






"""simple docstring"""


import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
           NezhaModel,
       )
       from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     List[str]=13,       _lowerCamelCase :     Optional[Any]=7,       _lowerCamelCase :     str=True,       _lowerCamelCase :     Dict=True,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     Dict=True,       _lowerCamelCase :     Dict=99,       _lowerCamelCase :     Optional[int]=32,       _lowerCamelCase :     List[Any]=5,       _lowerCamelCase :     Optional[int]=4,       _lowerCamelCase :     Union[str, Any]=37,       _lowerCamelCase :     Tuple="gelu",       _lowerCamelCase :     Any=0.1,       _lowerCamelCase :     int=0.1,       _lowerCamelCase :     Optional[int]=1_28,       _lowerCamelCase :     Any=32,       _lowerCamelCase :     List[str]=16,       _lowerCamelCase :     str=2,       _lowerCamelCase :     List[str]=0.02,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Any=4,       _lowerCamelCase :     Dict=None,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= seq_length
     __A									= is_training
     __A									= use_input_mask
     __A									= use_token_type_ids
     __A									= use_labels
     __A									= vocab_size
     __A									= hidden_size
     __A									= num_hidden_layers
     __A									= num_attention_heads
     __A									= intermediate_size
     __A									= hidden_act
     __A									= hidden_dropout_prob
     __A									= attention_probs_dropout_prob
     __A									= max_position_embeddings
     __A									= type_vocab_size
     __A									= type_sequence_label_size
     __A									= initializer_range
     __A									= num_labels
     __A									= num_choices
     __A									= scope


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     __A									= ids_tensor([self.batch_size, self.seq_length],       self.vocab_size  )

     __A									= None
     if self.use_input_mask:
       __A									= random_attention_mask([self.batch_size, self.seq_length]  )

     __A									= None
     if self.use_token_type_ids:
       __A									= ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size  )

     __A									= None
     __A									= None
     __A									= None
     if self.use_labels:
       __A									= ids_tensor([self.batch_size],       self.type_sequence_label_size  )
       __A									= ids_tensor([self.batch_size, self.seq_length],       self.num_labels  )
       __A									= ids_tensor([self.batch_size],       self.num_choices  )

     __A									= self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return NezhaConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=_lowerCamelCase,       initializer_range=self.initializer_range,       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     (
         (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    
     )									= self.prepare_config_and_inputs()

     __A									= True
     __A									= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
     __A									= ids_tensor([self.batch_size, self.seq_length],       vocab_size=2  )

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )


   def 				_SCREAMING_SNAKE_CASE				(	self :     int,       _lowerCamelCase :     Any,       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= NezhaModel(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase  )
     __A									= model(_lowerCamelCase,       token_type_ids=_lowerCamelCase  )
     __A									= model(_lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape,       (self.batch_size, self.seq_length, self.hidden_size)  )
     self.parent.assertEqual(result.pooler_output.shape,       (self.batch_size, self.hidden_size)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     Tuple,       _lowerCamelCase :     List[Any],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     List[Any],       _lowerCamelCase :     Any,       _lowerCamelCase :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     int,       ):

     '''simple docstring'''
     __A									= True
     __A									= NezhaModel(_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       encoder_hidden_states=_lowerCamelCase,       encoder_attention_mask=_lowerCamelCase,       )
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       encoder_hidden_states=_lowerCamelCase,       )
     __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase  )
     self.parent.assertEqual(result.last_hidden_state.shape,       (self.batch_size, self.seq_length, self.hidden_size)  )
     self.parent.assertEqual(result.pooler_output.shape,       (self.batch_size, self.hidden_size)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     Any,       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     str,       _lowerCamelCase :     Tuple,       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A									= NezhaForMaskedLM(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.seq_length, self.vocab_size)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     int,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     str,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     str  ):

     '''simple docstring'''
     __A									= NezhaForNextSentencePrediction(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase,       )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, 2)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     str,       _lowerCamelCase :     str,       _lowerCamelCase :     Tuple  ):

     '''simple docstring'''
     __A									= NezhaForPreTraining(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase,       next_sentence_label=_lowerCamelCase,       )
     self.parent.assertEqual(result.prediction_logits.shape,       (self.batch_size, self.seq_length, self.vocab_size)  )
     self.parent.assertEqual(result.seq_relationship_logits.shape,       (self.batch_size, 2)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     Dict,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     Any,       _lowerCamelCase :     List[str],       _lowerCamelCase :     Any  ):

     '''simple docstring'''
     __A									= NezhaForQuestionAnswering(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       start_positions=_lowerCamelCase,       end_positions=_lowerCamelCase,       )
     self.parent.assertEqual(result.start_logits.shape,       (self.batch_size, self.seq_length)  )
     self.parent.assertEqual(result.end_logits.shape,       (self.batch_size, self.seq_length)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Any,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     int,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     List[Any],       _lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     __A									= self.num_labels
     __A									= NezhaForSequenceClassification(_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.num_labels)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Optional[int],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     Tuple,       _lowerCamelCase :     str,       _lowerCamelCase :     str,       _lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     __A									= self.num_labels
     __A									= NezhaForTokenClassification(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.seq_length, self.num_labels)  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple,       _lowerCamelCase :     Union[str, Any],       _lowerCamelCase :     int,       _lowerCamelCase :     Tuple,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     Dict,       _lowerCamelCase :     List[str]  ):

