





"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    _lowerCAmelCase  (	_a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[int]						    =   UnCLIPImageVariationPipeline
       __magic_name__	:Dict						    =   IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
       __magic_name__	:Optional[Any]						    =   IMAGE_VARIATION_BATCH_PARAMS

       __magic_name__	:Tuple						    =   [
           """generator""",
           """return_dict""",
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]
       __magic_name__	:Optional[Any]						    =   False






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 3_2






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 3_2






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.time_input_dim






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return self.time_input_dim * 4






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return 1_0_0






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
         return tokenizer






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )
         lowerCAmelCase__     :List[str]											=				CLIPTextConfig(
             bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=self.text_embedder_hidden_size       ,					projection_dim=self.text_embedder_hidden_size       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					)
         return CLIPTextModelWithProjection(snake_case_ )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )
         lowerCAmelCase__     :Optional[Any]											=				CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size       ,					projection_dim=self.text_embedder_hidden_size       ,					num_hidden_layers=5       ,					num_attention_heads=4       ,					image_size=3_2       ,					intermediate_size=3_7       ,					patch_size=1       ,					)
         return CLIPVisionModelWithProjection(snake_case_ )






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )

         lowerCAmelCase__     :Optional[int]											=				{
             """clip_embeddings_dim""": self.text_embedder_hidden_size,
             """time_embed_dim""": self.time_embed_dim,
             """cross_attention_dim""": self.cross_attention_dim,
         }

         lowerCAmelCase__     :Optional[Any]											=				UnCLIPTextProjModel(**snake_case_ )
         return model






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )

         lowerCAmelCase__     :List[Any]											=				{
             """sample_size""": 3_2,
             # RGB in channels
             """in_channels""": 3,
             # Out channels is double in channels because predicts mean and variance
             """out_channels""": 6,
             """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
             """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
             """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
             """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
             """layers_per_block""": 1,
             """cross_attention_dim""": self.cross_attention_dim,
             """attention_head_dim""": 4,
             """resnet_time_scale_shift""": """scale_shift""",
             """class_embed_type""": """identity""",
         }

         lowerCAmelCase__     :Union[str, Any]											=				UNetaDConditionModel(**snake_case_ )
         return model






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )

         lowerCAmelCase__     :Optional[Any]											=				UNetaDModel(**self.dummy_super_res_kwargs )
         return model






       @property
       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(1 )

         lowerCAmelCase__     :List[str]											=				UNetaDModel(**self.dummy_super_res_kwargs )
         return model






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				self.dummy_decoder
         lowerCAmelCase__     :Optional[int]											=				self.dummy_text_proj
         lowerCAmelCase__     :Any											=				self.dummy_text_encoder
         lowerCAmelCase__     :List[Any]											=				self.dummy_tokenizer
         lowerCAmelCase__     :Dict											=				self.dummy_super_res_first
         lowerCAmelCase__     :List[Any]											=				self.dummy_super_res_last

         lowerCAmelCase__     :Tuple											=				UnCLIPScheduler(
             variance_type='learned_range'       ,					prediction_type='epsilon'       ,					num_train_timesteps=1_0_0_0       ,					)

         lowerCAmelCase__     :List[Any]											=				UnCLIPScheduler(
             variance_type='fixed_small_log'       ,					prediction_type='epsilon'       ,					num_train_timesteps=1_0_0_0       ,					)

         lowerCAmelCase__     :str											=				CLIPImageProcessor(crop_size=3_2       ,					size=3_2 )

         lowerCAmelCase__     :int											=				self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0       ,					__UpperCAmelCase=True ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(snake_case_ ) ).to(snake_case_ )
         if str(snake_case_ ).startswith('mps' ):
           lowerCAmelCase__     :Any											=				torch.manual_seed(snake_case_ )
         else:
           lowerCAmelCase__     :Dict											=				torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )

