import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort





class      A							(				a__		,				unittest.TestCase     ):
   # FIXME: add fast tests
   pass





@nightly
@require_onnxruntime
@require_torch_gpu
class      A							(				unittest.TestCase     ):


   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				Optional[int]:
         """simple docstring"""



         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )


   @property
   def 			lowercase_      (self     :						Optional[int]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  ort.SessionOptions()
         UpperCAmelCase__								=  False
         return options


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
         UpperCAmelCase__								=  OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,  revision="onnx"       ,  safety_checker=lowerCAmelCase__       ,  feature_extractor=lowerCAmelCase__       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

         UpperCAmelCase__								=  "A red cat sitting on a park bench"

         UpperCAmelCase__								=  np.random.RandomState(0		)
         UpperCAmelCase__								=  pipe(
             prompt=lowerCAmelCase__       ,  image=lowerCAmelCase__       ,  mask_image=lowerCAmelCase__       ,  guidance_scale=7.5       ,  num_inference_steps=1_0       ,  generator=lowerCAmelCase__       ,  output_type="np"       ,  )
         UpperCAmelCase__								=  output.images
         UpperCAmelCase__								=  images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCAmelCase__								=  np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3





   def 			lowercase_      (self     :						Tuple		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png"		)
         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"		)
         UpperCAmelCase__								=  LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,  subfolder="scheduler"       ,  revision="onnx"		)
         UpperCAmelCase__								=  OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,  revision="onnx"       ,  scheduler=lowerCAmelCase__       ,  safety_checker=lowerCAmelCase__       ,  feature_extractor=lowerCAmelCase__       ,  provider=self.gpu_provider       ,  sess_options=self.gpu_options       ,  )
         pipe.set_progress_bar_config(disable=lowerCAmelCase__		)

         UpperCAmelCase__								=  "A red cat sitting on a park bench"

         UpperCAmelCase__								=  np.random.RandomState(0		)
         UpperCAmelCase__								=  pipe(
             prompt=lowerCAmelCase__       ,  image=lowerCAmelCase__       ,  mask_image=lowerCAmelCase__       ,  guidance_scale=7.5       ,  num_inference_steps=2_0       ,  generator=lowerCAmelCase__       ,  output_type="np"       ,  )
         UpperCAmelCase__								=  output.images
         UpperCAmelCase__								=  images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCAmelCase__								=  np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3










"""simple docstring"""




import torch
from torch import nn

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


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		Any	=				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]

								a      :		List[str]	=				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, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				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"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


