



"""simple docstring"""







import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     FunnelBaseModel,
     FunnelForMaskedLM,
     FunnelForMultipleChoice,
     FunnelForPreTraining,
     FunnelForQuestionAnswering,
     FunnelForSequenceClassification,
     FunnelForTokenClassification,
     FunnelModel,
 )

class       UpperCamelCase  :





     """simple docstring"""






     def __init__(					self :							List[str]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							str=1_3	,      _lowerCamelCase :							str=7	,      _lowerCamelCase :							str=True	,      _lowerCamelCase :							List[Any]=True	,      _lowerCamelCase :							Union[str, Any]=True	,      _lowerCamelCase :							Dict=True	,      _lowerCamelCase :							Optional[int]=9_9	,      _lowerCamelCase :							List[Any]=[1, 1, 2]	,      _lowerCamelCase :							List[str]=1	,      _lowerCamelCase :							Any=3_2	,      _lowerCamelCase :							int=4	,      _lowerCamelCase :							Tuple=8	,      _lowerCamelCase :							str=3_7	,      _lowerCamelCase :							Any="gelu_new"	,      _lowerCamelCase :							List[str]=0.1	,      _lowerCamelCase :							str=0.1	,      _lowerCamelCase :							int=0.0	,      _lowerCamelCase :							List[Any]=5_1_2	,      _lowerCamelCase :							Union[str, Any]=3	,      _lowerCamelCase :							str=0.02	,      _lowerCamelCase :							Optional[int]=3	,      _lowerCamelCase :							Tuple=4	,      _lowerCamelCase :							Optional[int]=None	,      _lowerCamelCase :							Tuple=False	,      ):
          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__				=   block_sizes
          A__				=   num_decoder_layers
          A__				=   d_model
          A__				=   n_head
          A__				=   d_head
          A__				=   d_inner
          A__				=   hidden_act
          A__				=   hidden_dropout
          A__				=   attention_dropout
          A__				=   activation_dropout
          A__				=   max_position_embeddings
          A__				=   type_vocab_size
          A__				=   2
          A__				=   num_labels
          A__				=   num_choices
          A__				=   scope
          A__				=   initializer_std

          # Used in the tests to check the size of the first attention layer
          A__				=   n_head
          # Used in the tests to check the size of the first hidden state
          A__				=   self.d_model
          # Used in the tests to check the number of output hidden states/attentions
          A__				=   sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
          # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
          # the last hidden state of the first block (which is the first hidden state of the decoder).
          if not base:
               A__				=   self.num_hidden_layers + 2





     def 						A__  (					self :							Any ):
          A__				=   ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size )

          A__				=   None
          if self.use_input_mask:
               A__				=   ids_tensor([self.batch_size, self.seq_length]	,      vocab_size=2 )

          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__				=   ids_tensor([self.batch_size, self.seq_length]	,      1 )

          A__				=   self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_mask,
              sequence_labels,
              token_labels,
              choice_labels,
              fake_token_labels,
          )





     def 						A__  (					self :							Union[str, Any] ):
          return FunnelConfig(
              vocab_size=self.vocab_size	,      block_sizes=self.block_sizes	,      num_decoder_layers=self.num_decoder_layers	,      d_model=self.d_model	,      n_head=self.n_head	,      d_head=self.d_head	,      d_inner=self.d_inner	,      hidden_act=self.hidden_act	,      hidden_dropout=self.hidden_dropout	,      attention_dropout=self.attention_dropout	,      activation_dropout=self.activation_dropout	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      initializer_std=self.initializer_std	,      )





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							str	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							List[Any]	,      ):
          A__				=   FunnelModel(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.d_model) )

          A__				=   False
          A__				=   model(_lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.d_model) )

          A__				=   False
          A__				=   model(_lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.d_model) )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[Any]	,      ):
          A__				=   FunnelBaseModel(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, 2, self.d_model) )

          A__				=   False
          A__				=   model(_lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, 3, self.d_model) )

