Union[str, Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=True		,					UpperCAmelCase__     :		Optional[Any]="relu"		,					UpperCAmelCase__     :		List[str]=256		,					UpperCAmelCase__     :		Union[str, Any]=0.1		,					UpperCAmelCase__     :		List[Any]=0.0		,					UpperCAmelCase__     :		Optional[Any]=0.0		,					UpperCAmelCase__     :		Tuple=0.02		,					UpperCAmelCase__     :		int=2		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		str=1		,					UpperCAmelCase__     :		Optional[Any]=0		,					UpperCAmelCase__     :		Optional[int]=2		,					UpperCAmelCase__     :		List[Any]=1024		,					**UpperCAmelCase__     :		Dict		,					):
										'''simple docstring'''
										lowercase :							List[str]    		=vocab_size
										lowercase :							Optional[int]    		=d_model
										lowercase :							Optional[Any]    		=decoder_ffn_dim
										lowercase :							Any    		=decoder_layers
										lowercase :							Dict    		=decoder_attention_heads
										lowercase :							List[Any]    		=dropout
										lowercase :							List[Any]    		=attention_dropout
										lowercase :							Any    		=activation_dropout
										lowercase :							Optional[Any]    		=activation_function
										lowercase :							Optional[int]    		=init_std
										lowercase :							Dict    		=decoder_layerdrop
										lowercase :							Optional[int]    		=use_cache
										lowercase :							Optional[Any]    		=decoder_layers
										lowercase :							List[str]    		=scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase :							str    		=max_target_positions

										super().__init__(
										    pad_token_id=UpperCAmelCase__		,					bos_token_id=UpperCAmelCase__		,					eos_token_id=UpperCAmelCase__		,					decoder_start_token_id=UpperCAmelCase__		,					**UpperCAmelCase__		,					)





