




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
       from transformers import (
           FlaxBertModel,
           FlaxCLIPVisionModel,
           FlaxVisionTextDualEncoderModel,
           FlaxViTModel,
           VisionTextDualEncoderConfig,
           VisionTextDualEncoderProcessor,
       )
       from transformers.modeling_flax_pytorch_utils import (
           convert_pytorch_state_dict_to_flax,
           load_flax_weights_in_pytorch_model,
       )


if is_torch_available():
       import torch

       from transformers import VisionTextDualEncoderModel

if is_vision_available():
       from PIL import Image

def      A      (      _SCREAMING_SNAKE_CASE    )	-> List[Any]:
       if isinstance(_SCREAMING_SNAKE_CASE ,collections.abc.Iterable    ):
              return x
       return (x, x)




@require_flax
class 		UpperCamelCase__	:

    '''simple docstring'''



    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           pass







    def     _lowercase     (    self     )     ->      Any:
           pass







    def     _lowercase     (    self     )     ->      Optional[Any]:
           pass







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Union[str, Any]			 =					np.abs((a - b)     ).max()
           self.assertLessEqual(UpperCamelCase__     ,  UpperCamelCase__     ,  F'''Difference between torch and flax is {diff} (>= {tol}).'''     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Tuple			 =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      int			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					model(input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     )

           self.assertEqual(output["text_embeds"].shape     ,  (input_ids.shape[0], config.projection_dim)     )
           self.assertEqual(output["image_embeds"].shape     ,  (pixel_values.shape[0], config.projection_dim)     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					model(input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     )

           self.assertEqual(output["text_embeds"].shape     ,  (input_ids.shape[0], model.config.projection_dim)     )
           self.assertEqual(output["image_embeds"].shape     ,  (pixel_values.shape[0], model.config.projection_dim)     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      str			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Union[str, Any]			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					model(input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     )
           lowerCamelCase   :      int			 =					output[0]

           with tempfile.TemporaryDirectory() as tmpdirname:
                  model.save_pretrained(UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     )

                  lowerCamelCase   :      int			 =					model(input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     )
                  lowerCamelCase   :      int			 =					after_output[0]
                  lowerCamelCase   :      Optional[Any]			 =					np.amax(np.abs(out_a - out_a     )     )
                  self.assertLessEqual(UpperCamelCase__     ,  1e-3     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.get_vision_text_model(UpperCamelCase__     ,  UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					{"vision_model": vision_model, "text_model": text_model}
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**UpperCamelCase__     )

           lowerCamelCase   :      Union[str, Any]			 =					model(
               input_ids=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     ,  output_attentions=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					output.vision_model_output.attentions
           self.assertEqual(len(UpperCamelCase__     )     ,  vision_config.num_hidden_layers     )

           # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowerCamelCase   :      Tuple			 =					to_atuple(vision_model.config.image_size     )
           lowerCamelCase   :      str			 =					to_atuple(vision_model.config.patch_size     )
           lowerCamelCase   :      List[Any]			 =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
           lowerCamelCase   :      Union[str, Any]			 =					num_patches + 1
           self.assertEqual(vision_attentions[0].shape[-3:]     ,  (vision_config.num_attention_heads, seq_len, seq_len)     )

           lowerCamelCase   :      Optional[Any]			 =					output.text_model_output.attentions
           self.assertEqual(len(UpperCamelCase__     )     ,  text_config.num_hidden_layers     )

           self.assertEqual(
               text_attentions[0].shape[-3:]     ,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           pt_model.to(UpperCamelCase__     )
           pt_model.eval()

           # prepare inputs
           lowerCamelCase   :      int			 =					inputs_dict
           lowerCamelCase   :      Any			 =					{k: torch.tensor(v.tolist()     ) for k, v in flax_inputs.items()}

           with torch.no_grad():
                  lowerCamelCase   :      int			 =					pt_model(**UpperCamelCase__     ).to_tuple()

           lowerCamelCase   :      Any			 =					fx_model(**UpperCamelCase__     ).to_tuple()
           self.assertEqual(len(UpperCamelCase__     )     ,  len(UpperCamelCase__     )     ,  "Output lengths differ between Flax and PyTorch"     )
           for fx_output, pt_output in zip(fx_outputs[:4]     ,  pt_outputs[:4]     ):
                  self.assert_almost_equals(UpperCamelCase__     ,  pt_output.numpy()     ,  4e-2     )

