



import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


_lowercase    	=		logging.getLogger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    # save results
    if os.path.exists(UpperCamelCase_						):
        if os.path.exists(os.path.join(UpperCamelCase_			,							'''config.json'''						)						) and os.path.isfile(
            os.path.join(UpperCamelCase_			,							'''config.json'''						)						):
            os.remove(os.path.join(UpperCamelCase_			,							'''config.json'''						)						)
        if os.path.exists(os.path.join(UpperCamelCase_			,							'''pytorch_model.bin'''						)						) and os.path.isfile(
            os.path.join(UpperCamelCase_			,							'''pytorch_model.bin'''						)						):
            os.remove(os.path.join(UpperCamelCase_			,							'''pytorch_model.bin'''						)						)
    else:
        os.makedirs(UpperCamelCase_						)
    model.save_pretrained(UpperCamelCase_						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Optional[Any]=False						)->	List[Any]:
    A__					=       2
    if unlogit:
        A__					=       torch.pow(UpperCamelCase_			,							UpperCamelCase_						)
    A__					=       p * torch.log(UpperCamelCase_						)
    A__					=       0
    return -plogp.sum(dim=-1						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	List[str]:
    logger.info('''lv, h >\t''' + '''\t'''.join(f"{x + 1}" for x in range(len(UpperCamelCase_						)						)						)						)
    for row in range(len(UpperCamelCase_						)						):
        if tensor.dtype != torch.long:
            logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:.5f}" for x in tensor[row].cpu().data						)						)
        else:
            logger.info(f"layer {row + 1}:\t" + '''\t'''.join(f"{x:d}" for x in tensor[row].cpu().data						)						)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Dict			,							UpperCamelCase_					:							Union[str, Any]=True			,							UpperCamelCase_					:							str=True			,							UpperCamelCase_					:							Tuple=None			,							UpperCamelCase_					:							Optional[int]=False						)->	Tuple:
    A__			,						A__					=       model.config.num_hidden_layers, model.config.num_attention_heads
    A__					=       torch.zeros(UpperCamelCase_			,							UpperCamelCase_						).to(args.device						)
    A__					=       torch.zeros(UpperCamelCase_			,							UpperCamelCase_						).to(args.device						)

    if head_mask is None:
        A__					=       torch.ones(UpperCamelCase_			,							UpperCamelCase_						).to(args.device						)

    head_mask.requires_grad_(requires_grad=UpperCamelCase_						)
    # If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
    if actually_pruned:
        A__					=       None

    A__					=       0.0
    A__					=       0.0
    for step, inputs in enumerate(tqdm(UpperCamelCase_			,							desc='''Iteration'''			,							disable=args.local_rank not in [-1, 0]						)						):
        A__					=       tuple(t.to(args.device						) for t in inputs						)
        ((A__)			,						)					=       inputs

        # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
        A__					=       model(UpperCamelCase_			,							labels=UpperCamelCase_			,							head_mask=UpperCamelCase_						)
        #  (loss), lm_logits, presents, (all hidden_states), (attentions)
        A__			,						A__			,						A__					=       (
            outputs[0],
            outputs[1],
            outputs[-1],
        )  # Loss and logits are the first, attention the last
        loss.backward()  # Backpropagate to populate the gradients in the head mask
        total_loss += loss.detach().cpu().numpy()
        if compute_entropy:
            for layer, attn in enumerate(UpperCamelCase_						):
                A__					=       entropy(attn.detach()			,							UpperCamelCase_						)
                attn_entropy[layer] += masked_entropy.sum(-1						).sum(0						).sum(0						).detach()

        if compute_importance:
            head_importance += head_mask.grad.abs().detach()
        tot_tokens += torch.ones_like(UpperCamelCase_						).float().detach().sum().data

    # Normalize
    attn_entropy /= tot_tokens
    head_importance /= tot_tokens
    # Layerwise importance normalization
    if not args.dont_normalize_importance_by_layer:
        A__					=       2
        A__					=       torch.pow(torch.pow(UpperCamelCase_			,							UpperCamelCase_						).sum(-1						)			,							1 / exponent						)
        head_importance /= norm_by_layer.unsqueeze(-1						) + 1E-20

    if not args.dont_normalize_global_importance:
        A__					=       (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

