

import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    logging.get_logger(__name__)  # pylint: disable=invalid-name




def  lowerCAmelCase(			self ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ )->	Optional[Any]:



    """simple docstring"""






    UpperCamelCase_      =	image.to(device=SCREAMING_SNAKE_CASE_ )
    UpperCamelCase_      =	image  # take image as image_embeddings
    UpperCamelCase_      =	image_embeddings.unsqueeze(1 )

    # duplicate image embeddings for each generation per prompt, using mps friendly method
    UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	image_embeddings.shape
    UpperCamelCase_      =	image_embeddings.repeat(1 ,	SCREAMING_SNAKE_CASE_ ,	1 )
    UpperCamelCase_      =	image_embeddings.view(bs_embed * num_images_per_prompt ,	SCREAMING_SNAKE_CASE_ ,	-1 )

    if do_classifier_free_guidance:
        UpperCamelCase_      =	torch.zeros_like(SCREAMING_SNAKE_CASE_ )

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        UpperCamelCase_      =	torch.cat([uncond_embeddings, image_embeddings] )

    return image_embeddings






class 	__magic_name__				(       snake_case							):






     def __init__(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	)->    int:
         super().__init__()

         UpperCamelCase_      =	{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

         UpperCamelCase_      =	decoder_pipe_kwargs.get("torch_dtype"		,	_lowercase  ) or prior.dtype

         UpperCamelCase_      =	StableDiffusionImageVariationPipeline.from_pretrained(
             "lambdalabs/sd-image-variations-diffusers"		,	**_lowercase  )

         # replace `_encode_image` method
         UpperCamelCase_      =	types.MethodType(_encode_image		,	self.decoder_pipe  )

         self.register_modules(
             prior=_lowercase		,	tokenizer=_lowercase		,	text_encoder=_lowercase		,	prior_scheduler=_lowercase		,	)






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = None		,	_lowercase = None		,	)->    List[str]:
         if text_model_output is None:
             UpperCamelCase_      =	len(_lowercase  ) if isinstance(_lowercase		,	_lowercase  ) else 1
             # get prompt text embeddings
             UpperCamelCase_      =	self.tokenizer(
                 _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	return_tensors="pt"		,	)
             UpperCamelCase_      =	text_inputs.input_ids
             UpperCamelCase_      =	text_inputs.attention_mask.bool().to(_lowercase  )

             if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                 UpperCamelCase_      =	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]  )
                 logger.warning(
                     "The following part of your input was truncated because CLIP can only handle sequences up to"
                     F" {self.tokenizer.model_max_length} tokens: {removed_text}"  )
                 UpperCamelCase_      =	text_input_ids[:, : self.tokenizer.model_max_length]

             UpperCamelCase_      =	self.text_encoder(text_input_ids.to(_lowercase  )  )

             UpperCamelCase_      =	text_encoder_output.text_embeds
             UpperCamelCase_      =	text_encoder_output.last_hidden_state

         else:
             UpperCamelCase_      =	text_model_output[0].shape[0]
             UpperCamelCase_     ,					UpperCamelCase_      =	text_model_output[0], text_model_output[1]
             UpperCamelCase_      =	text_attention_mask

         UpperCamelCase_      =	text_embeddings.repeat_interleave(_lowercase		,	dim=0  )
         UpperCamelCase_      =	text_encoder_hidden_states.repeat_interleave(_lowercase		,	dim=0  )
         UpperCamelCase_      =	text_mask.repeat_interleave(_lowercase		,	dim=0  )

         if do_classifier_free_guidance:
             UpperCamelCase_      =	[""] * batch_size

             UpperCamelCase_      =	self.tokenizer(
                 _lowercase		,	padding="max_length"		,	max_length=self.tokenizer.model_max_length		,	truncation=_lowercase		,	return_tensors="pt"		,	)
             UpperCamelCase_      =	uncond_input.attention_mask.bool().to(_lowercase  )
             UpperCamelCase_      =	self.text_encoder(uncond_input.input_ids.to(_lowercase  )  )

             UpperCamelCase_      =	uncond_embeddings_text_encoder_output.text_embeds
             UpperCamelCase_      =	uncond_embeddings_text_encoder_output.last_hidden_state

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

             UpperCamelCase_      =	uncond_embeddings.shape[1]
             UpperCamelCase_      =	uncond_embeddings.repeat(1		,	_lowercase  )
             UpperCamelCase_      =	uncond_embeddings.view(batch_size * num_images_per_prompt		,	_lowercase  )

             UpperCamelCase_      =	uncond_text_encoder_hidden_states.shape[1]
             UpperCamelCase_      =	uncond_text_encoder_hidden_states.repeat(1		,	_lowercase		,	1  )
             UpperCamelCase_      =	uncond_text_encoder_hidden_states.view(
                 batch_size * num_images_per_prompt		,	_lowercase		,	-1  )
             UpperCamelCase_      =	uncond_text_mask.repeat_interleave(_lowercase		,	dim=0  )