           # PT -> Flax
           with tempfile.TemporaryDirectory() as tmpdirname:
                  pt_model.save_pretrained(UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     ,  from_pt=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					fx_model_loaded(**UpperCamelCase__     ).to_tuple()
           self.assertEqual(len(UpperCamelCase__     )     ,  len(UpperCamelCase__     )     ,  "Output lengths differ between Flax and PyTorch"     )
           for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]     ,  pt_outputs[:4]     ):
                  self.assert_almost_equals(UpperCamelCase__     ,  pt_output.numpy()     ,  4e-2     )

           # Flax -> PT
           with tempfile.TemporaryDirectory() as tmpdirname:
                  fx_model.save_pretrained(UpperCamelCase__     )
                  lowerCamelCase   :      Any			 =					VisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     ,  from_flax=UpperCamelCase__     )

           pt_model_loaded.to(UpperCamelCase__     )
           pt_model_loaded.eval()

           with torch.no_grad():
                  lowerCamelCase   :      List[str]			 =					pt_model_loaded(**UpperCamelCase__     ).to_tuple()

           self.assertEqual(len(UpperCamelCase__     )     ,  len(UpperCamelCase__     )     ,  "Output lengths differ between Flax and PyTorch"     )
           for fx_output, pt_output_loaded in zip(fx_outputs[:4]     ,  pt_outputs_loaded[:4]     ):
                  self.assert_almost_equals(UpperCamelCase__     ,  pt_output_loaded.numpy()     ,  4e-2     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      Optional[Any]			 =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      str			 =					VisionTextDualEncoderModel(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      Optional[int]			 =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[Any]			 =					fx_state

           self.check_pt_flax_equivalence(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      Any			 =					VisionTextDualEncoderConfig.from_vision_text_configs(UpperCamelCase__     ,  UpperCamelCase__     )

           lowerCamelCase   :      int			 =					VisionTextDualEncoderModel(UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					FlaxVisionTextDualEncoderModel(UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					load_flax_weights_in_pytorch_model(UpperCamelCase__     ,  fx_model.params     )

           self.check_pt_flax_equivalence(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[Any]			 =					self.prepare_config_and_inputs()
           self.check_model_from_pretrained_configs(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Any			 =					self.prepare_config_and_inputs()
           self.check_vision_text_dual_encoder_from_pretrained(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      List[str]			 =					self.prepare_config_and_inputs()
           self.check_save_load(**UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Any			 =					self.prepare_config_and_inputs()
           self.check_vision_text_output_attention(**UpperCamelCase__     )







    @is_pt_flax_cross_test
    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      int			 =					self.prepare_config_and_inputs()
           lowerCamelCase   :      Union[str, Any]			 =					config_inputs_dict.pop("vision_config"     )
           lowerCamelCase   :      Tuple			 =					config_inputs_dict.pop("text_config"     )

           lowerCamelCase   :      Dict			 =					config_inputs_dict

           self.check_equivalence_pt_to_flax(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )
           self.check_equivalence_flax_to_pt(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase ,      lowerCamelCase   :      List[Any]			 =					self.get_pretrained_model_and_inputs()

           lowerCamelCase   :      Any			 =					model_a(**UpperCamelCase__     )
           lowerCamelCase   :      Any			 =					outputs[0]

           with tempfile.TemporaryDirectory() as tmp_dirname:
                  model_a.save_pretrained(UpperCamelCase__     )
                  lowerCamelCase   :      Optional[int]			 =					FlaxVisionTextDualEncoderModel.from_pretrained(UpperCamelCase__     )

                  lowerCamelCase   :      int			 =					model_a(**UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					after_outputs[0]
                  lowerCamelCase   :      Tuple			 =					np.amax(np.abs(out_a - out_a     )     )
                  self.assertLessEqual(UpperCamelCase__     ,  1e-5     )




@require_flax
class 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      List[Any]			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               "hf-internal-testing/tiny-random-vit"     ,  "hf-internal-testing/tiny-bert"     ,  vision_from_pt=UpperCamelCase__     ,  text_from_pt=UpperCamelCase__     ,  )
           lowerCamelCase   :      str			 =					13
           lowerCamelCase   :      List[str]			 =					floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]     )
           lowerCamelCase   :      Dict			 =					ids_tensor([batch_size, 4]     ,  model.config.text_config.vocab_size     )
           lowerCamelCase   :      Dict			 =					random_attention_mask([batch_size, 4]     )
           lowerCamelCase   :      Optional[int]			 =					{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

           return model, inputs







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase   :      Dict			 =					FlaxViTModel(UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					FlaxBertModel(UpperCamelCase__     )
           return vision_model, text_model







