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      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      ):
      if attention_mask is None:
            SCREAMING_SNAKE_CASE_          =  input_ids.ne(config.pad_token_id				)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_          =  decoder_input_ids.ne(config.pad_token_id				)
      if head_mask is None:
            SCREAMING_SNAKE_CASE_          =  torch.ones(config.encoder_layers     ,      config.encoder_attention_heads     ,      device=__UpperCamelCase				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_          =  torch.ones(config.decoder_layers     ,      config.decoder_attention_heads     ,      device=__UpperCamelCase				)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_          =  torch.ones(config.decoder_layers     ,      config.decoder_attention_heads     ,      device=__UpperCamelCase				)
      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   lowerCamelCase					:
  """simple docstring"""







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







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size   )
        SCREAMING_SNAKE_CASE_          =  self.eos_token_id  # Eos Token
        SCREAMING_SNAKE_CASE_          =  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
        SCREAMING_SNAKE_CASE_          =  input_ids.clamp(self.pad_token_id + 1   )
        SCREAMING_SNAKE_CASE_          =  decoder_input_ids.clamp(self.pad_token_id + 1   )

        SCREAMING_SNAKE_CASE_          =  self.get_config()
        SCREAMING_SNAKE_CASE_          =  prepare_mam_aaa_inputs_dict(__magic_name__  ,     __magic_name__  ,     __magic_name__   )
        return config, inputs_dict







  def    __A     (							self :       Optional[int]   )  ->	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    __A     (							self :       Dict   )  ->	Dict:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        return config, inputs_dict







  def    __A     (							self :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  MaMaaaModel(config=__magic_name__   ).get_decoder().to(__magic_name__   ).eval()
        SCREAMING_SNAKE_CASE_          =  inputs_dict["input_ids"]
        SCREAMING_SNAKE_CASE_          =  inputs_dict["attention_mask"]
        SCREAMING_SNAKE_CASE_          =  inputs_dict["head_mask"]

        # first forward pass
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     head_mask=__magic_name__  ,     use_cache=__magic_name__   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  outputs.to_tuple()

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

        # append to next input_ids and
        SCREAMING_SNAKE_CASE_          =  torch.cat([input_ids, next_tokens]  ,     dim=-1   )
        SCREAMING_SNAKE_CASE_          =  torch.cat([attention_mask, next_attn_mask]  ,     dim=-1   )

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__   )["last_hidden_state"]
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     past_key_values=__magic_name__   )[
            "last_hidden_state"
        ]

        # select random slice
        SCREAMING_SNAKE_CASE_          =  ids_tensor((1,)  ,     output_from_past.shape[-1]   ).item()
        SCREAMING_SNAKE_CASE_          =  output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_SNAKE_CASE_          =  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(__magic_name__  ,     __magic_name__  ,     atol=1e-2   )   )







  def    __A     (							self :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  MaMaaaModel(config=__magic_name__   ).to(__magic_name__   ).eval()
        SCREAMING_SNAKE_CASE_          =  model(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  outputs.encoder_last_hidden_state
        SCREAMING_SNAKE_CASE_          =  outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE_          =  model.get_encoder()
              encoder.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  MaMaaaEncoder.from_pretrained(__magic_name__   ).to(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  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:
              SCREAMING_SNAKE_CASE_          =  model.get_decoder()
              decoder.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  MaMaaaDecoder.from_pretrained(__magic_name__   ).to(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  decoder(
            input_ids=inputs_dict["decoder_input_ids"]  ,     attention_mask=inputs_dict["decoder_attention_mask"]  ,     encoder_hidden_states=__magic_name__  ,     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   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(
      (
          MaMaaaModel,
          MaMaaaForConditionalGeneration,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase__     =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
  lowerCamelCase__     =						(
      {
          '''conversational''': MaMaaaForConditionalGeneration,
          '''feature-extraction''': MaMaaaModel,
          '''summarization''': MaMaaaForConditionalGeneration,
          '''text2text-generation''': MaMaaaForConditionalGeneration,
          '''translation''': MaMaaaForConditionalGeneration,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase__     =						True
  lowerCamelCase__     =						True
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[Any]   )  ->	Dict:
        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    __A     (							self :       Dict   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  MaMaaaModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__   )







  def    __A     (							self :       List[Any]   )  ->	Dict:
        self.config_tester.run_common_tests()







  def    __A     (							self :       Tuple   )  ->	str:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )

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







  def    __A     (							self :       List[Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
              SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
              model.to(__magic_name__   )
              model.eval()

              SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self._prepare_for_class(__magic_name__  ,     __magic_name__   )   )

              if not self.is_encoder_decoder:
                    SCREAMING_SNAKE_CASE_          =  inputs["input_ids"]
                    del inputs["input_ids"]
              else:
                    SCREAMING_SNAKE_CASE_          =  inputs["input_ids"]
                    SCREAMING_SNAKE_CASE_          =  inputs.get("decoder_input_ids"  ,     __magic_name__   )
                    del inputs["input_ids"]
                    inputs.pop("decoder_input_ids"  ,     __magic_name__   )

              SCREAMING_SNAKE_CASE_          =  model.get_input_embeddings()
              if not self.is_encoder_decoder:
                    SCREAMING_SNAKE_CASE_          =  wte(__magic_name__   )
              else:
                    SCREAMING_SNAKE_CASE_          =  wte(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  wte(__magic_name__   )