    # Print matrices
    if compute_entropy:
        logger.info('''Attention entropies'''						)
        print_ad_tensor(UpperCamelCase_						)
    if compute_importance:
        logger.info('''Head importance scores'''						)
        print_ad_tensor(UpperCamelCase_						)
    logger.info('''Head ranked by importance scores'''						)
    A__					=       torch.zeros(head_importance.numel()			,							dtype=torch.long			,							device=args.device						)
    A__					=       torch.arange(
        head_importance.numel()			,							device=args.device						)
    A__					=       head_ranks.view_as(UpperCamelCase_						)
    print_ad_tensor(UpperCamelCase_						)
    return attn_entropy, head_importance, total_loss



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Any			,							UpperCamelCase_					:							Optional[Any]			,							UpperCamelCase_					:							List[Any]						)->	str:
    A__			,						A__			,						A__					=       compute_heads_importance(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							compute_entropy=UpperCamelCase_						)
    A__					=       1 / loss  # instead of downsteam score use the LM loss
    logger.info('''Pruning: original score: %f, threshold: %f'''			,							UpperCamelCase_			,							original_score * args.masking_threshold						)

    A__					=       torch.ones_like(UpperCamelCase_						)
    A__					=       max(1			,							int(new_head_mask.numel() * args.masking_amount						)						)

    A__					=       original_score
    while current_score >= original_score * args.masking_threshold:
        A__					=       new_head_mask.clone().detach()  # save current head mask
        # heads from least important to most - keep only not-masked heads
        A__					=       float('''Inf'''						)
        A__					=       head_importance.view(-1						).sort()[1]

        if len(UpperCamelCase_						) <= num_to_mask:
            print('''BREAK BY num_to_mask'''						)
            break

        # mask heads
        A__					=       current_heads_to_mask[:num_to_mask]
        logger.info('''Heads to mask: %s'''			,							str(current_heads_to_mask.tolist()						)						)
        A__					=       new_head_mask.view(-1						)
        A__					=       0.0
        A__					=       new_head_mask.view_as(UpperCamelCase_						)
        A__					=       new_head_mask.clone().detach()
        print_ad_tensor(UpperCamelCase_						)

        # Compute metric and head importance again
        A__			,						A__			,						A__					=       compute_heads_importance(
            UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							compute_entropy=UpperCamelCase_			,							head_mask=UpperCamelCase_						)
        A__					=       1 / loss
        logger.info(
            '''Masking: current score: %f, remaining heads %d (%.1f percents)'''			,							UpperCamelCase_			,							new_head_mask.sum()			,							new_head_mask.sum() / new_head_mask.numel() * 1_0_0			,							)

    logger.info('''Final head mask'''						)
    print_ad_tensor(UpperCamelCase_						)
    np.save(os.path.join(args.output_dir			,							'''head_mask.npy'''						)			,							head_mask.detach().cpu().numpy()						)

    return head_mask



def 		lowerCAmelCase__     (       UpperCamelCase_					:							str			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]						)->	Union[str, Any]:
    A__					=       datetime.now()
    A__			,						A__			,						A__					=       compute_heads_importance(
        UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							compute_entropy=UpperCamelCase_			,							compute_importance=UpperCamelCase_			,							head_mask=UpperCamelCase_						)
    A__					=       1 / loss
    A__					=       datetime.now() - before_time

    A__					=       sum(p.numel() for p in model.parameters()						)
    A__					=       {
        layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCamelCase_						)						)
    }

    for k, v in heads_to_prune.items():
        if isinstance(UpperCamelCase_			,							UpperCamelCase_						):
            A__					=       [
                v,
            ]

    assert sum(len(UpperCamelCase_						) for h in heads_to_prune.values()						) == (1 - head_mask.long()).sum().item()
    model.prune_heads(UpperCamelCase_						)
    A__					=       sum(p.numel() for p in model.parameters()						)

    A__					=       datetime.now()
    A__			,						A__			,						A__					=       compute_heads_importance(
        UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							compute_entropy=UpperCamelCase_			,							compute_importance=UpperCamelCase_			,							head_mask=UpperCamelCase_			,							actually_pruned=UpperCamelCase_			,							)

