
'''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, KandinskyInpaintPipeline, 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    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						KandinskyInpaintPipeline
 UpperCAmelCase_		    =						["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
 UpperCAmelCase_		    =						[
     """prompt""",
     """negative_prompt""",
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
     """mask_image""",
 ]
 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        snake_case__		(    self):
     return 32





 @property
 def        snake_case__		(    self):
     return 32





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





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





 @property
 def        snake_case__		(    self):
     return 1_00





 @property
 def        snake_case__		(    self):
     snake_case_ :    Dict     			=						XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
     return tokenizer





 @property
 def        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    Optional[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=10_05 ,       )

     snake_case_ :    Union[str, Any]     			=						MultilingualCLIP(lowercase_)
     snake_case_ :    Dict     			=						text_encoder.eval()

     return text_encoder





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

     snake_case_ :    Optional[int]     			=						{
         "in_channels": 9,
         # 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,
     }

     snake_case_ :    Optional[int]     			=						UNetaDConditionModel(**lowercase_)
     return model





 @property
 def        snake_case__		(    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        snake_case__		(    self):
     torch.manual_seed(0)
     snake_case_ :    Optional[int]     			=						VQModel(**self.dummy_movq_kwargs)
     return model





 def        snake_case__		(    self):
     snake_case_ :    Optional[Any]     			=						self.dummy_text_encoder
     snake_case_ :    Tuple     			=						self.dummy_tokenizer
     snake_case_ :    str     			=						self.dummy_unet
     snake_case_ :    Optional[Any]     			=						self.dummy_movq

     snake_case_ :    Any     			=						DDIMScheduler(
         num_train_timesteps=10_00 ,       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_ ,       )

     snake_case_ :    Union[str, Any]     			=						{
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

     return components





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=0):
     snake_case_ :    str     			=						floats_tensor((1, self.cross_attention_dim) ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    int     			=						floats_tensor((1, self.cross_attention_dim) ,       rng=random.Random(seed + 1)).to(lowercase_)
     # create init_image
     snake_case_ :    Any     			=						floats_tensor((1, 3, 64, 64) ,       rng=random.Random(lowercase_)).to(lowercase_)
     snake_case_ :    Union[str, Any]     			=						image.cpu().permute(0 ,       2 ,       3 ,       1)[0]
     snake_case_ :    str     			=						Image.fromarray(np.uinta(lowercase_)).convert("RGB").resize((2_56, 2_56))
     # create mask
     snake_case_ :    Dict     			=						np.ones((64, 64) ,       dtype=np.floataa)
     snake_case_ :    List[Any]     			=						0

     if str(lowercase_).startswith("mps"):
         snake_case_ :    List[Any]     			=						torch.manual_seed(lowercase_)
     else:
         snake_case_ :    Union[str, Any]     			=						torch.Generator(device=lowercase_).manual_seed(lowercase_)
     snake_case_ :    Tuple     			=						{
         "prompt": "horse",
         "image": init_image,
         "mask_image": mask,
         "image_embeds": image_embeds,
         "negative_image_embeds": negative_image_embeds,
         "generator": generator,
         "height": 64,
         "width": 64,
         "num_inference_steps": 2,
         "guidance_scale": 4.0,
         "output_type": "np",
     }
     return inputs





 def        snake_case__		(    self):
     snake_case_ :    Tuple     			=						"cpu"

     snake_case_ :    Union[str, Any]     			=						self.get_dummy_components()

     snake_case_ :    Optional[int]     			=						self.pipeline_class(**lowercase_)
     snake_case_ :    List[Any]     			=						pipe.to(lowercase_)

     pipe.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Optional[Any]     			=						pipe(**self.get_dummy_inputs(lowercase_))
     snake_case_ :    Any     			=						output.images

     snake_case_ :    Any     			=						pipe(
         **self.get_dummy_inputs(lowercase_) ,       return_dict=lowercase_ ,       )[0]

     snake_case_ :    Optional[int]     			=						image[0, -3:, -3:, -1]
     snake_case_ :    List[str]     			=						image_from_tuple[0, -3:, -3:, -1]

     print(F'image.shape {image.shape}')

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

     snake_case_ :    str     			=						np.array(
         [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786])

     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()}'





 def        snake_case__		(    self):
     super().test_inference_batch_single_identical(expected_max_diff=3E-3)






@slow
@require_torch_gpu
class    UpperCAmelCase_				(	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):
     snake_case_ :    List[Any]     			=						load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy")

     snake_case_ :    Optional[Any]     			=						load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
     snake_case_ :    List[Any]     			=						np.ones((7_68, 7_68) ,       dtype=np.floataa)
     snake_case_ :    Union[str, Any]     			=						0

     snake_case_ :    str     			=						"a hat"

