






import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

__UpperCamelCase					:		List[str]	  =						logging.getLogger(__name__)


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       field(
							    default=1_2_8    ,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }    ,  )


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Train language if it is different from the evaluation language."}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,  )
							_UpperCAmelCase     =       field(
							    default="main"    ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=a_    ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,  )


def      a_ (      )			->			Union[str, Any]:







				"""simple docstring"""
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				snake_case__									=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
				snake_case__							,						snake_case__							,						snake_case__									=     parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry('run_xnli'      ,   _A  )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,   datefmt='%m/%d/%Y %H:%M:%S'      ,   handlers=[logging.StreamHandler(sys.stdout  )]      ,   )

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				snake_case__									=     training_args.get_process_log_level()
				logger.setLevel(_A  )
				datasets.utils.logging.set_verbosity(_A  )
				transformers.utils.logging.set_verbosity(_A  )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
				    + f'''distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}'''  )
				logger.info(f'''Training/evaluation parameters {training_args}'''  )

				# Detecting last checkpoint.
				snake_case__									=     None
				if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case__									=     get_last_checkpoint(training_args.output_dir  )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
												raise ValueError(
												    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
												    'Use --overwrite_output_dir to overcome.'  )
								elif last_checkpoint is not None:
												logger.info(
												    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
												    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Set seed before initializing model.
				set_seed(training_args.seed  )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												snake_case__									=     load_dataset(
												    'xnli'      ,   model_args.language      ,   split='train'      ,   cache_dir=model_args.cache_dir      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
								else:
												snake_case__									=     load_dataset(
												    'xnli'      ,   model_args.train_language      ,   split='train'      ,   cache_dir=model_args.cache_dir      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
								snake_case__									=     train_dataset.features['label'].names

				if training_args.do_eval:
								snake_case__									=     load_dataset(
								    'xnli'      ,   model_args.language      ,   split='validation'      ,   cache_dir=model_args.cache_dir      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
								snake_case__									=     eval_dataset.features['label'].names

				if training_args.do_predict:
								snake_case__									=     load_dataset(
								    'xnli'      ,   model_args.language      ,   split='test'      ,   cache_dir=model_args.cache_dir      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
								snake_case__									=     predict_dataset.features['label'].names

				# Labels
				snake_case__									=     len(_A  )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				snake_case__									=     AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,   num_labels=_A      ,   idalabel={str(_A  ): label for i, label in enumerate(_A  )}      ,   labelaid={label: i for i, label in enumerate(_A  )}      ,   finetuning_task='xnli'      ,   cache_dir=model_args.cache_dir      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
				snake_case__									=     AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,   do_lower_case=model_args.do_lower_case      ,   cache_dir=model_args.cache_dir      ,   use_fast=model_args.use_fast_tokenizer      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   )
				snake_case__									=     AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path      ,   from_tf=bool('.ckpt' in model_args.model_name_or_path  )      ,   config=_A      ,   cache_dir=model_args.cache_dir      ,   revision=model_args.model_revision      ,   use_auth_token=True if model_args.use_auth_token else None      ,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,   )

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								snake_case__									=     'max_length'
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								snake_case__									=     False

				def preprocess_function(_A  ):
								# Tokenize the texts
								return tokenizer(
								    examples['premise']      ,   examples['hypothesis']      ,   padding=_A      ,   max_length=data_args.max_seq_length      ,   truncation=_A      ,   )

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												snake_case__									=     min(len(_A  )      ,   data_args.max_train_samples  )
												snake_case__									=     train_dataset.select(range(_A  )  )
								with training_args.main_process_first(desc='train dataset map pre-processing'  ):
												snake_case__									=     train_dataset.map(
												    _A      ,   batched=_A      ,   load_from_cache_file=not data_args.overwrite_cache      ,   desc='Running tokenizer on train dataset'      ,   )
								# Log a few random samples from the training set:
								for index in random.sample(range(len(_A  )  )      ,   3  ):
												logger.info(f'''Sample {index} of the training set: {train_dataset[index]}.'''  )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												snake_case__									=     min(len(_A  )      ,   data_args.max_eval_samples  )
												snake_case__									=     eval_dataset.select(range(_A  )  )
								with training_args.main_process_first(desc='validation dataset map pre-processing'  ):
												snake_case__									=     eval_dataset.map(
												    _A      ,   batched=_A      ,   load_from_cache_file=not data_args.overwrite_cache      ,   desc='Running tokenizer on validation dataset'      ,   )

