
"""simple docstring"""

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

_A				=					logging.get_logger(__name__)

_A				=					{'vocab_file': 'tokenizer.model'}

_A				=					{
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
_A				=					{
    'hf-internal-testing/llama-tokenizer': 2_0_4_8,
}
_A				=					'▁'

_A	,    _A				=					'[INST]', '[/INST]'
_A	,    _A				=					'<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
_A				=					'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'






class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      VOCAB_FILES_NAMES
	lowercase_								=      PRETRAINED_VOCAB_FILES_MAP
	lowercase_								=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase_								=      ['input_ids', 'attention_mask']




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_="<unk>"						,					UpperCAmelCase_="<s>"						,					UpperCAmelCase_="</s>"						,					UpperCAmelCase_=None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_=True						,					UpperCAmelCase_=False						,					UpperCAmelCase_=False						,					UpperCAmelCase_=True						,					**UpperCAmelCase_						,					)						->				Any:
			lowerCamelCase	:     int            =       {} if sp_model_kwargs is None else sp_model_kwargs
			lowerCamelCase	:     Tuple            =       AddedToken(UpperCAmelCase_						,					lstrip=UpperCAmelCase_						,					rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) else bos_token
			lowerCamelCase	:     List[Any]            =       AddedToken(UpperCAmelCase_						,					lstrip=UpperCAmelCase_						,					rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) else eos_token
			lowerCamelCase	:     List[str]            =       AddedToken(UpperCAmelCase_						,					lstrip=UpperCAmelCase_						,					rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) else unk_token
			lowerCamelCase	:     Tuple            =       AddedToken(UpperCAmelCase_						,					lstrip=UpperCAmelCase_						,					rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_						,					eos_token=UpperCAmelCase_						,					unk_token=UpperCAmelCase_						,					pad_token=UpperCAmelCase_						,					add_bos_token=UpperCAmelCase_						,					add_eos_token=UpperCAmelCase_						,					sp_model_kwargs=self.sp_model_kwargs						,					clean_up_tokenization_spaces=UpperCAmelCase_						,					legacy=UpperCAmelCase_						,					**UpperCAmelCase_						,					)
			if legacy:
					logger.warning_once(
					    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
					    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'   )
			lowerCamelCase	:     Union[str, Any]            =       legacy
			lowerCamelCase	:     Optional[int]            =       vocab_file
			lowerCamelCase	:     Tuple            =       add_bos_token
			lowerCamelCase	:     str            =       add_eos_token
			lowerCamelCase	:     Tuple            =       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )




	def __getstate__(					self   )						->				Optional[int]:
			lowerCamelCase	:     int            =       self.__dict__.copy()
			lowerCamelCase	:     str            =       None
			lowerCamelCase	:     Any            =       self.sp_model.serialized_model_proto()
			return state




	def __setstate__(					self						,					UpperCAmelCase_   )						->				List[Any]:
			lowerCamelCase	:     Dict            =       d
			lowerCamelCase	:     int            =       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )




	@property
	def      _UpperCamelCase       (					self   )						->				List[str]:
			return self.sp_model.get_piece_size()




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     Any            =       {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					**UpperCAmelCase_   )						->				List[str]:
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					lowerCamelCase	:     Optional[Any]            =       SPIECE_UNDERLINE + text.replace(UpperCAmelCase_						,					' '   )
			return super().tokenize(UpperCAmelCase_						,					**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Tuple:
			if not self.legacy:
					lowerCamelCase	:     Dict            =       text.startswith(UpperCAmelCase_   )
					if is_first:
							lowerCamelCase	:     List[str]            =       text[1:]

			lowerCamelCase	:     Any            =       self.sp_model.encode(UpperCAmelCase_						,					out_type=UpperCAmelCase_   )

			if not self.legacy and not is_first and not text.startswith(' '   ) and tokens[0].startswith(UpperCAmelCase_   ):
					lowerCamelCase	:     Optional[int]            =       ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
			return tokens




