



"""simple docstring"""







import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

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


if is_torch_available():
					import torch

					from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel







class 				UpperCamelCase__       :




						"""simple docstring"""




						def __init__(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Any=1_3   ,      SCREAMING_SNAKE_CASE_    :				Dict=7   ,      SCREAMING_SNAKE_CASE_    :				List[str]=True   ,      SCREAMING_SNAKE_CASE_    :				int=True   ,      SCREAMING_SNAKE_CASE_    :				Any=False   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				Any=9_9   ,      SCREAMING_SNAKE_CASE_    :				str=3_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=5   ,      SCREAMING_SNAKE_CASE_    :				Dict=4   ,      SCREAMING_SNAKE_CASE_    :				int=3_7   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0.1   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.1   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=4   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      ):
								lowerCAmelCase_       :    str								=			parent
								lowerCAmelCase_       :    Optional[Any]								=			batch_size
								lowerCAmelCase_       :    int								=			seq_length
								lowerCAmelCase_       :    Dict								=			is_training
								lowerCAmelCase_       :    Union[str, Any]								=			use_input_mask
								lowerCAmelCase_       :    Optional[Any]								=			use_token_type_ids
								lowerCAmelCase_       :    Any								=			use_labels
								lowerCAmelCase_       :    List[Any]								=			vocab_size
								lowerCAmelCase_       :    int								=			hidden_size
								lowerCAmelCase_       :    Dict								=			num_hidden_layers
								lowerCAmelCase_       :    Union[str, Any]								=			num_attention_heads
								lowerCAmelCase_       :    str								=			intermediate_size
								lowerCAmelCase_       :    int								=			hidden_act
								lowerCAmelCase_       :    Union[str, Any]								=			hidden_dropout_prob
								lowerCAmelCase_       :    List[Any]								=			attention_probs_dropout_prob
								lowerCAmelCase_       :    List[str]								=			max_position_embeddings
								lowerCAmelCase_       :    Dict								=			type_vocab_size
								lowerCAmelCase_       :    str								=			type_sequence_label_size
								lowerCAmelCase_       :    Optional[int]								=			initializer_range
								lowerCAmelCase_       :    List[str]								=			num_labels
								lowerCAmelCase_       :    int								=			num_choices
								lowerCAmelCase_       :    Union[str, Any]								=			scope



						def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
								lowerCAmelCase_       :    Optional[int]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size	)

								lowerCAmelCase_       :    Dict								=			None
								if self.use_input_mask:
										lowerCAmelCase_       :    Any								=			random_attention_mask([self.batch_size, self.seq_length]	)

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

								lowerCAmelCase_       :    Union[str, Any]								=			None
								lowerCAmelCase_       :    List[Any]								=			None
								lowerCAmelCase_       :    str								=			None
								if self.use_labels:
										lowerCAmelCase_       :    List[Any]								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size	)
										lowerCAmelCase_       :    Union[str, Any]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels	)
										lowerCAmelCase_       :    Optional[int]								=			ids_tensor([self.batch_size]   ,      self.num_choices	)

								lowerCAmelCase_       :    Optional[Any]								=			self.get_config()

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



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
								return OpenLlamaConfig(
								    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=SCREAMING_SNAKE_CASE_   ,      initializer_range=self.initializer_range   ,      use_stable_embedding=SCREAMING_SNAKE_CASE_   ,      )



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				str	):
								lowerCAmelCase_       :    int								=			OpenLlamaModel(config=SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)
								lowerCAmelCase_       :    Union[str, Any]								=			model(SCREAMING_SNAKE_CASE_	)
								self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      ):
								lowerCAmelCase_       :    Optional[int]								=			True
								lowerCAmelCase_       :    List[str]								=			OpenLlamaModel(SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    int								=			model(
								    SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,      encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,      )
								lowerCAmelCase_       :    Union[str, Any]								=			model(
								    SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,      )
								lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)
								self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      ):
								lowerCAmelCase_       :    List[str]								=			OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
								self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      ):
								lowerCAmelCase_       :    str								=			True
								lowerCAmelCase_       :    Union[str, Any]								=			True
								lowerCAmelCase_       :    str								=			OpenLlamaForCausalLM(config=SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()

								# first forward pass
								lowerCAmelCase_       :    int								=			model(
								    SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,      encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,      use_cache=SCREAMING_SNAKE_CASE_   ,      )
								lowerCAmelCase_       :    str								=			outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowerCAmelCase_       :    int								=			ids_tensor((self.batch_size, 3)   ,      config.vocab_size	)
								lowerCAmelCase_       :    Tuple								=			ids_tensor((self.batch_size, 3)   ,      vocab_size=2	)

