





"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()






@skip_mps
class        lowercase__ (      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Dict								=     StableDiffusionModelEditingPipeline
			__lowerCAmelCase    :							Tuple								=     TEXT_TO_IMAGE_PARAMS
			__lowerCAmelCase    :							int								=     TEXT_TO_IMAGE_BATCH_PARAMS
			__lowerCAmelCase    :							List[str]								=     TEXT_TO_IMAGE_IMAGE_PARAMS
			__lowerCAmelCase    :							Dict								=     TEXT_TO_IMAGE_IMAGE_PARAMS







			def      _a			(			self						):






									'''simple docstring'''







									torch.manual_seed(0						)
									UpperCamelCase							:		str							      = UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)	,      layers_per_block=2	,      sample_size=3_2	,      in_channels=4	,      out_channels=4	,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")	,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")	,      cross_attention_dim=3_2	,      )
									UpperCamelCase							:		Optional[int]							      = DDIMScheduler()
									torch.manual_seed(0						)
									UpperCamelCase							:		List[str]							      = AutoencoderKL(
									    block_out_channels=[3_2, 6_4]	,      in_channels=3	,      out_channels=3	,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]	,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]	,      latent_channels=4	,      )
									torch.manual_seed(0						)
									UpperCamelCase							:		int							      = CLIPTextConfig(
									    bos_token_id=0	,      eos_token_id=2	,      hidden_size=3_2	,      intermediate_size=3_7	,      layer_norm_eps=1e-0_5	,      num_attention_heads=4	,      num_hidden_layers=5	,      pad_token_id=1	,      vocab_size=1_0_0_0	,      )
									UpperCamelCase							:		Dict							      = CLIPTextModel(_A						)
									UpperCamelCase							:		Dict							      = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

									UpperCamelCase							:		List[str]							      = {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components







			def      _a			(			self	,      _A	,      _A=0						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = torch.manual_seed(_A						)
									UpperCamelCase							:		Union[str, Any]							      = {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    """height""": None,
									    """width""": None,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase							:		Union[str, Any]							      = self.get_dummy_components()
									UpperCamelCase							:		str							      = StableDiffusionModelEditingPipeline(**_A						)
									UpperCamelCase							:		Tuple							      = sd_pipe.to(_A						)
									sd_pipe.set_progress_bar_config(disable=_A						)

									UpperCamelCase							:		Dict							      = self.get_dummy_inputs(_A						)
									UpperCamelCase							:		List[str]							      = sd_pipe(**_A						).images
									UpperCamelCase							:		List[Any]							      = image[0, -3:, -3:, -1]
									assert image.shape == (1, 6_4, 6_4, 3)

									UpperCamelCase							:		Union[str, Any]							      = np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]						)

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







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase							:		int							      = self.get_dummy_components()
									UpperCamelCase							:		Optional[int]							      = StableDiffusionModelEditingPipeline(**_A						)
									UpperCamelCase							:		str							      = sd_pipe.to(_A						)
									sd_pipe.set_progress_bar_config(disable=_A						)

									UpperCamelCase							:		int							      = self.get_dummy_inputs(_A						)
									UpperCamelCase							:		Union[str, Any]							      = """french fries"""
									UpperCamelCase							:		Optional[int]							      = sd_pipe(**_A	,      negative_prompt=_A						)
									UpperCamelCase							:		Optional[int]							      = output.images
									UpperCamelCase							:		Dict							      = image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									UpperCamelCase							:		int							      = np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]						)

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







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase							:		Optional[int]							      = self.get_dummy_components()
									UpperCamelCase							:		int							      = EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85	,      beta_end=0.0_12	,      beta_schedule="""scaled_linear"""						)
									UpperCamelCase							:		Dict							      = StableDiffusionModelEditingPipeline(**_A						)
									UpperCamelCase							:		str							      = sd_pipe.to(_A						)
									sd_pipe.set_progress_bar_config(disable=_A						)

									UpperCamelCase							:		List[Any]							      = self.get_dummy_inputs(_A						)
									UpperCamelCase							:		Dict							      = sd_pipe(**_A						).images
									UpperCamelCase							:		List[str]							      = image[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									UpperCamelCase							:		Optional[Any]							      = np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]						)

