





import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class   _UpperCamelCase  (       __a						,       __a  ):







     '''simple docstring'''

     @register_to_config
     def __init__(						self    :  str ,					*,
         __lowercase    :  int = 4 ,					__lowercase    :  int = 7_68 ,					__lowercase    :  int ,					__lowercase    :  str ,					):






      '''simple docstring'''







      super().__init__()

      UpperCAmelCase_								=     nn.Parameter(torch.zeros(a_		)		)

      # parameters for additional clip time embeddings
      UpperCAmelCase_								=     nn.Linear(a_ ,					a_		)
      UpperCAmelCase_								=     nn.Linear(a_ ,					a_		)

      # parameters for encoder hidden states
      UpperCAmelCase_								=     clip_extra_context_tokens
      UpperCAmelCase_								=     nn.Linear(
          a_ ,					self.clip_extra_context_tokens * cross_attention_dim		)
      UpperCAmelCase_								=     nn.Linear(a_ ,					a_		)
      UpperCAmelCase_								=     nn.LayerNorm(a_		)





     def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					*, __lowercase    :  Union[str, Any] ,					__lowercase    :  List[Any] ,					__lowercase    :  Optional[Any] ,					__lowercase    :  Dict		):






      '''simple docstring'''







      if do_classifier_free_guidance:
       # Add the classifier free guidance embeddings to the image embeddings
       UpperCAmelCase_								=     image_embeddings.shape[0]
       UpperCAmelCase_								=     self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
       UpperCAmelCase_								=     classifier_free_guidance_embeddings.expand(
           a_ ,					-1		)
       UpperCAmelCase_								=     torch.cat([classifier_free_guidance_embeddings, image_embeddings] ,					dim=0		)

      # The image embeddings batch size and the text embeddings batch size are equal
      assert image_embeddings.shape[0] == prompt_embeds.shape[0]

      UpperCAmelCase_								=     prompt_embeds.shape[0]

      # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
      # adding CLIP embeddings to the existing timestep embedding, ...
      UpperCAmelCase_								=     self.embedding_proj(a_		)
      UpperCAmelCase_								=     self.clip_image_embeddings_project_to_time_embeddings(a_		)
      UpperCAmelCase_								=     time_projected_image_embeddings + time_projected_prompt_embeds

      # ... and by projecting CLIP embeddings into four
      # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
      UpperCAmelCase_								=     self.clip_extra_context_tokens_proj(a_		)
      UpperCAmelCase_								=     clip_extra_context_tokens.reshape(a_ ,					-1 ,					self.clip_extra_context_tokens		)
      UpperCAmelCase_								=     clip_extra_context_tokens.permute(0 ,					2 ,					1		)

      UpperCAmelCase_								=     self.encoder_hidden_states_proj(a_		)
      UpperCAmelCase_								=     self.text_encoder_hidden_states_norm(a_		)
      UpperCAmelCase_								=     torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] ,					dim=1		)

      return text_encoder_hidden_states, additive_clip_time_embeddings









from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Union[List[PIL.Image.Image], np.ndarray]
					lowerCamelCase     :     Optional[List[bool]]


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_cycle_diffusion import CycleDiffusionPipeline
				from .pipeline_stable_diffusion import StableDiffusionPipeline
				from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
				from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
				from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
				from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
				from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
				from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
				from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
				from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
				from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
				from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
				from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
				from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
				from .pipeline_stable_unclip import StableUnCLIPPipeline
				from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
				from .safety_checker import StableDiffusionSafetyChecker
				from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
				from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import (
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				)
else:
				from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
				from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
				from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
				if not (
				    is_torch_available()
				    and is_transformers_available()
				    and is_k_diffusion_available()
				    and is_k_diffusion_version(""">=""", """0.0.12""")
				):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not (is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_onnx_objects import *  # noqa F403
else:
				from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
				from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
				from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
				from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
				from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
				import flax







				@flax.struct.dataclass
				class   _UpperCamelCase  (       A_  ):







									'''simple docstring'''

									lowerCamelCase     :     np.ndarray
									lowerCamelCase     :     List[bool]


				from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
				from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
				from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
				from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
				from .safety_checker_flax import FlaxStableDiffusionSafetyChecker


