



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 							_a							(      unittest.TestCase				):

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

			@property
			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Tuple       			=    1
						UpperCAmelCase_:		Dict       			=    3
						UpperCAmelCase_:		Optional[int]       			=    (32, 32)

						UpperCAmelCase_:		int       			=    floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE_				)
						return image

			@property
			def     __snake_case					(self				)					->      Optional[Any]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    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     __snake_case					(self				)					->      str:
						torch.manual_seed(0				)
						UpperCAmelCase_:		int       			=    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     __snake_case					(self				)					->      List[str]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    CLIPTextConfig(
						    bos_token_id=0,      eos_token_id=2,      hidden_size=32,      intermediate_size=37,      layer_norm_eps=1E-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=1000,      )
						return CLIPTextModel(SCREAMING_SNAKE_CASE_				)

			@property
			def     __snake_case					(self				)					->      str:
						def extract(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				):







									class 							_a							:

												def __init__(self				)					->      Any:
															UpperCAmelCase_:		Optional[Any]       			=    torch.ones([0]				)

												def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_				)
															return self






									return Out()

						return extract

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Any       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Union[str, Any]       			=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      beta_schedule="""scaled_linear""",      clip_sample=SCREAMING_SNAKE_CASE_,      set_alpha_to_one=SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		str       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""

						UpperCAmelCase_:		Tuple       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Dict       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)
						UpperCAmelCase_:		Tuple       			=    output.images

						UpperCAmelCase_:		List[Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[str]       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		int       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[str]       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Dict       			=    np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]				)

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

			def     __snake_case					(self				)					->      Any:
						UpperCAmelCase_:		List[Any]       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Tuple       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Optional[int]       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_vae
						UpperCAmelCase_:		Any       			=    self.dummy_text_encoder
						UpperCAmelCase_:		List[Any]       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		List[str]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Union[str, Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[Any]       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)

						UpperCAmelCase_:		Optional[int]       			=    output.images

						UpperCAmelCase_:		Any       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Tuple       			=    np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]				)

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

			def     __snake_case					(self				)					->      List[str]:
						UpperCAmelCase_:		Dict       			=    StableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""",      safety_checker=SCREAMING_SNAKE_CASE_				)
						assert isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
						assert isinstance(pipe.scheduler,      SCREAMING_SNAKE_CASE_				)
						assert pipe.safety_checker is None

						UpperCAmelCase_:		Optional[Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Optional[int]       			=    StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_				)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						UpperCAmelCase_:		Union[str, Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

			@unittest.skipIf(torch_device != """cuda""",      """This test requires a GPU"""				)
			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Any       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Tuple       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# put models in fp16
						UpperCAmelCase_:		Dict       			=    unet.half()
						UpperCAmelCase_:		Optional[Any]       			=    vae.half()
						UpperCAmelCase_:		Union[str, Any]       			=    bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		str       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe([prompt],      num_inference_steps=2,      output_type="""np"""				).images

						assert image.shape == (1, 64, 64, 3)







@nightly
@require_torch_gpu
class 							_a							(      unittest.TestCase				):