    A__					=       1 / loss
    A__					=       datetime.now() - before_time

    logger.info(
        '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''			,							UpperCamelCase_			,							UpperCamelCase_			,							pruned_num_params / original_num_params * 1_0_0			,							)
    logger.info('''Pruning: score with masking: %f score with pruning: %f'''			,							UpperCamelCase_			,							UpperCamelCase_						)
    logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''			,							original_time / new_time * 1_0_0						)
    save_model(UpperCamelCase_			,							args.output_dir						)



def 		lowerCAmelCase__     (       )->	int:
    A__					=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--data_dir'''			,							default=UpperCamelCase_			,							type=UpperCamelCase_			,							required=UpperCamelCase_			,							help='''The input data dir. Should contain the .tsv files (or other data files) for the task.'''			,							)
    parser.add_argument(
        '''--model_name_or_path'''			,							default=UpperCamelCase_			,							type=UpperCamelCase_			,							required=UpperCamelCase_			,							help='''Path to pretrained model or model identifier from huggingface.co/models'''			,							)
    parser.add_argument(
        '''--output_dir'''			,							default=UpperCamelCase_			,							type=UpperCamelCase_			,							required=UpperCamelCase_			,							help='''The output directory where the model predictions and checkpoints will be written.'''			,							)

    # Other parameters
    parser.add_argument(
        '''--config_name'''			,							default=''''''			,							type=UpperCamelCase_			,							help='''Pretrained config name or path if not the same as model_name_or_path'''			,							)
    parser.add_argument(
        '''--tokenizer_name'''			,							default=''''''			,							type=UpperCamelCase_			,							help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''			,							)
    parser.add_argument(
        '''--cache_dir'''			,							default=UpperCamelCase_			,							type=UpperCamelCase_			,							help='''Where do you want to store the pre-trained models downloaded from s3'''			,							)
    parser.add_argument(
        '''--data_subset'''			,							type=UpperCamelCase_			,							default=-1			,							help='''If > 0: limit the data to a subset of data_subset instances.'''						)
    parser.add_argument(
        '''--overwrite_output_dir'''			,							action='''store_true'''			,							help='''Whether to overwrite data in output directory'''						)
    parser.add_argument(
        '''--overwrite_cache'''			,							action='''store_true'''			,							help='''Overwrite the cached training and evaluation sets'''						)

    parser.add_argument(
        '''--dont_normalize_importance_by_layer'''			,							action='''store_true'''			,							help='''Don\'t normalize importance score by layers'''						)
    parser.add_argument(
        '''--dont_normalize_global_importance'''			,							action='''store_true'''			,							help='''Don\'t normalize all importance scores between 0 and 1'''			,							)

    parser.add_argument(
        '''--try_masking'''			,							action='''store_true'''			,							help='''Whether to try to mask head until a threshold of accuracy.'''						)
    parser.add_argument(
        '''--masking_threshold'''			,							default=0.9			,							type=UpperCamelCase_			,							help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''			,							)
    parser.add_argument(
        '''--masking_amount'''			,							default=0.1			,							type=UpperCamelCase_			,							help='''Amount to heads to masking at each masking step.'''						)
    parser.add_argument('''--metric_name'''			,							default='''acc'''			,							type=UpperCamelCase_			,							help='''Metric to use for head masking.'''						)

    parser.add_argument(
        '''--max_seq_length'''			,							default=1_2_8			,							type=UpperCamelCase_			,							help=(
            '''The maximum total input sequence length after WordPiece tokenization. \n'''
            '''Sequences longer than this will be truncated, sequences shorter padded.'''
        )			,							)
    parser.add_argument('''--batch_size'''			,							default=1			,							type=UpperCamelCase_			,							help='''Batch size.'''						)

    parser.add_argument('''--seed'''			,							type=UpperCamelCase_			,							default=4_2						)
    parser.add_argument('''--local_rank'''			,							type=UpperCamelCase_			,							default=-1			,							help='''local_rank for distributed training on gpus'''						)
    parser.add_argument('''--no_cuda'''			,							action='''store_true'''			,							help='''Whether not to use CUDA when available'''						)
    parser.add_argument('''--server_ip'''			,							type=UpperCamelCase_			,							default=''''''			,							help='''Can be used for distant debugging.'''						)
    parser.add_argument('''--server_port'''			,							type=UpperCamelCase_			,							default=''''''			,							help='''Can be used for distant debugging.'''						)
    A__					=       parser.parse_args()

    if args.server_ip and args.server_port:
        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
        import ptvsd

        print('''Waiting for debugger attach'''						)
        ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,							redirect_output=UpperCamelCase_						)
        ptvsd.wait_for_attach()

