import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	None
    if token is not None:
        __UpperCamelCase  :Tuple    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :int    						=	f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    __UpperCamelCase  :Optional[int]    						=	requests.get(SCREAMING_SNAKE_CASE						,    headers=SCREAMING_SNAKE_CASE			).json()
    __UpperCamelCase  :Tuple    						=	{}

    try:
        job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}			)
        __UpperCamelCase  :str    						=	math.ceil((result['''total_count'''] - 100) / 100			)

        for i in range(SCREAMING_SNAKE_CASE			):
            __UpperCamelCase  :int    						=	requests.get(url + f"""&page={i + 2}"""						,    headers=SCREAMING_SNAKE_CASE			).json()
            job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}			)

        return job_links
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""			)

    return {}



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	None
    if token is not None:
        __UpperCamelCase  :int    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :Optional[Any]    						=	f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    __UpperCamelCase  :Any    						=	requests.get(SCREAMING_SNAKE_CASE						,    headers=SCREAMING_SNAKE_CASE			).json()
    __UpperCamelCase  :Optional[int]    						=	{}

    try:
        artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}			)
        __UpperCamelCase  :str    						=	math.ceil((result['''total_count'''] - 100) / 100			)

        for i in range(SCREAMING_SNAKE_CASE			):
            __UpperCamelCase  :str    						=	requests.get(url + f"""&page={i + 2}"""						,    headers=SCREAMING_SNAKE_CASE			).json()
            artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}			)

        return artifacts
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""			)

    return {}



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	None
    if token is not None:
        __UpperCamelCase  :List[str]    						=	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}

    __UpperCamelCase  :str    						=	requests.get(SCREAMING_SNAKE_CASE						,    headers=SCREAMING_SNAKE_CASE						,    allow_redirects=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	result.headers['''Location''']
    __UpperCamelCase  :List[str]    						=	requests.get(SCREAMING_SNAKE_CASE						,    allow_redirects=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	os.path.join(SCREAMING_SNAKE_CASE						,    f"""{artifact_name}.zip"""			)
    with open(SCREAMING_SNAKE_CASE						,    '''wb'''			) as fp:
        fp.write(response.content			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	[]
    __UpperCamelCase  :List[str]    						=	[]
    __UpperCamelCase  :List[str]    						=	None

    with zipfile.ZipFile(SCREAMING_SNAKE_CASE			) as z:
        for filename in z.namelist():
            if not os.path.isdir(SCREAMING_SNAKE_CASE			):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(SCREAMING_SNAKE_CASE			) as f:
                        for line in f:
                            __UpperCamelCase  :Tuple    						=	line.decode('''UTF-8'''			).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    __UpperCamelCase  :Optional[int]    						=	line[: line.index(''': '''			)]
                                    __UpperCamelCase  :Union[str, Any]    						=	line[line.index(''': '''			) + len(''': '''			) :]
                                    errors.append([error_line, error]			)
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith('''FAILED '''			):
                                # `test` is the test method that failed
                                __UpperCamelCase  :Optional[int]    						=	line[len('''FAILED '''			) :]
                                failed_tests.append(SCREAMING_SNAKE_CASE			)
                            elif filename == "job_name.txt":
                                __UpperCamelCase  :Union[str, Any]    						=	line

    if len(SCREAMING_SNAKE_CASE			) != len(SCREAMING_SNAKE_CASE			):
        raise ValueError(
            f"""`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE			)} for `errors` """
            f"""and {len(SCREAMING_SNAKE_CASE			)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            ''' problem.'''			)

    __UpperCamelCase  :Dict    						=	None
    if job_name and job_links:
        __UpperCamelCase  :Any    						=	job_links.get(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # A list with elements of the form (line of error, error, failed test)
    __UpperCamelCase  :List[str]    						=	[x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)]

    return result



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	[]

    __UpperCamelCase  :List[str]    						=	[os.path.join(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			) for p in os.listdir(SCREAMING_SNAKE_CASE			) if p.endswith('''.zip'''			)]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE						,    job_links=SCREAMING_SNAKE_CASE			)			)

    return errors



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	Counter()
    counter.update([x[1] for x in logs]			)
    __UpperCamelCase  :str    						=	counter.most_common()
    __UpperCamelCase  :Dict    						=	{}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            __UpperCamelCase  :str    						=	{'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

