


import argparse

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
#
# 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
#
########################################################################


SCREAMING_SNAKE_CASE       :      Optional[int]												= 16
SCREAMING_SNAKE_CASE       :      List[Any]												= 32



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ = 16 )    ->						Optional[Any]:
    _lowercase       :							Optional[Any]      		=		AutoTokenizer.from_pretrained('bert-base-cased' )
    _lowercase       :							Any      		=		load_dataset('glue'							, 'mrpc' )

    def tokenize_function(lowerCamelCase_ ):
        # max_length=None => use the model max length (it's actually the default)
        _lowercase       :							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():
        _lowercase       :							Union[str, Any]      		=		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
    _lowercase       :							Tuple      		=		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.
        _lowercase       :							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":
            _lowercase       :							Union[str, Any]      		=		16
        elif accelerator.mixed_precision != "no":
            _lowercase       :							Any      		=		8
        else:
            _lowercase       :							List[Any]      		=		None

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

    # Instantiate dataloaders.
    _lowercase       :							str      		=		DataLoader(
        tokenized_datasets['train']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_							, drop_last=lowerCamelCase_ )
    _lowercase       :							str      		=		DataLoader(
        tokenized_datasets['validation']							, shuffle=lowerCamelCase_							, collate_fn=lowerCamelCase_							, batch_size=lowerCamelCase_							, drop_last=(accelerator.mixed_precision == 'fp8')							, )

    return train_dataloader, eval_dataloader



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    # Initialize accelerator
    _lowercase       :							Union[str, Any]      		=		Accelerator(cpu=args.cpu							, mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase       :							Tuple      		=		config['lr']
    _lowercase       :							Any      		=		int(config['num_epochs'] )
    _lowercase       :							Optional[int]      		=		int(config['seed'] )
    _lowercase       :							List[str]      		=		int(config['batch_size'] )

    _lowercase       :							List[Any]      		=		evaluate.load('glue'							, 'mrpc' )

    # If the batch size is too big we use gradient accumulation
    _lowercase       :							Any      		=		1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        _lowercase       :							List[str]      		=		batch_size // MAX_GPU_BATCH_SIZE
        _lowercase       :							Any      		=		MAX_GPU_BATCH_SIZE

    set_seed(lowerCamelCase_ )
    _lowercase		,				_lowercase       :							Optional[int]      		=		get_dataloaders(lowerCamelCase_							, lowerCamelCase_ )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase       :							List[Any]      		=		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).
    _lowercase       :							Optional[Any]      		=		model.to(accelerator.device )
    # Instantiate optimizer
    _lowercase       :							str      		=		AdamW(params=model.parameters()							, lr=lowerCamelCase_ )

    # Instantiate scheduler
    _lowercase       :							Optional[Any]      		=		get_linear_schedule_with_warmup(
        optimizer=lowerCamelCase_							, num_warmup_steps=100							, num_training_steps=(len(lowerCamelCase_ ) * num_epochs) // gradient_accumulation_steps							, )

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

    _lowercase		,				_lowercase		,				_lowercase		,				_lowercase		,				_lowercase       :							int      		=		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 )
            _lowercase       :							str      		=		model(**lowerCamelCase_ )
            _lowercase       :							Tuple      		=		outputs.loss
            _lowercase       :							str      		=		loss / gradient_accumulation_steps
            accelerator.backward(lowerCamelCase_ )
            if step % gradient_accumulation_steps == 0:
                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():
                _lowercase       :							List[str]      		=		model(**lowerCamelCase_ )
            _lowercase       :							Dict      		=		outputs.logits.argmax(dim=-1 )
            _lowercase		,				_lowercase       :							str      		=		accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=lowerCamelCase_							, references=lowerCamelCase_							, )

