


import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.framework.errors_impl import ResourceExhaustedError

 from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
 import pyanvml.pyanvml as nvml

SCREAMING_SNAKE_CASE       :      str												= logging.get_logger(__name__)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    def run_func(lowerCamelCase_ ):
        @wraps(lowerCamelCase_ )
        def run_in_eager_mode(*lowerCamelCase_							, **lowerCamelCase_ ):
            return func(*lowerCamelCase_							, **lowerCamelCase_ )

        @wraps(lowerCamelCase_ )
        @tf.function(experimental_compile=lowerCamelCase_ )
        def run_in_graph_mode(*lowerCamelCase_							, **lowerCamelCase_ ):
            return func(*lowerCamelCase_							, **lowerCamelCase_ )

        if do_eager_mode is True:
            if use_xla is not False:
                raise ValueError(
                    'Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.' )
            return run_in_eager_mode
        else:
            return run_in_graph_mode

    return run_func



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						["tf.Tensor"]:
    _lowercase       :							int      		=		random.Random()
    _lowercase       :							List[Any]      		=		[rng.randint(0							, vocab_size - 1 ) for i in range(batch_size * sequence_length )]
    return tf.constant(lowerCamelCase_							, shape=(batch_size, sequence_length)							, dtype=tf.intaa )






class _lowerCamelCase(						_a   ):
       lowercase_ :	TensorFlowBenchmarkArguments
       lowercase_ :	PretrainedConfig
       lowercase_ :	str     =			"TensorFlow"
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return tf.__version__
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   float:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		self.args.strategy
           if strategy is None:
               raise ValueError('A device strategy has to be initialized before using TensorFlow.')
           _lowercase       :							Optional[int]      		=		self._prepare_inference_func(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           return self._measure_speed(_inference)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   float:




           """simple docstring"""

           _lowercase       :							Dict      		=		self.args.strategy
           if strategy is None:
               raise ValueError('A device strategy has to be initialized before using TensorFlow.')
           _lowercase       :							str      		=		self._prepare_train_func(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           return self._measure_speed(_train)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   [Memory, Optional[MemorySummary]]:




           """simple docstring"""

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],      lowerCamelCase)
           _lowercase       :							Optional[int]      		=		self.args.strategy
           if strategy is None:
               raise ValueError('A device strategy has to be initialized before using TensorFlow.')
           _lowercase       :							Any      		=		self._prepare_inference_func(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           return self._measure_memory(_inference)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   [Memory, Optional[MemorySummary]]:




           """simple docstring"""

           if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx],      lowerCamelCase)
           _lowercase       :							Optional[int]      		=		self.args.strategy
           if strategy is None:
               raise ValueError('A device strategy has to be initialized before using TensorFlow.')

           _lowercase       :							Union[str, Any]      		=		self._prepare_train_func(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           return self._measure_memory(_train)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Callable[[], None]:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.config_dict[model_name]

           if self.args.fpaa:
               raise NotImplementedError('Mixed precision is currently not supported.')

           _lowercase       :							Union[str, Any]      		=		(
               hasattr(lowerCamelCase,      'architectures')
               and isinstance(config.architectures,      lowerCamelCase)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   _lowercase       :							List[Any]      		=		'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _lowercase       :							Union[str, Any]      		=		__import__('transformers',      fromlist=[model_class])
                   _lowercase       :							Dict      		=		getattr(lowerCamelCase,      lowerCamelCase)
                   _lowercase       :							Union[str, Any]      		=		model_cls(lowerCamelCase)
               except ImportError:
                   raise ImportError(
                       F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                       ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.')
           else:
               _lowercase       :							Dict      		=		TF_MODEL_MAPPING[config.__class__](lowerCamelCase)

           # encoder-decoder has vocab size saved differently
           _lowercase       :							Tuple      		=		config.vocab_size if hasattr(lowerCamelCase,      'vocab_size') else config.encoder.vocab_size
           _lowercase       :							List[Any]      		=		random_input_ids(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           @run_with_tf_optimizations(self.args.eager_mode,      self.args.use_xla)
           def encoder_decoder_forward():
               return model(lowerCamelCase,      decoder_input_ids=lowerCamelCase,      training=lowerCamelCase)

           @run_with_tf_optimizations(self.args.eager_mode,      self.args.use_xla)
           def encoder_forward():
               return model(lowerCamelCase,      training=lowerCamelCase)

           _lowercase       :							str      		=		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

           return _inference
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Callable[[], None]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.config_dict[model_name]

           if self.args.eager_mode is not False:
               raise ValueError('Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.')

           if self.args.fpaa:
               raise NotImplementedError('Mixed precision is currently not supported.')

