

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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       SCREAMING_SNAKE_CASE__					(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_		,       unittest.TestCase     ):
    __lowerCamelCase						:       Tuple														=     CycleDiffusionPipeline
    __lowerCamelCase						:       Optional[int]														=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
        "negative_prompt",
        "height",
        "width",
        "negative_prompt_embeds",
    }
    __lowerCamelCase						:       str														=     PipelineTesterMixin.required_optional_params - {"latents"}
    __lowerCamelCase						:       Optional[Any]														=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}     )
    __lowerCamelCase						:       Tuple														=     IMAGE_TO_IMAGE_IMAGE_PARAMS
    __lowerCamelCase						:       List[str]														=     IMAGE_TO_IMAGE_IMAGE_PARAMS





    def       snake_case_				(      self):
         torch.manual_seed(0)
         lowercase__    :   int      =       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						,   )
         lowercase__    :   str      =       DDIMScheduler(
             beta_start=0.00_085						,   beta_end=0.012						,   beta_schedule='scaled_linear'						,   num_train_timesteps=1000						,   clip_sample=a__						,   set_alpha_to_one=a__						,   )
         torch.manual_seed(0)
         lowercase__    :   Optional[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						,   )
         torch.manual_seed(0)
         lowercase__    :   Any      =       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						,   )
         lowercase__    :   Tuple      =       CLIPTextModel(a__)
         lowercase__    :   Dict      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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





    def       snake_case_				(      self						,   a						,   a=0):
         lowercase__    :   str      =       floats_tensor((1, 3, 32, 32)						,   rng=random.Random(a__)).to(a__)
         lowercase__    :   str      =       image / 2 + 0.5
         if str(a__).startswith('mps'):
              lowercase__    :   Union[str, Any]      =       torch.manual_seed(a__)
         else:
              lowercase__    :   Any      =       torch.Generator(device=a__).manual_seed(a__)
         lowercase__    :   Optional[int]      =       {
             """prompt""": """An astronaut riding an elephant""",
             """source_prompt""": """An astronaut riding a horse""",
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """eta""": 0.1,
             """strength""": 0.8,
             """guidance_scale""": 3,
             """source_guidance_scale""": 1,
             """output_type""": """numpy""",
         }
         return inputs





    def       snake_case_				(      self):
         lowercase__    :   Any      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator

         lowercase__    :   List[Any]      =       self.get_dummy_components()
         lowercase__    :   List[str]      =       CycleDiffusionPipeline(**a__)
         lowercase__    :   List[str]      =       pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         lowercase__    :   int      =       self.get_dummy_inputs(a__)
         lowercase__    :   Optional[Any]      =       pipe(**a__)
         lowercase__    :   Optional[Any]      =       output.images

         lowercase__    :   Optional[int]      =       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         lowercase__    :   Dict      =       np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179])

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





    @unittest.skipIf(torch_device != 'cuda'						,   'This test requires a GPU')
    def       snake_case_				(      self):
         lowercase__    :   str      =       self.get_dummy_components()
         for name, module in components.items():
              if hasattr(a__						,   'half'):
                   lowercase__    :   Any      =       module.half()
         lowercase__    :   int      =       CycleDiffusionPipeline(**a__)
         lowercase__    :   Dict      =       pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         lowercase__    :   Optional[int]      =       self.get_dummy_inputs(a__)
         lowercase__    :   Optional[int]      =       pipe(**a__)
         lowercase__    :   List[str]      =       output.images

         lowercase__    :   str      =       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         lowercase__    :   int      =       np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116])

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





    @skip_mps
    def       snake_case_				(      self):
         return super().test_save_load_local()





    @unittest.skip('non-deterministic pipeline')
    def       snake_case_				(      self):
         return super().test_inference_batch_single_identical()





    @skip_mps
    def       snake_case_				(      self):
         return super().test_dict_tuple_outputs_equivalent()





    @skip_mps
    def       snake_case_				(      self):
         return super().test_save_load_optional_components()





