import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()


class     lowercase     (  A__					,							A__					,							A__					,							unittest.TestCase  ):
							lowercase__    :							str										=				StableUnCLIPPipeline
							lowercase__    :							List[Any]										=				TEXT_TO_IMAGE_PARAMS
							lowercase__    :							Optional[int]										=				TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase__    :							Optional[int]										=				TEXT_TO_IMAGE_IMAGE_PARAMS
							lowercase__    :							Optional[int]										=				TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							lowercase__    :							Union[str, Any]										=				False




							def 						__snake_case(    self :       Tuple   )       ->					Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							32
												SCREAMING_SNAKE_CASE           =							embedder_hidden_size

												# prior components

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

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							CLIPTextModelWithProjection(
												    CLIPTextConfig(
												        bos_token_id=0						,				eos_token_id=2						,				hidden_size=_lowerCamelCase						,				projection_dim=_lowerCamelCase						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1_000						,				)   )

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							PriorTransformer(
												    num_attention_heads=2						,				attention_head_dim=12						,				embedding_dim=_lowerCamelCase						,				num_layers=1						,				)

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							DDPMScheduler(
												    variance_type="fixed_small_log"						,				prediction_type="sample"						,				num_train_timesteps=1_000						,				clip_sample=_lowerCamelCase						,				clip_sample_range=5.0						,				beta_schedule="squaredcos_cap_v2"						,				)

												# regular denoising components

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							DDPMScheduler(beta_schedule="squaredcos_cap_v2"   )

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

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							CLIPTextModel(
												    CLIPTextConfig(
												        bos_token_id=0						,				eos_token_id=2						,				hidden_size=_lowerCamelCase						,				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=1_000						,				)   )

												torch.manual_seed(0   )
												SCREAMING_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=_lowerCamelCase						,				layers_per_block=1						,				upcast_attention=_lowerCamelCase						,				use_linear_projection=_lowerCamelCase						,				)

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							DDIMScheduler(
												    beta_schedule="scaled_linear"						,				beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				prediction_type="v_prediction"						,				set_alpha_to_one=_lowerCamelCase						,				steps_offset=1						,				)

												torch.manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							AutoencoderKL()

												SCREAMING_SNAKE_CASE           =							{
												    # prior components
												    'prior_tokenizer': prior_tokenizer,
												    'prior_text_encoder': prior_text_encoder,
												    'prior': prior,
												    'prior_scheduler': prior_scheduler,
												    # image noising components
												    'image_normalizer': image_normalizer,
												    'image_noising_scheduler': image_noising_scheduler,
												    # regular denoising components
												    'tokenizer': tokenizer,
												    'text_encoder': text_encoder,
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												}

												return components




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       Dict=0   )       ->					int:



												'''simple docstring'''

												if str(_lowerCamelCase   ).startswith("mps"   ):
																	SCREAMING_SNAKE_CASE           =							torch.manual_seed(_lowerCamelCase   )
												else:
																	SCREAMING_SNAKE_CASE           =							torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
												SCREAMING_SNAKE_CASE           =							{
												    'prompt': 'A painting of a squirrel eating a burger',
												    'generator': generator,
												    'num_inference_steps': 2,
												    'prior_num_inference_steps': 2,
												    'output_type': 'numpy',
												}
												return inputs




							def 						__snake_case(    self :       Any   )       ->					str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							torch_device == 'cpu'

												self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase   )





							def 						__snake_case(    self :       str   )       ->					Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							torch_device in ['cpu', 'mps']

												self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase   )



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




							def 						__snake_case(    self :       List[str]   )       ->					int:



												'''simple docstring'''

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




							def 						__snake_case(    self :       int   )       ->					Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"   )

												SCREAMING_SNAKE_CASE           =							StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa   )
												pipe.to(_lowerCamelCase   )
												pipe.set_progress_bar_config(disable=_lowerCamelCase   )
												# 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()

												SCREAMING_SNAKE_CASE           =							torch.Generator(device="cpu"   ).manual_seed(0   )
												SCREAMING_SNAKE_CASE           =							pipe("anime turle"						,				generator=_lowerCamelCase						,				output_type="np"   )

												SCREAMING_SNAKE_CASE           =							output.images[0]

