





"""simple docstring"""






import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  DownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ResnetDownsampleBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnDownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  CrossAttnDownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  SimpleCrossAttnDownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_encoder_hidden_states=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       @unittest.skipIf(torch_device == 'mps'    ,  'MPS result is not consistent'     )
       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  SkipDownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_skip_sample=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnSkipDownBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_skip_sample=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  DownEncoderBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_temb=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'in_channels': 32,
                  'out_channels': 32,
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnDownEncoderBlockaD  # noqa F405
       lowerCAmelCase__						 =  """down"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_temb=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'in_channels': 32,
                  'out_channels': 32,
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetMidBlockaD  # noqa F405
       lowerCAmelCase__						 =  """mid"""






       def 							__A (   self     ):
              _UpperCAmelCase     =     {
                  'in_channels': 32,
                  'temb_channels': 1_28,
              }
              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetMidBlockaDCrossAttn  # noqa F405
       lowerCAmelCase__						 =  """mid"""






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UNetMidBlockaDSimpleCrossAttn  # noqa F405
       lowerCAmelCase__						 =  """mid"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_encoder_hidden_states=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  ResnetUpsampleBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  CrossAttnUpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  SimpleCrossAttnUpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__    ,  include_encoder_hidden_states=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     super().prepare_init_args_and_inputs_for_common()
              _UpperCAmelCase     =     32
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnUpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       @unittest.skipIf(torch_device == 'mps'    ,  'MPS result is not consistent'     )
       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  SkipUpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnSkipUpBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_res_hidden_states_tuple=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  UpDecoderBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_temb=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {'in_channels': 32, 'out_channels': 32}

              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
              super().test_output(a__     )



class 	lowerCAmelCase					(      snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  AttnUpDecoderBlockaD  # noqa F405
       lowerCAmelCase__						 =  """up"""






       @property
       def 							__A (   self     ):
              return super().get_dummy_input(include_temb=a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     {'in_channels': 32, 'out_channels': 32}

              _UpperCAmelCase     =     self.dummy_input
              return init_dict, inputs_dict






       def 							__A (   self     ):
              _UpperCAmelCase     =     [0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
              super().test_output(a__     )













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


lowerCAmelCase_			=							16
lowerCAmelCase_			=							32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = 16							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     AutoTokenizer.from_pretrained('bert-base-cased'							)
       _UpperCAmelCase     =     load_dataset('glue','mrpc'							)

       def tokenize_function(SCREAMING_SNAKE_CASE							):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase     =     tokenizer(examples['sentence1'],examples['sentence2'],truncation=SCREAMING_SNAKE_CASE,max_length=SCREAMING_SNAKE_CASE							)
              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(
                  SCREAMING_SNAKE_CASE,batched=SCREAMING_SNAKE_CASE,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(SCREAMING_SNAKE_CASE							):
              # 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(
                  SCREAMING_SNAKE_CASE,padding='longest',max_length=SCREAMING_SNAKE_CASE,pad_to_multiple_of=SCREAMING_SNAKE_CASE,return_tensors='pt',)

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

       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

    lowerCAmelCase_			=							mocked_dataloaders  # noqa: F811

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       if os.environ.get('TESTING_MOCKED_DATALOADERS',SCREAMING_SNAKE_CASE							) == "1":
              _UpperCAmelCase     =     2
       # New Code #
       _UpperCAmelCase     =     int(args.gradient_accumulation_steps							)
       # Initialize accelerator
       _UpperCAmelCase     =     Accelerator(
           cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
       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
       _UpperCAmelCase     =     config['lr']
       _UpperCAmelCase     =     int(config['num_epochs']							)
       _UpperCAmelCase     =     int(config['seed']							)
       _UpperCAmelCase     =     int(config['batch_size']							)

       _UpperCAmelCase     =     evaluate.load('glue','mrpc'							)

       set_seed(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     get_dataloaders(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       # 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=SCREAMING_SNAKE_CASE							)

       # 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=SCREAMING_SNAKE_CASE							)

       # Instantiate scheduler
       _UpperCAmelCase     =     get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE,num_warmup_steps=100,num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs),)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     accelerator.prepare(
           SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE							):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                     # 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(SCREAMING_SNAKE_CASE							):
                            _UpperCAmelCase     =     model(**SCREAMING_SNAKE_CASE							)
                            _UpperCAmelCase     =     output.loss
                            accelerator.backward(SCREAMING_SNAKE_CASE							)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              _UpperCAmelCase     =     metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F"""epoch {epoch}:""",SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




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


if __name__ == "__main__":
    main()






