
'''simple docstring'''

import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCAmelCase__      :       Union[str, Any] 		=						logging.get_logger(__name__)

UpperCAmelCase__      :       List[str] 		=						{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

UpperCAmelCase__      :       int 		=						{
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

UpperCAmelCase__      :       Optional[int] 		=						{
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

UpperCAmelCase__      :       str 		=						{
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}







def      A   (					)    ->				Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__									=							(
			    list(range(ord("!"					)  ,		ord("~"					) + 1					)					) + list(range(ord("¡"					)  ,		ord("¬"					) + 1					)					) + list(range(ord("®"					)  ,		ord("ÿ"					) + 1					)					)
			)
			lowerCAmelCase__									=							bs[:]
			lowerCAmelCase__									=							0
			for b in range(2**8					):
						if b not in bs:
									bs.append(lowerCAmelCase__					)
									cs.append(2**8 + n					)
									n += 1
			lowerCAmelCase__									=							[chr(lowerCAmelCase__					) for n in cs]
			return dict(zip(lowerCAmelCase__  ,		lowerCAmelCase__					)					)







def      A   (					UpperCamelCase_    :   str					)    ->				int:







			'''simple docstring'''



			lowerCAmelCase__									=							set()
			lowerCAmelCase__									=							word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)					)
						lowerCAmelCase__									=							char
			return pairs






class       A   (		SCREAMING_SNAKE_CASE__			):
	snake_case__					:Optional[Any]         =   VOCAB_FILES_NAMES
	snake_case__					:int         =   PRETRAINED_VOCAB_FILES_MAP
	snake_case__					:List[str]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__					:Union[str, Any]         =   ['input_ids', 'attention_mask', 'token_type_ids']





	def __init__(    self	:				List[str]					,							__magic_name__	:				Optional[Any]					,							__magic_name__	:				str					,							__magic_name__	:				Any="replace"					,							__magic_name__	:				str="[CLS]"					,							__magic_name__	:				Optional[Any]="[SEP]"					,							__magic_name__	:				Optional[int]="[SEP]"					,							__magic_name__	:				List[str]="[CLS]"					,							__magic_name__	:				Union[str, Any]="[UNK]"					,							__magic_name__	:				List[str]="[PAD]"					,							__magic_name__	:				Optional[Any]="[MASK]"					,							__magic_name__	:				Any=False					,							__magic_name__	:				List[Any]=False					,							**__magic_name__	:				Union[str, Any]					,							):


				"""simple docstring"""

				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else bos_token
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else eos_token
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else sep_token
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else cls_token
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else unk_token
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else pad_token

				# Mask token behave like a normal word, i.e. include the space before it
				lowerCAmelCase__									=							AddedToken(lowercase__					,							lstrip=lowercase__					,							rstrip=lowercase__							) if isinstance(lowercase__					,							lowercase__							) else mask_token

				super().__init__(
				    errors=lowercase__					,							bos_token=lowercase__					,							eos_token=lowercase__					,							unk_token=lowercase__					,							sep_token=lowercase__					,							cls_token=lowercase__					,							pad_token=lowercase__					,							mask_token=lowercase__					,							add_prefix_space=lowercase__					,							add_bos_token=lowercase__					,							**lowercase__					,							)
				lowerCAmelCase__									=							add_bos_token

				with open(lowercase__					,							encoding="utf-8"							) as vocab_handle:
							lowerCAmelCase__									=							json.load(lowercase__							)
				lowerCAmelCase__									=							{v: k for k, v in self.encoder.items()}
				lowerCAmelCase__									=							errors  # how to handle errors in decoding
				lowerCAmelCase__									=							bytes_to_unicode()
				lowerCAmelCase__									=							{v: k for k, v in self.byte_encoder.items()}
				with open(lowercase__					,							encoding="utf-8"							) as merges_handle:
							lowerCAmelCase__									=							merges_handle.read().split("\n"							)[1:-1]
				lowerCAmelCase__									=							[tuple(merge.split()							) for merge in bpe_merges]
				lowerCAmelCase__									=							dict(zip(lowercase__					,							range(len(lowercase__							)							)							)							)
				lowerCAmelCase__									=							{}
				lowerCAmelCase__									=							add_prefix_space

				# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
				lowerCAmelCase__									=							re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)





	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def 							__SCREAMING_SNAKE_CASE     (    self	:				int							):


				"""simple docstring"""

				return len(self.encoder							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				str							):


				"""simple docstring"""

				return dict(self.encoder					,							**self.added_tokens_encoder							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				int					,							__magic_name__	:				Any							):


				"""simple docstring"""

				if token in self.cache:
							return self.cache[token]
				lowerCAmelCase__									=							tuple(lowercase__							)
				lowerCAmelCase__									=							get_pairs(lowercase__							)

				if not pairs:
							return token

				while True:
							lowerCAmelCase__									=							min(lowercase__					,							key=lambda __magic_name__							: self.bpe_ranks.get(lowercase__					,							float("inf"							)							)							)
							if bigram not in self.bpe_ranks:
										break
							lowerCAmelCase__		,lowerCAmelCase__									=							bigram
							lowerCAmelCase__									=							[]
							lowerCAmelCase__									=							0
							while i < len(lowercase__							):
										try:
													lowerCAmelCase__									=							word.index(lowercase__					,							lowercase__							)
										except ValueError:
													new_word.extend(word[i:]							)
													break
										else:
													new_word.extend(word[i:j]							)
													lowerCAmelCase__									=							j

										if word[i] == first and i < len(lowercase__							) - 1 and word[i + 1] == second:
													new_word.append(first + second							)
													i += 2
										else:
													new_word.append(word[i]							)
													i += 1
							lowerCAmelCase__									=							tuple(lowercase__							)
							lowerCAmelCase__									=							new_word
							if len(lowercase__							) == 1:
										break
							else:
										lowerCAmelCase__									=							get_pairs(lowercase__							)
				lowerCAmelCase__									=							" ".join(lowercase__							)
				lowerCAmelCase__									=							word
				return word





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[str]					,							__magic_name__	:				List[int]					,							__magic_name__	:				Optional[List[int]] = None							):


				"""simple docstring"""

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				lowerCAmelCase__									=							[self.cls_token_id]
				lowerCAmelCase__									=							[self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]					,							__magic_name__	:				List[int]					,							__magic_name__	:				Optional[List[int]] = None					,							__magic_name__	:				bool = False							):


				"""simple docstring"""

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase__					,							token_ids_a=lowercase__					,							already_has_special_tokens=lowercase__							)

				if token_ids_a is None:
							return [1] + ([0] * len(lowercase__							)) + [1]
				return [1] + ([0] * len(lowercase__							)) + [1] + ([0] * len(lowercase__							)) + [1]





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[Any]					,							__magic_name__	:				List[int]					,							__magic_name__	:				Optional[List[int]] = None							):


				"""simple docstring"""

				lowerCAmelCase__									=							[self.sep_token_id]
				lowerCAmelCase__									=							[self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep							) * [0]
				return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





	def 							__SCREAMING_SNAKE_CASE     (    self	:				int					,							__magic_name__	:				List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for token in re.findall(self.pat					,							lowercase__							):
							lowerCAmelCase__									=							"".join(
							    self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__							).split(" "							)							)
				return bpe_tokens





	def 							__SCREAMING_SNAKE_CASE     (    self	:				str					,							__magic_name__	:				Optional[Any]							):


				"""simple docstring"""

				return self.encoder.get(lowercase__					,							self.encoder.get(self.unk_token							)							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				str					,							__magic_name__	:				int							):


				"""simple docstring"""

				return self.decoder.get(lowercase__							)





	def 							__SCREAMING_SNAKE_CASE     (    self	:				Optional[int]					,							__magic_name__	:				Union[str, Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							"".join(lowercase__							)
				lowerCAmelCase__									=							bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"					,							errors=self.errors							)
				return text





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[Any]					,							__magic_name__	:				str					,							__magic_name__	:				Optional[str] = None							):