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[Any]:
			return self.sp_model.piece_to_id(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase	:     str            =       self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				List[str]:
			lowerCamelCase	:     Any            =       []
			lowerCamelCase	:     int            =       ''
			lowerCamelCase	:     Union[str, Any]            =       False
			for i, token in enumerate(UpperCAmelCase_   ):
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special and i != 0:
									out_string += " "
							out_string += self.sp_model.decode(UpperCAmelCase_   ) + token
							lowerCamelCase	:     Any            =       True
							lowerCamelCase	:     str            =       []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							lowerCamelCase	:     Optional[Any]            =       False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None   )						->				Tuple[str]:
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			lowerCamelCase	:     str            =       os.path.join(
			    UpperCAmelCase_						,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

			if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file						,					UpperCAmelCase_   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(UpperCAmelCase_						,					'wb'   ) as fi:
							lowerCamelCase	:     List[Any]            =       self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				List[Any]:
			lowerCamelCase	:     Optional[Any]            =       [self.bos_token_id] if self.add_bos_token else []
			lowerCamelCase	:     Optional[int]            =       [self.eos_token_id] if self.add_eos_token else []

			lowerCamelCase	:     int            =       bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					lowerCamelCase	:     Union[str, Any]            =       output + bos_token_id + token_ids_a + eos_token_id

			return output




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = False   )						->				List[int]:
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCAmelCase_						,					token_ids_a=UpperCAmelCase_						,					already_has_special_tokens=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       [1] if self.add_bos_token else []
			lowerCamelCase	:     Union[str, Any]            =       [1] if self.add_eos_token else []

			if token_ids_a is None:
					return bos_token_id + ([0] * len(UpperCAmelCase_   )) + eos_token_id
			return (
			    bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			    + bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None   )						->				List[int]:
			lowerCamelCase	:     Dict            =       [self.bos_token_id] if self.add_bos_token else []
			lowerCamelCase	:     Tuple            =       [self.eos_token_id] if self.add_eos_token else []

			lowerCamelCase	:     str            =       [0] * len(bos_token_id + token_ids_a + eos_token_id   )

			if token_ids_a is not None:
					output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

			return output




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				List[int]:
			lowerCamelCase	:     str            =       list(conversation.iter_texts()   )
			if not all([is_user for is_user, msg in dialogue[::2]]   ) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]   ):
					raise ValueError(
					    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'   )

			lowerCamelCase	:     List[int]            =       []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							lowerCamelCase	:     List[Any]            =       (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					lowerCamelCase	:     List[str]            =       (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """						,					add_special_tokens=UpperCAmelCase_   )
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]						,					dialogue[1::2]   )
			    ]						,					[]						,					)
			if not (dialogue[-1][0]):
					raise ValueError(F"""Last message must be from user, got {dialogue[-1]['role']}"""   )
			dialog_tokens += [self.bos_token_id] + self.encode(
			    F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""						,					add_special_tokens=UpperCAmelCase_   )
			return dialog_tokens








"""simple docstring"""

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 			_lowercase  (							__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			unittest.TestCase						):
	lowercase_								=      StableDiffusionImageVariationPipeline
	lowercase_								=      IMAGE_VARIATION_PARAMS
	lowercase_								=      IMAGE_VARIATION_BATCH_PARAMS
	lowercase_								=      frozenset([]						)
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase_								=      frozenset([]						)




	def      _UpperCamelCase       (					self   )						->				int:
			torch.manual_seed(0   )
			lowerCamelCase	:     List[str]            =       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	:     List[Any]            =       PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
			torch.manual_seed(0   )
			lowerCamelCase	:     str            =       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	:     int            =       CLIPVisionConfig(
			    hidden_size=32						,					projection_dim=32						,					intermediate_size=37						,					layer_norm_eps=1E-0_5						,					num_attention_heads=4						,					num_hidden_layers=5						,					image_size=32						,					patch_size=4						,					)
			lowerCamelCase	:     Optional[int]            =       CLIPVisionModelWithProjection(UpperCAmelCase_   )
			lowerCamelCase	:     int            =       CLIPImageProcessor(crop_size=32						,					size=32   )

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




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=0   )						->				Optional[int]:
			lowerCamelCase	:     int            =       floats_tensor((1, 3, 32, 32)						,					rng=random.Random(UpperCAmelCase_   )   )
			lowerCamelCase	:     List[str]            =       image.cpu().permute(0						,					2						,					3						,					1   )[0]
			lowerCamelCase	:     str            =       Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert('RGB'   ).resize((32, 32)   )
			if str(UpperCAmelCase_   ).startswith('mps'   ):
					lowerCamelCase	:     Tuple            =       torch.manual_seed(UpperCAmelCase_   )
			else:
					lowerCamelCase	:     int            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       {
			    'image': image,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'guidance_scale': 6.0,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     Tuple            =       'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase	:     List[str]            =       self.get_dummy_components()
			lowerCamelCase	:     Tuple            =       StableDiffusionImageVariationPipeline(**UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.get_dummy_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe(**UpperCAmelCase_   ).images
			lowerCamelCase	:     Dict            =       image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase	:     str            =       np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]   )