        _lowercase       :							Any      		=		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'''epoch {epoch}:'''							, lowerCamelCase_ )



def 				UpperCamelCase_(      )    ->						Tuple:
    _lowercase       :							List[Any]      		=		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.'							, )
    parser.add_argument('--cpu'							, action='store_true'							, help='If passed, will train on the CPU.' )
    _lowercase       :							Optional[int]      		=		parser.parse_args()
    _lowercase       :							Dict      		=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCamelCase_							, lowerCamelCase_ )


if __name__ == "__main__":
 main()









import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

@skip_mps
class 		UpperCamelCase__ (      lowerCAmelCase_							,      unittest.TestCase   ):



     '''simple docstring'''

     __snake_case    :	List[str]			=			TextToVideoSDPipeline
     __snake_case    :	int			=			TEXT_TO_IMAGE_PARAMS
     __snake_case    :	Tuple			=			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     __snake_case    :	Dict			=			frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]   )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDConditionModel(
               block_out_channels=(32, 64, 64, 64)						,layers_per_block=2						,sample_size=32						,in_channels=4						,out_channels=4						,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")						,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")						,cross_attention_dim=32						,attention_head_dim=4						,)
           SCREAMING_SNAKE_CASE		=						DDIMScheduler(
               beta_start=0.00085						,beta_end=0.012						,beta_schedule="""scaled_linear"""						,clip_sample=lowerCamelCase__						,set_alpha_to_one=lowerCamelCase__						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						AutoencoderKL(
               block_out_channels=[32, 64]						,in_channels=3						,out_channels=3						,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,latent_channels=4						,sample_size=128						,)
           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						CLIPTextConfig(
               bos_token_id=0						,eos_token_id=2						,hidden_size=32						,intermediate_size=37						,layer_norm_eps=1e-0_5						,num_attention_heads=4						,num_hidden_layers=5						,pad_token_id=1						,vocab_size=1000						,hidden_act="""gelu"""						,projection_dim=512						,)
           SCREAMING_SNAKE_CASE		=						CLIPTextModel(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

           SCREAMING_SNAKE_CASE		=						{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
           }
           return components






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    Optional[Any]						,lowerCamelCase__						:    int=0       )     ->		List[Any]:




           '''simple docstring'''


           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 SCREAMING_SNAKE_CASE		=						torch.manual_seed(lowerCamelCase__       )
           else:
                 SCREAMING_SNAKE_CASE		=						torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						{
               """prompt""": """A painting of a squirrel eating a burger""",
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 6.0,
               """output_type""": """pt""",
           }
           return inputs






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE		=						self.get_dummy_components()
           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						sd_pipe.to(lowerCamelCase__       )
           sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						self.get_dummy_inputs(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						"""np"""
           SCREAMING_SNAKE_CASE		=						sd_pipe(**lowerCamelCase__       ).frames
           SCREAMING_SNAKE_CASE		=						frames[0][-3:, -3:, -1]

           assert frames[0].shape == (64, 64, 3)
           SCREAMING_SNAKE_CASE		=						np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0]       )

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		int:




           '''simple docstring'''


           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=3e-3       )






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()						,reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,)
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Any:




           '''simple docstring'''


           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__						,expected_max_diff=1e-2       )






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[Any]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""       )
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Tuple:




           '''simple docstring'''


           pass






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[str]       )     ->		Optional[Any]:




           '''simple docstring'''


           return super().test_progress_bar()

@slow
@skip_mps
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=25						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""       )

           SCREAMING_SNAKE_CASE		=						TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""       )
           SCREAMING_SNAKE_CASE		=						pipe.to("""cuda"""       )

           SCREAMING_SNAKE_CASE		=						"""Spiderman is surfing"""
           SCREAMING_SNAKE_CASE		=						torch.Generator(device="""cpu"""       ).manual_seed(0       )

           SCREAMING_SNAKE_CASE		=						pipe(lowerCamelCase__						,generator=lowerCamelCase__						,num_inference_steps=2						,output_type="""pt"""       ).frames
           SCREAMING_SNAKE_CASE		=						video_frames.cpu().numpy()

           assert np.abs(expected_video - video       ).mean() < 5e-2