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												snake_case__									=     min(len(_A  )      ,   data_args.max_predict_samples  )
												snake_case__									=     predict_dataset.select(range(_A  )  )
								with training_args.main_process_first(desc='prediction dataset map pre-processing'  ):
												snake_case__									=     predict_dataset.map(
												    _A      ,   batched=_A      ,   load_from_cache_file=not data_args.overwrite_cache      ,   desc='Running tokenizer on prediction dataset'      ,   )

    # Get the metric function
				snake_case__									=     evaluate.load('xnli'  )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(_A  ):
								snake_case__									=     p.predictions[0] if isinstance(p.predictions      ,   _A  ) else p.predictions
								snake_case__									=     np.argmax(_A      ,   axis=1  )
								return metric.compute(predictions=_A      ,   references=p.label_ids  )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								snake_case__									=     default_data_collator
				elif training_args.fpaa:
								snake_case__									=     DataCollatorWithPadding(_A      ,   pad_to_multiple_of=8  )
				else:
								snake_case__									=     None

				# Initialize our Trainer
				snake_case__									=     Trainer(
				    model=_A      ,   args=_A      ,   train_dataset=train_dataset if training_args.do_train else None      ,   eval_dataset=eval_dataset if training_args.do_eval else None      ,   compute_metrics=_A      ,   tokenizer=_A      ,   data_collator=_A      ,   )

				# Training
				if training_args.do_train:
								snake_case__									=     None
								if training_args.resume_from_checkpoint is not None:
												snake_case__									=     training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case__									=     last_checkpoint
								snake_case__									=     trainer.train(resume_from_checkpoint=_A  )
								snake_case__									=     train_result.metrics
								snake_case__									=     (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_A  )
								)
								snake_case__									=     min(_A      ,   len(_A  )  )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics('train'      ,   _A  )
								trainer.save_metrics('train'      ,   _A  )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info('*** Evaluate ***'  )
								snake_case__									=     trainer.evaluate(eval_dataset=_A  )

								snake_case__									=     data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_A  )
								snake_case__									=     min(_A      ,   len(_A  )  )

								trainer.log_metrics('eval'      ,   _A  )
								trainer.save_metrics('eval'      ,   _A  )

				# Prediction
				if training_args.do_predict:
								logger.info('*** Predict ***'  )
								snake_case__							,						snake_case__							,						snake_case__									=     trainer.predict(_A      ,   metric_key_prefix='predict'  )

								snake_case__									=     (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_A  )
								)
								snake_case__									=     min(_A      ,   len(_A  )  )

								trainer.log_metrics('predict'      ,   _A  )
								trainer.save_metrics('predict'      ,   _A  )

								snake_case__									=     np.argmax(_A      ,   axis=1  )
								snake_case__									=     os.path.join(training_args.output_dir      ,   'predictions.txt'  )
								if trainer.is_world_process_zero():
												with open(_A      ,   'w'  ) as writer:
																writer.write('index\tprediction\n'  )
																for index, item in enumerate(_A  ):
																				snake_case__									=     label_list[item]
																				writer.write(f'''{index}\t{item}\n'''  )


if __name__ == "__main__":
							main()












import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableUnCLIPImgaImgPipeline
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS
							_UpperCAmelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase     =       frozenset(
							    []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							_UpperCAmelCase     =       frozenset([]	)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Union[str, Any]:
											snake_case__									=     32
											snake_case__									=     embedder_hidden_size

											# image encoding components

											snake_case__									=     CLIPImageProcessor(crop_size=32       ,	size=32						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPVisionModelWithProjection(
											    CLIPVisionConfig(
											        hidden_size=UpperCamelCase       ,	projection_dim=UpperCamelCase       ,	num_hidden_layers=5       ,	num_attention_heads=4       ,	image_size=32       ,	intermediate_size=37       ,	patch_size=1       ,	)						)

											# regular denoising components

											torch.manual_seed(0						)
											snake_case__									=     StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase						)
											snake_case__									=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											torch.manual_seed(0						)
											snake_case__									=     CLIPTextModel(
											    CLIPTextConfig(
											        bos_token_id=0       ,	eos_token_id=2       ,	hidden_size=UpperCamelCase       ,	projection_dim=32       ,	intermediate_size=37       ,	layer_norm_eps=1e-05       ,	num_attention_heads=4       ,	num_hidden_layers=5       ,	pad_token_id=1       ,	vocab_size=10_00       ,	)						)