				"""simple docstring"""

				if not os.path.isdir(lowercase__							):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
							return
				lowerCAmelCase__									=							os.path.join(
				    lowercase__					,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
				lowerCAmelCase__									=							os.path.join(
				    lowercase__					,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

				with open(lowercase__					,							"w"					,							encoding="utf-8"							) as f:
							f.write(json.dumps(self.encoder					,							indent=2					,							sort_keys=lowercase__					,							ensure_ascii=lowercase__							) + "\n"							)

				lowerCAmelCase__									=							0
				with open(lowercase__					,							"w"					,							encoding="utf-8"							) as writer:
							writer.write("#version: 0.2\n"							)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,							key=lambda __magic_name__							: kv[1]							):
										if index != token_index:
													logger.warning(
													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
													    " Please check that the tokenizer is not corrupted!"							)
													lowerCAmelCase__									=							token_index
										writer.write(" ".join(lowercase__							) + "\n"							)
										index += 1

				return vocab_file, merge_file





	def 							__SCREAMING_SNAKE_CASE     (    self	:				List[Any]					,							__magic_name__	:				Optional[Any]					,							__magic_name__	:				Any=False					,							**__magic_name__	:				int							):


				"""simple docstring"""

				lowerCAmelCase__									=							kwargs.pop("add_prefix_space"					,							self.add_prefix_space							)
				if (is_split_into_words or add_prefix_space) and (len(lowercase__							) > 0 and not text[0].isspace()):
							lowerCAmelCase__									=							" " + text
				return (text, kwargs)







	def 							__SCREAMING_SNAKE_CASE     (    self	:				int					,							__magic_name__	:				"Conversation"							):


				"""simple docstring"""

				lowerCAmelCase__									=							[]
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowercase__					,							add_special_tokens=lowercase__							) + [self.eos_token_id]							)
				if len(lowercase__							) > self.model_max_length:
							lowerCAmelCase__									=							input_ids[-self.model_max_length :]
				return input_ids






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				a_ (						SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     SCREAMING_SNAKE_CASE       ,     unittest.TestCase						):






 '''simple docstring'''






 UpperCAmelCase_	 =					StableDiffusionImageVariationPipeline
 UpperCAmelCase_	 =					IMAGE_VARIATION_PARAMS
 UpperCAmelCase_	 =					IMAGE_VARIATION_BATCH_PARAMS
 UpperCAmelCase_	 =					frozenset([]						)
 # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 UpperCAmelCase_	 =					frozenset([]						)




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        torch.manual_seed(0)
        lowerCAmelCase__     	=					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			,    )
        lowerCAmelCase__     	=					PNDMScheduler(skip_prk_steps=lowercase__)
        torch.manual_seed(0)
        lowerCAmelCase__     	=					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)
        lowerCAmelCase__     	=					CLIPVisionConfig(
            hidden_size=32			,    projection_dim=32			,    intermediate_size=37			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    image_size=32			,    patch_size=4			,    )
        lowerCAmelCase__     	=					CLIPVisionModelWithProjection(lowercase__)
        lowerCAmelCase__     	=					CLIPImageProcessor(crop_size=32			,    size=32)

        lowerCAmelCase__     	=					{
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'image_encoder': image_encoder,
            'feature_extractor': feature_extractor,
            'safety_checker': None,
        }
        return components




 def 		__snake_case						(			self					:  Optional[int]			,    lowercase__					:  Dict			,    lowercase__					:  Tuple=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					floats_tensor((1, 3, 32, 32)			,    rng=random.Random(lowercase__))
        lowerCAmelCase__     	=					image.cpu().permute(0			,    2			,    3			,    1)[0]
        lowerCAmelCase__     	=					Image.fromarray(np.uinta(lowercase__)).convert('RGB').resize((32, 32))
        if str(lowercase__).startswith('mps'):
               lowerCAmelCase__     	=					torch.manual_seed(lowercase__)
        else:
               lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					{
            'image': image,
            'generator': generator,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.5_239, 0.5_723, 0.4_796, 0.5_049, 0.5_550, 0.4_685, 0.5_329, 0.4_891, 0.4_921])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					'cpu'  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase__     	=					self.get_dummy_components()
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline(**lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_dummy_inputs(lowercase__)
        lowerCAmelCase__     	=					2 * [inputs['image']]
        lowerCAmelCase__     	=					sd_pipe(**lowercase__)

        lowerCAmelCase__     	=					output.images

        lowerCAmelCase__     	=					image[-1, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)
        lowerCAmelCase__     	=					np.array([0.6_892, 0.5_637, 0.5_836, 0.5_771, 0.6_254, 0.6_409, 0.5_580, 0.5_569, 0.5_289])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




 def 		__snake_case						(			self					:  Optional[int]):