          A__				=   False
          A__				=   model(_lowerCamelCase )
          self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, 2, self.d_model) )





     def 						A__  (					self :							Any	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							Union[str, Any]	,      ):
          A__				=   self.num_labels
          A__				=   FunnelForPreTraining(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) )





     def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Dict	,      ):
          A__				=   FunnelForMaskedLM(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 						A__  (					self :							str	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Optional[Any]	,      ):
          A__				=   self.num_labels
          A__				=   FunnelForSequenceClassification(_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 						A__  (					self :							List[Any]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							str	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							Optional[int]	,      ):
          A__				=   self.num_choices
          A__				=   FunnelForMultipleChoice(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 						A__  (					self :							Any	,      _lowerCamelCase :							Optional[int]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[str]	,      _lowerCamelCase :							Dict	,      ):
          A__				=   self.num_labels
          A__				=   FunnelForTokenClassification(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 						A__  (					self :							Tuple	,      _lowerCamelCase :							Any	,      _lowerCamelCase :							Tuple	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Union[str, Any]	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							List[str]	,      ):
          A__				=   FunnelForQuestionAnswering(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 						A__  (					self :							Any ):
          A__				=   self.prepare_config_and_inputs()
          (
              (
              A__ 
          )	,     (
              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       UpperCamelCase  (					snake_case_      , snake_case_      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =False
     _lowerCamelCase					: List[Any]				   =False
     _lowerCamelCase					: Optional[int]				   =(
         (
             FunnelModel,
             FunnelForMaskedLM,
             FunnelForPreTraining,
             FunnelForQuestionAnswering,
             FunnelForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     _lowerCamelCase					: List[str]				   =(
         {
             "feature-extraction": (FunnelBaseModel, FunnelModel),
             "fill-mask": FunnelForMaskedLM,
             "question-answering": FunnelForQuestionAnswering,
             "text-classification": FunnelForSequenceClassification,
             "token-classification": FunnelForTokenClassification,
             "zero-shot": FunnelForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )





     def 						A__  (					self :							List[str]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Any=False ):
          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 )
          return inputs_dict





     def 						A__  (					self :							Optional[Any] ):
          A__				=   FunnelModelTester(self )
          A__				=   ConfigTester(self	,      config_class=_lowerCamelCase )





     def 						A__  (					self :							Optional[int] ):
          self.config_tester.run_common_tests()





     def 						A__  (					self :							Optional[Any] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase )





     def 						A__  (					self :							Union[str, Any] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase )





     def 						A__  (					self :							Optional[Any] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase )





     def 						A__  (					self :							Union[str, Any] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase )





     def 						A__  (					self :							Tuple ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase )





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							int ):
          if hasattr(_lowerCamelCase	,      '''weight''' ) and module.weight is not None:
               module.weight.data.fill_(3 )
          if hasattr(_lowerCamelCase	,      '''bias''' ) and module.bias is not None:
               module.bias.data.fill_(3 )

          for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(_lowerCamelCase	,      _lowerCamelCase ) and getattr(_lowerCamelCase	,      _lowerCamelCase ) is not None:
                    A__				=   getattr(_lowerCamelCase	,      _lowerCamelCase )
                    weight.data.fill_(3 )

@require_torch
class       UpperCamelCase  (					snake_case_      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Union[str, Any]				   =False
     _lowerCamelCase					: Tuple				   =False
     _lowerCamelCase					: List[str]				   =(
         (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
     )





     def 						A__  (					self :							str ):
          A__				=   FunnelModelTester(self	,      base=_lowerCamelCase )
          A__				=   ConfigTester(self	,      config_class=_lowerCamelCase )





     def 						A__  (					self :							int ):
          self.config_tester.run_common_tests()





     def 						A__  (					self :							Union[str, Any] ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_base_model(*_lowerCamelCase )





     def 						A__  (					self :							Any ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase )





     def 						A__  (					self :							Any ):
          A__				=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_lowerCamelCase )





     def 						A__  (					self :							str ):
          A__	,     A__				=   self.model_tester.prepare_config_and_inputs_for_common()
          A__				=   True

          for model_class in self.all_model_classes:
               if model_class.__name__ == "FunnelBaseModel":
                    continue
               A__				=   model_class(_lowerCamelCase )
               model.to(_lowerCamelCase )
               model.train()
               A__				=   self._prepare_for_class(_lowerCamelCase	,      _lowerCamelCase	,      return_labels=_lowerCamelCase )
               A__				=   model(**_lowerCamelCase ).loss
               loss.backward()





     def 						A__  (					self :							str	,      _lowerCamelCase :							Tuple ):
          if hasattr(_lowerCamelCase	,      '''weight''' ) and module.weight is not None:
               module.weight.data.fill_(3 )
          if hasattr(_lowerCamelCase	,      '''bias''' ) and module.bias is not None:
               module.bias.data.fill_(3 )

          for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
               if hasattr(_lowerCamelCase	,      _lowerCamelCase ) and getattr(_lowerCamelCase	,      _lowerCamelCase ) is not None:
                    A__				=   getattr(_lowerCamelCase	,      _lowerCamelCase )
                    weight.data.fill_(3 )

