import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
      from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder

def 			lowercase_				(			_lowerCamelCase							:       List[str]		,	_lowerCamelCase							:       Any		,	_lowerCamelCase							:       Union[str, Any]		,	_lowerCamelCase							:       str=None		,	_lowerCamelCase							:       Union[str, Any]=None		,	_lowerCamelCase							:       Tuple=None		,	_lowerCamelCase							:       Dict=None		,	_lowerCamelCase							:       Union[str, Any]=None		,	):
    if attention_mask is None:
        lowercase__ :   Dict    					=				input_ids.ne(config.pad_token_id)
    if decoder_attention_mask is None:
        lowercase__ :   Any    					=				decoder_input_ids.ne(config.pad_token_id)
    if head_mask is None:
        lowercase__ :   Optional[int]    					=				torch.ones(config.encoder_layers		,	config.encoder_attention_heads		,	device=_lowerCamelCase)
    if decoder_head_mask is None:
        lowercase__ :   Dict    					=				torch.ones(config.decoder_layers		,	config.decoder_attention_heads		,	device=_lowerCamelCase)
    if cross_attn_head_mask is None:
        lowercase__ :   List[str]    					=				torch.ones(config.decoder_layers		,	config.decoder_attention_heads		,	device=_lowerCamelCase)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }



class   snake_case_       :






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     Any	,						lowercase_				:     Optional[int]=13	,						lowercase_				:     Any=7	,						lowercase_				:     Tuple=True	,						lowercase_				:     Union[str, Any]=False	,						lowercase_				:     Any=99	,						lowercase_				:     Tuple=16	,						lowercase_				:     int=2	,						lowercase_				:     List[str]=4	,						lowercase_				:     List[str]=4	,						lowercase_				:     Any="relu"	,						lowercase_				:     Optional[Any]=0.1	,						lowercase_				:     Optional[int]=0.1	,						lowercase_				:     int=0.0	,						lowercase_				:     Dict=0.0	,						lowercase_				:     List[str]=20	,						lowercase_				:     Dict=2	,						lowercase_				:     int=1	,						lowercase_				:     Dict=0	,						)					->						str:
       lowercase__ :   List[Any]    					=				parent
       lowercase__ :   int    					=				batch_size
       lowercase__ :   Optional[Any]    					=				seq_length
       lowercase__ :   Dict    					=				is_training
       lowercase__ :   List[str]    					=				use_labels
       lowercase__ :   Dict    					=				vocab_size
       lowercase__ :   List[Any]    					=				hidden_size
       lowercase__ :   Optional[int]    					=				num_hidden_layers
       lowercase__ :   Any    					=				num_attention_heads
       lowercase__ :   str    					=				intermediate_size
       lowercase__ :   Optional[Any]    					=				hidden_act
       lowercase__ :   str    					=				hidden_dropout_prob
       lowercase__ :   Union[str, Any]    					=				attention_probs_dropout_prob
       lowercase__ :   Optional[Any]    					=				encoder_layerdrop
       lowercase__ :   Optional[int]    					=				decoder_layerdrop
       lowercase__ :   Dict    					=				max_position_embeddings
       lowercase__ :   List[Any]    					=				eos_token_id
       lowercase__ :   Dict    					=				pad_token_id
       lowercase__ :   Union[str, Any]    					=				bos_token_id






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						int:
       lowercase__ :   Optional[Any]    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )
       lowercase__ :   Optional[int]    					=				self.eos_token_id  # Eos Token
       lowercase__ :   int    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for M2M100 the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       lowercase__ :   Union[str, Any]    					=				input_ids.clamp(self.pad_token_id + 1  )
       lowercase__ :   Any    					=				decoder_input_ids.clamp(self.pad_token_id + 1  )

       lowercase__ :   Tuple    					=				self.get_config()
       lowercase__ :   Tuple    					=				prepare_mam_aaa_inputs_dict(lowercase_	,						lowercase_	,						lowercase_  )
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     Dict  )					->						Optional[int]:
       return MaMaaaConfig(
           vocab_size=self.vocab_size	,						d_model=self.hidden_size	,						encoder_layers=self.num_hidden_layers	,						decoder_layers=self.num_hidden_layers	,						encoder_attention_heads=self.num_attention_heads	,						decoder_attention_heads=self.num_attention_heads	,						encoder_ffn_dim=self.intermediate_size	,						decoder_ffn_dim=self.intermediate_size	,						dropout=self.hidden_dropout_prob	,						attention_dropout=self.attention_probs_dropout_prob	,						encoder_layerdrop=self.encoder_layerdrop	,						decoder_layerdrop=self.decoder_layerdrop	,						max_position_embeddings=self.max_position_embeddings	,						eos_token_id=self.eos_token_id	,						bos_token_id=self.bos_token_id	,						pad_token_id=self.pad_token_id	,						)






