





# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
#     ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
#     --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
#     --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
#    1. --tf32 0 --fp16 0
#    2. --tf32 0 --fp16 1
#    3. --tf32 0 --bf16 1
#    4. --tf32 1 --fp16 0
#    5. --tf32 1 --fp16 1
#    6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
#    --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
#    --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
#    1. --tf32 0
#    2. --tf32 0 --fp16
#    3. --tf32 0 --bf16
#    4. --tf32 1
#    5. --tf32 1 --fp16
#    6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
#    `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation       |     Train |   Diff |   Train |
# |                 |   samples |      % |    loss |
# |                 |       per |        |         |
# |                 |    second |        |         |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0        |    285.11 |      0 |    2.51 |
# | --tf32 1        |    342.09 |     20 |    2.51 |
# | --fp16 --tf32 0 |    423.49 |     49 |    2.51 |
# | --fp16 --tf32 1 |    423.13 |     48 |    2.51 |
# | --bf16 --tf32 0 |    416.80 |     46 |    2.52 |
# | --bf16 --tf32 1 |    415.87 |     46 |    2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
#    --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#

import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean

import pandas as pd
import torch
from tqdm import tqdm

import transformers


_snake_case  :    Any						    =  float('nan')



class 				_UpperCAmelCase    :

  """simple docstring"""


  def __init__(				self :		Optional[int]					,  lowerCAmelCase_ :		Tuple     )							->						Optional[int]:
     __lowerCAmelCase								=	sys.stdout
     __lowerCAmelCase								=	open(lowerCAmelCase_					,  'a'     )






  def __getattr__(				self :		Optional[Any]					,  lowerCAmelCase_ :		Union[str, Any]     )							->						Optional[Any]:
     return getattr(self.stdout					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		Optional[int]     )							->						str:
     self.stdout.write(lowerCAmelCase_     )
     # strip tqdm codes
     self.file.write(re.sub(R'^.*\r'					,  ''					,  lowerCAmelCase_					,  0					,  re.M     )     )





def   a_    ( lowerCAmelCase_      :			Optional[Any]=80,	lowerCAmelCase_      :			Optional[int]=False	):
   __lowerCAmelCase								=	[]

   # deal with critical env vars
   __lowerCAmelCase								=	['CUDA_VISIBLE_DEVICES']
   for key in env_keys:
      __lowerCAmelCase								=	os.environ.get(lowerCAmelCase_,	lowerCAmelCase_	)
      if val is not None:
         cmd.append(F"""{key}={val}"""	)

    # python executable (not always needed if the script is executable)
   __lowerCAmelCase								=	sys.executable if full_python_path else sys.executable.split('/'	)[-1]
   cmd.append(lowerCAmelCase_	)

   # now the normal args
   cmd += list(map(shlex.quote,	sys.argv	)	)

   # split up into up to MAX_WIDTH lines with shell multi-line escapes
   __lowerCAmelCase								=	[]
   __lowerCAmelCase								=	''
   while len(lowerCAmelCase_	) > 0:
      current_line += F"""{cmd.pop(0	)} """
      if len(lowerCAmelCase_	) == 0 or len(lowerCAmelCase_	) + len(cmd[0]	) + 1 > max_width - 1:
         lines.append(lowerCAmelCase_	)
         __lowerCAmelCase								=	''
   return "\\\n".join(lowerCAmelCase_	)

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			int	):
   # unwrap multi-line input
   __lowerCAmelCase								=	re.sub(R'[\\\n]+',	' ',	args.base_cmd	)

   # remove --output_dir if any and set our own
   __lowerCAmelCase								=	re.sub('--output_dir\s+[^\s]+',	'',	args.base_cmd	)
   args.base_cmd += F""" --output_dir {output_dir}"""

   # ensure we have --overwrite_output_dir
   __lowerCAmelCase								=	re.sub('--overwrite_output_dir\s+',	'',	args.base_cmd	)
   args.base_cmd += " --overwrite_output_dir"

   return [sys.executable] + shlex.split(args.base_cmd	)

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			int	):
   # Enable to debug everything but the run itself, to do it fast and see the progress.
   # This is useful for debugging the output formatting quickly - we can remove it later once
   # everybody is happy with the output
   if 0:
      import random
      from time import sleep

      sleep(0	)
      return dict(
          {k: random.uniform(0,	100	) for k in metric_keys},	**{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222]	)},	)