@require_torch
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase  (					unittest.TestCase				):





     """simple docstring"""






     def 						A__  (					self :							Optional[Any] ):
          A__				=   1_3
          A__				=   7
          A__				=   torch.arange(0	,      batch_size * sequence_length ).long().reshape(_lowerCamelCase	,      _lowerCamelCase )
          A__				=   [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
          A__				=   torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths] )

          A__				=   FunnelModel.from_pretrained('''sgugger/funnel-random-tiny''' )
          A__				=   model(_lowerCamelCase	,      token_type_ids=_lowerCamelCase )[0].abs()

          A__				=   torch.tensor(2_344.8_352 )
          A__				=   torch.tensor(0.8_052 )
          self.assertTrue(torch.allclose(output.sum()	,      _lowerCamelCase	,      atol=1E-4 ) )
          self.assertTrue(torch.allclose(output.mean()	,      _lowerCamelCase	,      atol=1E-4 ) )

          A__				=   torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]] )
          A__				=   model(_lowerCamelCase	,      attention_mask=_lowerCamelCase	,      token_type_ids=_lowerCamelCase )[0].abs()

          A__				=   torch.tensor(2_343.8_425 )
          A__				=   torch.tensor(0.8_049 )
          self.assertTrue(torch.allclose(output.sum()	,      _lowerCamelCase	,      atol=1E-4 ) )
          self.assertTrue(torch.allclose(output.mean()	,      _lowerCamelCase	,      atol=1E-4 ) )





     @slow
     def 						A__  (					self :							Optional[int] ):
          A__				=   FunnelTokenizer.from_pretrained('''huggingface/funnel-small''' )
          A__				=   FunnelModel.from_pretrained('''huggingface/funnel-small''' )
          A__				=   tokenizer('''Hello! I am the Funnel Transformer model.'''	,      return_tensors='''pt''' )
          A__				=   model(**_lowerCamelCase )[0]

          A__				=   torch.tensor(235.7_246 )
          A__				=   torch.tensor(0.0_256 )
          self.assertTrue(torch.allclose(output.sum()	,      _lowerCamelCase	,      atol=1E-4 ) )
          self.assertTrue(torch.allclose(output.mean()	,      _lowerCamelCase	,      atol=1E-4 ) )








"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case	:				Tuple  					=			16
__snake_case	:				Any  					=			32



def   a_	(			__a		,							__a      ):
     # Initialize accelerator
     if args.with_tracking:
          A__				=   Accelerator(
              cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							log_with='''all'''		,							project_dir=args.project_dir      )
     else:
          A__				=   Accelerator(cpu=args.cpu		,							mixed_precision=args.mixed_precision      )

     if hasattr(args.checkpointing_steps		,							'''isdigit'''      ):
          if args.checkpointing_steps == "epoch":
               A__				=   args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               A__				=   int(args.checkpointing_steps      )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''      )
     else:
          A__				=   None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A__				=   config['''lr''']
     A__				=   int(config['''num_epochs''']      )
     A__				=   int(config['''seed''']      )
     A__				=   int(config['''batch_size''']      )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          A__				=   os.path.split(__a      )[-1].split('''.'''      )[0]
          accelerator.init_trackers(__a		,							__a      )

     A__				=   AutoTokenizer.from_pretrained('''bert-base-cased'''      )
     A__				=   load_dataset('''glue'''		,							'''mrpc'''      )
     A__				=   evaluate.load('''glue'''		,							'''mrpc'''      )

     def tokenize_function(__a      ):
          # max_length=None => use the model max length (it's actually the default)
          A__				=   tokenizer(examples['''sentence1''']		,							examples['''sentence2''']		,							truncation=__a		,							max_length=__a      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          A__				=   datasets.map(
              __a		,							batched=__a		,							remove_columns=['''idx''', '''sentence1''', '''sentence2''']		,							)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     A__				=   tokenized_datasets.rename_column('''label'''		,							'''labels'''      )

     # If the batch size is too big we use gradient accumulation
     A__				=   1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          A__				=   batch_size // MAX_GPU_BATCH_SIZE
          A__				=   MAX_GPU_BATCH_SIZE

     def collate_fn(__a      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A__				=   128 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               A__				=   16
          elif accelerator.mixed_precision != "no":
               A__				=   8
          else:
               A__				=   None

          return tokenizer.pad(
              __a		,							padding='''longest'''		,							max_length=__a		,							pad_to_multiple_of=__a		,							return_tensors='''pt'''		,							)