								# append to next input_ids and
								lowerCAmelCase_       :    Union[str, Any]								=			torch.cat([input_ids, next_tokens]   ,      dim=-1	)
								lowerCAmelCase_       :    Dict								=			torch.cat([input_mask, next_mask]   ,      dim=-1	)

								lowerCAmelCase_       :    int								=			model(
								    SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,      encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,      output_hidden_states=SCREAMING_SNAKE_CASE_   ,      )['hidden_states'][0]
								lowerCAmelCase_       :    int								=			model(
								    SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,      encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,      past_key_values=SCREAMING_SNAKE_CASE_   ,      output_hidden_states=SCREAMING_SNAKE_CASE_   ,      )['hidden_states'][0]

								# select random slice
								lowerCAmelCase_       :    Optional[int]								=			ids_tensor((1,)   ,      output_from_past.shape[-1]	).item()
								lowerCAmelCase_       :    Optional[int]								=			output_from_no_past[:, -3:, random_slice_idx].detach()
								lowerCAmelCase_       :    Optional[int]								=			output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]	)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-3	)	)





						def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
								lowerCAmelCase_       :    Any								=			self.prepare_config_and_inputs()
								(
								    lowerCAmelCase_
								)       :    Dict								=			config_and_inputs
								lowerCAmelCase_       :    Dict								=			{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, lowercase_, unittest.TestCase  ):




						"""simple docstring"""




						_SCREAMING_SNAKE_CASE      =					(
						    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
						)
						_SCREAMING_SNAKE_CASE      =					(OpenLlamaForCausalLM,) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE      =					(
						    {
						        """feature-extraction""": OpenLlamaModel,
						        """text-classification""": OpenLlamaForSequenceClassification,
						        """text-generation""": OpenLlamaForCausalLM,
						        """zero-shot""": OpenLlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_SCREAMING_SNAKE_CASE      =					False
						_SCREAMING_SNAKE_CASE      =					False



						def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
								lowerCAmelCase_       :    int								=			OpenLlamaModelTester(self	)
								lowerCAmelCase_       :    List[Any]								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
								self.config_tester.run_common_tests()



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
								lowerCAmelCase_       :    Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
								lowerCAmelCase_       :    Optional[Any]								=			self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										lowerCAmelCase_       :    str								=			type
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Any	):
								lowerCAmelCase_       :    List[str]								=			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase_       :    Union[str, Any]								=			3
								lowerCAmelCase_       :    Any								=			input_dict['input_ids']
								lowerCAmelCase_       :    Dict								=			input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
								lowerCAmelCase_       :    Optional[Any]								=			ids_tensor([self.model_tester.batch_size]   ,      self.model_tester.type_sequence_label_size	)
								lowerCAmelCase_       :    List[Any]								=			OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
								self.assertEqual(result.logits.shape   ,      (self.model_tester.batch_size, self.model_tester.num_labels)	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
								lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase_       :    Tuple								=			3
								lowerCAmelCase_       :    int								=			'single_label_classification'
								lowerCAmelCase_       :    int								=			input_dict['input_ids']
								lowerCAmelCase_       :    str								=			input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
								lowerCAmelCase_       :    str								=			ids_tensor([self.model_tester.batch_size]   ,      self.model_tester.type_sequence_label_size	)
								lowerCAmelCase_       :    List[str]								=			OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
								self.assertEqual(result.logits.shape   ,      (self.model_tester.batch_size, self.model_tester.num_labels)	)



						def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
								lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase_       :    Tuple								=			3
								lowerCAmelCase_       :    List[str]								=			'multi_label_classification'
								lowerCAmelCase_       :    Tuple								=			input_dict['input_ids']
								lowerCAmelCase_       :    int								=			input_ids.ne(1	).to(SCREAMING_SNAKE_CASE_	)
								lowerCAmelCase_       :    int								=			ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]   ,      self.model_tester.type_sequence_label_size	).to(torch.float	)
								lowerCAmelCase_       :    List[str]								=			OpenLlamaForSequenceClassification(SCREAMING_SNAKE_CASE_	)
								model.to(SCREAMING_SNAKE_CASE_	)
								model.eval()
								lowerCAmelCase_       :    Optional[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
								self.assertEqual(result.logits.shape   ,      (self.model_tester.batch_size, self.model_tester.num_labels)	)



						@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'	)
						def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
								pass