   __lowerCAmelCase								=	subprocess.run(lowerCAmelCase_,	capture_output=lowerCAmelCase_,	text=lowerCAmelCase_	)

   if verbose:
      print('STDOUT',	result.stdout	)
      print('STDERR',	result.stderr	)

   # save the streams
   __lowerCAmelCase								=	variation.replace(' ',	'-'	)
   with open(Path(lowerCAmelCase_	) / F"""log.{prefix}.stdout.txt""",	'w'	) as f:
      f.write(result.stdout	)
   with open(Path(lowerCAmelCase_	) / F"""log.{prefix}.stderr.txt""",	'w'	) as f:
      f.write(result.stderr	)

   if result.returncode != 0:
      if verbose:
         print('failed'	)
      return {target_metric_key: nan}

   with io.open(F"""{output_dir}/all_results.json""",	'r',	encoding='utf-8'	) as f:
      __lowerCAmelCase								=	json.load(lowerCAmelCase_	)

   # filter out just the keys we want
   return {k: v for k, v in metrics.items() if k in metric_keys}

def   a_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Tuple,	):
   __lowerCAmelCase								=	[]
   __lowerCAmelCase								=	[]
   __lowerCAmelCase								=	F"""{id}: {variation:<{longest_variation_len}}"""
   __lowerCAmelCase								=	F"""{preamble}: """
   __lowerCAmelCase								=	set(report_metric_keys + [target_metric_key]	)
   for i in tqdm(range(lowerCAmelCase_	),	desc=lowerCAmelCase_,	leave=lowerCAmelCase_	):
      __lowerCAmelCase								=	process_run_single(
          lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)
      __lowerCAmelCase								=	single_run_metrics[target_metric_key]
      if not math.isnan(lowerCAmelCase_	):
         metrics.append(lowerCAmelCase_	)
         results.append(lowerCAmelCase_	)
         outcome += "✓"
      else:
         outcome += "✘"
   __lowerCAmelCase								=	F"""\33[2K\r{outcome}"""
   if len(lowerCAmelCase_	) > 0:
      __lowerCAmelCase								=	{k: fmean([x[k] for x in metrics]	) for k in metrics[0].keys()}
      __lowerCAmelCase								=	round(mean_metrics[target_metric_key],	2	)
      __lowerCAmelCase								=	F"""{outcome} {mean_target}"""
      if len(lowerCAmelCase_	) > 1:
         results_str += F""" {tuple(round(lowerCAmelCase_,	2	) for x in results	)}"""
      print(lowerCAmelCase_	)
      __lowerCAmelCase								=	variation
      return mean_metrics
   else:
      print(lowerCAmelCase_	)
      return {variation_key: variation, target_metric_key: nan}

def   a_    ( ):
   __lowerCAmelCase								=	torch.cuda.get_device_properties(torch.device('cuda'	)	)
   return F"""
Datetime    : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"	)}

Software:
transformers: {transformers.__version__}
torch       : {torch.__version__}
cuda        : {torch.version.cuda}
python      : {platform.python_version()}

Hardware:
{torch.cuda.device_count()} GPUs      : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""

def   a_    ( lowerCAmelCase_      :			str,	lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase								=	pd.DataFrame(lowerCAmelCase_	)
   __lowerCAmelCase								=	'variation'
   __lowerCAmelCase								=	'diff_%'

   __lowerCAmelCase								=	nan
   if base_variation is not None and len(df[df[variation_key] == base_variation]	):
      # this may still return nan
      __lowerCAmelCase								=	df.loc[df[variation_key] == base_variation][target_metric_key].item()
   if math.isnan(lowerCAmelCase_	):
      # as a fallback, use the minimal value as the sentinel
      __lowerCAmelCase								=	df.loc[df[target_metric_key] != nan][target_metric_key].min()

   # create diff column if possible
   if not math.isnan(lowerCAmelCase_	):
      __lowerCAmelCase								=	df.apply(
          lambda lowerCAmelCase_	: round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value	)
          if not math.isnan(r[target_metric_key]	)
          else 0,	axis='columns',	)

