



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

from accelerate.utils.dataclasses import DistributedType


class 				snake_case :



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



       def __len__(				self      )  ->							str:
           return self.length







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





class 				snake_case (torch.nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_=0     ,UpperCAmelCase_=0     ,UpperCAmelCase_=False      )  ->							Tuple:
           super().__init__()
           lowercase__		       =						torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
           lowercase__		       =						torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
           lowercase__		       =						True







       def 							_a      (				self     ,UpperCAmelCase_=None      )  ->							int:




           if self.first_batch:
               print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
               lowercase__		       =						False
           return x * self.a[0] + self.b[0]





class 				snake_case (torch.nn.Module   ):



       def __init__(				self     ,UpperCAmelCase_=0     ,UpperCAmelCase_=0     ,UpperCAmelCase_=False      )  ->							str:
           super().__init__()
           lowercase__		       =						torch.nn.Parameter(torch.tensor(UpperCAmelCase_      ).float()      )
           lowercase__		       =						torch.nn.Parameter(torch.tensor(UpperCAmelCase_      ).float()      )
           lowercase__		       =						True







       def 							_a      (				self     ,UpperCAmelCase_=None      )  ->							Any:




           if self.first_batch:
               print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
               lowercase__		       =						False
           return x * self.a + self.b





def        lowerCamelCase  (				_snake_case  :						Optional[Any]     ,_snake_case  :						Tuple = 16       ):





    '''simple docstring'''


    from datasets import load_dataset
    from transformers import AutoTokenizer

    lowercase__		       =						AutoTokenizer.from_pretrained("bert-base-cased"       )
    lowercase__		       =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
    lowercase__		       =						load_dataset("csv"     ,data_files=_snake_case       )
    lowercase__		       =						datasets["""train"""].unique("label"       )

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

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

    def collate_fn(_snake_case  :						Union[str, 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(_snake_case     ,padding="max_length"     ,max_length=128     ,return_tensors="pt"       )
        return tokenizer.pad(_snake_case     ,padding="longest"     ,return_tensors="pt"       )

    # Instantiate dataloaders.
    lowercase__		       =						DataLoader(tokenized_datasets["train"]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=2       )
    lowercase__		       =						DataLoader(tokenized_datasets["validation"]     ,shuffle=_snake_case     ,collate_fn=_snake_case     ,batch_size=1       )

    return train_dataloader, eval_dataloader









'''simple docstring'''





import copy
import random

from transformers import CLIPTokenizer


class 				snake_case (UpperCamelCase   ):



       def __init__(				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           super().__init__(*UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						{}



       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						super().add_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           if num_added_tokens == 0:
               raise ValueError(
                   F'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
                   " `placeholder_token` that is not already in the tokenizer."      )



       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=1     ,**UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						[]
           if num_vec_per_token == 1:
               self.try_adding_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
               output.append(UpperCAmelCase_      )
           else:
               lowercase__		       =						[]
               for i in range(UpperCAmelCase_      ):
                   lowercase__		       =						placeholder_token + F'''_{i}'''
                   self.try_adding_tokens(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
                   output.append(UpperCAmelCase_      )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
           for token in self.token_map:
               if token in placeholder_token:
                   raise ValueError(
                       F'''The tokenizer already has placeholder token {token} that can get confused with'''
                       F''' {placeholder_token}keep placeholder tokens independent'''      )
           lowercase__		       =						output



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0      )  ->							Optional[int]:
           if isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
               lowercase__		       =						[]
               for i in range(len(UpperCAmelCase_      )      ):
                   output.append(self.replace_placeholder_tokens_in_text(text[i]     ,vector_shuffle=UpperCAmelCase_      )      )
               return output
           for placeholder_token in self.token_map:
               if placeholder_token in text:
                   lowercase__		       =						self.token_map[placeholder_token]
                   lowercase__		       =						tokens[: 1 + int(len(UpperCAmelCase_      ) * prop_tokens_to_load      )]
                   if vector_shuffle:
                       lowercase__		       =						copy.copy(UpperCAmelCase_      )
                       random.shuffle(UpperCAmelCase_      )
                   lowercase__		       =						text.replace(UpperCAmelCase_     ," ".join(UpperCAmelCase_      )      )
           return text



       def __call__(				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0     ,**UpperCAmelCase_      )  ->							int:
           return super().__call__(
               self.replace_placeholder_tokens_in_text(
                   UpperCAmelCase_     ,vector_shuffle=UpperCAmelCase_     ,prop_tokens_to_load=UpperCAmelCase_      )     ,*UpperCAmelCase_     ,**UpperCAmelCase_     ,)







       def 							_a      (				self     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,UpperCAmelCase_=False     ,UpperCAmelCase_=1.0     ,**UpperCAmelCase_      )  ->							Optional[int]:
           return super().encode(
               self.replace_placeholder_tokens_in_text(
                   UpperCAmelCase_     ,vector_shuffle=UpperCAmelCase_     ,prop_tokens_to_load=UpperCAmelCase_      )     ,*UpperCAmelCase_     ,**UpperCAmelCase_     ,)




