

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

from accelerate.utils.dataclasses import DistributedType


class 				SCREAMING_SNAKE_CASE__ :





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





    def __len__(				self):
         return self.length





    def __getitem__(				self						,       a):
         return {"x": self.x[i], "y": self.y[i]}



class 				SCREAMING_SNAKE_CASE__ (torch.nn.Module					):





    def __init__(				self						,       a=0						,       a=0						,       a=False):
         super().__init__()
         lowercase__   :       int				=	torch.nn.Parameter(torch.tensor([2, 3]).float())
         lowercase__   :       str				=	torch.nn.Parameter(torch.tensor([2, 3]).float())
         lowercase__   :       Dict				=	True





    def 				snake_case_    (				self						,       a=None):
         if self.first_batch:
              print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
              lowercase__   :       Tuple				=	False
         return x * self.a[0] + self.b[0]



class 				SCREAMING_SNAKE_CASE__ (torch.nn.Module					):





    def __init__(				self						,       a=0						,       a=0						,       a=False):
         super().__init__()
         lowercase__   :       int				=	torch.nn.Parameter(torch.tensor(a).float())
         lowercase__   :       str				=	torch.nn.Parameter(torch.tensor(a).float())
         lowercase__   :       List[Any]				=	True





    def 				snake_case_    (				self						,       a=None):
         if self.first_batch:
              print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
              lowercase__   :       Optional[Any]				=	False
         return x * self.a + self.b



def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   int = 16      ):



     '''simple docstring'''





     from datasets import load_dataset
     from transformers import AutoTokenizer

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

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

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

     def collate_fn(SCREAMING_SNAKE_CASE_		:   Optional[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(SCREAMING_SNAKE_CASE_     ,					padding='max_length'     ,					max_length=128     ,					return_tensors='pt'      )
          return tokenizer.pad(SCREAMING_SNAKE_CASE_     ,					padding='longest'     ,					return_tensors='pt'      )

     # Instantiate dataloaders.
     lowercase__   :       Optional[int]				=	DataLoader(tokenized_datasets['train']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=2      )
     lowercase__   :       Union[str, Any]				=	DataLoader(tokenized_datasets['validation']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=1      )

     return train_dataloader, eval_dataloader




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Tuple														=					["""image_processor""", """tokenizer"""]
    __lowerCamelCase						:	int														=					"""ViltImageProcessor"""
    __lowerCamelCase						:	Union[str, Any]														=					("""BertTokenizer""", """BertTokenizerFast""")





    def __init__(				self						,       a=None						,       a=None						,       **a):
         lowercase__   :       Any				=	None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'						,       a						,       )
              lowercase__   :       Union[str, Any]				=	kwargs.pop('feature_extractor')

         lowercase__   :       List[Any]				=	image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.')
         if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.')

         super().__init__(a						,       a)
         lowercase__   :       str				=	self.image_processor





    def __call__(				self						,       a						,       a = None						,       a = True						,       a = False						,       a = None						,       a = None						,       a = 0						,       a = None						,       a = None						,       a = None						,       a = False						,       a = False						,       a = False						,       a = False						,       a = True						,       a = None						,       **a						,       ):
         lowercase__   :       Optional[Any]				=	self.tokenizer(
             text=a						,       add_special_tokens=a						,       padding=a						,       truncation=a						,       max_length=a						,       stride=a						,       pad_to_multiple_of=a						,       return_token_type_ids=a						,       return_attention_mask=a						,       return_overflowing_tokens=a						,       return_special_tokens_mask=a						,       return_offsets_mapping=a						,       return_length=a						,       verbose=a						,       return_tensors=a						,       **a						,       )
         # add pixel_values + pixel_mask
         lowercase__   :       str				=	self.image_processor(a						,       return_tensors=a)
         encoding.update(a)

         return encoding





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.batch_decode(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         return self.tokenizer.decode(*a						,       **a)





    @property
    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.tokenizer.model_input_names
         lowercase__   :       List[str]				=	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,       a						,       )
         return self.image_processor_class





    @property
    def 				snake_case_    (				self):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,       a						,       )
         return self.image_processor