    # Setup devices and distributed training
    if args.local_rank == -1 or args.no_cuda:
        A__					=       torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
        A__					=       0 if args.no_cuda else torch.cuda.device_count()
    else:
        torch.cuda.set_device(args.local_rank						)
        A__					=       torch.device('''cuda'''			,							args.local_rank						)
        A__					=       1
        torch.distributed.init_process_group(backend='''nccl'''						)  # Initializes the distributed backend

    # Setup logging
    logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN						)
    logger.info('''device: {} n_gpu: {}, distributed: {}'''.format(args.device			,							args.n_gpu			,							bool(args.local_rank != -1						)						)						)

    A__					=       GPTaLMHeadModel.from_pretrained(args.model_name_or_path						)

    # Distributed and parallel training
    model.to(args.device						)
    if args.local_rank != -1:
        A__					=       nn.parallel.DistributedDataParallel(
            UpperCamelCase_			,							device_ids=[args.local_rank]			,							output_device=args.local_rank			,							find_unused_parameters=UpperCamelCase_						)
    elif args.n_gpu > 1:
        A__					=       nn.DataParallel(UpperCamelCase_						)

    # Print/save training arguments
    os.makedirs(args.output_dir			,							exist_ok=UpperCamelCase_						)
    torch.save(UpperCamelCase_			,							os.path.join(args.output_dir			,							'''run_args.bin'''						)						)
    logger.info('''Training/evaluation parameters %s'''			,							UpperCamelCase_						)

    # Prepare dataset
    A__					=       np.concatenate(
        [
            np.loadtxt(args.data_dir			,							dtype=np.intaa						),
        ]						)
    A__					=       (torch.from_numpy(UpperCamelCase_						),)
    A__					=       TensorDataset(*UpperCamelCase_						)
    A__					=       RandomSampler(UpperCamelCase_						)
    A__					=       DataLoader(UpperCamelCase_			,							sampler=UpperCamelCase_			,							batch_size=args.batch_size						)

    # Compute head entropy and importance score
    compute_heads_importance(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)

    # Try head masking (set heads to zero until the score goes under a threshole)
    # and head pruning (remove masked heads and see the effect on the network)
    if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
        A__					=       mask_heads(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)
        prune_heads(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)


if __name__ == "__main__":
   main()





import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    A__					=       [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]						)



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionLatentUpscalePipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCamelCase__		     =					frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCamelCase__		     =					frozenset([]  )

     UpperCamelCase__		     =					True




     @property
     def 		snake_case_		(					self):
         A__					=       1
         A__					=       4
         A__					=       (1_6, 1_6)

         A__					=       floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0)).to(a__)
         return image




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             act_fn='''gelu'''			,    attention_head_dim=8			,    norm_num_groups=a__			,    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    time_cond_proj_dim=1_6_0			,    conv_in_kernel=1			,    conv_out_kernel=1			,    cross_attention_dim=3_2			,    down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,    in_channels=8			,    mid_block_type=a__			,    only_cross_attention=a__			,    out_channels=5			,    resnet_time_scale_shift='''scale_shift'''			,    time_embedding_type='''fourier'''			,    timestep_post_act='''gelu'''			,    up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,    )
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    )
         A__					=       EulerDiscreteScheduler(prediction_type='''sample''')
         A__					=       CLIPTextConfig(
             bos_token_id=0			,    eos_token_id=2			,    hidden_size=3_2			,    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			,    hidden_act='''quick_gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         A__					=       '''cpu'''

         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)
         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       pipe(**a__).images
         A__					=       image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,    (1, 2_5_6, 2_5_6, 3))
         A__					=       np.array(
             [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
         A__					=       np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(a__			,    1e-3)




     def 		snake_case_		(					self):
         super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_cpu_offload_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_local(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_optional_components(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         A__					=       [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         A__					=       self.get_dummy_inputs(a__)
         A__					=       2

         A__					=       []
         for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                 # no sigma schedulers are not supported
                 # no schedulers
                 continue

             A__					=       getattr(a__			,    scheduler_enum.name)
             A__					=       scheduler_cls.from_config(pipe.scheduler.config)
             A__					=       pipe(**a__)[0]
             outputs.append(a__)

         assert check_same_shape(a__)







@require_torch_gpu
@slow
class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,    torch_dtype=torch.floataa)
         pipe.to('''cuda''')

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         A__					=       pipe(a__			,    generator=a__			,    output_type='''latent''').images

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''')
         assert np.abs((expected_image - image).mean()) < 5e-2




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''')

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''')
         assert np.abs((expected_image - image).max()) < 5e-2