     '''simple docstring'''
     __A									= self.num_choices
     __A									= NezhaForMultipleChoice(config=_lowerCamelCase  )
     model.to(_lowerCamelCase  )
     model.eval()
     __A									= input_ids.unsqueeze(1  ).expand(-1,       self.num_choices,       -1  ).contiguous()
     __A									= token_type_ids.unsqueeze(1  ).expand(-1,       self.num_choices,       -1  ).contiguous()
     __A									= input_mask.unsqueeze(1  ).expand(-1,       self.num_choices,       -1  ).contiguous()
     __A									= model(
         _lowerCamelCase,       attention_mask=_lowerCamelCase,       token_type_ids=_lowerCamelCase,       labels=_lowerCamelCase,       )
     self.parent.assertEqual(result.logits.shape,       (self.batch_size, self.num_choices)  )






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     (
         (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    
     )									= config_and_inputs
     __A									= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict





@require_torch
class 					snake_case		(							_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[Any]						 = (
       (
           NezhaModel,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   A_						:	Optional[int]						 = (
       {
           "feature-extraction": NezhaModel,
           "fill-mask": NezhaForMaskedLM,
           "question-answering": NezhaForQuestionAnswering,
           "text-classification": NezhaForSequenceClassification,
           "token-classification": NezhaForTokenClassification,
           "zero-shot": NezhaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A_						:	List[Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Dict,       _lowerCamelCase :     str=False  ):

     '''simple docstring'''
     __A									= super()._prepare_for_class(_lowerCamelCase,       _lowerCamelCase,       return_labels=_lowerCamelCase  )

     if return_labels:
       if model_class in get_values(_lowerCamelCase  ):
         __A									= torch.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length),       dtype=torch.long,       device=_lowerCamelCase  )
         __A									= torch.zeros(
             self.model_tester.batch_size,       dtype=torch.long,       device=_lowerCamelCase  )
     return inputs_dict


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= NezhaModelTester(self  )
     __A									= ConfigTester(self,       config_class=_lowerCamelCase,       hidden_size=37  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     # This regression test was failing with PyTorch < 1.3
     (
         (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    (
         __A
     )			,    
     )									= self.model_tester.prepare_config_and_inputs_for_decoder()

     __A									= None

     self.model_tester.create_and_check_model_as_decoder(
         _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_next_sequence_prediction(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )


   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A									= NezhaModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )






   @slow
   @require_torch_gpu
   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A			,    __A									= self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
       # NezhaForMultipleChoice behaves incorrectly in JIT environments.
       if model_class == NezhaForMultipleChoice:
         return

       __A									= True
       __A									= model_class(config=_lowerCamelCase  )

       __A									= self._prepare_for_class(_lowerCamelCase,       _lowerCamelCase  )
       __A									= torch.jit.trace(
           _lowerCamelCase,       (inputs_dict['''input_ids'''].to('''cpu'''  ), inputs_dict['''attention_mask'''].to('''cpu'''  ))  )

       with tempfile.TemporaryDirectory() as tmp:
         torch.jit.save(_lowerCamelCase,       os.path.join(_lowerCamelCase,       '''bert.pt'''  )  )
         __A									= torch.jit.load(os.path.join(_lowerCamelCase,       '''bert.pt'''  ),       map_location=_lowerCamelCase  )
         loaded(inputs_dict['''input_ids'''].to(_lowerCamelCase  ),       inputs_dict['''attention_mask'''].to(_lowerCamelCase  )  )





@require_torch
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''  )
     __A									= torch.tensor([[0, 1, 2, 3, 4, 5]]  )
     __A									= torch.tensor([[0, 1, 1, 1, 1, 1]]  )
     with torch.no_grad():
       __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase  )[0]
     __A									= torch.Size((1, 6, 7_68)  )
     self.assertEqual(output.shape,       _lowerCamelCase  )
     __A									= torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]  )

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4],       _lowerCamelCase,       atol=1e-4  )  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''  )
     __A									= torch.tensor([[0, 1, 2, 3, 4, 5]]  )
     __A									= torch.tensor([[1, 1, 1, 1, 1, 1]]  )
     with torch.no_grad():
       __A									= model(_lowerCamelCase,       attention_mask=_lowerCamelCase  )[0]
     __A									= torch.Size((1, 6, 2_11_28)  )
     self.assertEqual(output.shape,       _lowerCamelCase  )
     __A									= torch.tensor(
         [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]  )

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4],       _lowerCamelCase,       atol=1e-4  )  )












"""simple docstring"""


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 :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= 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 :     Dict,       _lowerCamelCase :     Optional[int]  ):

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





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

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





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

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

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  __A									= {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)
    __A									= tokenizer(
        examples['''sentence1''']		,       examples['''sentence2''']		,       truncation=__UpperCamelCase		,       max_length=__UpperCamelCase		,       padding='''max_length''' )
    if "label" in examples:
      __A									= [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
  __A									= 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=1_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

  # Instantiate dataloaders.
  __A									= DataLoader(tokenized_datasets['''train''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=2 )
  __A									= DataLoader(tokenized_datasets['''validation''']		,       shuffle=__UpperCamelCase		,       collate_fn=__UpperCamelCase		,       batch_size=1 )

  return train_dataloader, eval_dataloader