             # done duplicates

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCamelCase_      =	torch.cat([uncond_embeddings, text_embeddings]  )
             UpperCamelCase_      =	torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )

             UpperCamelCase_      =	torch.cat([uncond_text_mask, text_mask]  )

         return text_embeddings, text_encoder_hidden_states, text_mask






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         if self.device != torch.device("meta"  ) or not hasattr(self.prior		,	"_hf_hook"  ):
             return self.device
         for module in self.prior.modules():
             if (
                 hasattr(_lowercase		,	"_hf_hook"  )
                 and hasattr(module._hf_hook		,	"execution_device"  )
                 and module._hf_hook.execution_device is not None
             ):
                 return torch.device(module._hf_hook.execution_device  )
         return self.device






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[int]:
         if latents is None:
             UpperCamelCase_      =	randn_tensor(_lowercase		,	generator=_lowercase		,	device=_lowercase		,	dtype=_lowercase  )
         else:
             if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
             UpperCamelCase_      =	latents.to(_lowercase  )

         UpperCamelCase_      =	latents * scheduler.init_noise_sigma
         return latents






     def   UpperCAmelCase_							(							self		,	_lowercase = None  )->    Union[str, Any]:
         self.decoder_pipe.to(_lowercase  )
         super().to(_lowercase  )






     @torch.no_grad()
     def __call__(							self		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = 1		,	_lowercase = 25		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = None		,	_lowercase = 4.0		,	_lowercase = 8.0		,	_lowercase = 50		,	_lowercase = 1		,	_lowercase = 0.0		,	_lowercase = "pil"		,	_lowercase = True		,	)->    List[Any]:
         if prompt is not None:
             if isinstance(_lowercase		,	_lowercase  ):
                 UpperCamelCase_      =	1
             elif isinstance(_lowercase		,	_lowercase  ):
                 UpperCamelCase_      =	len(_lowercase  )
             else:
                 raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowercase  )}"  )
         else:
             UpperCamelCase_      =	text_model_output[0].shape[0]

         UpperCamelCase_      =	self._execution_device

         UpperCamelCase_      =	batch_size * num_images_per_prompt

         UpperCamelCase_      =	prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

         UpperCamelCase_     ,					UpperCamelCase_     ,					UpperCamelCase_      =	self._encode_prompt(
             _lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )

         # prior

         self.prior_scheduler.set_timesteps(_lowercase		,	device=_lowercase  )
         UpperCamelCase_      =	self.prior_scheduler.timesteps

         UpperCamelCase_      =	self.prior.config.embedding_dim

         UpperCamelCase_      =	self.prepare_latents(
             (batch_size, embedding_dim)		,	text_embeddings.dtype		,	_lowercase		,	_lowercase		,	_lowercase		,	self.prior_scheduler		,	)

         for i, t in enumerate(self.progress_bar(_lowercase  )  ):
             # expand the latents if we are doing classifier free guidance
             UpperCamelCase_      =	torch.cat([prior_latents] * 2  ) if do_classifier_free_guidance else prior_latents

             UpperCamelCase_      =	self.prior(
                 _lowercase		,	timestep=_lowercase		,	proj_embedding=_lowercase		,	encoder_hidden_states=_lowercase		,	attention_mask=_lowercase		,	).predicted_image_embedding

             if do_classifier_free_guidance:
                 UpperCamelCase_     ,					UpperCamelCase_      =	predicted_image_embedding.chunk(2  )
                 UpperCamelCase_      =	predicted_image_embedding_uncond + prior_guidance_scale * (
                     predicted_image_embedding_text - predicted_image_embedding_uncond
                 )

             if i + 1 == prior_timesteps_tensor.shape[0]:
                 UpperCamelCase_      =	None
             else:
                 UpperCamelCase_      =	prior_timesteps_tensor[i + 1]

             UpperCamelCase_      =	self.prior_scheduler.step(
                 _lowercase		,	timestep=_lowercase		,	sample=_lowercase		,	generator=_lowercase		,	prev_timestep=_lowercase		,	).prev_sample

         UpperCamelCase_      =	self.prior.post_process_latents(_lowercase  )

         UpperCamelCase_      =	prior_latents

         UpperCamelCase_      =	self.decoder_pipe(
             image=_lowercase		,	height=_lowercase		,	width=_lowercase		,	num_inference_steps=_lowercase		,	guidance_scale=_lowercase		,	generator=_lowercase		,	output_type=_lowercase		,	return_dict=_lowercase		,	num_images_per_prompt=_lowercase		,	eta=_lowercase		,	)
         return output