    @skip_mps
    def       snake_case_				(      self):
         return super().test_attention_slicing_forward_pass()



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





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





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png')
         lowercase__    :   Optional[Any]      =       load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy')
         lowercase__    :   Optional[int]      =       init_image.resize((512, 512))

         lowercase__    :   Optional[Any]      =       """CompVis/stable-diffusion-v1-4"""
         lowercase__    :   List[str]      =       DDIMScheduler.from_pretrained(a__						,   subfolder='scheduler')
         lowercase__    :   Tuple      =       CycleDiffusionPipeline.from_pretrained(
             a__						,   scheduler=a__						,   safety_checker=a__						,   torch_dtype=torch.floataa						,   revision='fp16')

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         pipe.enable_attention_slicing()

         lowercase__    :   Dict      =       """A black colored car"""
         lowercase__    :   str      =       """A blue colored car"""

         lowercase__    :   int      =       torch.manual_seed(0)
         lowercase__    :   Optional[int]      =       pipe(
             prompt=a__						,   source_prompt=a__						,   image=a__						,   num_inference_steps=100						,   eta=0.1						,   strength=0.85						,   guidance_scale=3						,   source_guidance_scale=1						,   generator=a__						,   output_type='np'						,   )
         lowercase__    :   int      =       output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image).max() < 5e-1





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png')
         lowercase__    :   str      =       load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy')
         lowercase__    :   List[Any]      =       init_image.resize((512, 512))

         lowercase__    :   Tuple      =       """CompVis/stable-diffusion-v1-4"""
         lowercase__    :   Dict      =       DDIMScheduler.from_pretrained(a__						,   subfolder='scheduler')
         lowercase__    :   Optional[int]      =       CycleDiffusionPipeline.from_pretrained(a__						,   scheduler=a__						,   safety_checker=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         pipe.enable_attention_slicing()

         lowercase__    :   Union[str, Any]      =       """A black colored car"""
         lowercase__    :   Any      =       """A blue colored car"""

         lowercase__    :   Optional[int]      =       torch.manual_seed(0)
         lowercase__    :   Dict      =       pipe(
             prompt=a__						,   source_prompt=a__						,   image=a__						,   num_inference_steps=100						,   eta=0.1						,   strength=0.85						,   guidance_scale=3						,   source_guidance_scale=1						,   generator=a__						,   output_type='np'						,   )
         lowercase__    :   Optional[int]      =       output.images

         assert np.abs(image - expected_image).max() < 2e-2


"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		KandinskyImgaImgPipeline
     _UpperCamelCase							:			Optional[Any]							=		["prompt", "image_embeds", "negative_image_embeds", "image"]
     _UpperCamelCase							:			List[Any]							=		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     _UpperCamelCase							:			Dict							=		[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     _UpperCamelCase							:			Union[str, Any]							=		False






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return 32






     @property
     def     __A  (		self  ):
       return self.time_input_dim






     @property
     def     __A  (		self  ):
       return self.time_input_dim * 4






     @property
     def     __A  (		self  ):
       return 100






     @property
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
       return tokenizer






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[Any]  			=     MCLIPConfig(
           numDims=self.cross_attention_dim				,     transformerDimensions=self.text_embedder_hidden_size				,     hidden_size=self.text_embedder_hidden_size				,     intermediate_size=37				,     num_attention_heads=4				,     num_hidden_layers=5				,     vocab_size=1005				,     )

       _lowerCAmelCase						:       int  			=     MultilingualCLIP(a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     text_encoder.eval()

       return text_encoder






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )

       _lowerCAmelCase						:       str  			=     {
           """in_channels""": 4,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 8,
           """addition_embed_type""": """text_image""",
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """encoder_hid_dim""": self.text_embedder_hidden_size,
           """encoder_hid_dim_type""": """text_image_proj""",
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": None,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     UNetaDConditionModel(**a__  )
       return model






     @property
     def     __A  (		self  ):
       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }






     @property
     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     VQModel(**self.dummy_movq_kwargs  )
       return model