    __UpperCamelCase  :Tuple    						=	dict(sorted(r.items()						,    key=lambda SCREAMING_SNAKE_CASE			: item[1]["count"]						,    reverse=SCREAMING_SNAKE_CASE			)			)
    return r



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	test.split('''::'''			)[0]
    if test.startswith('''tests/models/'''			):
        __UpperCamelCase  :List[str]    						=	test.split('''/'''			)[2]
    else:
        __UpperCamelCase  :Tuple    						=	None

    return test



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	[(x[0], x[1], get_model(x[2]			)) for x in logs]
    __UpperCamelCase  :int    						=	[x for x in logs if x[2] is not None]
    __UpperCamelCase  :List[str]    						=	{x[2] for x in logs}

    __UpperCamelCase  :Optional[int]    						=	{}
    for test in tests:
        __UpperCamelCase  :List[str]    						=	Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]			)
        __UpperCamelCase  :Optional[Any]    						=	counter.most_common()
        __UpperCamelCase  :List[str]    						=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        __UpperCamelCase  :Optional[Any]    						=	sum(error_counts.values()			)
        if n_errors > 0:
            __UpperCamelCase  :Union[str, Any]    						=	{'''count''': n_errors, '''errors''': error_counts}

    __UpperCamelCase  :Optional[Any]    						=	dict(sorted(r.items()						,    key=lambda SCREAMING_SNAKE_CASE			: item[1]["count"]						,    reverse=SCREAMING_SNAKE_CASE			)			)
    return r



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	'''| no. | error | status |'''
    __UpperCamelCase  :Dict    						=	'''|-:|:-|:-|'''
    __UpperCamelCase  :List[Any]    						=	[header, sep]
    for error in reduced_by_error:
        __UpperCamelCase  :List[Any]    						=	reduced_by_error[error]['''count''']
        __UpperCamelCase  :Any    						=	f"""| {count} | {error[:100]} |  |"""
        lines.append(SCREAMING_SNAKE_CASE			)

    return "\n".join(SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	'''| model | no. of errors | major error | count |'''
    __UpperCamelCase  :Union[str, Any]    						=	'''|-:|-:|-:|-:|'''
    __UpperCamelCase  :int    						=	[header, sep]
    for model in reduced_by_model:
        __UpperCamelCase  :List[Any]    						=	reduced_by_model[model]['''count''']
        __UpperCamelCase  :Tuple    						=	list(reduced_by_model[model]['''errors'''].items()			)[0]
        __UpperCamelCase  :str    						=	f"""| {model} | {count} | {error[:60]} | {_count} |"""
        lines.append(SCREAMING_SNAKE_CASE			)

    return "\n".join(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
    parser.add_argument(
        '''--output_dir''',
        type=str,
        required=True,
        help='''Where to store the downloaded artifacts and other result files.''',
    )
    parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
    __lowercase											=      parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    __lowercase											=      get_job_links(args.workflow_run_id, token=args.token)
    __lowercase											=      {}
    # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
    # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
    if _job_links:
        for k, v in _job_links.items():
            # This is how GitHub actions combine job names.
            if " / " in k:
                __lowercase											=      k.find(''' / ''')
                __lowercase											=      k[index + len(''' / ''') :]
            __lowercase											=      v
    with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
        json.dump(job_links, fp, ensure_ascii=False, indent=4)

    __lowercase											=      get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
        json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    for idx, (name, url) in enumerate(artifacts.items()):
        download_artifact(name, url, args.output_dir, args.token)
        # Be gentle to GitHub
        time.sleep(1)

    __lowercase											=      get_all_errors(args.output_dir, job_links=job_links)

    # `e[1]` is the error
    __lowercase											=      Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    __lowercase											=      counter.most_common(30)
    for item in most_common:
        print(item)

    with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
        json.dump(errors, fp, ensure_ascii=False, indent=4)

    __lowercase											=      reduce_by_error(errors)
    __lowercase											=      reduce_by_model(errors)

    __lowercase											=      make_github_table(reduced_by_error)
    __lowercase											=      make_github_table_per_model(reduced_by_model)

    with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
        fp.write(sa)
    with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
        fp.write(sa)






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 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[str]      			=   StableUnCLIPImgaImgPipeline
    a__			:		Optional[Any]      			=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    a__			:		Optional[int]      			=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a__			:		str      			=   frozenset(
        []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    a__			:		List[Any]      			=   frozenset([]				)







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	32
        __UpperCamelCase  :int    						=	embedder_hidden_size