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







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



def      a__ (		__UpperCamelCase				):
      return torch.tensor(__UpperCamelCase     ,      dtype=torch.long     ,      device=__UpperCamelCase				)


A     :  List[str]					     =       1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @cached_property
  def    __A     (							self :       List[Any]   )  ->	int:
        return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"   )







  def    __A     (							self :       Optional[int]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  MaMaaaModel.from_pretrained("facebook/m2m100_418M"   ).to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]   )
        SCREAMING_SNAKE_CASE_          =  _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]   )
        SCREAMING_SNAKE_CASE_          =  prepare_mam_aaa_inputs_dict(model.config  ,     __magic_name__  ,     __magic_name__   )
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(**__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 11, 1_024)   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        # change to expected output here
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]  ,     device=__magic_name__   )
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,     __magic_name__  ,     atol=__magic_name__   )   )







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(__magic_name__   )

        # change to intended input
        SCREAMING_SNAKE_CASE_          =  _long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]   )
        SCREAMING_SNAKE_CASE_          =  _long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]   )
        SCREAMING_SNAKE_CASE_          =  prepare_mam_aaa_inputs_dict(model.config  ,     __magic_name__  ,     __magic_name__   )
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(**__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  torch.Size((1, 11, model.config.vocab_size)   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        # change to expected output here
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]  ,     device=__magic_name__   )
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,     __magic_name__  ,     atol=__magic_name__   )   )







  def    __A     (							self :       Any   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"   ).to(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"  ,     src_lang="fr"  ,     tgt_lang="en"   )

        SCREAMING_SNAKE_CASE_          =  [
            "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
        SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     padding=__magic_name__  ,     return_tensors="pt"   )

        SCREAMING_SNAKE_CASE_          =  model.generate(
            input_ids=dct["input_ids"].to(__magic_name__   )  ,     attention_mask=dct["attention_mask"].to(__magic_name__   )  ,     num_beams=5  ,     forced_bos_token_id=tokenizer.get_lang_id("en"   )  ,     )

        SCREAMING_SNAKE_CASE_          =  [
            "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.",
        ]

        SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(
            hypotheses_batch.tolist()  ,     clean_up_tokenization_spaces=__magic_name__  ,     skip_special_tokens=__magic_name__   )
        assert generated == expected_en



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
# and perform gradient accumulation
#
# 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)
#
# 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
#
########################################################################


A     :  Optional[Any]					     =       16
A     :  Optional[Any]					     =       32


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 1_6				):
      SCREAMING_SNAKE_CASE_          =  AutoTokenizer.from_pretrained("bert-base-cased"				)
      SCREAMING_SNAKE_CASE_          =  load_dataset("glue"     ,      "mrpc"				)

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

      def collate_fn(__UpperCamelCase				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE_          =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE_          =  1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE_          =  8
            else:
                  SCREAMING_SNAKE_CASE_          =  None

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

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

      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

      A     :  Tuple					     =       mocked_dataloaders  # noqa: F811


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      # For testing only
      if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,      __UpperCamelCase				) == "1":
            SCREAMING_SNAKE_CASE_          =  2
      # New Code #
      SCREAMING_SNAKE_CASE_          =  int(args.gradient_accumulation_steps				)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_          =  Accelerator(
          cpu=args.cpu     ,      mixed_precision=args.mixed_precision     ,      gradient_accumulation_steps=__UpperCamelCase				)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_          =  config["lr"]
      SCREAMING_SNAKE_CASE_          =  int(config["num_epochs"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["seed"]				)
      SCREAMING_SNAKE_CASE_          =  int(config["batch_size"]				)

      SCREAMING_SNAKE_CASE_          =  evaluate.load("glue"     ,      "mrpc"				)

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

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

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_          =  AdamW(params=model.parameters()     ,      lr=__UpperCamelCase				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE_          =  get_linear_schedule_with_warmup(
          optimizer=__UpperCamelCase     ,      num_warmup_steps=1_0_0     ,      num_training_steps=(len(__UpperCamelCase				) * 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.
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  accelerator.prepare(
          __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # Now we train the model
      for epoch in range(__UpperCamelCase				):
            model.train()
            for step, batch in enumerate(__UpperCamelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				)
                        SCREAMING_SNAKE_CASE_          =  output.loss
                        accelerator.backward(__UpperCamelCase				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

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

            SCREAMING_SNAKE_CASE_          =  metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'''epoch {epoch}:'''     ,      __UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  argparse.ArgumentParser(description="Simple example of training script."				)
      parser.add_argument(
          "--mixed_precision"     ,      type=__UpperCamelCase     ,      default=__UpperCamelCase     ,      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."     ,      )
      # New Code #
      parser.add_argument(
          "--gradient_accumulation_steps"     ,      type=__UpperCamelCase     ,      default=1     ,      help="The number of minibatches to be ran before gradients are accumulated."     ,      )
      parser.add_argument("--cpu"     ,      action="store_true"     ,      help="If passed, will train on the CPU."				)
      SCREAMING_SNAKE_CASE_          =  parser.parse_args()
      SCREAMING_SNAKE_CASE_          =  {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
      training_function(__UpperCamelCase     ,      __UpperCamelCase				)


if __name__ == "__main__":
      main()


