






from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput




@dataclass
class 			snake_case__   (							__A):

							'''simple docstring'''
							lowerCamelCase							:				Optional[Any] 				= None
							lowerCamelCase							:				Dict 				= None
							lowerCamelCase							:				Optional[Any] 				= None
							lowerCamelCase							:				Dict 				= None




class 			snake_case__   (							__A):

							'''simple docstring'''



							def __init__(							self			,						a__=1			,						a__=0			,						a__=2			,						a__=5_12			,						a__="cls"			,						a__=False			,						a__=True			,						**a__			,						) ->      int:






												'''simple docstring'''





												super().__init__(pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						**a__    )
												__snake_case			:List[str]          =						project_dim
												__snake_case			:int          =						pooler_fn
												__snake_case			:int          =						learn_encoder
												__snake_case			:List[Any]          =						use_attention_mask




class 			snake_case__   (							__A):

							'''simple docstring'''
							lowerCamelCase							:				Tuple 				= [r"pooler", r"logit_scale"]
							lowerCamelCase							:				int 				= [r"position_ids", r"predictions.decoder.bias"]
							lowerCamelCase							:				Union[str, Any] 				= "roberta"
							lowerCamelCase							:				int 				= RobertaSeriesConfig



							def __init__(							self			,						a__    ) ->      Any:






												'''simple docstring'''





												super().__init__(a__    )
												__snake_case			:Tuple          =						XLMRobertaModel(a__    )
												__snake_case			:Optional[Any]          =						nn.Linear(config.hidden_size			,						config.project_dim    )
												__snake_case			:Tuple          =						getattr(a__			,						"""has_pre_transformation"""			,						a__    )
												if self.has_pre_transformation:
																	__snake_case			:Optional[int]          =						nn.Linear(config.hidden_size			,						config.project_dim    )
																	__snake_case			:List[Any]          =						nn.LayerNorm(config.hidden_size			,						eps=config.layer_norm_eps    )
												self.post_init()



							def 					__lowercase						(							self			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						) ->      Optional[int]:






												'''simple docstring'''





												__snake_case			:Dict          =						return_dict if return_dict is not None else self.config.use_return_dict

												__snake_case			:Optional[Any]          =						self.base_model(
												    input_ids=a__			,						attention_mask=a__			,						token_type_ids=a__			,						position_ids=a__			,						head_mask=a__			,						inputs_embeds=a__			,						encoder_hidden_states=a__			,						encoder_attention_mask=a__			,						output_attentions=a__			,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states			,						return_dict=a__			,						)

												if self.has_pre_transformation:
																	__snake_case			:Any          =						outputs['''hidden_states'''][-2]
																	__snake_case			:Dict          =						self.pre_LN(a__    )
																	__snake_case			:str          =						self.transformation_pre(a__    )

																	return TransformationModelOutput(
																	    projection_state=a__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)
												else:
																	__snake_case			:Dict          =						self.transformation(outputs.last_hidden_state    )
																	return TransformationModelOutput(
																	    projection_state=a__			,						last_hidden_state=outputs.last_hidden_state			,						hidden_states=outputs.hidden_states			,						attentions=outputs.attentions			,						)










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

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





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



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





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




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=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			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''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   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

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

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

     def collate_fn(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(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