   # re-order columns
   __lowerCAmelCase								=	[variation_key, target_metric_key, diff_key, *report_metric_keys]
   __lowerCAmelCase								=	df.reindex(lowerCAmelCase_,	axis='columns'	)  # reorder cols

   # capitalize
   __lowerCAmelCase								=	df.rename(str.capitalize,	axis='columns'	)

   # make the cols as narrow as possible
   __lowerCAmelCase								=	df.rename(lambda lowerCAmelCase_	: c.replace('_',	'<br>'	),	axis='columns'	)
   __lowerCAmelCase								=	df.rename(lambda lowerCAmelCase_	: c.replace('_',	'\n'	),	axis='columns'	)

   __lowerCAmelCase								=	['', 'Copy between the cut-here-lines and paste as is to github or a forum']
   report += ["----------8<-----------------8<--------"]
   report += ["*** Results:", df_github.to_markdown(index=lowerCAmelCase_,	floatfmt='.2f'	)]
   report += ["```"]
   report += ["*** Setup:", get_versions()]
   report += ["*** The benchmark command line was:", get_original_command()]
   report += ["```"]
   report += ["----------8<-----------------8<--------"]
   report += ["*** Results (console):", df_console.to_markdown(index=lowerCAmelCase_,	floatfmt='.2f'	)]

   print('\n\n'.join(lowerCAmelCase_	)	)

def   a_    ( ):
   __lowerCAmelCase								=	argparse.ArgumentParser()
   parser.add_argument(
       '--base-cmd',	default=lowerCAmelCase_,	type=lowerCAmelCase_,	required=lowerCAmelCase_,	help='Base cmd',	)
   parser.add_argument(
       '--variations',	default=lowerCAmelCase_,	type=lowerCAmelCase_,	nargs='+',	required=lowerCAmelCase_,	help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'',	)
   parser.add_argument(
       '--base-variation',	default=lowerCAmelCase_,	type=lowerCAmelCase_,	help='Baseline variation to compare to. if None the minimal target value will be used to compare against',	)
   parser.add_argument(
       '--target-metric-key',	default=lowerCAmelCase_,	type=lowerCAmelCase_,	required=lowerCAmelCase_,	help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second',	)
   parser.add_argument(
       '--report-metric-keys',	default='',	type=lowerCAmelCase_,	help='Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples',	)
   parser.add_argument(
       '--repeat-times',	default=1,	type=lowerCAmelCase_,	help='How many times to re-run each variation - an average will be reported',	)
   parser.add_argument(
       '--output_dir',	default='output_benchmark',	type=lowerCAmelCase_,	help='The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked',	)
   parser.add_argument(
       '--verbose',	default=lowerCAmelCase_,	action='store_true',	help='Whether to show the outputs of each run or just the benchmark progress',	)
   __lowerCAmelCase								=	parser.parse_args()

   __lowerCAmelCase								=	args.output_dir
   Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
   __lowerCAmelCase								=	get_base_command(lowerCAmelCase_,	lowerCAmelCase_	)

   # split each dimension into its --foo variations
   __lowerCAmelCase								=	[list(map(str.strip,	re.split(R'\|',	lowerCAmelCase_	)	)	) for x in args.variations]
   # build a cartesian product of dimensions and convert those back into cmd-line arg strings,
   # while stripping white space for inputs that were empty
   __lowerCAmelCase								=	list(map(str.strip,	map(' '.join,	itertools.product(*lowerCAmelCase_	)	)	)	)
   __lowerCAmelCase								=	max(len(lowerCAmelCase_	) for x in variations	)

   # split wanted keys
   __lowerCAmelCase								=	args.report_metric_keys.split()

   # capture prints into a log file for convenience
   __lowerCAmelCase								=	F"""benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S"	)}.txt"""
   print(F"""\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt"""	)
   print(F"""and this script's output is also piped into {report_fn}"""	)

   __lowerCAmelCase								=	Tee(lowerCAmelCase_	)

   print(F"""\n*** Running {len(lowerCAmelCase_	)} benchmarks:"""	)
   print(F"""Base command: {" ".join(lowerCAmelCase_	)}"""	)

