


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

from accelerate.utils.dataclasses import DistributedType







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=2	,   _UpperCAmelCase=3	,   _UpperCAmelCase=64	,   _UpperCAmelCase=None      ):
       snake_case_ =   np.random.default_rng(_UpperCAmelCase      )
       snake_case_ =   length
       snake_case_ =   rng.normal(size=(length,)      ).astype(np.floataa      )
       snake_case_ =   a * self.x + b + rng.normal(scale=0.1	,   size=(length,)      ).astype(np.floataa      )



 def __len__(			self      ):
       return self.length





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







class lowerCAmelCase_		(				torch.nn.Module ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=0	,   _UpperCAmelCase=0	,   _UpperCAmelCase=False      ):
       super().__init__()
       snake_case_ =   torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
       snake_case_ =   torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
       snake_case_ =   True





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=None      ):
       if self.first_batch:
             print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
             snake_case_ =   False
       return x * self.a[0] + self.b[0]







class lowerCAmelCase_		(				torch.nn.Module ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=0	,   _UpperCAmelCase=0	,   _UpperCAmelCase=False      ):
       super().__init__()
       snake_case_ =   torch.nn.Parameter(torch.tensor(_UpperCAmelCase      ).float()      )
       snake_case_ =   torch.nn.Parameter(torch.tensor(_UpperCAmelCase      ).float()      )
       snake_case_ =   True





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase=None      ):
       if self.first_batch:
             print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
             snake_case_ =   False
       return x * self.a + self.b






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = 16      )->						Union[str, Any]:




      """simple docstring"""

      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      snake_case_ =   {v: i for i, v in enumerate(SCREAMING_SNAKE_CASE      )}

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

      def collate_fn(SCREAMING_SNAKE_CASE      ):
            # 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.
      snake_case_ =   DataLoader(tokenized_datasets['''train''']      ,      shuffle=SCREAMING_SNAKE_CASE      ,      collate_fn=SCREAMING_SNAKE_CASE      ,      batch_size=2      )
      snake_case_ =   DataLoader(tokenized_datasets['''validation''']      ,      shuffle=SCREAMING_SNAKE_CASE      ,      collate_fn=SCREAMING_SNAKE_CASE      ,      batch_size=1      )

      return train_dataloader, eval_dataloader



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 lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None
 __snake_case				=						None







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase="cls"	,   _UpperCAmelCase=False	,   _UpperCAmelCase=True	,   **_UpperCAmelCase	,   ):
       super().__init__(pad_token_id=_UpperCAmelCase	,   bos_token_id=_UpperCAmelCase	,   eos_token_id=_UpperCAmelCase	,   **_UpperCAmelCase      )
       snake_case_ =   project_dim
       snake_case_ =   pooler_fn
       snake_case_ =   learn_encoder
       snake_case_ =   use_attention_mask







class lowerCAmelCase_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						[R"pooler", R"logit_scale"]
 __snake_case				=						[R"position_ids", R"predictions.decoder.bias"]
 __snake_case				=						"roberta"
 __snake_case				=						RobertaSeriesConfig



 def __init__(			self	,   _UpperCAmelCase      ):
       super().__init__(_UpperCAmelCase      )
       snake_case_ =   XLMRobertaModel(_UpperCAmelCase      )
       snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
       snake_case_ =   getattr(_UpperCAmelCase	,   '''has_pre_transformation'''	,   _UpperCAmelCase      )
       if self.has_pre_transformation:
             snake_case_ =   nn.Linear(config.hidden_size	,   config.project_dim      )
             snake_case_ =   nn.LayerNorm(config.hidden_size	,   eps=config.layer_norm_eps      )
       self.post_init()





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   _UpperCAmelCase = None	,   ):
       snake_case_ =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case_ =   self.base_model(
           input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   token_type_ids=_UpperCAmelCase	,   position_ids=_UpperCAmelCase	,   head_mask=_UpperCAmelCase	,   inputs_embeds=_UpperCAmelCase	,   encoder_hidden_states=_UpperCAmelCase	,   encoder_attention_mask=_UpperCAmelCase	,   output_attentions=_UpperCAmelCase	,   output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,   return_dict=_UpperCAmelCase	,   )

       if self.has_pre_transformation:
             snake_case_ =   outputs['''hidden_states'''][-2]
             snake_case_ =   self.pre_LN(_UpperCAmelCase      )
             snake_case_ =   self.transformation_pre(_UpperCAmelCase      )

             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )
       else:
             snake_case_ =   self.transformation(outputs.last_hidden_state      )
             return TransformationModelOutput(
                 projection_state=_UpperCAmelCase	,   last_hidden_state=outputs.last_hidden_state	,   hidden_states=outputs.hidden_states	,   attentions=outputs.attentions	,   )