        '''simple docstring'''



        super().test_inference_batch_single_identical(expected_max_diff=3e-3)






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






 '''simple docstring'''






 def 		__snake_case						(			self					:  List[Any]):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  List[Any]			,    lowercase__					:  List[str]			,    lowercase__					:  Optional[int]="cpu"			,    lowercase__					:  List[str]=torch.floataa			,    lowercase__					:  Dict=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'lambdalabs/sd-image-variations-diffusers'			,    safety_checker=lowercase__)
        lowerCAmelCase__     	=					sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images
        lowerCAmelCase__     	=					image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 512, 3)
        lowerCAmelCase__     	=					np.array([0.84_491, 0.90_789, 0.75_708, 0.78_734, 0.83_485, 0.70_099, 0.66_938, 0.68_727, 0.61_379])
        assert np.abs(image_slice - expected_slice).max() < 6e-3




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					0

        def callback_fn(lowercase__					:  int			,    lowercase__					:  int			,    lowercase__					:  torch.FloatTensor) -> None:
               lowerCAmelCase__     	=					True
               nonlocal number_of_steps
               number_of_steps += 1
               if step == 1:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array(
                          [-0.1_621, 0.2_837, -0.7_979, -0.1_221, -1.3_057, 0.7_681, -2.1_191, 0.0_464, 1.6_309])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
               elif step == 2:
                      lowerCAmelCase__     	=					latents.detach().cpu().numpy()
                      assert latents.shape == (1, 4, 64, 64)
                      lowerCAmelCase__     	=					latents[0, -3:, -3:, -1]
                      lowerCAmelCase__     	=					np.array([0.6_299, 1.7_500, 1.1_992, -2.1_582, -1.8_994, 0.7_334, -0.7_090, 1.0_137, 1.5_273])

                      assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2

        lowerCAmelCase__     	=					False

        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            'fusing/sd-image-variations-diffusers'			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa			,    )
        pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        pipe(**lowercase__			,    callback=lowercase__			,    callback_steps=1)
        assert callback_fn.has_been_called
        assert number_of_steps == inputs["num_inference_steps"]




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        lowerCAmelCase__     	=					'fusing/sd-image-variations-diffusers'
        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained(
            lowercase__			,    safety_checker=lowercase__			,    torch_dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe.to(lowercase__)
        pipe.set_progress_bar_config(disable=lowercase__)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        lowerCAmelCase__     	=					self.get_inputs(lowercase__			,    dtype=torch.floataa)
        lowerCAmelCase__     	=					pipe(**lowercase__)

        lowerCAmelCase__     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 2.6 GB is allocated
        assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



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




 def 		__snake_case						(			self					:  int			,    lowercase__					:  List[str]			,    lowercase__					:  Dict="cpu"			,    lowercase__					:  Optional[int]=torch.floataa			,    lowercase__					:  Optional[Any]=0):







        '''simple docstring'''



        lowerCAmelCase__     	=					torch.Generator(device=lowercase__).manual_seed(lowercase__)
        lowerCAmelCase__     	=					load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/input_image_vermeer.png')
        lowerCAmelCase__     	=					np.random.RandomState(lowercase__).standard_normal((1, 4, 64, 64))
        lowerCAmelCase__     	=					torch.from_numpy(lowercase__).to(device=lowercase__			,    dtype=lowercase__)
        lowerCAmelCase__     	=					{
            'image': init_image,
            'latents': latents,
            'generator': generator,
            'num_inference_steps': 50,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs




 def 		__snake_case						(			self					:  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3




 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers')
        lowerCAmelCase__     	=					DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
        sd_pipe.to(lowercase__)
        sd_pipe.set_progress_bar_config(disable=lowercase__)

        lowerCAmelCase__     	=					self.get_inputs(lowercase__)
        lowerCAmelCase__     	=					25
        lowerCAmelCase__     	=					sd_pipe(**lowercase__).images[0]

        lowerCAmelCase__     	=					load_numpy(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy')
        lowerCAmelCase__     	=					np.abs(expected_image - image).max()
        assert max_diff < 1e-3