         if pil_image:
           lowerCAmelCase__     :Optional[Any]											=				input_image * 0.5 + 0.5
           lowerCAmelCase__     :List[Any]											=				input_image.clamp(0       ,					1 )
           lowerCAmelCase__     :Optional[int]											=				input_image.cpu().permute(0       ,					2       ,					3       ,					1 ).float().numpy()
           lowerCAmelCase__     :Dict											=				DiffusionPipeline.numpy_to_pil(snake_case_ )[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[Any]											=				"""cpu"""

         lowerCAmelCase__     :List[Any]											=				self.get_dummy_components()

         lowerCAmelCase__     :str											=				self.pipeline_class(**snake_case_ )
         lowerCAmelCase__     :List[str]											=				pipe.to(snake_case_ )

         pipe.set_progress_bar_config(disable=snake_case_ )

         lowerCAmelCase__     :Optional[Any]											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )

         lowerCAmelCase__     :Dict											=				pipe(**snake_case_ )
         lowerCAmelCase__     :List[str]											=				output.images

         lowerCAmelCase__     :str											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )

         lowerCAmelCase__     :Optional[Any]											=				pipe(
             **snake_case_       ,					return_dict=snake_case_       ,					)[0]

         lowerCAmelCase__     :List[Any]											=				image[0, -3:, -3:, -1]
         lowerCAmelCase__     :List[Any]											=				image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCAmelCase__     :List[str]											=				np.array(
             [
                 0.99_97,
                 0.00_02,
                 0.99_97,
                 0.99_97,
                 0.99_69,
                 0.00_23,
                 0.99_97,
                 0.99_69,
                 0.99_70,
             ] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				"""cpu"""

         lowerCAmelCase__     :Optional[int]											=				self.get_dummy_components()

         lowerCAmelCase__     :Tuple											=				self.pipeline_class(**snake_case_ )
         lowerCAmelCase__     :Any											=				pipe.to(snake_case_ )

         pipe.set_progress_bar_config(disable=snake_case_ )

         lowerCAmelCase__     :List[str]											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )

         lowerCAmelCase__     :Optional[Any]											=				pipe(**snake_case_ )
         lowerCAmelCase__     :List[Any]											=				output.images

         lowerCAmelCase__     :Optional[int]											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )

         lowerCAmelCase__     :Optional[Any]											=				pipe(
             **snake_case_       ,					return_dict=snake_case_       ,					)[0]

         lowerCAmelCase__     :List[str]											=				image[0, -3:, -3:, -1]
         lowerCAmelCase__     :Optional[int]											=				image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)

         lowerCAmelCase__     :Optional[Any]											=				np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				"""cpu"""

         lowerCAmelCase__     :List[str]											=				self.get_dummy_components()

         lowerCAmelCase__     :Dict											=				self.pipeline_class(**snake_case_ )
         lowerCAmelCase__     :int											=				pipe.to(snake_case_ )

         pipe.set_progress_bar_config(disable=snake_case_ )

         lowerCAmelCase__     :Any											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )
         lowerCAmelCase__     :str											=				[
             pipeline_inputs["""image"""],
             pipeline_inputs["""image"""],
         ]

         lowerCAmelCase__     :str											=				pipe(**snake_case_ )
         lowerCAmelCase__     :List[str]											=				output.images

         lowerCAmelCase__     :List[Any]											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )
         lowerCAmelCase__     :Any											=				[
             tuple_pipeline_inputs["""image"""],
             tuple_pipeline_inputs["""image"""],
         ]

         lowerCAmelCase__     :List[str]											=				pipe(
             **snake_case_       ,					return_dict=snake_case_       ,					)[0]

         lowerCAmelCase__     :Tuple											=				image[0, -3:, -3:, -1]
         lowerCAmelCase__     :Union[str, Any]											=				image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)

         lowerCAmelCase__     :Optional[int]											=				np.array(
             [
                 0.99_97,
                 0.99_89,
                 0.00_08,
                 0.00_21,
                 0.99_60,
                 0.00_18,
                 0.00_14,
                 0.00_02,
                 0.99_33,
             ] )

         assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				torch.device('cpu' )






         class    _lowerCAmelCase  :
                """simple docstring"""