						@parameterized.expand([('linear',), ('dynamic',)]	)
						def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any	):
								lowerCAmelCase_       :    Any								=			self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase_       :    Optional[Any]								=			ids_tensor([1, 1_0]   ,      config.vocab_size	)
								lowerCAmelCase_       :    List[str]								=			ids_tensor([1, int(config.max_position_embeddings * 1.5	)]   ,      config.vocab_size	)

								set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
								lowerCAmelCase_       :    Tuple								=			OpenLlamaModel(SCREAMING_SNAKE_CASE_	)
								original_model.to(SCREAMING_SNAKE_CASE_	)
								original_model.eval()
								lowerCAmelCase_       :    str								=			original_model(SCREAMING_SNAKE_CASE_	).last_hidden_state
								lowerCAmelCase_       :    Dict								=			original_model(SCREAMING_SNAKE_CASE_	).last_hidden_state

								set_seed(4_2	)  # Fixed seed at init time so the two models get the same random weights
								lowerCAmelCase_       :    Optional[Any]								=			{'type': scaling_type, 'factor': 10.0}
								lowerCAmelCase_       :    Optional[Any]								=			OpenLlamaModel(SCREAMING_SNAKE_CASE_	)
								scaled_model.to(SCREAMING_SNAKE_CASE_	)
								scaled_model.eval()
								lowerCAmelCase_       :    Optional[Any]								=			scaled_model(SCREAMING_SNAKE_CASE_	).last_hidden_state
								lowerCAmelCase_       :    str								=			scaled_model(SCREAMING_SNAKE_CASE_	).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
										self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-5	)	)
								else:
										self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-5	)	)

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-5	)	)










"""simple docstring"""







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


lowercase__		:    Optional[int]				   =     1_6
lowercase__		:    List[str]				   =     3_2

def     UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset('glue'   ,       'mrpc'						)

  def tokenize_function(lowerCAmelCase__				:    Tuple						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_       :    Dict								=			tokenizer(examples['sentence1']   ,       examples['sentence2']   ,       truncation=lowerCAmelCase__   ,       max_length=lowerCAmelCase__						)
    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():
    lowerCAmelCase_       :    Dict								=			datasets.map(
        lowerCAmelCase__   ,       batched=lowerCAmelCase__   ,       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
  lowerCAmelCase_       :    int								=			tokenized_datasets.rename_column('label'   ,       'labels'						)

  def collate_fn(lowerCAmelCase__				:    List[str]						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCAmelCase_       :    Any								=			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":
      lowerCAmelCase_       :    Dict								=			16
    elif accelerator.mixed_precision != "no":
      lowerCAmelCase_       :    str								=			8
    else:
      lowerCAmelCase_       :    str								=			None

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

  # Instantiate dataloaders.
  lowerCAmelCase_       :    List[Any]								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			DataLoader(
      tokenized_datasets['validation']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     lowercase__		:    int				   =     mocked_dataloaders  # noqa: F811

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[int]:

  """simple docstring"""




  if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,       lowerCAmelCase__						) == "1":
    lowerCAmelCase_       :    Optional[int]								=			2
  # Initialize accelerator
  lowerCAmelCase_       :    str								=			Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCAmelCase_       :    Optional[int]								=			config['lr']
  lowerCAmelCase_       :    Tuple								=			int(config['num_epochs']						)
  lowerCAmelCase_       :    int								=			int(config['seed']						)
  lowerCAmelCase_       :    str								=			int(config['batch_size']						)

  lowerCAmelCase_       :    str								=			evaluate.load('glue'   ,       'mrpc'						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowerCAmelCase__						)
  def inner_training_loop(lowerCAmelCase__				:    Optional[int]						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowerCAmelCase__						)

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

    # 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).
    lowerCAmelCase_       :    int								=			model.to(accelerator.device						)

    # Instantiate optimizer
    lowerCAmelCase_       :    List[Any]								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)
    lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Instantiate scheduler
    lowerCAmelCase_       :    Dict								=			get_linear_schedule_with_warmup(
        optimizer=lowerCAmelCase__   ,       num_warmup_steps=100   ,       num_training_steps=(len(lowerCAmelCase__						) * num_epochs)   ,       )

    # 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.
    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			accelerator.prepare(
        lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Now we train the model
    for epoch in range(lowerCAmelCase__						):
      model.train()
      for step, batch in enumerate(lowerCAmelCase__						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        lowerCAmelCase_       :    List[str]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    Optional[Any]								=			outputs.loss
        accelerator.backward(lowerCAmelCase__						)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

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

      lowerCAmelCase_       :    str								=			metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"   ,       lowerCAmelCase__						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()

def     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			argparse.ArgumentParser(description='Simple example of training script.'						)
  parser.add_argument(
      '--mixed_precision'   ,       type=lowerCAmelCase__   ,       default=lowerCAmelCase__   ,       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.'						)
  lowerCAmelCase_       :    str								=			parser.parse_args()
  lowerCAmelCase_       :    Union[str, Any]								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__   ,       lowerCAmelCase__						)


if __name__ == "__main__":
     main()