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




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     List[Any]            =       'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase	:     List[str]            =       self.get_dummy_components()
			lowerCamelCase	:     Union[str, Any]            =       StableDiffusionImageVariationPipeline(**UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Any            =       self.get_dummy_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       2 * [inputs['image']]
			lowerCamelCase	:     Dict            =       sd_pipe(**UpperCAmelCase_   )

			lowerCamelCase	:     List[str]            =       output.images

			lowerCamelCase	:     Dict            =       image[-1, -3:, -3:, -1]

			assert image.shape == (2, 64, 64, 3)
			lowerCamelCase	:     Dict            =       np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]   )

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




	def      _UpperCamelCase       (					self   )						->				List[str]:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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




	def      _UpperCamelCase       (					self   )						->				Dict:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_="cpu"						,					UpperCAmelCase_=torch.floataa						,					UpperCAmelCase_=0   )						->				Optional[int]:
			lowerCamelCase	:     List[Any]            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     List[str]            =       load_image(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
			lowerCamelCase	:     Union[str, Any]            =       np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 4, 64, 64)   )
			lowerCamelCase	:     int            =       torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       {
			    'image': init_image,
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     List[Any]            =       StableDiffusionImageVariationPipeline.from_pretrained(
			    'lambdalabs/sd-image-variations-diffusers'						,					safety_checker=UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Optional[int]            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe(**UpperCAmelCase_   ).images
			lowerCamelCase	:     Union[str, Any]            =       image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase	:     List[str]            =       np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]   )
			assert np.abs(image_slice - expected_slice   ).max() < 6E-3




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Optional[Any]            =       0

			def callback_fn(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) -> None:
					lowerCamelCase	:     int            =       True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
							lowerCamelCase	:     List[Any]            =       latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase	:     Dict            =       latents[0, -3:, -3:, -1]
							lowerCamelCase	:     List[Any]            =       np.array(
							    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]   )

							assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
					elif step == 2:
							lowerCamelCase	:     Union[str, Any]            =       latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase	:     Optional[int]            =       latents[0, -3:, -3:, -1]
							lowerCamelCase	:     Optional[Any]            =       np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]   )

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

			lowerCamelCase	:     List[str]            =       False

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

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




	def      _UpperCamelCase       (					self   )						->				str:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCamelCase	:     Any            =       'fusing/sd-image-variations-diffusers'
			lowerCamelCase	:     Optional[int]            =       StableDiffusionImageVariationPipeline.from_pretrained(
			    UpperCAmelCase_						,					safety_checker=UpperCAmelCase_						,					torch_dtype=torch.floataa   )
			lowerCamelCase	:     Any            =       pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
			pipe.enable_attention_slicing(1   )
			pipe.enable_sequential_cpu_offload()

			lowerCamelCase	:     Optional[int]            =       self.get_inputs(UpperCAmelCase_						,					dtype=torch.floataa   )
			lowerCamelCase	:     int            =       pipe(**UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       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 			_lowercase  (							unittest.TestCase						):




	def      _UpperCamelCase       (					self   )						->				Dict:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_="cpu"						,					UpperCAmelCase_=torch.floataa						,					UpperCAmelCase_=0   )						->				Any:
			lowerCamelCase	:     List[str]            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       load_image(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
			lowerCamelCase	:     Union[str, Any]            =       np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 4, 64, 64)   )
			lowerCamelCase	:     List[Any]            =       torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )
			lowerCamelCase	:     List[Any]            =       {
			    'image': init_image,
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 50,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     int            =       StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
			sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     int            =       sd_pipe(**UpperCAmelCase_   ).images[0]

			lowerCamelCase	:     List[Any]            =       load_numpy(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'   )
			lowerCamelCase	:     str            =       np.abs(expected_image - image   ).max()
			assert max_diff < 1E-3




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     List[Any]            =       StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
			lowerCamelCase	:     Optional[int]            =       DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config   )
			sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       25
			lowerCamelCase	:     List[str]            =       sd_pipe(**UpperCAmelCase_   ).images[0]

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






