



# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile



def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       List[Any] ,       UpperCamelCase_				:       Dict ,       UpperCamelCase_				:       Tuple	)->  Optional[Any]:
    A__		 =					multiprocessing.Manager()
    A__		 =					manager.list()

    A__		 =					multiprocessing.Process(target=a__ ,       args=(check_program, result, timeout)	)
    p.start()
    p.join(timeout=timeout + 1	)
    if p.is_alive():
        p.kill()

    if not result:
        result.append('''timed out'''	)

    return {
        "task_id": task_id,
        "passed": result[0] == "passed",
        "result": result[0],
        "completion_id": completion_id,
    }



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       List[str]	)->  Tuple:

    with create_tempdir():
        # These system calls are needed when cleaning up tempdir.
        import os
        import shutil

        A__		 =					shutil.rmtree
        A__		 =					os.rmdir
        A__		 =					os.chdir

        # Disable functionalities that can make destructive changes to the test.
        reliability_guard()

        # Run program.
        try:
            A__		 =					{}
            with swallow_io():
                with time_limit(a__	):
                    exec(a__ ,       a__	)
            result.append('''passed'''	)
        except TimeoutException:
            result.append('''timed out'''	)
        except BaseException as e:
            result.append(f"failed: {e}"	)

        # Needed for cleaning up.
        A__		 =					rmtree
        A__		 =					rmdir
        A__		 =					chdir



@contextlib.contextmanager
def 		lowerCAmelCase__    (					UpperCamelCase_				:       List[str]	)->  List[str]:

    def signal_handler(UpperCamelCase_				:       Tuple ,       UpperCamelCase_				:       int	):
        raise TimeoutException('''Timed out!'''	)

    signal.setitimer(signal.ITIMER_REAL ,       a__	)
    signal.signal(signal.SIGALRM ,       a__	)
    try:
        yield
    finally:
        signal.setitimer(signal.ITIMER_REAL ,       0	)



@contextlib.contextmanager
def 		lowerCAmelCase__    (					)->  Dict:
    A__		 =					WriteOnlyStringIO()
    with contextlib.redirect_stdout(a__	):
        with contextlib.redirect_stderr(a__	):
            with redirect_stdin(a__	):
                yield



@contextlib.contextmanager
def 		lowerCAmelCase__    (					)->  Dict:

    with tempfile.TemporaryDirectory() as dirname:
        with chdir(a__	):
            yield dirname



class     _UpperCAmelCase			(       A__     ):
     pass







class     _UpperCAmelCase			(       io.StringIO     ):




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         raise OSError




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         raise OSError




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         raise OSError




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         return False







class     _UpperCAmelCase			(       contextlib._RedirectStream     ):  # type: ignore
     UpperCamelCase__		    =				'''stdin'''







@contextlib.contextmanager
def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Union[str, Any]:

    if root == ".":
        yield
        return
    A__		 =					os.getcwd()
    os.chdir(a__	)
    try:
        yield
    except BaseException as exc:
        raise exc
    finally:
        os.chdir(a__	)



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Any=None	)->  Dict:

    if maximum_memory_bytes is not None:
        import resource

        resource.setrlimit(resource.RLIMIT_AS ,       (maximum_memory_bytes, maximum_memory_bytes)	)
        resource.setrlimit(resource.RLIMIT_DATA ,       (maximum_memory_bytes, maximum_memory_bytes)	)
        if not platform.uname().system == "Darwin":
            resource.setrlimit(resource.RLIMIT_STACK ,       (maximum_memory_bytes, maximum_memory_bytes)	)

    faulthandler.disable()

    import builtins

    A__		 =					None
    A__		 =					None

    import os

    A__		 =					'''1'''

    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None

    import shutil

    A__		 =					None
    A__		 =					None
    A__		 =					None

    import subprocess

    A__		 =					None  # type: ignore

    A__		 =					None

    import sys

    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None
    A__		 =					None





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin



@skip_mps
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				IFPipeline
     UpperCamelCase__		    =				TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     UpperCamelCase__		    =				TEXT_TO_IMAGE_BATCH_PARAMS
     UpperCamelCase__		    =				PipelineTesterMixin.required_optional_params - {'''latents'''}




     def 		snake_case_		(				self):
         return self._get_dummy_components()




     def 		snake_case_		(				self ,						a__ ,						a__=0):
         if str(a__).startswith('''mps'''):
             A__		 =					torch.manual_seed(a__)
         else:
             A__		 =					torch.Generator(device=a__).manual_seed(a__)

         A__		 =					{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs




     def 		snake_case_		(				self):
         self._test_save_load_optional_components()




     @unittest.skipIf(torch_device != '''cuda''' ,						reason='''float16 requires CUDA''')
     def 		snake_case_		(				self):
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1)




     def 		snake_case_		(				self):
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)




     def 		snake_case_		(				self):
         self._test_save_load_local()




     def 		snake_case_		(				self):
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,						)




     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available() ,						reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,						)
     def 		snake_case_		(				self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







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




     def 		snake_case_		(				self):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(				self):
         # if

         A__		 =					IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa)

         A__		 =					IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0''' ,						variant='''fp16''' ,						torch_dtype=torch.floataa ,						text_encoder=a__ ,						tokenizer=a__)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda''')

         A__ ,	A__		 =					pipe_a.encode_prompt('''anime turtle''' ,						device='''cuda''')

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         A__		 =					None
         A__		 =					None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A__		 =					IFImgaImgPipeline(**pipe_a.components)
         A__		 =					IFImgaImgSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_imgaimg(a__ ,						a__ ,						a__ ,						a__)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A__		 =					IFInpaintingPipeline(**pipe_a.components)
         A__		 =					IFInpaintingSuperResolutionPipeline(**pipe_a.components)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

         self._test_if_inpainting(a__ ,						a__ ,						a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_3 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         # pipeline 1

         _start_torch_memory_measurement()

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(1)).to(a__)

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)
         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						num_inference_steps=2 ,						generator=a__ ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (6_4, 6_4, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 1_0 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
         assert_mean_pixel_difference(a__ ,						a__)

         # pipeline 2

         _start_torch_memory_measurement()

         A__		 =					torch.Generator(device='''cpu''').manual_seed(0)

         A__		 =					floats_tensor((1, 3, 6_4, 6_4) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(0)).to(a__)
         A__		 =					floats_tensor((1, 3, 2_5_6, 2_5_6) ,						rng=random.Random(1)).to(a__)

         A__		 =					pipe_a(
             prompt_embeds=a__ ,						negative_prompt_embeds=a__ ,						image=a__ ,						mask_image=a__ ,						original_image=a__ ,						generator=a__ ,						num_inference_steps=2 ,						output_type='''np''' ,						)

         A__		 =					output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         A__		 =					torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 1_0**9

         A__		 =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
         assert_mean_pixel_difference(a__ ,						a__)







def 		lowerCAmelCase__    (					)->  List[Any]:
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()