        # image encoding components

        __UpperCamelCase  :Dict    						=	CLIPImageProcessor(crop_size=32				,				size=32)

        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	CLIPVisionModelWithProjection(
            CLIPVisionConfig(
                hidden_size=__lowercase				,				projection_dim=__lowercase				,				num_hidden_layers=5				,				num_attention_heads=4				,				image_size=32				,				intermediate_size=37				,				patch_size=1				,				))

        # regular denoising components

        torch.manual_seed(0)
        __UpperCamelCase  :Union[str, Any]    						=	StableUnCLIPImageNormalizer(embedding_dim=__lowercase)
        __UpperCamelCase  :Any    						=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2''')

        torch.manual_seed(0)
        __UpperCamelCase  :List[Any]    						=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        torch.manual_seed(0)
        __UpperCamelCase  :str    						=	CLIPTextModel(
            CLIPTextConfig(
                bos_token_id=0				,				eos_token_id=2				,				hidden_size=__lowercase				,				projection_dim=32				,				intermediate_size=37				,				layer_norm_eps=1E-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				))

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	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=__lowercase				,				layers_per_block=1				,				upcast_attention=__lowercase				,				use_linear_projection=__lowercase				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Dict    						=	DDIMScheduler(
            beta_schedule='''scaled_linear'''				,				beta_start=0.0_00_85				,				beta_end=0.0_12				,				prediction_type='''v_prediction'''				,				set_alpha_to_one=__lowercase				,				steps_offset=1				,				)

        torch.manual_seed(0)
        __UpperCamelCase  :Any    						=	AutoencoderKL()

        __UpperCamelCase  :Dict    						=	{
            # 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     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0				,				__lowercase=True) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :int    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :int    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Tuple    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if pil_image:
            __UpperCamelCase  :Tuple    						=	input_image * 0.5 + 0.5
            __UpperCamelCase  :Any    						=	input_image.clamp(0				,				1)
            __UpperCamelCase  :Any    						=	input_image.cpu().permute(0				,				2				,				3				,				1).float().numpy()
            __UpperCamelCase  :int    						=	DiffusionPipeline.numpy_to_pil(__lowercase)[0]

        return {
            "prompt": "An anime racoon running a marathon",
            "image": input_image,
            "generator": generator,
            "num_inference_steps": 2,
            "output_type": "np",
        }







    @skip_mps
    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :int    						=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        __UpperCamelCase  :Dict    						=	self.get_dummy_components()
        __UpperCamelCase  :Dict    						=	StableUnCLIPImgaImgPipeline(**__lowercase)
        __UpperCamelCase  :int    						=	sd_pipe.to(__lowercase)
        sd_pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	self.get_dummy_inputs(__lowercase)
        inputs.update({'''image_embeds''': None})
        __UpperCamelCase  :Any    						=	sd_pipe(**__lowercase).images
        __UpperCamelCase  :int    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :Union[str, Any]    						=	np.array([0.38_72, 0.72_24, 0.56_01, 0.47_41, 0.68_72, 0.58_14, 0.46_36, 0.38_67, 0.50_78])

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







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Union[str, Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_attention_slicing_forward_pass(test_max_difference=__lowercase)







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        __UpperCamelCase  :List[Any]    						=	torch_device in ['''cpu''', '''mps''']

        self._test_inference_batch_single_identical(test_max_difference=__lowercase)







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


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






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







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

        __UpperCamelCase  :str    						=	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''')

        __UpperCamelCase  :Optional[int]    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-l-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # 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()

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :str    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png''')

        __UpperCamelCase  :str    						=	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''')

        __UpperCamelCase  :Tuple    						=	StableUnCLIPImgaImgPipeline.from_pretrained(
            '''fusing/stable-unclip-2-1-h-img2img'''				,				torch_dtype=torch.floataa)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        # 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()

        __UpperCamelCase  :int    						=	torch.Generator(device='''cpu''').manual_seed(0)
        __UpperCamelCase  :int    						=	pipe(__lowercase				,				'''anime turle'''				,				generator=__lowercase				,				output_type='''np''')

        __UpperCamelCase  :List[str]    						=	output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        __UpperCamelCase  :List[Any]    						=	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()

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

        __UpperCamelCase  :Tuple    						=	pipe(
            __lowercase				,				'''anime turtle'''				,				num_inference_steps=2				,				output_type='''np'''				,				)

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