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Any:
       lowercase__					,   lowercase__ :   Tuple    					=				self.prepare_config_and_inputs()
       return config, inputs_dict






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     List[Any]	,						lowercase_				:     int  )					->						Union[str, Any]:
       lowercase__ :   str    					=				MaMaaaModel(config=lowercase_  ).get_decoder().to(lowercase_  ).eval()
       lowercase__ :   Tuple    					=				inputs_dict["input_ids"]
       lowercase__ :   List[Any]    					=				inputs_dict["attention_mask"]
       lowercase__ :   Any    					=				inputs_dict["head_mask"]

       # first forward pass
       lowercase__ :   Optional[int]    					=				model(lowercase_	,						attention_mask=lowercase_	,						head_mask=lowercase_	,						use_cache=lowercase_  )

       lowercase__					,   lowercase__ :   List[Any]    					=				outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       lowercase__ :   List[Any]    					=				ids_tensor((self.batch_size, 3)	,						config.vocab_size  )
       lowercase__ :   Any    					=				ids_tensor((self.batch_size, 3)	,						2  )

       # append to next input_ids and
       lowercase__ :   List[str]    					=				torch.cat([input_ids, next_tokens]	,						dim=-1  )
       lowercase__ :   Tuple    					=				torch.cat([attention_mask, next_attn_mask]	,						dim=-1  )

       lowercase__ :   Tuple    					=				model(lowercase_	,						attention_mask=lowercase_  )["last_hidden_state"]
       lowercase__ :   List[Any]    					=				model(lowercase_	,						attention_mask=lowercase_	,						past_key_values=lowercase_  )[
           "last_hidden_state"
       ]

       # select random slice
       lowercase__ :   Union[str, Any]    					=				ids_tensor((1,)	,						output_from_past.shape[-1]  ).item()
       lowercase__ :   int    					=				output_from_no_past[:, -3:, random_slice_idx].detach()
       lowercase__ :   Union[str, Any]    					=				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(lowercase_	,						lowercase_	,						atol=1E-2  )  )







   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[Any]  )					->						Dict:
       lowercase__ :   Optional[Any]    					=				MaMaaaModel(config=lowercase_  ).to(lowercase_  ).eval()
       lowercase__ :   str    					=				model(**lowercase_  )

       lowercase__ :   Union[str, Any]    					=				outputs.encoder_last_hidden_state
       lowercase__ :   Dict    					=				outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   List[str]    					=				model.get_encoder()
           encoder.save_pretrained(lowercase_  )
           lowercase__ :   int    					=				MaMaaaEncoder.from_pretrained(lowercase_  ).to(lowercase_  )

       lowercase__ :   str    					=				encoder(inputs_dict["input_ids"]	,						attention_mask=inputs_dict["attention_mask"]  )[
           0
       ]

       self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3  )

       with tempfile.TemporaryDirectory() as tmpdirname:
           lowercase__ :   Optional[int]    					=				model.get_decoder()
           decoder.save_pretrained(lowercase_  )
           lowercase__ :   int    					=				MaMaaaDecoder.from_pretrained(lowercase_  ).to(lowercase_  )

       lowercase__ :   List[str]    					=				decoder(
           input_ids=inputs_dict["decoder_input_ids"]	,						attention_mask=inputs_dict["decoder_attention_mask"]	,						encoder_hidden_states=lowercase_	,						encoder_attention_mask=inputs_dict["attention_mask"]	,						)[0]

       self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3  )



@require_torch
class   snake_case_       (		__A  ,__A  ,__A  ,unittest.TestCase       ):
   __A	:		Optional[Any]         =				(
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   __A	:		Optional[Any]         =				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   __A	:		Dict         =				(
       {
           "conversational": MaMaaaForConditionalGeneration,
           "feature-extraction": MaMaaaModel,
           "summarization": MaMaaaForConditionalGeneration,
           "text2text-generation": MaMaaaForConditionalGeneration,
           "translation": MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   __A	:		str         =				True
   __A	:		List[str]         =				True
   __A	:		List[Any]         =				False
   __A	:		List[Any]         =				False