   __lowerCAmelCase								=	'variation'
   __lowerCAmelCase								=	[]
   for id, variation in enumerate(tqdm(lowerCAmelCase_,	desc='Total completion: ',	leave=lowerCAmelCase_	)	):
      __lowerCAmelCase								=	base_cmd + variation.split()
      results.append(
          process_run(
              id + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	args.target_metric_key,	lowerCAmelCase_,	args.repeat_times,	lowerCAmelCase_,	args.verbose,	)	)

   process_results(lowerCAmelCase_,	args.target_metric_key,	lowerCAmelCase_,	args.base_variation,	lowerCAmelCase_	)


if __name__ == "__main__":
     main()








import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 				_UpperCAmelCase    (    _UpperCamelCase		,    _UpperCamelCase		,    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = StableDiffusionInstructPixaPixPipeline
  a_        = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
  a_        = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  a_        = IMAGE_TO_IMAGE_IMAGE_PARAMS
  a_        = IMAGE_TO_IMAGE_IMAGE_PARAMS






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     torch.manual_seed(0     )
     __lowerCAmelCase								=	UNetaDConditionModel(
         block_out_channels=(3_2, 6_4)					,  layers_per_block=2					,  sample_size=3_2					,  in_channels=8					,  out_channels=4					,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,  cross_attention_dim=3_2					,  )
     __lowerCAmelCase								=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_     )
     torch.manual_seed(0     )
     __lowerCAmelCase								=	AutoencoderKL(
         block_out_channels=[3_2, 6_4]					,  in_channels=3					,  out_channels=3					,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,  latent_channels=4					,  )
     torch.manual_seed(0     )
     __lowerCAmelCase								=	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					,  )
     __lowerCAmelCase								=	CLIPTextModel(lowerCAmelCase_     )
     __lowerCAmelCase								=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

     __lowerCAmelCase								=	{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Tuple=0     )							->						Dict:
     __lowerCAmelCase								=	floats_tensor((1, 3, 3_2, 3_2)					,  rng=random.Random(lowerCAmelCase_     )     ).to(lowerCAmelCase_     )
     __lowerCAmelCase								=	image.cpu().permute(0					,  2					,  3					,  1     )[0]
     __lowerCAmelCase								=	Image.fromarray(np.uinta(lowerCAmelCase_     )     ).convert('RGB'     )
     if str(lowerCAmelCase_     ).startswith('mps'     ):
        __lowerCAmelCase								=	torch.manual_seed(lowerCAmelCase_     )
     else:
        __lowerCAmelCase								=	torch.Generator(device=lowerCAmelCase_     ).manual_seed(lowerCAmelCase_     )
     __lowerCAmelCase								=	{
         'prompt': 'A painting of a squirrel eating a burger',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'guidance_scale': 6.0,
         'image_guidance_scale': 1,
         'output_type': 'numpy',
     }
     return inputs






  def 				lowercase		(				self :		Tuple     )							->						List[Any]:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]
     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		List[str]     )							->						Dict:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	'french fries'
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_					,  negative_prompt=lowerCAmelCase_     )
     __lowerCAmelCase								=	output.images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	[inputs['prompt']] * 2

     __lowerCAmelCase								=	np.array(inputs['image']     ).astype(np.floataa     ) / 2_55.0
     __lowerCAmelCase								=	torch.from_numpy(lowerCAmelCase_     ).unsqueeze(0     ).to(lowerCAmelCase_     )
     __lowerCAmelCase								=	image / 2 + 0.5
     __lowerCAmelCase								=	image.permute(0					,  3					,  1					,  2     )
     __lowerCAmelCase								=	image.repeat(2					,  1					,  1					,  1     )

     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[-1, -3:, -3:, -1]

     assert image.shape == (2, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Dict     )							->						Optional[Any]:
     __lowerCAmelCase								=	'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85					,  beta_end=0.0_12					,  beta_schedule='scaled_linear'     )
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe.to(lowerCAmelCase_     )
     sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	self.get_dummy_inputs(lowerCAmelCase_     )
     __lowerCAmelCase								=	sd_pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1]

