
from __future__ import annotations

from typing import Any






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> None:  # noqa: A002





													'''simple docstring'''






													__UpperCamelCase  	=      item
													__UpperCamelCase  	=      next






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      0



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> None:





													'''simple docstring'''






													__UpperCamelCase  	=      Node(SCREAMING_SNAKE_CASE_  ,						self.head      )
													self.size += 1



							def      A__		(	self      )-> Any:





													'''simple docstring'''






													if self.head is None:
																			return None
													else:
																			__UpperCamelCase  	=      self.head.item
																			__UpperCamelCase  	=      self.head.next
																			self.size -= 1
																			return item



							def      A__		(	self      )-> bool:





													'''simple docstring'''






													return self.head is None



							def __str__(	self      )-> str:





													'''simple docstring'''






													if self.is_empty():
																			return ""
													else:
																			__UpperCamelCase  	=      self.head
																			__UpperCamelCase  	=      ''''''
																			__UpperCamelCase  	=      []
																			while iterate:
																									item_list.append(str(iterate.item      )      )
																									__UpperCamelCase  	=      iterate.next

																			__UpperCamelCase  	=      ''' --> '''.join(SCREAMING_SNAKE_CASE_      )

																			return item_str



							def __len__(	self      )-> int:





													'''simple docstring'''






													return self.size










"""simple docstring"""
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  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__			,				unittest.TestCase				):



 '''simple docstring'''





 __UpperCAmelCase		   =							KandinskyPipeline
 __UpperCAmelCase		   =							[
     """prompt""",
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __UpperCAmelCase		   =							["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
 __UpperCAmelCase		   =							[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __UpperCAmelCase		   =							False






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 32






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 32






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return self.time_input_dim






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return self.time_input_dim * 4






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 1_00






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
  return tokenizer






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )
  _UpperCamelCase      :	List[str]					      =  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=10_05					,     )

  _UpperCamelCase      :	Optional[Any]					      =  MultilingualCLIP(lowerCAmelCase__   )
  _UpperCamelCase      :	List[str]					      =  text_encoder.eval()

  return text_encoder






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )

  _UpperCamelCase      :	Dict					      =  {
      "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      :	int					      =  UNetaDConditionModel(**lowerCAmelCase__   )
  return model






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  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 lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )
  _UpperCamelCase      :	Dict					      =  VQModel(**self.dummy_movq_kwargs   )
  return model






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[str]					      =  self.dummy_text_encoder
  _UpperCamelCase      :	str					      =  self.dummy_tokenizer
  _UpperCamelCase      :	List[str]					      =  self.dummy_unet
  _UpperCamelCase      :	Tuple					      =  self.dummy_movq

  _UpperCamelCase      :	Dict					      =  DDIMScheduler(
      num_train_timesteps=10_00					,     beta_schedule="linear"					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type="epsilon"					,     thresholding=lowerCAmelCase__					,     )

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






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__=0   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__   )   ).to(lowerCAmelCase__   )
  _UpperCamelCase      :	int					      =  floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1   )   ).to(lowerCAmelCase__   )
  if str(lowerCAmelCase__   ).startswith("mps"   ):
   _UpperCamelCase      :	Optional[Any]					      =  torch.manual_seed(lowerCAmelCase__   )
  else:
   _UpperCamelCase      :	List[Any]					      =  torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
  _UpperCamelCase      :	Optional[Any]					      =  {
      "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 lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  "cpu"

  _UpperCamelCase      :	Any					      =  self.get_dummy_components()

  _UpperCamelCase      :	Tuple					      =  self.pipeline_class(**lowerCAmelCase__   )
  _UpperCamelCase      :	Optional[int]					      =  pipe.to(lowerCAmelCase__   )

  pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

  _UpperCamelCase      :	Union[str, Any]					      =  pipe(**self.get_dummy_inputs(lowerCAmelCase__   )   )
  _UpperCamelCase      :	Union[str, Any]					      =  output.images

  _UpperCamelCase      :	str					      =  pipe(
      **self.get_dummy_inputs(lowerCAmelCase__   )					,     return_dict=lowerCAmelCase__					,     )[0]

  _UpperCamelCase      :	Tuple					      =  image[0, -3:, -3:, -1]
  _UpperCamelCase      :	Optional[int]					      =  image_from_tuple[0, -3:, -3:, -1]

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

  _UpperCamelCase      :	Union[str, Any]					      =  np.array(
      [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953]   )

  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  _SCREAMING_SNAKE_CASE       (     unittest.TestCase				):



 '''simple docstring'''





 def lowercase_      (self   ):

  '''simple docstring'''







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






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  load_numpy(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
      "/kandinsky/kandinsky_text2img_cat_fp16.npy"   )

  _UpperCamelCase      :	Optional[int]					      =  KandinskyPriorPipeline.from_pretrained(
      "kandinsky-community/kandinsky-2-1-prior"					,     torch_dtype=torch.floataa   )
  pipe_prior.to(lowerCAmelCase__   )

  _UpperCamelCase      :	List[str]					      =  KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"					,     torch_dtype=torch.floataa   )
  _UpperCamelCase      :	List[Any]					      =  pipeline.to(lowerCAmelCase__   )
  pipeline.set_progress_bar_config(disable=lowerCAmelCase__   )

  _UpperCamelCase      :	Optional[Any]					      =  "red cat, 4k photo"

  _UpperCamelCase      :	str					      =  torch.Generator(device="cuda"   ).manual_seed(0   )
  _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  pipe_prior(
      lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=""					,     ).to_tuple()

  _UpperCamelCase      :	Optional[Any]					      =  torch.Generator(device="cuda"   ).manual_seed(0   )
  _UpperCamelCase      :	Any					      =  pipeline(
      lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     output_type="np"					,     )

  _UpperCamelCase      :	Optional[Any]					      =  output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__   )