                __magic_name__	:Union[str, Any]						    =   1






         lowerCAmelCase__     :Union[str, Any]											=				self.get_dummy_components()

         lowerCAmelCase__     :Union[str, Any]											=				self.pipeline_class(**snake_case_ )
         lowerCAmelCase__     :Tuple											=				pipe.to(snake_case_ )

         pipe.set_progress_bar_config(disable=snake_case_ )

         lowerCAmelCase__     :Optional[int]											=				torch.Generator(device=snake_case_ ).manual_seed(0 )
         lowerCAmelCase__     :Union[str, Any]											=				pipe.decoder.dtype
         lowerCAmelCase__     :List[str]											=				1

         lowerCAmelCase__     :str											=				(
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         lowerCAmelCase__     :List[Any]											=				pipe.prepare_latents(
             snake_case_       ,					dtype=snake_case_       ,					device=snake_case_       ,					generator=snake_case_       ,					latents=snake_case_       ,					scheduler=DummyScheduler() )

         lowerCAmelCase__     :List[str]											=				(
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         lowerCAmelCase__     :Union[str, Any]											=				pipe.prepare_latents(
             snake_case_       ,					dtype=snake_case_       ,					device=snake_case_       ,					generator=snake_case_       ,					latents=snake_case_       ,					scheduler=DummyScheduler() )

         lowerCAmelCase__     :Dict											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )

         lowerCAmelCase__     :Dict											=				pipe(
             **snake_case_       ,					decoder_latents=snake_case_       ,					super_res_latents=snake_case_ ).images

         lowerCAmelCase__     :int											=				self.get_dummy_inputs(snake_case_       ,					pil_image=snake_case_ )
         # Don't pass image, instead pass embedding
         lowerCAmelCase__     :Union[str, Any]											=				pipeline_inputs.pop('image' )
         lowerCAmelCase__     :List[Any]											=				pipe.image_encoder(snake_case_ ).image_embeds

         lowerCAmelCase__     :str											=				pipe(
             **snake_case_       ,					decoder_latents=snake_case_       ,					super_res_latents=snake_case_       ,					image_embeddings=snake_case_       ,					).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a ).max() < 1E-4






       @skip_mps
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				torch_device == """cpu"""

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         lowerCAmelCase__     :Any											=				1E-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=snake_case_       ,					expected_max_diff=snake_case_ )






       @skip_mps
       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :List[str]											=				torch_device == """cpu"""
         lowerCAmelCase__     :Any											=				True
         lowerCAmelCase__     :List[str]											=				[
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=snake_case_       ,					relax_max_difference=snake_case_       ,					additional_params_copy_to_batched_inputs=snake_case_       ,					)






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Dict											=				[
             """decoder_num_inference_steps""",
             """super_res_num_inference_steps""",
         ]

         if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           lowerCAmelCase__     :Any											=				[2, 3]
           self._test_inference_batch_consistent(
               batch_sizes=snake_case_       ,					additional_params_copy_to_batched_inputs=snake_case_       ,					)
         else:
           self._test_inference_batch_consistent(
               additional_params_copy_to_batched_inputs=snake_case_ )






       @skip_mps
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().test_save_load_local()






       @skip_mps
       def 	snake_case		(       self ):



         '''simple docstring'''

         return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Optional[Any]											=				load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png' )
         lowerCAmelCase__     :Dict											=				load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/unclip/karlo_v1_alpha_cat_variation_fp16.npy' )

         lowerCAmelCase__     :Optional[Any]											=				UnCLIPImageVariationPipeline.from_pretrained(
             'kakaobrain/karlo-v1-alpha-image-variations'       ,					torch_dtype=torch.floataa )
         lowerCAmelCase__     :Any											=				pipeline.to(snake_case_ )
         pipeline.set_progress_bar_config(disable=snake_case_ )

         lowerCAmelCase__     :Tuple											=				torch.Generator(device='cpu' ).manual_seed(0 )
         lowerCAmelCase__     :List[Any]											=				pipeline(
             snake_case_       ,					generator=snake_case_       ,					output_type='np'       ,					)

         lowerCAmelCase__     :Union[str, Any]											=				output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(snake_case_       ,					snake_case_       ,					1_5 )