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







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = """cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase							:		Optional[Any]							      = self.get_dummy_components()
									UpperCamelCase							:		List[Any]							      = PNDMScheduler()
									UpperCamelCase							:		Dict							      = StableDiffusionModelEditingPipeline(**_A						)
									UpperCamelCase							:		List[str]							      = sd_pipe.to(_A						)
									sd_pipe.set_progress_bar_config(disable=_A						)

									UpperCamelCase							:		int							      = self.get_dummy_inputs(_A						)
									# the pipeline does not expect pndm so test if it raises error.
									with self.assertRaises(_A						):
															UpperCamelCase							:		Optional[int]							      = sd_pipe(**_A						).images







			def      _a			(			self						):






									'''simple docstring'''







									super().test_inference_batch_single_identical(expected_max_diff=5e-3						)







			def      _a			(			self						):






									'''simple docstring'''







									super().test_attention_slicing_forward_pass(expected_max_diff=5e-3						)






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

			"""simple docstring"""







			def      _a			(			self						):






									'''simple docstring'''







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







			def      _a			(			self	,      _A=0						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = torch.manual_seed(_A						)
									UpperCamelCase							:		Tuple							      = {
									    """prompt""": """A field of roses""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = """CompVis/stable-diffusion-v1-4"""
									UpperCamelCase							:		int							      = StableDiffusionModelEditingPipeline.from_pretrained(_A	,      safety_checker=_A						)
									pipe.to(_A						)
									pipe.set_progress_bar_config(disable=_A						)
									pipe.enable_attention_slicing()

									UpperCamelCase							:		Dict							      = self.get_inputs()
									UpperCamelCase							:		Tuple							      = pipe(**_A						).images
									UpperCamelCase							:		List[Any]							      = image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									UpperCamelCase							:		Tuple							      = np.array(
									    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]						)

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

									# make sure image changes after editing
									pipe.edit_model("""A pack of roses"""	,      """A pack of blue roses"""						)

									UpperCamelCase							:		Dict							      = pipe(**_A						).images
									UpperCamelCase							:		Union[str, Any]							      = image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 5_1_2, 5_1_2, 3)

									assert np.abs(expected_slice - image_slice						).max() > 1e-1







			def      _a			(			self						):






									'''simple docstring'''







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

									UpperCamelCase							:		Tuple							      = """CompVis/stable-diffusion-v1-4"""
									UpperCamelCase							:		Optional[Any]							      = DDIMScheduler.from_pretrained(_A	,      subfolder="""scheduler"""						)
									UpperCamelCase							:		Optional[int]							      = StableDiffusionModelEditingPipeline.from_pretrained(
									    _A	,      scheduler=_A	,      safety_checker=_A						)
									UpperCamelCase							:		str							      = pipe.to(_A						)
									pipe.set_progress_bar_config(disable=_A						)
									pipe.enable_attention_slicing(1						)
									pipe.enable_sequential_cpu_offload()

									UpperCamelCase							:		Dict							      = self.get_inputs()
									UpperCamelCase							:		Optional[Any]							      = pipe(**_A						)

									UpperCamelCase							:		Any							      = torch.cuda.max_memory_allocated()
									# make sure that less than 4.4 GB is allocated
									assert mem_bytes < 4.4 * 1_0**9










"""simple docstring"""


from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin






@dataclass
class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							torch.FloatTensor






class        lowercase__ (      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""







			@register_to_config
			def __init__(			self	,      _A = 1_6	,      _A = 8_8	,      _A = None	,      _A = None	,      _A = 1	,      _A = 0.0	,      _A = 3_2	,      _A = None	,      _A = False	,      _A = None	,      _A = None	,      _A = None	,      _A = "geglu"	,      _A = None	,      _A = False	,      _A = False	,      _A = False	,      _A = "layer_norm"	,      _A = True	,      ):






									'''simple docstring'''







									super().__init__()
									UpperCamelCase							:		int							      = use_linear_projection
									UpperCamelCase							:		str							      = num_attention_heads
									UpperCamelCase							:		Union[str, Any]							      = attention_head_dim
									UpperCamelCase							:		List[Any]							      = num_attention_heads * attention_head_dim

									# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
									# Define whether input is continuous or discrete depending on configuration
									UpperCamelCase							:		int							      = (in_channels is not None) and (patch_size is None)
									UpperCamelCase							:		str							      = num_vector_embeds is not None
									UpperCamelCase							:		Tuple							      = in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
															UpperCamelCase							:		Tuple							      = (
															    f"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
															    """ incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."""
															    """ Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"""
															    """ results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"""
															    """ would be very nice if you could open a Pull request for the `transformer/config.json` file"""
															)
															deprecate("""norm_type!=num_embeds_ada_norm"""	,      """1.0.0"""	,      _A	,      standard_warn=_A						)
															UpperCamelCase							:		Optional[int]							      = """ada_norm"""

									if self.is_input_continuous and self.is_input_vectorized:
															raise ValueError(
															    f"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
															    """ sure that either `in_channels` or `num_vector_embeds` is None."""						)
									elif self.is_input_vectorized and self.is_input_patches:
															raise ValueError(
															    f"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
															    """ sure that either `num_vector_embeds` or `num_patches` is None."""						)
									elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
															raise ValueError(
															    f"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
															    f""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""						)

									# 2. Define input layers
									if self.is_input_continuous:
															UpperCamelCase							:		Union[str, Any]							      = in_channels

															UpperCamelCase							:		List[str]							      = torch.nn.GroupNorm(num_groups=_A	,      num_channels=_A	,      eps=1e-6	,      affine=_A						)
															if use_linear_projection:
																					UpperCamelCase							:		Dict							      = nn.Linear(_A	,      _A						)
															else:
																					UpperCamelCase							:		int							      = nn.Convad(_A	,      _A	,      kernel_size=1	,      stride=1	,      padding=0						)
									elif self.is_input_vectorized:
															assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
															assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

															UpperCamelCase							:		Tuple							      = sample_size
															UpperCamelCase							:		str							      = sample_size
															UpperCamelCase							:		int							      = num_vector_embeds
															UpperCamelCase							:		Any							      = self.height * self.width

															UpperCamelCase							:		Any							      = ImagePositionalEmbeddings(
															    num_embed=_A	,      embed_dim=_A	,      height=self.height	,      width=self.width						)
									elif self.is_input_patches:
															assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

															UpperCamelCase							:		Optional[Any]							      = sample_size
															UpperCamelCase							:		int							      = sample_size

															UpperCamelCase							:		int							      = patch_size
															UpperCamelCase							:		Optional[Any]							      = PatchEmbed(
															    height=_A	,      width=_A	,      patch_size=_A	,      in_channels=_A	,      embed_dim=_A	,      )

									# 3. Define transformers blocks
									UpperCamelCase							:		List[Any]							      = nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            _A	,      _A	,      _A	,      dropout=_A	,      cross_attention_dim=_A	,      activation_fn=_A	,      num_embeds_ada_norm=_A	,      attention_bias=_A	,      only_cross_attention=_A	,      upcast_attention=_A	,      norm_type=_A	,      norm_elementwise_affine=_A	,      )
									        for d in range(_A						)
									    ]						)

									# 4. Define output layers
									UpperCamelCase							:		Union[str, Any]							      = in_channels if out_channels is None else out_channels
									if self.is_input_continuous:
															# TODO: should use out_channels for continuous projections
															if use_linear_projection:
																					UpperCamelCase							:		Optional[int]							      = nn.Linear(_A	,      _A						)
															else:
																					UpperCamelCase							:		str							      = nn.Convad(_A	,      _A	,      kernel_size=1	,      stride=1	,      padding=0						)
									elif self.is_input_vectorized:
															UpperCamelCase							:		str							      = nn.LayerNorm(_A						)
															UpperCamelCase							:		Tuple							      = nn.Linear(_A	,      self.num_vector_embeds - 1						)
									elif self.is_input_patches:
															UpperCamelCase							:		Any							      = nn.LayerNorm(_A	,      elementwise_affine=_A	,      eps=1e-6						)
															UpperCamelCase							:		Optional[Any]							      = nn.Linear(_A	,      2 * inner_dim						)
															UpperCamelCase							:		List[Any]							      = nn.Linear(_A	,      patch_size * patch_size * self.out_channels						)







			def      _a			(			self	,      _A	,      _A = None	,      _A = None	,      _A = None	,      _A = None	,      _A = None	,      _A = None	,      _A = True	,      ):






									'''simple docstring'''







									if attention_mask is not None and attention_mask.ndim == 2:
															# assume that mask is expressed as:
															#   (1 = keep,      0 = discard)
															# convert mask into a bias that can be added to attention scores:
															#       (keep = +0,     discard = -10000.0)
															UpperCamelCase							:		List[str]							      = (1 - attention_mask.to(hidden_states.dtype						)) * -1_00_00.0
															UpperCamelCase							:		Any							      = attention_mask.unsqueeze(1						)

