




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class       a__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[Any]		 =     StableDiffusionXLImgaImgPipeline
     a		:      Dict		 =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
     a		:      Optional[Any]		 =     PipelineTesterMixin.required_optional_params - {"""latents"""}
     a		:      str		 =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     a		:      str		 =     IMAGE_TO_IMAGE_IMAGE_PARAMS
     a		:      List[Any]		 =     IMAGE_TO_IMAGE_IMAGE_PARAMS







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



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				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")  ,		attention_head_dim=(2, 4)  ,		use_linear_projection=A  ,		addition_embed_type="text_time"  ,		addition_time_embed_dim=8  ,		transformer_layers_per_block=(1, 2)  ,		projection_class_embeddings_input_dim=80  ,		cross_attention_dim=64  ,		)
          a     =				EulerDiscreteScheduler(
              beta_start=0.0_0_0_8_5  ,		beta_end=0.0_1_2  ,		steps_offset=1  ,		beta_schedule="scaled_linear"  ,		timestep_spacing="leading"  ,		)
          torch.manual_seed(0			)
          a     =				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  ,		sample_size=128  ,		)
          torch.manual_seed(0			)
          a     =				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  ,		hidden_act="gelu"  ,		projection_dim=32  ,		)
          a     =				CLIPTextModel(A			)
          a     =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,		local_files_only=A			)

          a     =				CLIPTextModelWithProjection(A			)
          a     =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  ,		local_files_only=A			)

          a     =				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_encoder_2": text_encoder_a,
              "tokenizer_2": tokenizer_a,
              # "safety_checker": None,
              # "feature_extractor": None,
          }
          return components







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   int:



          '''simple docstring'''



          a     =				floats_tensor((1, 3, 32, 32)  ,		rng=random.Random(A			)			).to(A			)
          a     =				image / 2 + 0.5
          if str(A			).startswith("mps"			):
               a     =				torch.manual_seed(A			)
          else:
               a     =				torch.Generator(device=A			).manual_seed(A			)
          a     =				{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 5.0,
              "output_type": "numpy",
              "strength": 0.7_5,
          }
          return inputs







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



          '''simple docstring'''



          a     =				"cpu"  # ensure determinism for the device-dependent torch.Generator
          a     =				self.get_dummy_components()
          a     =				StableDiffusionXLImgaImgPipeline(**A			)
          a     =				sd_pipe.to(A			)
          sd_pipe.set_progress_bar_config(disable=A			)

          a     =				self.get_dummy_inputs(A			)
          a     =				sd_pipe(**A			).images
          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)

          a     =				np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]			)

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







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.get_dummy_components()
          a     =				StableDiffusionXLImgaImgPipeline(**A			)
          a     =				sd_pipe.to(A			)
          a     =				sd_pipe.to(A			)
          sd_pipe.set_progress_bar_config(disable=A			)

          # forward without prompt embeds
          a     =				self.get_dummy_inputs(A			)
          a     =				3 * ["this is a negative prompt"]
          a     =				negative_prompt
          a     =				3 * [inputs["prompt"]]

          a     =				sd_pipe(**A			)
          a     =				output.images[0, -3:, -3:, -1]

          # forward with prompt embeds
          a     =				self.get_dummy_inputs(A			)
          a     =				3 * ["this is a negative prompt"]
          a     =				3 * [inputs.pop("prompt"			)]

          (
              (
              a
          )  ,				(
              a
          )  ,				(
              a
          )  ,				(
              a
          )  ,				
          )     =				sd_pipe.encode_prompt(A  ,		negative_prompt=A			)

          a     =				sd_pipe(
              **A  ,		prompt_embeds=A  ,		negative_prompt_embeds=A  ,		pooled_prompt_embeds=A  ,		negative_pooled_prompt_embeds=A  ,		)
          a     =				output.images[0, -3:, -3:, -1]

          # make sure that it's equal
          assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()			).max() < 1e-4







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







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A  ,		A="cpu"  ,		A=torch.floataa  ,		A=0			)  ->   List[Any]:



          '''simple docstring'''



          a     =				torch.Generator(device=A			).manual_seed(A			)
          a     =				np.random.RandomState(A			).standard_normal((1, 4, 64, 64)			)
          a     =				torch.from_numpy(A			).to(device=A  ,		dtype=A			)
          a     =				{
              "prompt": "a photograph of an astronaut riding a horse",
              "latents": latents,
              "generator": generator,
              "num_inference_steps": 3,
              "guidance_scale": 7.5,
              "output_type": "numpy",
          }
          return inputs







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"			)
          pipe.to(A			)
          pipe.set_progress_bar_config(disable=A			)

          a     =				self.get_inputs(A			)
          a     =				pipe(**A			).images
          a     =				image[0, -3:, -3:, -1].flatten()

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]			)
          assert np.abs(image_slice - expected_slice			).max() < 7e-3







lowercase__				:      List[Any]	   = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowercase__				:      str	   = {value: key for key, value in encode_dict.items()}

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				""
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception("encode() accepts only letters of the alphabet and spaces")
     return encoded

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     if set(__UpperCamelCase) - {"A", "B", " "} != set():
          raise Exception("decode() accepts only 'A', 'B' and spaces")
     a     =				""
     for word in coded.split():
          while len(__UpperCamelCase) != 0:
               decoded += decode_dict[word[:5]]
               a     =				word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()