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.dummy_text_encoder
       _lowerCAmelCase						:       List[Any]  			=     self.dummy_tokenizer
       _lowerCAmelCase						:       int  			=     self.dummy_unet
       _lowerCAmelCase						:       Dict  			=     self.dummy_movq

       _lowerCAmelCase						:       Tuple  			=     {
           """num_train_timesteps""": 1000,
           """beta_schedule""": """linear""",
           """beta_start""": 0.0_0_0_8_5,
           """beta_end""": 0.0_1_2,
           """clip_sample""": False,
           """set_alpha_to_one""": False,
           """steps_offset""": 0,
           """prediction_type""": """epsilon""",
           """thresholding""": False,
       }

       _lowerCAmelCase						:       Optional[Any]  			=     DDIMScheduler(**a__  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
           """unet""": unet,
           """scheduler""": scheduler,
           """movq""": movq,
       }

       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       _lowerCAmelCase						:       Optional[int]  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     floats_tensor((1, self.cross_attention_dim)				,     rng=random.Random(seed + 1  )  ).to(a__  )
       # create init_image
       _lowerCAmelCase						:       List[Any]  			=     floats_tensor((1, 3, 64, 64)				,     rng=random.Random(a__  )  ).to(a__  )
       _lowerCAmelCase						:       int  			=     image.cpu().permute(0				,     2				,     3				,     1  )[0]
       _lowerCAmelCase						:       List[Any]  			=     Image.fromarray(np.uinta(a__  )  ).convert("""RGB"""  ).resize((256, 256)  )

       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Tuple  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     {
           """prompt""": """horse""",
           """image""": init_image,
           """image_embeds""": image_embeds,
           """negative_image_embeds""": negative_image_embeds,
           """generator""": generator,
           """height""": 64,
           """width""": 64,
           """num_inference_steps""": 10,
           """guidance_scale""": 7.0,
           """strength""": 0.2,
           """output_type""": """np""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     """cpu"""

       _lowerCAmelCase						:       int  			=     self.get_dummy_components()

       _lowerCAmelCase						:       int  			=     self.pipeline_class(**a__  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to(a__  )

       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Tuple  			=     pipe(**self.get_dummy_inputs(a__  )  )
       _lowerCAmelCase						:       List[Any]  			=     output.images

       _lowerCAmelCase						:       Tuple  			=     pipe(
           **self.get_dummy_inputs(a__  )				,     return_dict=a__				,     )[0]

       _lowerCAmelCase						:       Dict  			=     image[0, -3:, -3:, -1]
       _lowerCAmelCase						:       List[Any]  			=     image_from_tuple[0, -3:, -3:, -1]

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

       _lowerCAmelCase						:       str  			=     np.array(
           [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3]  )
       assert (
           np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1e-2
       ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







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






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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/kandinsky/kandinsky_img2img_frog.npy"""  )

       _lowerCAmelCase						:       List[str]  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     """A red cartoon frog, 4k"""

       _lowerCAmelCase						:       int  			=     KandinskyPriorPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1-prior"""				,     torch_dtype=torch.floataa  )
       pipe_prior.to(a__  )

       _lowerCAmelCase						:       Tuple  			=     KandinskyImgaImgPipeline.from_pretrained(
           """kandinsky-community/kandinsky-2-1"""				,     torch_dtype=torch.floataa  )
       _lowerCAmelCase						:       Any  			=     pipeline.to(a__  )

       pipeline.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Any  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     pipe_prior(
           a__				,     generator=a__				,     num_inference_steps=5				,     negative_prompt=""""""				,     ).to_tuple()

       _lowerCAmelCase						:       Union[str, Any]  			=     pipeline(
           a__				,     image=a__				,     image_embeds=a__				,     negative_image_embeds=a__				,     generator=a__				,     num_inference_steps=100				,     height=768				,     width=768				,     strength=0.2				,     output_type="""np"""				,     )

       _lowerCAmelCase						:       Dict  			=     output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(a__				,     a__  )




