


import unittest

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

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class   __a			(    lowerCAmelCase_       ,       unittest.TestCase      ):
 __lowercase :							Dict        = KandinskyVaaPriorPipeline
 __lowercase :							List[Any]        = ['prompt']
 __lowercase :							Optional[Any]        = ['prompt', 'negative_prompt']
 __lowercase :							Union[str, Any]        = [
     'num_images_per_prompt',
     'generator',
     'num_inference_steps',
     'latents',
     'negative_prompt',
     'guidance_scale',
     'output_type',
     'return_dict',
 ]
 __lowercase :							Union[str, Any]        = False
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        return 32
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        return self.time_input_dim
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        return self.time_input_dim * 4
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return 100
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
        return tokenizer
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        torch.manual_seed(0 )
        lowercase__:   Union[str, Any]  			=   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=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1_000				, )
        return CLIPTextModelWithProjection(__lowerCAmelCase )
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        torch.manual_seed(0 )

        lowercase__:   Any  			=   {
            'num_attention_heads': 2,
            'attention_head_dim': 12,
            'embedding_dim': self.text_embedder_hidden_size,
            'num_layers': 1,
        }

        lowercase__:   Optional[int]  			=   PriorTransformer(**__lowerCAmelCase )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        lowercase__:   List[Any]  			=   nn.Parameter(torch.ones(model.clip_std.shape ) )
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        torch.manual_seed(0 )
        lowercase__:   List[str]  			=   CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size				, image_size=224				, projection_dim=self.text_embedder_hidden_size				, intermediate_size=37				, num_attention_heads=4				, num_channels=3				, num_hidden_layers=5				, patch_size=14				, )

        lowercase__:   Optional[int]  			=   CLIPVisionModelWithProjection(__lowerCAmelCase )
        return model
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   CLIPImageProcessor(
            crop_size=224				, do_center_crop=__lowerCAmelCase				, do_normalize=__lowerCAmelCase				, do_resize=__lowerCAmelCase				, image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				, image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				, resample=3				, size=224				, )

        return image_processor
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.dummy_prior
        lowercase__:   Any  			=   self.dummy_image_encoder
        lowercase__:   Union[str, Any]  			=   self.dummy_text_encoder
        lowercase__:   Tuple  			=   self.dummy_tokenizer
        lowercase__:   int  			=   self.dummy_image_processor

        lowercase__:   int  			=   UnCLIPScheduler(
            variance_type='fixed_small_log'				, prediction_type='sample'				, num_train_timesteps=1_000				, clip_sample=__lowerCAmelCase				, clip_sample_range=1_0.0				, )

        lowercase__:   Union[str, Any]  			=   {
            'prior': prior,
            'image_encoder': image_encoder,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'scheduler': scheduler,
            'image_processor': image_processor,
        }

        return components
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=0 )					->				Optional[Any]:




        '''simple docstring'''
        if str(__lowerCAmelCase ).startswith('mps' ):
               lowercase__:   List[str]  			=   torch.manual_seed(__lowerCAmelCase )
        else:
               lowercase__:   Optional[Any]  			=   torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__:   Any  			=   {
            'prompt': 'horse',
            'generator': generator,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   'cpu'

        lowercase__:   Any  			=   self.get_dummy_components()

        lowercase__:   Optional[Any]  			=   self.pipeline_class(**__lowerCAmelCase )
        lowercase__:   Union[str, Any]  			=   pipe.to(__lowerCAmelCase )

        pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__:   Optional[int]  			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase ) )
        lowercase__:   int  			=   output.image_embeds

        lowercase__:   Optional[Any]  			=   pipe(
            **self.get_dummy_inputs(__lowerCAmelCase )				, return_dict=__lowerCAmelCase				, )[0]

        lowercase__:   List[str]  			=   image[0, -10:]
        lowercase__:   List[Any]  			=   image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        lowercase__:   int  			=   np.array(
            [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   torch_device == 'cpu'
        lowercase__:   str  			=   True
        lowercase__:   Tuple  			=   False

        self._test_inference_batch_single_identical(
            test_max_difference=__lowerCAmelCase				, relax_max_difference=__lowerCAmelCase				, test_mean_pixel_difference=__lowerCAmelCase				, )
 @skip_mps
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   int  			=   torch_device == 'cpu'
        lowercase__:   Optional[Any]  			=   False

        self._test_attention_slicing_forward_pass(
            test_max_difference=__lowerCAmelCase				, test_mean_pixel_difference=__lowerCAmelCase				, )



"""simple docstring"""


import argparse
import os

# New Code #
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


UpperCAmelCase__     						=  1_6
UpperCAmelCase__     						=  3_2






def 			__UpperCAmelCase    (	lowercase      ,lowercase = 16						):







  """simple docstring"""
  _UpperCAmelCase         =					AutoTokenizer.from_pretrained("""bert-base-cased"""						)
  _UpperCAmelCase         =					load_dataset("""glue"""      ,"""mrpc"""						)

  def tokenize_function(lowercase						):
    # max_length=None => use the model max length (it's actually the default)
    _UpperCAmelCase         =					tokenizer(examples["""sentence1"""]      ,examples["""sentence2"""]      ,truncation=lowercase      ,max_length=lowercase						)
    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(
        lowercase      ,batched=lowercase      ,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(lowercase						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _UpperCAmelCase         =					1_28 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(
        lowercase      ,padding="""longest"""      ,max_length=lowercase      ,pad_to_multiple_of=lowercase      ,return_tensors="""pt"""      ,)

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

  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

    UpperCAmelCase__     						=  mocked_dataloaders  # noqa: F811






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  # For testing only
  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,lowercase						) == "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"""						)

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=lowercase						)
  def inner_training_loop(lowercase						):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(lowercase						)

    # 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=lowercase						)

    # 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=lowercase						)
    _UpperCAmelCase       , _UpperCAmelCase         =					get_dataloaders(lowercase      ,lowercase						)

    # Instantiate scheduler
    _UpperCAmelCase         =					get_linear_schedule_with_warmup(
        optimizer=lowercase      ,num_warmup_steps=1_00      ,num_training_steps=(len(lowercase						) * 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(
        lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						)

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

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

      _UpperCAmelCase         =					metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''      ,lowercase						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					argparse.ArgumentParser(description="""Simple example of training script."""						)
  parser.add_argument(
      """--mixed_precision"""      ,type=lowercase      ,default=lowercase      ,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(lowercase      ,lowercase						)


if __name__ == "__main__":
    main()
