"""simple docstring"""




import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Any							=		TextToVideoSDPipeline
     _UpperCamelCase							:			Union[str, Any]							=		TEXT_TO_IMAGE_PARAMS
     _UpperCamelCase							:			Tuple							=		TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     _UpperCamelCase							:			List[str]							=		frozenset(
         [
             "num_inference_steps",
             "generator",
             "latents",
             "return_dict",
             "callback",
             "callback_steps",
         ]		)






     def     __A  (		self  ):
       torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     UNetaDConditionModel(
           block_out_channels=(32, 64, 64, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""")				,     up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""")				,     cross_attention_dim=32				,     attention_head_dim=4				,     )
       _lowerCAmelCase						:       List[Any]  			=     DDIMScheduler(
           beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="""scaled_linear"""				,     clip_sample=a__				,     set_alpha_to_one=a__				,     )
       torch.manual_seed(0  )
       _lowerCAmelCase						:       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				,     sample_size=128				,     )
       torch.manual_seed(0  )
       _lowerCAmelCase						:       List[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				,     hidden_act="""gelu"""				,     projection_dim=512				,     )
       _lowerCAmelCase						:       Any  			=     CLIPTextModel(a__  )
       _lowerCAmelCase						:       Dict  			=     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

       _lowerCAmelCase						:       List[Any]  			=     {
           """unet""": unet,
           """scheduler""": scheduler,
           """vae""": vae,
           """text_encoder""": text_encoder,
           """tokenizer""": tokenizer,
       }
       return components






     def     __A  (		self				,     a__				,     a__=0  ):
       if str(a__  ).startswith("""mps"""  ):
         _lowerCAmelCase						:       List[str]  			=     torch.manual_seed(a__  )
       else:
         _lowerCAmelCase						:       Any  			=     torch.Generator(device=a__  ).manual_seed(a__  )
       _lowerCAmelCase						:       str  			=     {
           """prompt""": """A painting of a squirrel eating a burger""",
           """generator""": generator,
           """num_inference_steps""": 2,
           """guidance_scale""": 6.0,
           """output_type""": """pt""",
       }
       return inputs






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """cpu"""  # ensure determinism for the device-dependent torch.Generator
       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_dummy_components()
       _lowerCAmelCase						:       str  			=     TextToVideoSDPipeline(**a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     sd_pipe.to(a__  )
       sd_pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     self.get_dummy_inputs(a__  )
       _lowerCAmelCase						:       int  			=     """np"""
       _lowerCAmelCase						:       List[Any]  			=     sd_pipe(**a__  ).frames
       _lowerCAmelCase						:       Tuple  			=     frames[0][-3:, -3:, -1]

       assert frames[0].shape == (64, 64, 3)
       _lowerCAmelCase						:       Union[str, Any]  			=     np.array([1_5_8.0, 1_6_0.0, 1_5_3.0, 1_2_5.0, 1_0_0.0, 1_2_1.0, 1_1_1.0, 9_3.0, 1_1_3.0]  )

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






     def     __A  (		self  ):
       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=a__				,     expected_max_diff=3e-3  )






     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()				,     reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,     )
     def     __A  (		self  ):
       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a__				,     expected_max_diff=1e-2  )






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""  )
     def     __A  (		self  ):
       pass






     @unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline."""  )
     def     __A  (		self  ):
       pass






     @unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline."""  )
     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       return super().test_progress_bar()







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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy"""  )

       _lowerCAmelCase						:       Dict  			=     TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""  )
       _lowerCAmelCase						:       Optional[int]  			=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
       _lowerCAmelCase						:       Optional[int]  			=     pipe.to("""cuda"""  )

       _lowerCAmelCase						:       List[str]  			=     """Spiderman is surfing"""
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )

       _lowerCAmelCase						:       Dict  			=     pipe(a__				,     generator=a__				,     num_inference_steps=25				,     output_type="""pt"""  ).frames
       _lowerCAmelCase						:       Optional[int]  			=     video_frames.cpu().numpy()

       assert np.abs(expected_video - video  ).mean() < 5e-2






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"""  )

       _lowerCAmelCase						:       Optional[int]  			=     TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b"""  )
       _lowerCAmelCase						:       Dict  			=     pipe.to("""cuda"""  )

       _lowerCAmelCase						:       Any  			=     """Spiderman is surfing"""
       _lowerCAmelCase						:       List[Any]  			=     torch.Generator(device="""cpu"""  ).manual_seed(0  )

       _lowerCAmelCase						:       Optional[int]  			=     pipe(a__				,     generator=a__				,     num_inference_steps=2				,     output_type="""pt"""  ).frames
       _lowerCAmelCase						:       Tuple  			=     video_frames.cpu().numpy()

       assert np.abs(expected_video - video  ).mean() < 5e-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__  )