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     List[Any]	,						lowercase_				:     List[str]	,						lowercase_				:     List[str]	,						lowercase_				:     Tuple	,						lowercase_				:     int  )					->						str:
       if pipeline_test_casse_name == "TranslationPipelineTests":
           # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
           # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
           return True

       return False






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Dict:
       lowercase__ :   List[Any]    					=				MaMaaaModelTester(self  )
       lowercase__ :   str    					=				ConfigTester(self	,						config_class=lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Optional[Any]:
       self.config_tester.run_common_tests()






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Optional[int]:
       lowercase__					,   lowercase__ :   str    					=				self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           lowercase__ :   int    					=				model_class(lowercase_  )

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowercase_  )
               lowercase__					,   lowercase__ :   int    					=				model_class.from_pretrained(lowercase_	,						output_loading_info=lowercase_  )
           self.assertEqual(info["missing_keys"]	,						[]  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						str:
       lowercase__ :   List[str]    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Optional[Any]:
       lowercase__ :   Any    					=				self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Optional[Any]:
       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
           lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )
           model.to(lowercase_  )
           model.eval()

           lowercase__ :   List[Any]    					=				copy.deepcopy(self._prepare_for_class(lowercase_	,						lowercase_  )  )

           if not self.is_encoder_decoder:
               lowercase__ :   Optional[int]    					=				inputs["input_ids"]
               del inputs["input_ids"]
           else:
               lowercase__ :   str    					=				inputs["input_ids"]
               lowercase__ :   Optional[Any]    					=				inputs.get("decoder_input_ids"	,						lowercase_  )
               del inputs["input_ids"]
               inputs.pop("decoder_input_ids"	,						lowercase_  )

           lowercase__ :   Optional[int]    					=				model.get_input_embeddings()
           if not self.is_encoder_decoder:
               lowercase__ :   int    					=				wte(lowercase_  )
           else:
               lowercase__ :   str    					=				wte(lowercase_  )
               lowercase__ :   str    					=				wte(lowercase_  )

           with torch.no_grad():
               model(**lowercase_  )[0]







   def       __UpperCamelCase     (      self				:     Tuple  )					->						str:
       lowercase__					,   lowercase__ :   Tuple    					=				self.model_tester.prepare_config_and_inputs()
       lowercase__ :   Any    					=				input_dict["input_ids"]
       lowercase__ :   List[Any]    					=				input_ids.ne(1  ).to(lowercase_  )
       lowercase__ :   Optional[int]    					=				MaMaaaForConditionalGeneration(lowercase_  ).eval().to(lowercase_  )
       if torch_device == "cuda":
           model.half()
       model.generate(lowercase_	,						attention_mask=lowercase_  )
       model.generate(num_beams=4	,						do_sample=lowercase_	,						early_stopping=lowercase_	,						num_return_sequences=3  )



def 			lowercase_				(			_lowerCamelCase							:       Any):
    return torch.tensor(_lowerCamelCase		,	dtype=torch.long		,	device=_lowerCamelCase)


UpperCamelCase  							=						1E-4



@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   snake_case_       (		unittest.TestCase       ):






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						Tuple:
       return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  )






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       lowercase__ :   Optional[Any]    					=				MaMaaaModel.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
       lowercase__ :   int    					=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
       lowercase__ :   Optional[Any]    					=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
       lowercase__ :   int    					=				prepare_mam_aaa_inputs_dict(model.config	,						lowercase_	,						lowercase_  )
       with torch.no_grad():
           lowercase__ :   Any    					=				model(**lowercase_  )[0]
       lowercase__ :   Any    					=				torch.Size((1, 11, 10_24)  )
       self.assertEqual(output.shape	,						lowercase_  )
       # change to expected output here
       lowercase__ :   Tuple    					=				torch.tensor(
           [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]	,						device=lowercase_  )
       self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=lowercase_  )  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[str]:
       lowercase__ :   Optional[Any]    					=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )

       # change to intended input
       lowercase__ :   Optional[Any]    					=				_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]  )
       lowercase__ :   Any    					=				_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]  )
       lowercase__ :   Optional[int]    					=				prepare_mam_aaa_inputs_dict(model.config	,						lowercase_	,						lowercase_  )
       with torch.no_grad():
           lowercase__ :   List[str]    					=				model(**lowercase_  )[0]
       lowercase__ :   Optional[Any]    					=				torch.Size((1, 11, model.config.vocab_size)  )
       self.assertEqual(output.shape	,						lowercase_  )
       # change to expected output here
       lowercase__ :   List[Any]    					=				torch.tensor(
           [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]	,						device=lowercase_  )
       self.assertTrue(torch.allclose(output[:, :3, :3]	,						lowercase_	,						atol=lowercase_  )  )







   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       lowercase__ :   List[Any]    					=				MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"  ).to(lowercase_  )
       lowercase__ :   int    					=				MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"	,						src_lang="fr"	,						tgt_lang="en"  )

       lowercase__ :   Union[str, Any]    					=				[
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"
           " Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"
           " l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
       ]

       # The below article tests that we don't add any hypotheses outside of the top n_beams
       lowercase__ :   Optional[int]    					=				tokenizer(lowercase_	,						padding=lowercase_	,						return_tensors="pt"  )

       lowercase__ :   str    					=				model.generate(
           input_ids=dct["input_ids"].to(lowercase_  )	,						attention_mask=dct["attention_mask"].to(lowercase_  )	,						num_beams=5	,						forced_bos_token_id=tokenizer.get_lang_id("en"  )	,						)

       lowercase__ :   Optional[int]    					=				[
           "The NSA case highlights the total absence of intelligence debate",
           "I think there are two levels of response from the French government.",
           "When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."
           " Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"
           " communications in France.",
       ]

       lowercase__ :   List[str]    					=				tokenizer.batch_decode(
           hypotheses_batch.tolist()	,						clean_up_tokenization_spaces=lowercase_	,						skip_special_tokens=lowercase_  )
       assert generated == expected_en




"""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
#
########################################################################


UpperCAmelCase__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







  """simple docstring"""
  _UpperCAmelCase         =					AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  _UpperCAmelCase         =					load_dataset("""glue"""      ,"""mrpc"""						)

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

  def collate_fn(lowercase						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCAmelCase         =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _UpperCAmelCase         =					16
    elif accelerator.mixed_precision != "no":
      _UpperCAmelCase         =					8
    else:
      _UpperCAmelCase         =					None

    return tokenizer.pad(
        lowercase      ,padding="""longest"""      ,max_length=lowercase      ,pad_to_multiple_of=lowercase      ,return_tensors="""pt"""      ,)

  # Instantiate dataloaders.
  _UpperCAmelCase         =					DataLoader(
      tokenized_datasets["""train"""]      ,shuffle=lowercase      ,collate_fn=lowercase      ,batch_size=lowercase						)
  _UpperCAmelCase         =					DataLoader(
      tokenized_datasets["""validation"""]      ,shuffle=lowercase      ,collate_fn=lowercase      ,batch_size=lowercase						)

  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

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "1":
    _UpperCAmelCase         =					2
  # Initialize accelerator
  _UpperCAmelCase         =					Accelerator(cpu=args.cpu      ,mixed_precision=args.mixed_precision						)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _UpperCAmelCase         =					config["""lr"""]
  _UpperCAmelCase         =					int(config["""num_epochs"""]						)
  _UpperCAmelCase         =					int(config["""seed"""]						)
  _UpperCAmelCase         =					int(config["""batch_size"""]						)

  _UpperCAmelCase         =					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=lowercase						)
  def inner_training_loop(lowercase						):
    # 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(lowercase						)

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

    # 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).
    _UpperCAmelCase         =					model.to(accelerator.device						)

    # Instantiate optimizer
    _UpperCAmelCase         =					AdamW(params=model.parameters()      ,lr=lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,num_training_steps=(len(lowercase						) * 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.
    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase         =					accelerator.prepare(
        lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						)

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

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

      _UpperCAmelCase         =					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''      ,lowercase						)

    # 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    (	):







  """simple docstring"""
  _UpperCAmelCase         =					argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision"""      ,type=lowercase      ,default=lowercase      ,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."""						)
  _UpperCAmelCase         =					parser.parse_args()
  _UpperCAmelCase         =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(lowercase      ,lowercase						)


if __name__ == "__main__":
    main()