     __lowerCAmelCase								=	[round(lowerCAmelCase_					,  4     ) for x in image_slice.flatten().tolist()]
     print(','.join([str(lowerCAmelCase_     ) for x in slice]     )     )

     assert image.shape == (1, 3_2, 3_2, 3)
     __lowerCAmelCase								=	np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[int]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






  def 				lowercase		(				self :		Optional[Any]     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.get_dummy_components()
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_     )
     __lowerCAmelCase								=	VaeImageProcessor(do_resize=lowerCAmelCase_					,  do_normalize=lowerCAmelCase_     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

     __lowerCAmelCase								=	pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )     )[0]

     __lowerCAmelCase								=	components['vae']
     __lowerCAmelCase								=	self.get_dummy_inputs_by_type(lowerCAmelCase_					,  input_image_type='pt'     )

     for image_param in self.image_latents_params:
        if image_param in inputs.keys():
           __lowerCAmelCase								=	vae.encode(inputs[image_param]     ).latent_dist.mode()

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )[0]

     __lowerCAmelCase								=	np.abs(out - out_latents_inputs     ).max()
     self.assertLess(lowerCAmelCase_					,  1e-4					,  'passing latents as image input generate different result from passing image'     )





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

  """simple docstring"""


  def 				lowercase		(				self :		int     )							->						Optional[int]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		List[Any]=0     )							->						Any:
     __lowerCAmelCase								=	torch.manual_seed(lowerCAmelCase_     )
     __lowerCAmelCase								=	load_image(
         'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'     )
     __lowerCAmelCase								=	{
         'prompt': 'turn him into a cyborg',
         'image': image,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 7.5,
         'image_guidance_scale': 1.0,
         'output_type': 'numpy',
     }
     return inputs






  def 				lowercase		(				self :		List[Any]     )							->						str:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Tuple     )							->						List[str]:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[Any]     )							->						Dict:
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_     )
     __lowerCAmelCase								=	DDIMScheduler.from_config(pipe.scheduler.config     )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     ).images
     __lowerCAmelCase								=	image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_1_2, 5_1_2, 3)
     __lowerCAmelCase								=	np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]     )

     assert np.abs(expected_slice - image_slice     ).max() < 1e-3






  def 				lowercase		(				self :		Optional[int]     )							->						int:
     __lowerCAmelCase								=	0

     def callback_fn(lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		torch.FloatTensor     ) -> None:
        __lowerCAmelCase								=	True
        nonlocal number_of_steps
        number_of_steps += 1
        if step == 1:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
        elif step == 2:
           __lowerCAmelCase								=	latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 6_4, 6_4)
           __lowerCAmelCase								=	latents[0, -3:, -3:, -1]
           __lowerCAmelCase								=	np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]     )

           assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2

     __lowerCAmelCase								=	False

     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_					,  torch_dtype=torch.floataa     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	self.get_inputs()
     pipe(**lowerCAmelCase_					,  callback=lowerCAmelCase_					,  callback_steps=1     )
     assert callback_fn.has_been_called
     assert number_of_steps == 3






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         'timbrooks/instruct-pix2pix'					,  safety_checker=lowerCAmelCase_					,  torch_dtype=torch.floataa     )
     __lowerCAmelCase								=	pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing(1     )
     pipe.enable_sequential_cpu_offload()

     __lowerCAmelCase								=	self.get_inputs()
     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )

     __lowerCAmelCase								=	torch.cuda.max_memory_allocated()
     # make sure that less than 2.2 GB is allocated
     assert mem_bytes < 2.2 * 1_0**9






  def 				lowercase		(				self :		List[Any]     )							->						Any:
     __lowerCAmelCase								=	self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     __lowerCAmelCase								=	inputs['image'].resize((5_0_4, 5_0_4)     )

     __lowerCAmelCase								=	'timbrooks/instruct-pix2pix'
     __lowerCAmelCase								=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
         lowerCAmelCase_					,  safety_checker=lowerCAmelCase_					,  )
     pipe.to(lowerCAmelCase_     )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_     )
     pipe.enable_attention_slicing()

     __lowerCAmelCase								=	pipe(**lowerCAmelCase_     )
     __lowerCAmelCase								=	output.images[0]

     __lowerCAmelCase								=	image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert image.shape == (5_0_4, 5_0_4, 3)
     __lowerCAmelCase								=	np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]     )

     assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3