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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 	__magic_name__				(       snake_case ,			unittest.TestCase							):
     UpperCamelCase_  :Any								=			KandinskyPipeline
     UpperCamelCase_  :str								=			[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_  :Union[str, Any]								=			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_  :int								=			[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_  :Optional[Any]								=			False






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[Any]:
         return 32






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return self.time_input_dim






     @property
     def   UpperCAmelCase_							(							self  )->    Union[str, Any]:
         return self.time_input_dim * 4






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         return 100






     @property
     def   UpperCAmelCase_							(							self  )->    List[Any]:
         UpperCamelCase_      =	XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer






     @property
     def   UpperCAmelCase_							(							self  )->    int:
         torch.manual_seed(0  )
         UpperCamelCase_      =	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=1_005		,	)

         UpperCamelCase_      =	MultilingualCLIP(_lowercase  )
         UpperCamelCase_      =	text_encoder.eval()

         return text_encoder






     @property
     def   UpperCAmelCase_							(							self  )->    List[str]:
         torch.manual_seed(0  )

         UpperCamelCase_      =	{
             "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,
         }

         UpperCamelCase_      =	UNetaDConditionModel(**_lowercase  )
         return model






     @property
     def   UpperCAmelCase_							(							self  )->    Dict:
         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   UpperCAmelCase_							(							self  )->    Tuple:
         torch.manual_seed(0  )
         UpperCamelCase_      =	VQModel(**self.dummy_movq_kwargs  )
         return model






     def   UpperCAmelCase_							(							self  )->    Dict:
         UpperCamelCase_      =	self.dummy_text_encoder
         UpperCamelCase_      =	self.dummy_tokenizer
         UpperCamelCase_      =	self.dummy_unet
         UpperCamelCase_      =	self.dummy_movq

         UpperCamelCase_      =	DDIMScheduler(
             num_train_timesteps=1_000		,	beta_schedule="linear"		,	beta_start=0.00_085		,	beta_end=0.012		,	clip_sample=_lowercase		,	set_alpha_to_one=_lowercase		,	steps_offset=1		,	prediction_type="epsilon"		,	thresholding=_lowercase		,	)

         UpperCamelCase_      =	{
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase=0  )->    Union[str, Any]:
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(_lowercase  )  ).to(_lowercase  )
         UpperCamelCase_      =	floats_tensor((1, self.cross_attention_dim)		,	rng=random.Random(seed + 1  )  ).to(_lowercase  )
         if str(_lowercase  ).startswith("mps"  ):
             UpperCamelCase_      =	torch.manual_seed(_lowercase  )
         else:
             UpperCamelCase_      =	torch.Generator(device=_lowercase  ).manual_seed(_lowercase  )
         UpperCamelCase_      =	{
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs






     def   UpperCAmelCase_							(							self  )->    Tuple:
         UpperCamelCase_      =	"cpu"

         UpperCamelCase_      =	self.get_dummy_components()

         UpperCamelCase_      =	self.pipeline_class(**_lowercase  )
         UpperCamelCase_      =	pipe.to(_lowercase  )

         pipe.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	pipe(**self.get_dummy_inputs(_lowercase  )  )
         UpperCamelCase_      =	output.images

         UpperCamelCase_      =	pipe(
             **self.get_dummy_inputs(_lowercase  )		,	return_dict=_lowercase		,	)[0]

         UpperCamelCase_      =	image[0, -3:, -3:, -1]
         UpperCamelCase_      =	image_from_tuple[0, -3:, -3:, -1]

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

         UpperCamelCase_      =	np.array(
             [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

         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 	__magic_name__				(       unittest.TestCase							):






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






     def   UpperCAmelCase_							(							self  )->    str:
         UpperCamelCase_      =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

         UpperCamelCase_      =	KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior"		,	torch_dtype=torch.floataa  )
         pipe_prior.to(_lowercase  )

         UpperCamelCase_      =	KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"		,	torch_dtype=torch.floataa  )
         UpperCamelCase_      =	pipeline.to(_lowercase  )
         pipeline.set_progress_bar_config(disable=_lowercase  )

         UpperCamelCase_      =	"red cat, 4k photo"

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_     ,					UpperCamelCase_      =	pipe_prior(
             _lowercase		,	generator=_lowercase		,	num_inference_steps=5		,	negative_prompt=""		,	).to_tuple()

         UpperCamelCase_      =	torch.Generator(device="cuda"  ).manual_seed(0  )
         UpperCamelCase_      =	pipeline(
             _lowercase		,	image_embeds=_lowercase		,	negative_image_embeds=_lowercase		,	generator=_lowercase		,	num_inference_steps=100		,	output_type="np"		,	)

         UpperCamelCase_      =	output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(_lowercase		,	_lowercase  )