    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Optional[int]			 =					FlaxViTModelTester(self     )
           lowerCamelCase   :      Any			 =					FlaxBertModelTester(self     )
           lowerCamelCase   :      Union[str, Any]			 =					vit_model_tester.prepare_config_and_inputs()
           lowerCamelCase   :      str			 =					bert_model_tester.prepare_config_and_inputs()

           lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					vision_config_and_inputs

           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }




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

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
               "hf-internal-testing/tiny-random-clip"     ,  "hf-internal-testing/tiny-bert"     ,  vision_from_pt=UpperCamelCase__     ,  text_from_pt=UpperCamelCase__     ,  )
           lowerCamelCase   :      Optional[int]			 =					13
           lowerCamelCase   :      List[str]			 =					floats_tensor(
               [
                   batch_size,
                   model.config.vision_config.num_channels,
                   model.config.vision_config.image_size,
                   model.config.vision_config.image_size,
               ]     )
           lowerCamelCase   :      Tuple			 =					ids_tensor([batch_size, 4]     ,  model.config.text_config.vocab_size     )
           lowerCamelCase   :      Union[str, Any]			 =					random_attention_mask([batch_size, 4]     )
           lowerCamelCase   :      Optional[int]			 =					{"pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask}

           return model, inputs







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase   :      List[Any]			 =					FlaxCLIPVisionModel(UpperCamelCase__     )
           lowerCamelCase   :      Dict			 =					FlaxBertModel(UpperCamelCase__     )
           return vision_model, text_model







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Optional[Any]			 =					FlaxCLIPVisionModelTester(self     )
           lowerCamelCase   :      Optional[int]			 =					FlaxBertModelTester(self     )
           lowerCamelCase   :      List[Any]			 =					clip_model_tester.prepare_config_and_inputs()
           lowerCamelCase   :      int			 =					bert_model_tester.prepare_config_and_inputs()

           lowerCamelCase ,      lowerCamelCase   :      Any			 =					vision_config_and_inputs

           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      int			 =					text_config_and_inputs

           # make sure that cross attention layers are added
           return {
               "text_config": text_config,
               "vision_config": vision_config,
               "pixel_values": pixel_values,
               "attention_mask": attention_mask,
               "input_ids": input_ids,
               "token_type_ids": token_type_ids,
           }




@require_flax
@require_vision
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Tuple			 =					FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"     ,  logit_scale_init_value=1.0     )
           lowerCamelCase   :      Optional[int]			 =					VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"     )

           lowerCamelCase   :      Optional[Any]			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
           lowerCamelCase   :      Optional[int]			 =					processor(
               text=["una foto di un gatto", "una foto di un cane"]     ,  images=UpperCamelCase__     ,  padding=UpperCamelCase__     ,  return_tensors="np"     )

           lowerCamelCase   :      Dict			 =					model(**UpperCamelCase__     )

           # verify the logits
           self.assertEqual(outputs.logits_per_image.shape     ,  (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])     )
           self.assertEqual(
               outputs.logits_per_text.shape     ,  (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])     ,  )

           lowerCamelCase   :      Dict			 =					np.array([[1.2284727, 0.3104122]]     )

           self.assertTrue(np.allclose(outputs.logits_per_image     ,  UpperCamelCase__     ,  atol=1e-3     )     )







import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowerCamelCase			: Any							   =		16
__lowerCamelCase			: List[Any]							   =		32



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int = 16 ,     __UpperCamelCase    :       str = "bert-base-cased"	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      load_dataset("""glue""" ,     """mrpc"""	)

  def tokenize_function(__UpperCamelCase    :       Optional[Any]	):
    # 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
  SCREAMING_SNAKE_CASE__										=      datasets.map(
      __UpperCamelCase ,     batched=__UpperCamelCase ,     remove_columns=["""idx""", """sentence1""", """sentence2"""] ,     load_from_cache_file=__UpperCamelCase	)

