import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
         VideoMAEForPreTraining,
         VideoMAEForVideoClassification,
         VideoMAEModel,
     )
     from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VideoMAEImageProcessor



class     _A   :






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=13   ,				SCREAMING_SNAKE_CASE_=10   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=32   ,				SCREAMING_SNAKE_CASE_=5   ,				SCREAMING_SNAKE_CASE_=4   ,				SCREAMING_SNAKE_CASE_=37   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=10   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=0.9   ,				SCREAMING_SNAKE_CASE_=None   ,				)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      tubelet_size
       UpperCamelCase__										=      num_frames
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      type_sequence_label_size
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      scope

       # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
       UpperCamelCase__										=      (image_size // patch_size) ** 2
       UpperCamelCase__										=      (num_frames // tubelet_size) * self.num_patches_per_frame

       # use this variable to define bool_masked_pos
       UpperCamelCase__										=      int(mask_ratio * self.seq_length	)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]	)

       UpperCamelCase__										=      None
       if self.use_labels:
            UpperCamelCase__										=      ids_tensor([self.batch_size]   ,				self.type_sequence_label_size	)

       UpperCamelCase__										=      self.get_config()

       return config, pixel_values, labels






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       return VideoMAEConfig(
           image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				num_frames=self.num_frames   ,				tubelet_size=self.tubelet_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   ,				is_decoder=SCREAMING_SNAKE_CASE_   ,				initializer_range=self.initializer_range   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      VideoMAEModel(config=SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      VideoMAEForPreTraining(SCREAMING_SNAKE_CASE_	)
       model.to(SCREAMING_SNAKE_CASE_	)
       model.eval()
       # important: each video needs to have the same number of masked patches
       # hence we define a single mask, which we then repeat for each example in the batch
       UpperCamelCase__										=      torch.ones((self.num_masks,)	)
       UpperCamelCase__										=      torch.cat([mask, torch.zeros(self.seq_length - mask.size(0	)	)]	)
       UpperCamelCase__										=      mask.expand(self.batch_size   ,				-1	).bool()

       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       # model only returns predictions for masked patches
       UpperCamelCase__										=      mask.sum().item()
       UpperCamelCase__										=      3 * self.tubelet_size * self.patch_size**2
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, num_masked_patches, decoder_num_labels)	)






  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      config_and_inputs
       UpperCamelCase__										=      {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_torch
class     _A   (    __UpperCamelCase	, __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=(
      (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
  )
  SCREAMING_SNAKE_CASE_	:	str						=(
      {"feature-extraction": VideoMAEModel, "video-classification": VideoMAEForVideoClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE_	:	List[str]						=False
  SCREAMING_SNAKE_CASE_	:	Dict						=False
  SCREAMING_SNAKE_CASE_	:	str						=False
  SCREAMING_SNAKE_CASE_	:	str						=False






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      VideoMAEModelTester(self	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				has_text_modality=SCREAMING_SNAKE_CASE_   ,				hidden_size=37	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=False	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      copy.deepcopy(SCREAMING_SNAKE_CASE_	)

       if model_class == VideoMAEForPreTraining:
            # important: each video needs to have the same number of masked patches
            # hence we define a single mask, which we then repeat for each example in the batch
            UpperCamelCase__										=      torch.ones((self.model_tester.num_masks,)	)
            UpperCamelCase__										=      torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0	)	)]	)
            UpperCamelCase__										=      mask.expand(self.model_tester.batch_size   ,				-1	).bool()
            UpperCamelCase__										=      bool_masked_pos.to(SCREAMING_SNAKE_CASE_	)

       if return_labels:
            if model_class in [
                *get_values(SCREAMING_SNAKE_CASE_	),
            ]:
                 UpperCamelCase__										=      torch.zeros(
                     self.model_tester.batch_size   ,				dtype=torch.long   ,				device=SCREAMING_SNAKE_CASE_	)

       return inputs_dict






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  @unittest.skip(reason='''VideoMAE does not use inputs_embeds'''	)
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)	)
            UpperCamelCase__										=      model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_   ,				nn.Linear	)	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      inspect.signature(model.forward	)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCamelCase__										=      [*signature.parameters.keys()]

            UpperCamelCase__										=      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__										=      VideoMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       if not self.has_attentions:
            pass

       else:
            UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()
            UpperCamelCase__										=      True

            for model_class in self.all_model_classes:
                 UpperCamelCase__										=      self.model_tester.seq_length - self.model_tester.num_masks
                 UpperCamelCase__										=      (
                     num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                 )

                 UpperCamelCase__										=      True
                 UpperCamelCase__										=      False
                 UpperCamelCase__										=      True
                 UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
                 model.to(SCREAMING_SNAKE_CASE_	)
                 model.eval()
                 with torch.no_grad():
                      UpperCamelCase__										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)
                 UpperCamelCase__										=      outputs.attentions
                 self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				self.model_tester.num_hidden_layers	)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 UpperCamelCase__										=      True
                 UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
                 model.to(SCREAMING_SNAKE_CASE_	)
                 model.eval()
                 with torch.no_grad():
                      UpperCamelCase__										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)
                 UpperCamelCase__										=      outputs.attentions
                 self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				self.model_tester.num_hidden_layers	)

                 self.assertListEqual(
                     list(attentions[0].shape[-3:]	)   ,				[self.model_tester.num_attention_heads, seq_len, seq_len]   ,				)
                 UpperCamelCase__										=      len(SCREAMING_SNAKE_CASE_	)

                 # Check attention is always last and order is fine
                 UpperCamelCase__										=      True
                 UpperCamelCase__										=      True
                 UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
                 model.to(SCREAMING_SNAKE_CASE_	)
                 model.eval()
                 with torch.no_grad():
                      UpperCamelCase__										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)

                 self.assertEqual(out_len + 1   ,				len(SCREAMING_SNAKE_CASE_	)	)