'''simple docstring'''





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  			=						16
__a  			=						32





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase = 16						)						->					Optional[Any]:
      snake_case__    :       Optional[int]       		= AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      snake_case__    :       Optional[int]       		= load_dataset("""glue"""	,  """mrpc"""						)

      def tokenize_function(_lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            snake_case__    :       Union[str, Any]       		= tokenizer(examples["""sentence1"""]	,  examples["""sentence2"""]	,  truncation=_lowerCAmelCase	,  max_length=_lowerCAmelCase						)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            snake_case__    :       List[str]       		= datasets.map(
                _lowerCAmelCase	,  batched=_lowerCAmelCase	,  remove_columns=["""idx""", """sentence1""", """sentence2"""]	,  )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      snake_case__    :       Any       		= tokenized_datasets.rename_column("""label"""	,  """labels"""						)

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

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

      # Instantiate dataloaders.
      snake_case__    :       List[Any]       		= DataLoader(
          tokenized_datasets["""train"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)
      snake_case__    :       Dict       		= DataLoader(
          tokenized_datasets["""validation"""]	,  shuffle=_lowerCAmelCase	,  collate_fn=_lowerCAmelCase	,  batch_size=_lowerCAmelCase						)

      return train_dataloader, eval_dataloader


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

       __a  			=						mocked_dataloaders  # noqa: F811





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,  _lowerCAmelCase						) == "1":
            snake_case__    :       int       		= 2
      # New Code #
      snake_case__    :       Any       		= int(args.gradient_accumulation_steps						)
      # Initialize accelerator
      snake_case__    :       Any       		= Accelerator(
          cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  gradient_accumulation_steps=_lowerCAmelCase						)
      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
      snake_case__    :       List[Any]       		= config["""lr"""]
      snake_case__    :       Optional[Any]       		= int(config["""num_epochs"""]						)
      snake_case__    :       Union[str, Any]       		= int(config["""seed"""]						)
      snake_case__    :       List[str]       		= int(config["""batch_size"""]						)

      snake_case__    :       Union[str, Any]       		= evaluate.load("""glue"""	,  """mrpc"""						)

      set_seed(_lowerCAmelCase						)
      snake_case__		,							snake_case__    :       Tuple       		= get_dataloaders(_lowerCAmelCase	,  _lowerCAmelCase						)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      snake_case__    :       Optional[int]       		= AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,  return_dict=_lowerCAmelCase						)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      snake_case__    :       Tuple       		= model.to(accelerator.device						)

      # Instantiate optimizer
      snake_case__    :       Any       		= AdamW(params=model.parameters()	,  lr=_lowerCAmelCase						)

      # Instantiate scheduler
      snake_case__    :       List[Any]       		= get_linear_schedule_with_warmup(
          optimizer=_lowerCAmelCase	,  num_warmup_steps=100	,  num_training_steps=(len(_lowerCAmelCase						) * num_epochs)	,  )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      snake_case__		,							snake_case__		,							snake_case__		,							snake_case__		,							snake_case__    :       List[Any]       		= accelerator.prepare(
          _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)

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

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

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





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       List[str]       		= argparse.ArgumentParser(description="""Simple example of training script."""						)
      parser.add_argument(
          """--mixed_precision"""	,  type=_lowerCAmelCase	,  default=_lowerCAmelCase	,  choices=["""no""", """fp16""", """bf16""", """fp8"""]	,  help="""Whether to use mixed precision. Choose"""
          """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
          """and an Nvidia Ampere GPU."""	,  )
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,  type=_lowerCAmelCase	,  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."""						)
      snake_case__    :       Tuple       		= parser.parse_args()
      snake_case__    :       Dict       		= {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(_lowerCAmelCase	,  _lowerCAmelCase						)


if __name__ == "__main__":
       main()