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

     snake_case_ :    Any     			=						KandinskyInpaintPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-1-inpaint" ,       torch_dtype=torch.floataa)
     snake_case_ :    Dict     			=						pipeline.to(lowercase_)
     pipeline.set_progress_bar_config(disable=lowercase_)

     snake_case_ :    Optional[int]     			=						torch.Generator(device="cpu").manual_seed(0)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						pipe_prior(
         lowercase_ ,       generator=lowercase_ ,       num_inference_steps=5 ,       negative_prompt="" ,       ).to_tuple()

     snake_case_ :    Any     			=						pipeline(
         lowercase_ ,       image=lowercase_ ,       mask_image=lowercase_ ,       image_embeds=lowercase_ ,       negative_image_embeds=lowercase_ ,       generator=lowercase_ ,       num_inference_steps=1_00 ,       height=7_68 ,       width=7_68 ,       output_type="np" ,       )

     snake_case_ :    Optional[int]     			=						output.images[0]

     assert image.shape == (7_68, 7_68, 3)

     assert_mean_pixel_difference(lowercase_ ,       lowercase_)






'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

a_											=						TypeVar("T")
a_											=						TypeVar("U")






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Any     			=						key
     snake_case_ :    List[Any]     			=						val
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None
     snake_case_ :    DoubleLinkedListNode[T, U] | None     			=						None





 def __repr__(    self):
     return (
         F'Node: key: {self.key}, val: {self.val}, '
         F'has next: {bool(self.next)}, has prev: {bool(self.prev)}'
     )






class    UpperCAmelCase_				(	Generic[T, U]					):





 def __init__(    self):
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_ :    DoubleLinkedListNode[T, U]     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.rear, self.head





 def __repr__(    self):
     snake_case_ :    Dict     			=						["DoubleLinkedList"]
     snake_case_ :    Dict     			=						self.head
     while node.next is not None:
         rep.append(str(lowercase_))
         snake_case_ :    List[str]     			=						node.next
     rep.append(str(self.rear))
     return ",\n    ".join(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						self.rear.prev

     # All nodes other than self.head are guaranteed to have non-None previous
     assert previous is not None

     snake_case_ :    Tuple     			=						node
     snake_case_ :    str     			=						previous
     snake_case_ :    Optional[Any]     			=						node
     snake_case_ :    Any     			=						self.rear





 def        snake_case__		(    self ,       lowercase_):

     if node.prev is None or node.next is None:
         return None

     snake_case_ :    Union[str, Any]     			=						node.next
     snake_case_ :    Optional[int]     			=						node.prev
     snake_case_ :    str     			=						None
     snake_case_ :    int     			=						None
     return node






class    UpperCAmelCase_				(	Generic[T, U]					):
 UpperCAmelCase_		    =						{}





 def __init__(    self ,       lowercase_):
     snake_case_ :    DoubleLinkedList[T, U]     			=						DoubleLinkedList()
     snake_case_ :    List[str]     			=						capacity
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						0
     snake_case_ :    Union[str, Any]     			=						0
     snake_case_ :    dict[T, DoubleLinkedListNode[T, U]]     			=						{}





 def __repr__(    self):
     return (
         F'CacheInfo(hits={self.hits}, misses={self.miss}, '
         F'capacity={self.capacity}, current size={self.num_keys})'
     )





 def __contains__(    self ,       lowercase_):
     return key in self.cache





 def        snake_case__		(    self ,       lowercase_):
     # Note: pythonic interface would throw KeyError rather than return None

     if key in self.cache:
         self.hits += 1
         snake_case_ :    DoubleLinkedListNode[T, U]     			=						self.cache[key]
         snake_case_ :    Optional[Any]     			=						self.list.remove(self.cache[key])
         assert node == value_node

         # node is guaranteed not None because it is in self.cache
         assert node is not None
         self.list.add(lowercase_)
         return node.val
     self.miss += 1
     return None





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):

     if key not in self.cache:
         if self.num_keys >= self.capacity:
             # delete first node (oldest) when over capacity
             snake_case_ :    Optional[Any]     			=						self.list.head.next

             # guaranteed to have a non-None first node when num_keys > 0
             # explain to type checker via assertions
             assert first_node is not None
             assert first_node.key is not None
             assert (
                 self.list.remove(lowercase_) is not None
             )  # node guaranteed to be in list assert node.key is not None

             del self.cache[first_node.key]
             self.num_keys -= 1
         snake_case_ :    Dict     			=						DoubleLinkedListNode(lowercase_ ,       lowercase_)
         self.list.add(self.cache[key])
         self.num_keys += 1

     else:
         # bump node to the end of the list, update value
         snake_case_ :    List[Any]     			=						self.list.remove(self.cache[key])
         assert node is not None  # node guaranteed to be in list
         snake_case_ :    Optional[Any]     			=						value
         self.list.add(lowercase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ = 1_28):

     def cache_decorator_inner(lowercase_) -> Callable[..., U]:
         def cache_decorator_wrapper(*lowercase_) -> U:
             if func not in cls.decorator_function_to_instance_map:
                 snake_case_ :    List[str]     			=						LRUCache(lowercase_)

             snake_case_ :    Optional[Any]     			=						cls.decorator_function_to_instance_map[func].get(args[0])
             if result is None:
                 snake_case_ :    Any     			=						func(*lowercase_)
                 cls.decorator_function_to_instance_map[func].put(args[0] ,       lowercase_)
             return result

         def cache_info() -> LRUCache[T, U]:
             return cls.decorator_function_to_instance_map[func]

         setattr(lowercase_ ,       "cache_info" ,       lowercase_)  # noqa: B010

         return cache_decorator_wrapper

     return cache_decorator_inner


if __name__ == "__main__":
  import doctest

  doctest.testmod()