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

			def     __snake_case					(self				)					->      str:
						UpperCAmelCase_:		Optional[int]       			=    """cuda"""
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """a photo of a cat"""
						UpperCAmelCase_:		Tuple       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[str]       			=    3
						UpperCAmelCase_:		str       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.3_4_6_7_3_1_1_3,
						    0.3_8_4_9_2_7_3_3,
						    0.3_7_5_9_7_3_5_2,
						    0.3_4_0_8_6_3_3_5,
						    0.3_5_6_5_0_7_4_8,
						    0.3_5_5_7_9_2_0_5,
						    0.3_3_8_4_7_6_3,
						    0.3_4_3_4_0_2_3_6,
						    0.3_5_7_3_2_7_1,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		Any       			=    output.images
						UpperCAmelCase_:		Dict       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.4_1_8_8_7_8_2_6,
						    0.3_7_7_2_8_7_6_6,
						    0.3_0_1_3_8_2_7_2,
						    0.4_1_4_1_6_3_3_5,
						    0.4_1_6_6_4_9_8_5,
						    0.3_6_2_8_3_3_9_2,
						    0.3_6_1_9_1_2_4_6,
						    0.4_3_3_6_4_4_6_5,
						    0.4_3_0_0_1_7_3_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Tuple       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		List[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """an image of a crowded boulevard, realistic, 4k"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": """crowd, crowded, people""",
						    """reverse_editing_direction""": True,
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 8.3,
						    """edit_threshold""": 0.9,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		Dict       			=    9
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		List[Any]       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		int       			=    [
						    0.4_3_4_9_7_9_9_8,
						    0.9_1_8_1_4_0_6_5,
						    0.7_5_4_0_7_3_9,
						    0.5_5_5_8_0_2_0_5,
						    0.8_4_6_7_2_6_5,
						    0.5_3_8_9_6_9_1,
						    0.6_2_5_7_4_5_0_6,
						    0.5_8_8_9_7_7_6_3,
						    0.5_0_9_2_6_7_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[int]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		int       			=    output.images
						UpperCAmelCase_:		Any       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str       			=    [
						    0.3_0_8_9_7_1_9,
						    0.3_0_5_0_0_1_4_4,
						    0.2_9_0_1_6_0_4_2,
						    0.3_0_6_3_0_9_6_4,
						    0.3_2_5_6_8_7,
						    0.2_9_4_1_9_2_2_5,
						    0.2_9_0_8_0_9_1,
						    0.2_8_7_2_3_5_9_8,
						    0.2_7_6_9_6_2_9_4,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Union[str, Any]       			=    """cuda"""
						UpperCAmelCase_:		Optional[Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Tuple       			=    """a castle next to a river"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
						    """reverse_editing_direction""": False,
						    """edit_warmup_steps""": [15, 18],
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": [0.9, 0.8],
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		str       			=    48
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		int       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Tuple       			=    output.images
						UpperCAmelCase_:		Tuple       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    [
						    0.7_5_1_6_3_5_5_5,
						    0.7_6_0_3_7_1_4_5,
						    0.6_1_7_8_5,
						    0.9_1_8_9_6_7_3,
						    0.8_6_2_7_7_0_1,
						    0.8_5_1_8_9_6_9_4,
						    0.8_5_1_2_8_1_3,
						    0.8_7_0_1_2_0_7_6,
						    0.8_3_1_2_8_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Dict       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    output.images
						UpperCAmelCase_:		Optional[int]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[Any]       			=    [
						    0.7_3_5_5_3_3_6_5,
						    0.7_5_3_7_2_7_1,
						    0.7_4_3_4_1_9_0_5,
						    0.6_6_4_8_0_3_5_6,
						    0.6_4_7_2_9_2_5,
						    0.6_3_0_3_9_4_1_6,
						    0.6_4_8_1_2_9_0_5,
						    0.6_7_4_9_7_1_7,
						    0.6_5_1_7_1_0_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Union[str, Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""",      torch_dtype=torch.floataa				)
						UpperCAmelCase_:		Optional[int]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """a photo of a cat"""
						UpperCAmelCase_:		Dict       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[Any]       			=    3
						UpperCAmelCase_:		Dict       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		str       			=    output.images
						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.3_4_8_8_7_6_9_5,
						    0.3_8_7_6_9_5_3,
						    0.3_7_5,
						    0.3_4_4_2_3_8_2_8,
						    0.3_5_8_1_5_4_3,
						    0.3_5_7_1_7_7_7_3,
						    0.3_3_8_3_7_8_9,
						    0.3_4_5_7_0_3_1_2,
						    0.3_5_9_3_7_5,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[Any]       			=    output.images
						UpperCAmelCase_:		List[str]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.4_2_2_8_5_1_5_6,
						    0.3_6_9_1_4_0_6_2,
						    0.2_9_0_7_7_1_4_8,
						    0.4_2_0_4_1_0_1_6,
						    0.4_1_9_1_8_9_4_5,
						    0.3_5_4_9_8_0_4_7,
						    0.3_6_1_8_1_6_4,
						    0.4_4_2_3_8_2_8,
						    0.4_3_1_1_5_2_3_4,
						]

						assert image.shape == (1, 512, 512, 3)

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











import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


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







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			random.seed(lowerCAmelCase__     )
			np.random.seed(lowerCAmelCase__     )
			torch.manual_seed(lowerCAmelCase__     )
			torch.cuda.manual_seed_all(lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		Dict     ):





			"""simple docstring"""


			model.eval()
			UpperCAmelCase_:		Optional[Any]       			=    next(model.parameters()     ).device
			UpperCAmelCase_:		Tuple       			=    1
			UpperCAmelCase_:		Any       			=    torch.zeros((eval_batch_size, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[Any]       			=    torch.zeros(1		,	device=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    0
			for batch in eval_dataloader:
						batch.to(lowerCAmelCase__     )
						# pad
						context.zero_()
						for i in range(lowerCAmelCase__     ):
									UpperCAmelCase_:		Optional[int]       			=    batch[i]
						UpperCAmelCase_:		Union[str, Any]       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0     )
			UpperCAmelCase_:		Tuple       			=    eval_loss / nb_eval_examples
			UpperCAmelCase_:		List[Any]       			=    torch.exp(lowerCAmelCase__     )
			model.train()
			return perplexity







def    lowerCAmelCase_  (lowerCAmelCase__:		List[Any]="gpt2"     ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]       			=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase__		,	output_hidden_states=lowerCAmelCase__     )
			torch.save(model.state_dict()		,	model_name + """local.pt"""     )
			return model







def    lowerCAmelCase_  (lowerCAmelCase__:		Dict		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			model.to(lowerCAmelCase__     )