           _lowercase       :							Optional[Any]      		=		(
               hasattr(lowerCamelCase,      'architectures')
               and isinstance(config.architectures,      lowerCamelCase)
               and len(config.architectures) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                   _lowercase       :							List[Any]      		=		'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _lowercase       :							str      		=		__import__('transformers',      fromlist=[model_class])
                   _lowercase       :							Tuple      		=		getattr(lowerCamelCase,      lowerCamelCase)
                   _lowercase       :							Optional[int]      		=		model_cls(lowerCamelCase)
               except ImportError:
                   raise ImportError(
                       F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
                       ' set `--only_pretrain_model` or `args.only_pretrain_model=True`.')
           else:
               _lowercase       :							str      		=		TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase)

           # encoder-decoder has vocab size saved differently
           _lowercase       :							List[str]      		=		config.vocab_size if hasattr(lowerCamelCase,      'vocab_size') else config.encoder.vocab_size
           _lowercase       :							Union[str, Any]      		=		random_input_ids(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           @run_with_tf_optimizations(self.args.eager_mode,      self.args.use_xla)
           def encoder_decoder_train():
               _lowercase       :							Any      		=		model(lowerCamelCase,      decoder_input_ids=lowerCamelCase,      labels=lowerCamelCase,      training=lowerCamelCase)[0]
               _lowercase       :							List[str]      		=		tf.gradients(lowerCamelCase,      model.trainable_variables)
               return gradients

           @run_with_tf_optimizations(self.args.eager_mode,      self.args.use_xla)
           def encoder_train():
               _lowercase       :							Optional[Any]      		=		model(lowerCamelCase,      labels=lowerCamelCase,      training=lowerCamelCase)[0]
               _lowercase       :							List[str]      		=		tf.gradients(lowerCamelCase,      model.trainable_variables)
               return gradients

           _lowercase       :							str      		=		encoder_decoder_train if config.is_encoder_decoder else encoder_train

           return _train
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   float:




           """simple docstring"""

           with self.args.strategy.scope():
               try:
                   if self.args.is_tpu or self.args.use_xla:
                       # run additional 10 times to stabilize compilation for tpu
                       logger.info('Do inference on TPU. Running model 5 times to stabilize compilation')
                       timeit.repeat(lowerCamelCase,      repeat=1,      number=5)

                   # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                   _lowercase       :							Dict      		=		timeit.repeat(
                       lowerCamelCase,      repeat=self.args.repeat,      number=10,      )

                   return min(lowerCamelCase) / 1_0.0
               except ResourceExhaustedError as e:
                   self.print_fn(F'''Doesn\'t fit on GPU. {e}''')




       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   [Memory, MemorySummary]:




           """simple docstring"""

           logger.info(
               'Note that TensorFlow allocates more memory than '
               'it might need to speed up computation. '
               'The memory reported here corresponds to the memory '
               'reported by `nvidia-smi`, which can vary depending '
               'on total available memory on the GPU that is used.')
           with self.args.strategy.scope():
               try:
                   if self.args.trace_memory_line_by_line:
                       if not self.args.eager_mode:
                           raise ValueError(
                               '`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'
                               ' consumption line by line.')
                       _lowercase       :							str      		=		start_memory_tracing('transformers')