											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,	up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,	block_out_channels=(32, 64)       ,	attention_head_dim=(2, 4)       ,	class_embed_type='projection'       ,	projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,	cross_attention_dim=UpperCamelCase       ,	layers_per_block=1       ,	upcast_attention=UpperCamelCase       ,	use_linear_projection=UpperCamelCase       ,	)

											torch.manual_seed(0						)
											snake_case__									=     DDIMScheduler(
											    beta_schedule='scaled_linear'       ,	beta_start=0.00_085       ,	beta_end=0.012       ,	prediction_type='v_prediction'       ,	set_alpha_to_one=UpperCamelCase       ,	steps_offset=1       ,	)

											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL()

											snake_case__									=     {
											    # image encoding components
											    'feature_extractor': feature_extractor,
											    'image_encoder': image_encoder.eval(),
											    # image noising components
											    'image_normalizer': image_normalizer.eval(),
											    'image_noising_scheduler': image_noising_scheduler,
											    # regular denoising components
											    'tokenizer': tokenizer,
											    'text_encoder': text_encoder.eval(),
											    'unet': unet.eval(),
											    'scheduler': scheduler,
											    'vae': vae.eval(),
											}

											return components
							def lowerCAmelCase_ (     self:      str       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Tuple=0       ,	UpperCamelCase:      Union[str, Any]=True						)		->		Any:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)

											snake_case__									=     floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(UpperCamelCase						)						).to(UpperCamelCase						)

											if pil_image:
															snake_case__									=     input_image * 0.5 + 0.5
															snake_case__									=     input_image.clamp(0       ,	1						)
															snake_case__									=     input_image.cpu().permute(0       ,	2       ,	3       ,	1						).float().numpy()
															snake_case__									=     DiffusionPipeline.numpy_to_pil(UpperCamelCase						)[0]

											return {
											    "prompt": "An anime racoon running a marathon",
											    "image": input_image,
											    "generator": generator,
											    "num_inference_steps": 2,
											    "output_type": "np",
											}
							@skip_mps
							def lowerCAmelCase_ (     self:      Dict						)		->		Optional[Any]:
											snake_case__									=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case__									=     self.get_dummy_components()
											snake_case__									=     StableUnCLIPImgaImgPipeline(**UpperCamelCase						)
											snake_case__									=     sd_pipe.to(UpperCamelCase						)
											sd_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     self.get_dummy_inputs(UpperCamelCase						)
											inputs.update({'image_embeds': None}						)
											snake_case__									=     sd_pipe(**UpperCamelCase						).images
											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Union[str, Any]:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		int:
											snake_case__									=     torch_device in ['cpu', 'mps']

											self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase						)


							@unittest.skipIf(
							    torch_device != 'cuda' or not is_xformers_available()       ,	reason='XFormers attention is only available with CUDA and `xformers` installed'       ,	)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[int]:
											self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-l-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											snake_case__									=     load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'						)

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											# stable unclip will oom when integration tests are run on a V100,
											# so turn on memory savings
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     torch.Generator(device='cpu'						).manual_seed(0						)
											snake_case__									=     pipe(UpperCamelCase       ,	'anime turle'       ,	generator=UpperCamelCase       ,	output_type='np'						)

											snake_case__									=     output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		str:
											snake_case__									=     load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'						)

											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()
											torch.cuda.reset_peak_memory_stats()

											snake_case__									=     StableUnCLIPImgaImgPipeline.from_pretrained(
											    'fusing/stable-unclip-2-1-h-img2img'       ,	torch_dtype=torch.floataa						)
											snake_case__									=     pipe.to(UpperCamelCase						)
											pipe.set_progress_bar_config(disable=UpperCamelCase						)
											pipe.enable_attention_slicing()
											pipe.enable_sequential_cpu_offload()

											snake_case__									=     pipe(
											    UpperCamelCase       ,	'anime turtle'       ,	num_inference_steps=2       ,	output_type='np'       ,	)

											snake_case__									=     torch.cuda.max_memory_allocated()
											# make sure that less than 7 GB is allocated
											assert mem_bytes < 7 * 10**9