									# convert encoder_attention_mask to a bias the same way we do for attention_mask
									if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
															UpperCamelCase							:		Union[str, Any]							      = (1 - encoder_attention_mask.to(hidden_states.dtype						)) * -1_00_00.0
															UpperCamelCase							:		Dict							      = encoder_attention_mask.unsqueeze(1						)

									# 1. Input
									if self.is_input_continuous:
															UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase      ,	UpperCamelCase							:		int							      = hidden_states.shape
															UpperCamelCase							:		int							      = hidden_states

															UpperCamelCase							:		List[Any]							      = self.norm(_A						)
															if not self.use_linear_projection:
																					UpperCamelCase							:		Union[str, Any]							      = self.proj_in(_A						)
																					UpperCamelCase							:		Any							      = hidden_states.shape[1]
																					UpperCamelCase							:		Optional[int]							      = hidden_states.permute(0	,      2	,      3	,      1						).reshape(_A	,      height * width	,      _A						)
															else:
																					UpperCamelCase							:		Optional[int]							      = hidden_states.shape[1]
																					UpperCamelCase							:		Optional[Any]							      = hidden_states.permute(0	,      2	,      3	,      1						).reshape(_A	,      height * width	,      _A						)
																					UpperCamelCase							:		Optional[int]							      = self.proj_in(_A						)
									elif self.is_input_vectorized:
															UpperCamelCase							:		Dict							      = self.latent_image_embedding(_A						)
									elif self.is_input_patches:
															UpperCamelCase							:		str							      = self.pos_embed(_A						)

									# 2. Blocks
									for block in self.transformer_blocks:
															UpperCamelCase							:		Union[str, Any]							      = block(
															    _A	,      attention_mask=_A	,      encoder_hidden_states=_A	,      encoder_attention_mask=_A	,      timestep=_A	,      cross_attention_kwargs=_A	,      class_labels=_A	,      )

									# 3. Output
									if self.is_input_continuous:
															if not self.use_linear_projection:
																					UpperCamelCase							:		Optional[int]							      = hidden_states.reshape(_A	,      _A	,      _A	,      _A						).permute(0	,      3	,      1	,      2						).contiguous()
																					UpperCamelCase							:		Tuple							      = self.proj_out(_A						)
															else:
																					UpperCamelCase							:		List[Any]							      = self.proj_out(_A						)
																					UpperCamelCase							:		str							      = hidden_states.reshape(_A	,      _A	,      _A	,      _A						).permute(0	,      3	,      1	,      2						).contiguous()

															UpperCamelCase							:		int							      = hidden_states + residual
									elif self.is_input_vectorized:
															UpperCamelCase							:		List[str]							      = self.norm_out(_A						)
															UpperCamelCase							:		Dict							      = self.out(_A						)
															# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
															UpperCamelCase							:		Tuple							      = logits.permute(0	,      2	,      1						)

															# log(p(x_0))
															UpperCamelCase							:		str							      = F.log_softmax(logits.double()	,      dim=1						).float()
									elif self.is_input_patches:
															# TODO: cleanup!
															UpperCamelCase							:		Union[str, Any]							      = self.transformer_blocks[0].norma.emb(
															    _A	,      _A	,      hidden_dtype=hidden_states.dtype						)
															UpperCamelCase      ,	UpperCamelCase							:		Dict							      = self.proj_out_a(F.silu(_A						)						).chunk(2	,      dim=1						)
															UpperCamelCase							:		Union[str, Any]							      = self.norm_out(_A						) * (1 + scale[:, None]) + shift[:, None]
															UpperCamelCase							:		Dict							      = self.proj_out_a(_A						)

															# unpatchify
															UpperCamelCase							:		Tuple							      = int(hidden_states.shape[1] ** 0.5						)
															UpperCamelCase							:		Dict							      = hidden_states.reshape(
															    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)						)
															UpperCamelCase							:		Optional[Any]							      = torch.einsum("""nhwpqc->nchpwq"""	,      _A						)
															UpperCamelCase							:		Union[str, Any]							      = hidden_states.reshape(
															    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)						)

									if not return_dict:
															return (output,)

									return TransformeraDModelOutput(sample=_A						)