			UpperCAmelCase_:		Dict       			=    ["""bias""", """LayerNorm.weight"""]
			UpperCAmelCase_:		Optional[int]       			=    [
			    {
			        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )],
			        """weight_decay""": 0.0,
			    },
			    {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay     )], """weight_decay""": 0.0},
			]
			UpperCAmelCase_:		str       			=    AdamW(lowerCAmelCase__		,	lr=5e-5		,	eps=1e-8     )
			UpperCAmelCase_:		Tuple       			=    get_linear_schedule_with_warmup(lowerCAmelCase__		,	0		,	lowerCAmelCase__     )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler







def    lowerCAmelCase_  (lowerCAmelCase__:		int		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		str     ):





			"""simple docstring"""


			UpperCAmelCase_:		str       			=    np.array(lowerCAmelCase__     ).mean()
			UpperCAmelCase_:		Optional[Any]       			=    np.array(lowerCAmelCase__     ).std()
			UpperCAmelCase_:		Tuple       			=    (real_perps - avg) / std
			UpperCAmelCase_:		Tuple       			=    list(zip(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )     )
			joblib.dump(lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Any		,	lowerCAmelCase__:		Union[str, Any]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		Optional[Any]="dev.jbl"		,	lowerCAmelCase__:		Tuple=recopy_gpta		,	):





			"""simple docstring"""


			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []
			UpperCAmelCase_:		int       			=    []

			# Initialize the transformer model
			UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(lowerCAmelCase__     )
			orig_model.to(device="""cpu"""     )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		List[str]       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

			for step in tqdm(range(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Union[str, Any]       			=    torch.zeros((1, context_len)		,	dtype=torch.long		,	device=lowerCAmelCase__     )
						UpperCAmelCase_:		str       			=    random.choice(lowerCAmelCase__     )
						UpperCAmelCase_:		int       			=    random.randint(0		,	len(story[0]     ) - context_len - 1     )
						UpperCAmelCase_:		Optional[int]       			=    story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCAmelCase_:		Dict       			=    model(lowerCAmelCase__		,	labels=lowerCAmelCase__     )
						UpperCAmelCase_:		List[str]       			=    outputs[0]
						UpperCAmelCase_:		Optional[Any]       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()		,	3.0     )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						UpperCAmelCase_:		int       			=    compute_perplexity(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1_0_0_0 == 0 and step > 1:
									intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCAmelCase_			,       UpperCAmelCase_			,       UpperCAmelCase_:		str       			=    recopy_model(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )

						past_perps.append(past_perp.item()     )
						real_perps.append(orig_perp - real_perp.item()     )
						contexts.append(np.array(context.cpu()     )     )

			intermittent_save(lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__     )







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]		,	lowerCAmelCase__:		Any="data/tokenized_stories_train_wikitext103.jbl"		,	lowerCAmelCase__:		Any=1_0_0		,	lowerCAmelCase__:		Optional[int]=1_0_2_6		,	lowerCAmelCase__:		Optional[int]=True     ):





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    joblib.load(lowerCAmelCase__     )
			print("""data loaded"""     )
			UpperCAmelCase_:		List[Any]       			=    []
			if trim:
						for i, example in enumerate(lowerCAmelCase__     ):
									if len(example[0]     ) > min_len:
												UpperCAmelCase_:		Optional[int]       			=    random.randint(0		,	len(example[0]     ) - context_len - 1     )
												objective_set.append(example[0, start : start + context_len]     )
									if len(lowerCAmelCase__     ) >= number:
												break
						UpperCAmelCase_:		Union[str, Any]       			=    []
						for j in range(i + 1		,	len(lowerCAmelCase__     )     ):
									if len(data[j][0]     ) > min_len:
												train_data.append(data[j]     )
			else:
						UpperCAmelCase_:		Optional[int]       			=    data[0:number]
						UpperCAmelCase_:		Tuple       			=    data[number:]

			joblib.dump(lowerCAmelCase__		,	"""objective_set.jbl"""     )
			print("""objective set saved"""     )
			return train_data, objective_set







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[int]		,	lowerCAmelCase__:		List[str]		,	lowerCAmelCase__:		int		,	lowerCAmelCase__:		Tuple		,	lowerCAmelCase__:		int=5_0		,	lowerCAmelCase__:		List[Any]="secondary_learner.pt"     ):





			"""simple docstring"""