     # Instantiate dataloaders.
     A__				=   DataLoader(
         tokenized_datasets['''train''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )
     A__				=   DataLoader(
         tokenized_datasets['''validation''']		,							shuffle=__a		,							collate_fn=__a		,							batch_size=__a      )

     set_seed(__a      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A__				=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,							return_dict=__a      )

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     A__				=   model.to(accelerator.device      )

     # Instantiate optimizer
     A__				=   AdamW(params=model.parameters()		,							lr=__a      )

     # Instantiate scheduler
     A__				=   get_linear_schedule_with_warmup(
         optimizer=__a		,							num_warmup_steps=100		,							num_training_steps=(len(__a      ) * num_epochs) // gradient_accumulation_steps		,							)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     A__	,     A__	,     A__	,     A__	,     A__				=   accelerator.prepare(
         __a		,							__a		,							__a		,							__a		,							__a      )

     # We need to keep track of how many total steps we have iterated over
     A__				=   0
     # We also need to keep track of the stating epoch so files are named properly
     A__				=   0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''      )
               accelerator.load_state(args.resume_from_checkpoint      )
               A__				=   os.path.basename(args.resume_from_checkpoint      )
          else:
               # Get the most recent checkpoint
               A__				=   [f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
               dirs.sort(key=os.path.getctime      )
               A__				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          A__				=   os.path.splitext(__a      )[0]

          if "epoch" in training_difference:
               A__				=   int(training_difference.replace('''epoch_'''		,							''''''      )      ) + 1
               A__				=   None
          else:
               A__				=   int(training_difference.replace('''step_'''		,							''''''      )      )
               A__				=   resume_step // len(__a      )
               resume_step -= starting_epoch * len(__a      )

    # Now we train the model
     for epoch in range(__a		,							__a      ):
          model.train()
          if args.with_tracking:
               A__				=   0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               A__				=   accelerator.skip_first_batches(__a		,							__a      )
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               A__				=   train_dataloader
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               A__				=   model(**__a      )
               A__				=   outputs.loss
               A__				=   loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__a      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__a		,							__a      ):
                    A__				=   f'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              A__				=   os.path.join(args.output_dir		,							__a      )
                         accelerator.save_state(__a      )

          model.eval()
          for step, batch in enumerate(__a      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    A__				=   model(**__a      )
               A__				=   outputs.logits.argmax(dim=-1      )
               A__	,     A__				=   accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
               metric.add_batch(
                   predictions=__a		,							references=__a		,							)

          A__				=   metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'''epoch {epoch}:'''		,							__a      )
          if args.with_tracking:
               accelerator.log(
                   {
                       '''accuracy''': eval_metric['''accuracy'''],
                       '''f1''': eval_metric['''f1'''],
                       '''train_loss''': total_loss.item() / len(__a      ),
                       '''epoch''': epoch,
                   }		,							step=__a		,							)

          if checkpointing_steps == "epoch":
               A__				=   f'''epoch_{epoch}'''
               if args.output_dir is not None:
                    A__				=   os.path.join(args.output_dir		,							__a      )
               accelerator.save_state(__a      )

     if args.with_tracking:
          accelerator.end_training()



def   a_	(			):
     A__				=   argparse.ArgumentParser(description='''Simple example of training script.'''      )
     parser.add_argument(
         '''--mixed_precision'''		,							type=__a		,							default=__a		,							choices=['''no''', '''fp16''', '''bf16''', '''fp8''']		,							help='''Whether to use mixed precision. Choose'''
         '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
         '''and an Nvidia Ampere GPU.'''		,							)
     parser.add_argument('''--cpu'''		,							action='''store_true'''		,							help='''If passed, will train on the CPU.'''      )
     parser.add_argument(
         '''--checkpointing_steps'''		,							type=__a		,							default=__a		,							help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''		,							)
     parser.add_argument(
         '''--resume_from_checkpoint'''		,							type=__a		,							default=__a		,							help='''If the training should continue from a checkpoint folder.'''		,							)
     parser.add_argument(
         '''--with_tracking'''		,							action='''store_true'''		,							help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,							)
     parser.add_argument(
         '''--output_dir'''		,							type=__a		,							default='''.'''		,							help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,							)
     parser.add_argument(
         '''--project_dir'''		,							type=__a		,							default='''logs'''		,							help='''Location on where to store experiment tracking logs` and relevent project information'''		,							)
     A__				=   parser.parse_args()
     A__				=   {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__a		,							__a      )


if __name__ == "__main__":
 main()