                   if self.args.is_tpu:
                       # tpu
                       raise NotImplementedError(
                           'Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'
                           ' with `args.memory=False`')
                   elif self.args.is_gpu:
                       # gpu
                       if not is_pyanvml_available():
                           logger.warning(
                               'py3nvml not installed, we won\'t log GPU memory usage. '
                               'Install py3nvml (pip install py3nvml) to log information about GPU.')
                           _lowercase       :							Dict      		=		'N/A'
                       else:
                           logger.info(
                               'Measuring total GPU usage on GPU device. Make sure to not have additional processes'
                               ' running on the same GPU.')
                           # init nvml
                           nvml.nvmlInit()
                           func()
                           _lowercase       :							int      		=		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                           _lowercase       :							str      		=		nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase)
                           _lowercase       :							Dict      		=		meminfo.used
                           _lowercase       :							Any      		=		Memory(lowerCamelCase)
                           # shutdown nvml
                           nvml.nvmlShutdown()
                   else:
                       # cpu
                       if self.args.trace_memory_line_by_line:
                           logger.info(
                               'When enabling line by line tracing, the max peak memory for CPU is inaccurate in'
                               ' TensorFlow.')
                           _lowercase       :							str      		=		None
                       else:
                           _lowercase       :							Any      		=		measure_peak_memory_cpu(lowerCamelCase)
                           _lowercase       :							Dict      		=		Memory(lowerCamelCase) if isinstance(lowerCamelCase,      lowerCamelCase) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                       _lowercase       :							Optional[int]      		=		stop_memory_tracing(lowerCamelCase)
                       if memory is None:
                           _lowercase       :							List[str]      		=		summary.total
                   else:
                       _lowercase       :							Optional[int]      		=		None

                   return memory, summary
               except ResourceExhaustedError as e:
                   self.print_fn(F'''Doesn\'t fit on GPU. {e}''')
                   return "N/A", None






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		1
           _lowercase       :							Any      		=		3
           _lowercase       :							Tuple      		=		(32, 32)

           _lowercase       :							Tuple      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		UNetaDConditionModel(
               block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=4,      out_channels=4,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),      cross_attention_dim=32,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							str      		=		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,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		RobertaSeriesConfig(
               hidden_size=32,      project_dim=32,      intermediate_size=37,      layer_norm_eps=1E-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=50_06,      )
           return RobertaSeriesModelWithTransformation(lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           def extract(*lowerCamelCase,      **lowerCamelCase):






               class _lowerCamelCase:
                      def __init__(					self)       ->   Optional[Any]:




                          """simple docstring"""

                          _lowercase       :							Optional[int]      		=		torch.ones([0])




                      def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




                          """simple docstring"""

                          self.pixel_values.to(lowerCamelCase)
                          return self


               return Out()

           return extract
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Union[str, Any]      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.dummy_vae
           _lowercase       :							List[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Any      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Tuple      		=		77

           _lowercase       :							int      		=		self.dummy_image.to(lowerCamelCase)
           _lowercase       :							int      		=		init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[Any]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Dict      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Any      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      )

           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Optional[Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							int      		=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Tuple      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							str      		=		self.dummy_vae
           _lowercase       :							Optional[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Optional[Any]      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Optional[Any]      		=		77

           _lowercase       :							str      		=		self.dummy_image.to(lowerCamelCase)

           # put models in fp16
           _lowercase       :							List[str]      		=		unet.half()
           _lowercase       :							List[Any]      		=		vae.half()
           _lowercase       :							Any      		=		bert.half()

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[str]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Any      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Optional[Any]      		=		torch.manual_seed(0)
           _lowercase       :							Union[str, Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      ).images

           assert image.shape == (1, 32, 32, 3)




       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           # resize to resolution that is divisible by 8 but not 16 or 32
           _lowercase       :							str      		=		init_image.resize((7_60, 5_04))

           _lowercase       :							Optional[int]      		=		'BAAI/AltDiffusion'
           _lowercase       :							str      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							Any      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[str]      		=		output.images[0]

           _lowercase       :							Tuple      		=		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           _lowercase       :							Optional[Any]      		=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0])

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






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							str      		=		init_image.resize((7_68, 5_12))
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

           _lowercase       :							str      		=		'BAAI/AltDiffusion'
           _lowercase       :							Optional[Any]      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							int      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Union[str, Any]      		=		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image).max() < 1E-2