  # 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    :       List[Any]	):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__UpperCamelCase ,     padding="""max_length""" ,     max_length=1_28 ,     return_tensors="""pt"""	)
    return tokenizer.pad(__UpperCamelCase ,     padding="""longest""" ,     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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__										=      0
  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	)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.gather(
        (predictions, batch["""labels"""])	)  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(__UpperCamelCase	) - 1:
        SCREAMING_SNAKE_CASE__										=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
        SCREAMING_SNAKE_CASE__										=      references[: len(eval_dataloader.dataset	) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase ,     references=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      metric.compute()
  return eval_metric["accuracy"]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator()

  # 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__										=      args.model_name_or_path

  set_seed(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_dataloaders(__UpperCamelCase ,     __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(__UpperCamelCase ,     return_dict=__UpperCamelCase	)

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__										=      (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE__										=      optimizer_cls(params=model.parameters() ,     lr=__UpperCamelCase	)

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__										=      accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      (len(__UpperCamelCase	) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    SCREAMING_SNAKE_CASE__										=      get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase ,     num_warmup_steps=0 ,     num_training_steps=__UpperCamelCase ,     )
  else:
    SCREAMING_SNAKE_CASE__										=      DummyScheduler(__UpperCamelCase ,     total_num_steps=__UpperCamelCase ,     warmup_num_steps=0	)

  # 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	)

  # We need to keep track of how many total steps we have iterated over
  SCREAMING_SNAKE_CASE__										=      0
  # We also need to keep track of the stating epoch so files are named properly
  SCREAMING_SNAKE_CASE__										=      0
  SCREAMING_SNAKE_CASE__										=      evaluate.load("""glue""" ,     """mrpc"""	)
  SCREAMING_SNAKE_CASE__										=      num_epochs

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__										=      args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint	)
    SCREAMING_SNAKE_CASE__										=      args.resume_from_checkpoint.split("""epoch_"""	)[1]
    SCREAMING_SNAKE_CASE__										=      """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__										=      int(__UpperCamelCase	) + 1
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint performance:""" ,     __UpperCamelCase	)
    accelerator.print("""resumed checkpoint's scheduler's lr:""" ,     lr_scheduler.get_lr()[0]	)
    accelerator.print("""resumed optimizers's lr:""" ,     optimizer.param_groups[0]["""lr"""]	)
    with open(os.path.join(args.output_dir ,     f"""state_{starting_epoch-1}.json"""	) ,     """r"""	) as f:
      SCREAMING_SNAKE_CASE__										=      json.load(__UpperCamelCase	)
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  SCREAMING_SNAKE_CASE__										=      {}
  for epoch in range(__UpperCamelCase ,     __UpperCamelCase	):
    model.train()
    for step, batch in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      outputs.loss
      SCREAMING_SNAKE_CASE__										=      loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__										=      f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__										=      os.path.join(args.output_dir ,     __UpperCamelCase	)
    accelerator.save_state(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      accuracy
    SCREAMING_SNAKE_CASE__										=      lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__										=      optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__										=      epoch
    SCREAMING_SNAKE_CASE__										=      overall_step
    accelerator.print(f"""epoch {epoch}:""" ,     __UpperCamelCase	)

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir ,     f"""state_{epoch}.json"""	) ,     """w"""	) as f:
        json.dump(__UpperCamelCase ,     __UpperCamelCase	)



def 	__SCREAMING_SNAKE_CASE  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""	)
  parser.add_argument(
      """--model_name_or_path""" ,     type=__UpperCamelCase ,     default="""bert-base-cased""" ,     help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,     required=__UpperCamelCase ,     )
  parser.add_argument(
      """--output_dir""" ,     type=__UpperCamelCase ,     default=""".""" ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" ,     )
  parser.add_argument(
      """--resume_from_checkpoint""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If the training should continue from a checkpoint folder.""" ,     )
  parser.add_argument(
      """--partial_train_epoch""" ,     type=__UpperCamelCase ,     default=__UpperCamelCase ,     help="""If passed, the training will stop after this number of epochs.""" ,     )
  parser.add_argument(
      """--num_epochs""" ,     type=__UpperCamelCase ,     default=2 ,     help="""Number of train epochs.""" ,     )
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  SCREAMING_SNAKE_CASE__										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(__UpperCamelCase ,     __UpperCamelCase	)


if __name__ == "__main__":
   main()