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

												assert_mean_pixel_difference(_lowerCamelCase						,				_lowerCamelCase   )





							def 						__snake_case(    self :       Union[str, Any]   )       ->					Optional[int]:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =							StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa   )
												SCREAMING_SNAKE_CASE           =							pipe.to(_lowerCamelCase   )
												pipe.set_progress_bar_config(disable=_lowerCamelCase   )
												pipe.enable_attention_slicing()
												pipe.enable_sequential_cpu_offload()

												SCREAMING_SNAKE_CASE           =							pipe(
												    "anime turtle"						,				prior_num_inference_steps=2						,				num_inference_steps=2						,				output_type="np"						,				)

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






"""simple docstring"""






# 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       A_  (				__lowercase						,	__lowercase						,	__lowercase						,	__lowercase	):
 UpperCamelCase_     :					Tuple   	=multiprocessing.Manager()
 UpperCamelCase_     :					Dict   	=manager.list()

 UpperCamelCase_     :					Union[str, Any]   	=multiprocessing.Process(target=__lowercase						,	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       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 with create_tempdir():
  # These system calls are needed when cleaning up tempdir.
  import os
  import shutil

  UpperCamelCase_     :					Tuple   	=shutil.rmtree
  UpperCamelCase_     :					List[str]   	=os.rmdir
  UpperCamelCase_     :					Any   	=os.chdir

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

  # Run program.
  try:
   UpperCamelCase_     :					List[str]   	={}
   with swallow_io():
    with time_limit(__lowercase	):
     exec(__lowercase						,	__lowercase	)
   result.append('passed'	)
  except TimeoutException:
   result.append('timed out'	)
  except BaseException as e:
   result.append(F'''failed: {e}'''	)

  # Needed for cleaning up.
  UpperCamelCase_     :					Optional[Any]   	=rmtree
  UpperCamelCase_     :					str   	=rmdir
  UpperCamelCase_     :					Dict   	=chdir




@contextlib.contextmanager
def       A_  (				__lowercase	):
 def signal_handler(__lowercase						,	__lowercase	):
  raise TimeoutException('Timed out!'	)

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




@contextlib.contextmanager
def       A_  (				):
 UpperCamelCase_     :					str   	=WriteOnlyStringIO()
 with contextlib.redirect_stdout(__lowercase	):
  with contextlib.redirect_stderr(__lowercase	):
   with redirect_stdin(__lowercase	):
    yield




@contextlib.contextmanager
def       A_  (				):
 with tempfile.TemporaryDirectory() as dirname:
  with chdir(__lowercase	):
   yield dirname





class 				a__					(						A__       ):
     pass





class 				a__					(						io.StringIO       ):



     def  lowerCamelCase_						(							self :Any      ,  *_lowerCamelCase :Any      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      raise OSError



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  *_lowerCamelCase :Any      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      raise OSError



     def  lowerCamelCase_						(							self :Tuple      ,  *_lowerCamelCase :str      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      raise OSError




     def  lowerCamelCase_						(							self :List[str]      ,  *_lowerCamelCase :Optional[int]      ,  **_lowerCamelCase :str   ):







      '''simple docstring'''



      return False





class 				a__					(						contextlib._RedirectStream       ):  # type: ignore
     UpperCAmelCase__         =      '''stdin'''




@contextlib.contextmanager
def       A_  (				__lowercase	):
 if root == ".":
  yield
  return
 UpperCamelCase_     :					List[str]   	=os.getcwd()
 os.chdir(__lowercase	)
 try:
  yield
 except BaseException as exc:
  raise exc
 finally:
  os.chdir(__lowercase	)





def       A_  (				__lowercase=None	):

 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

 UpperCamelCase_     :					Any   	=None
 UpperCamelCase_     :					Any   	=None

 import os

 UpperCamelCase_     :					Union[str, Any]   	='1'

 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Any   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					Optional[int]   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					List[str]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					int   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					List[Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					Optional[int]   	=None
 UpperCamelCase_     :					str   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					int   	=None

 import shutil

 UpperCamelCase_     :					List[str]   	=None
 UpperCamelCase_     :					Optional[Any]   	=None
 UpperCamelCase_     :					Dict   	=None

 import subprocess

 UpperCamelCase_     :					Optional[int]   	=None  # type: ignore

 UpperCamelCase_     :					Union[str, Any]   	=None

 import sys

 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Tuple   	=None
 UpperCamelCase_     :					Union[str, Any]   	=None
 UpperCamelCase_     :					Dict   	=None
 UpperCamelCase_     :					Any   	=None
