





'''simple docstring'''

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

from accelerate.utils.dataclasses import DistributedType







class __a :





   def __init__(					self				:			Optional[Any]				,lowerCamelCase				:			List[str]=2				,lowerCamelCase				:			Tuple=3				,lowerCamelCase				:			Union[str, Any]=64				,lowerCamelCase				:			Any=None    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE     :   List[Any]      							=       np.random.default_rng(__a    )
      __SCREAMING_SNAKE_CASE     :   Dict      							=       length
      __SCREAMING_SNAKE_CASE     :   str      							=       rng.normal(size=(length,)    ).astype(np.floataa    )
      __SCREAMING_SNAKE_CASE     :   int      							=       a * self.x + b + rng.normal(scale=0.1				,size=(length,)    ).astype(np.floataa    )





   def __len__(					self				:			List[str]    ):

      '''simple docstring'''

      return self.length





   def __getitem__(					self				:			Optional[int]				,lowerCamelCase				:			int    ):

      '''simple docstring'''

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







class __a (   torch.nn.Module			):





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

      '''simple docstring'''

      super().__init__()
      __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
      __SCREAMING_SNAKE_CASE     :   List[Any]      							=       torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
      __SCREAMING_SNAKE_CASE     :   str      							=       True





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			Optional[int]=None    ):

      '''simple docstring'''

      if self.first_batch:
         print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
         __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       False
      return x * self.a[0] + self.b[0]







class __a (   torch.nn.Module			):





   def __init__(					self				:			Optional[Any]				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Optional[int]=0				,lowerCamelCase				:			Any=False    ):

      '''simple docstring'''

      super().__init__()
      __SCREAMING_SNAKE_CASE     :   int      							=       torch.nn.Parameter(torch.tensor(__a    ).float()    )
      __SCREAMING_SNAKE_CASE     :   Tuple      							=       torch.nn.Parameter(torch.tensor(__a    ).float()    )
      __SCREAMING_SNAKE_CASE     :   List[str]      							=       True





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			Optional[Any]=None    ):

      '''simple docstring'''

      if self.first_batch:
         print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
         __SCREAMING_SNAKE_CASE     :   Dict      							=       False
      return x * self.a + self.b





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = 16      )						->					int:

   '''simple docstring'''







   from datasets import load_dataset
   from transformers import AutoTokenizer

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

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

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

   def collate_fn(__UpperCAmelCase      ):
      # 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.
   __SCREAMING_SNAKE_CASE     :   Tuple      							=       DataLoader(tokenized_datasets["""train"""]							,	shuffle=__UpperCAmelCase							,	collate_fn=__UpperCAmelCase							,	batch_size=2      )
   __SCREAMING_SNAKE_CASE     :   Union[str, Any]      							=       DataLoader(tokenized_datasets["""validation"""]							,	shuffle=__UpperCAmelCase							,	collate_fn=__UpperCAmelCase							,	batch_size=1      )

   return train_dataloader, eval_dataloader












'''simple docstring'''

import functools
import operator

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


a        =						logging.get_logger(__name__)

a        =						{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'sew'





   def __init__(					self				:			str				,lowerCamelCase				:			Any=32				,lowerCamelCase				:			str=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			Union[str, Any]=12				,lowerCamelCase				:			Union[str, Any]=3072				,lowerCamelCase				:			int=2				,lowerCamelCase				:			Union[str, Any]="gelu"				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Optional[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Optional[Any]=0.02				,lowerCamelCase				:			List[str]=1E-5				,lowerCamelCase				:			Tuple="group"				,lowerCamelCase				:			Optional[Any]="gelu"				,lowerCamelCase				:			List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,lowerCamelCase				:			Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Optional[int]=False				,lowerCamelCase				:			Dict=128				,lowerCamelCase				:			Union[str, Any]=16				,lowerCamelCase				:			List[Any]=True				,lowerCamelCase				:			List[Any]=0.05				,lowerCamelCase				:			Optional[int]=10				,lowerCamelCase				:			Any=2				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Tuple=10				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple="mean"				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Dict=False				,lowerCamelCase				:			Optional[int]=256				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			Tuple=2				,**lowerCamelCase				:			Union[str, Any]				,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase				,pad_token_id=lowerCamelCase				,bos_token_id=lowerCamelCase				,eos_token_id=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       feat_extract_norm
      __SCREAMING_SNAKE_CASE      							=       feat_extract_activation
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       conv_bias
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embeddings
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embedding_groups
      __SCREAMING_SNAKE_CASE      							=       len(self.conv_dim    )
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       squeeze_factor
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       feat_proj_dropout
      __SCREAMING_SNAKE_CASE      							=       final_dropout
      __SCREAMING_SNAKE_CASE      							=       layerdrop
      __SCREAMING_SNAKE_CASE      							=       layer_norm_eps
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       vocab_size

      if (
          (len(self.conv_stride    ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
          or (len(self.conv_dim    ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             """Configuration for convolutional layers is incorrect."""
             """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
             f"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
             f"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __SCREAMING_SNAKE_CASE      							=       apply_spec_augment
      __SCREAMING_SNAKE_CASE      							=       mask_time_prob
      __SCREAMING_SNAKE_CASE      							=       mask_time_length
      __SCREAMING_SNAKE_CASE      							=       mask_time_min_masks
      __SCREAMING_SNAKE_CASE      							=       mask_feature_prob
      __SCREAMING_SNAKE_CASE      							=       mask_feature_length
      __SCREAMING_SNAKE_CASE      							=       mask_feature_min_masks

      # ctc loss
      __SCREAMING_SNAKE_CASE      							=       ctc_loss_reduction
      __SCREAMING_SNAKE_CASE      							=       ctc_zero_infinity

      # sequence classification
      __SCREAMING_SNAKE_CASE      							=       use_weighted_layer_sum
      __SCREAMING_SNAKE_CASE      							=       classifier_proj_size





   @property
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      return functools.reduce(operator.mul				,self.conv_stride				,1    )