                 UpperCamelCase__										=      outputs.attentions

                 self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				self.model_tester.num_hidden_layers	)
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:]	)   ,				[self.model_tester.num_attention_heads, seq_len, seq_len]   ,				)






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       def check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	):
            UpperCamelCase__										=      model_class(SCREAMING_SNAKE_CASE_	)
            model.to(SCREAMING_SNAKE_CASE_	)
            model.eval()

            with torch.no_grad():
                 UpperCamelCase__										=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)	)

            UpperCamelCase__										=      outputs.hidden_states
            UpperCamelCase__										=      self.model_tester.num_hidden_layers + 1
            self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      self.model_tester.seq_length - self.model_tester.num_masks
            UpperCamelCase__										=      num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

            self.assertListEqual(
                list(hidden_states[0].shape[-2:]	)   ,				[seq_length, self.model_tester.hidden_size]   ,				)

       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCamelCase__										=      True
            check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            UpperCamelCase__										=      True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       pass







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video''' ,							filename='''eating_spaghetti.npy''' ,							repo_type='''dataset'''							)
     UpperCamelCase__										=      np.load(A							)
     return list(A							)



@require_torch
@require_vision
class     _A   (    unittest.TestCase							):






  @cached_property
  def 		_a					(self	)		->      Union[str, Any]:





       '''simple docstring'''





       return (
           VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]   ,				image_std=[0.5, 0.5, 0.5]	)
           if is_vision_available()
           else None
       )






  @slow
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics'''	).to(
           SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_video()
       UpperCamelCase__										=      image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       # verify the logits
       UpperCamelCase__										=      torch.Size((1, 400)	)
       self.assertEqual(outputs.logits.shape   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor([0.3669, -0.0688, -0.2421]	).to(SCREAMING_SNAKE_CASE_	)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)






  @slow
  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       UpperCamelCase__										=      VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''	).to(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_video()
       UpperCamelCase__										=      image_processor(SCREAMING_SNAKE_CASE_   ,				return_tensors='''pt'''	).to(SCREAMING_SNAKE_CASE_	)

       # add boolean mask, indicating which patches to mask
       UpperCamelCase__										=      hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''   ,				filename='''bool_masked_pos.pt'''	)
       UpperCamelCase__										=      torch.load(SCREAMING_SNAKE_CASE_	)

       # forward pass
       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       # verify the logits
       UpperCamelCase__										=      torch.Size([1, 1408, 1536]	)
       UpperCamelCase__										=      torch.tensor(
           [[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]   ,				device=SCREAMING_SNAKE_CASE_	)
       self.assertEqual(outputs.logits.shape   ,				SCREAMING_SNAKE_CASE_	)
       self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       # verify the loss (`config.norm_pix_loss` = `True`)
       UpperCamelCase__										=      torch.tensor([0.5142]   ,				device=SCREAMING_SNAKE_CASE_	)
       self.assertTrue(torch.allclose(outputs.loss   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)

       # verify the loss (`config.norm_pix_loss` = `False`)
       UpperCamelCase__										=      VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''   ,				norm_pix_loss=SCREAMING_SNAKE_CASE_	).to(
           SCREAMING_SNAKE_CASE_	)

       with torch.no_grad():
            UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      torch.tensor(torch.tensor([0.6469]	)   ,				device=SCREAMING_SNAKE_CASE_	)
       self.assertTrue(torch.allclose(outputs.loss   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-4	)	)




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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


__magic_name__								=16
__magic_name__								=32







def  __UpperCamelCase    (						A ,							A = 16							):
     UpperCamelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     UpperCamelCase__										=      load_dataset('''glue''' ,							'''mrpc'''							)

     def tokenize_function(A							):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__										=      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():
          UpperCamelCase__										=      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
     UpperCamelCase__										=      tokenized_datasets.rename_column('''label''' ,							'''labels'''							)

     def collate_fn(A							):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCamelCase__										=      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":
               UpperCamelCase__										=      16
          elif accelerator.mixed_precision != "no":
               UpperCamelCase__										=      8
          else:
               UpperCamelCase__										=      None

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

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

     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

     __magic_name__								=mocked_dataloaders  # noqa: F811







def  __UpperCamelCase    (						A ,							A							):
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							A							) == "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'''							)

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

     set_seed(A							)
     UpperCamelCase__						,       UpperCamelCase__										=      get_dataloaders(A ,							A							)
     # 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=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).
     UpperCamelCase__										=      model.to(accelerator.device							)

     # Instantiate optimizer
     UpperCamelCase__										=      AdamW(params=model.parameters() ,							lr=A							)

     # Instantiate scheduler
     UpperCamelCase__										=      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.
     UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      accelerator.prepare(
         A ,							A ,							A ,							A ,							A							)

     # Now we train the model
     for epoch in range(A							):
          model.train()
          for step, batch in enumerate(A							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.loss
               UpperCamelCase__										=      loss / gradient_accumulation_steps
               accelerator.backward(A							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCamelCase__										=      0
          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():
                    UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.logits.argmax(dim=-1							)
               UpperCamelCase__						,       UpperCamelCase__										=      accelerator.gather((predictions, batch['''labels'''])							)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(A							) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         UpperCamelCase__										=      predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         UpperCamelCase__										=      references[: len(eval_dataloader.dataset							) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=A ,							references=A ,							)

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







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      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.'''							)
     UpperCamelCase__										=      parser.parse_args()
     UpperCamelCase__										=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A ,							A							)


if __name__ == "__main__":
     main()