			UpperCAmelCase_:		Tuple       			=    torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			UpperCAmelCase_:		int       			=    train_dataset[:5_1_2]
			UpperCAmelCase_:		Any       			=    train_dataset[5_1_2:]
			UpperCAmelCase_:		Union[str, Any]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    DataLoader(lowerCAmelCase__		,	shuffle=lowerCAmelCase__		,	batch_size=lowerCAmelCase__     )

			# secondary learner model set up
			UpperCAmelCase_:		Optional[int]       			=    nn.MSELoss()
			UpperCAmelCase_:		str       			=    nn.MSELoss(reduction="""sum"""     )
			secondary_learner.to(lowerCAmelCase__     )
			UpperCAmelCase_:		Optional[int]       			=    torch.optim.Adam(secondary_learner.parameters()		,	lr=0.00001     )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			UpperCAmelCase_:		Any       			=    float("""inf"""     )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(lowerCAmelCase__     )     ):
						UpperCAmelCase_:		Optional[Any]       			=    0.0
						secondary_learner.train()
						for step, batch in enumerate(lowerCAmelCase__     ):
									UpperCAmelCase_:		Union[str, Any]       			=    batch[0].to(lowerCAmelCase__     )
									UpperCAmelCase_:		List[Any]       			=    batch[1].to(lowerCAmelCase__     )
									UpperCAmelCase_:		Tuple       			=    secondary_learner(lowerCAmelCase__     )
									q_optimizer.zero_grad()
									UpperCAmelCase_:		Any       			=    loss(lowerCAmelCase__		,	real_q.float()     )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(lowerCAmelCase__     )):
												UpperCAmelCase_:		int       			=    tr_q_loss / (step + 1)

												secondary_learner.eval()
												UpperCAmelCase_:		Optional[Any]       			=    0.0
												UpperCAmelCase_:		Union[str, Any]       			=    0.0
												UpperCAmelCase_:		Any       			=    []
												UpperCAmelCase_:		Any       			=    []
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(lowerCAmelCase__     ):
															UpperCAmelCase_:		Optional[Any]       			=    batcha[0].to(lowerCAmelCase__     )
															UpperCAmelCase_:		int       			=    batcha[1].to(lowerCAmelCase__     )
															UpperCAmelCase_:		Dict       			=    secondary_learner(lowerCAmelCase__     )
															q_lossa += test_loss(lowerCAmelCase__		,	lowerCAmelCase__     ).item()
															sum_qa += torch.sum(lowerCAmelCase__     ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy()     ):
																		predicted.append(i.item()     )
															for ei, i in enumerate(real_qa.cpu().detach().numpy()     ):
																		actual.append(i.item()     )

												q_lossa /= len(lowerCAmelCase__     )
												print(
												    """Epoch: """		,	lowerCAmelCase__		,	"""step: """		,	lowerCAmelCase__		,	"""Avg. q:"""		,	sum_qa / len(lowerCAmelCase__     )		,	"""Train Loss: """		,	lowerCAmelCase__		,	"""Test Loss: """		,	lowerCAmelCase__		,	)
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)		,	"""pred_vs_actual.jbl"""     )
															torch.save(secondary_learner.state_dict()		,	lowerCAmelCase__     )
															UpperCAmelCase_:		str       			=    q_lossa

									secondary_learner.train()
			return secondary_learner







class 							_a							(      nn.Module				):

			def __init__(self,      SCREAMING_SNAKE_CASE_				)					->      str:
						super(SCREAMING_SNAKE_CASE_,      self				).__init__()
						UpperCAmelCase_:		List[str]       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						UpperCAmelCase_:		List[str]       			=    nn.Convad(self.embeddings.weight.size(1				),      256,      3,      padding=1				)
						UpperCAmelCase_:		Union[str, Any]       			=    nn.Sequential(nn.Linear(256,      32				),      nn.Dropout(p=0.1				),      nn.Linear(32,      32				),      nn.Linear(32,      1				)				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      Any:
						UpperCAmelCase_:		Dict       			=    torch.max(self.conv(self.embeddings(SCREAMING_SNAKE_CASE_				).squeeze(1				).transpose(1,      2				)				),      2				)[0]
						UpperCAmelCase_:		Dict       			=    self.fc(SCREAMING_SNAKE_CASE_				)
						return qs.squeeze(1				)

			@classmethod
			def     __snake_case					(cls,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						UpperCAmelCase_:		Optional[int]       			=    cls(SCREAMING_SNAKE_CASE_				)  # this calls __init__
						UpperCAmelCase_:		Any       			=    torch.load(SCREAMING_SNAKE_CASE_				)
						secondary_learner.load_state_dict(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    model.transformer.wte
						UpperCAmelCase_:		Optional[Any]       			=    copy.deepcopy(model.transformer.wte.weight				)
						return secondary_learner






