'''simple docstring'''




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

import sentencepiece as spm

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


_a   :	Optional[Any] 							=   logging.get_logger(__name__)

_a   :	Tuple 							=   "▁"

_a   :	Dict 							=   {"vocab_file": "sentencepiece.bpe.model"}

_a   :	str 							=   {
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

_a   :	Tuple 							=   {
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
_a   :	str 							=   ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]






class 		__A       (__magic_name__   ):
							snake_case       :Optional[Any]				    =					VOCAB_FILES_NAMES
							snake_case       :int				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case       :Union[str, Any]				    =					PRETRAINED_VOCAB_FILES_MAP
							snake_case       :Dict				    =					["input_ids", "attention_mask"]

							snake_case       :List[int]				    =					[]
							snake_case       :List[int]				    =					[]






							def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="</s>"				,     UpperCamelCase_="<s>"				,     UpperCamelCase_="<unk>"				,     UpperCamelCase_="<pad>"				,     UpperCamelCase_="<mask>"				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_ = None				,     UpperCamelCase_=None				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
														# Mask token behave like a normal word, i.e. include the space before it
														__UpperCAmelCase  :				List[str]       				=		AddedToken(UpperCamelCase_				,     lstrip=UpperCamelCase_				,     rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_				,     UpperCamelCase_      ) else mask_token

														__UpperCAmelCase  :				Optional[int]       				=		{} if sp_model_kwargs is None else sp_model_kwargs
														__UpperCAmelCase  :				Any       				=		legacy_behaviour

														super().__init__(
														    bos_token=UpperCamelCase_				,     eos_token=UpperCamelCase_				,     unk_token=UpperCamelCase_				,     sep_token=UpperCamelCase_				,     cls_token=UpperCamelCase_				,     pad_token=UpperCamelCase_				,     mask_token=UpperCamelCase_				,     tokenizer_file=UpperCamelCase_				,     src_lang=UpperCamelCase_				,     tgt_lang=UpperCamelCase_				,     additional_special_tokens=UpperCamelCase_				,     sp_model_kwargs=self.sp_model_kwargs				,     legacy_behaviour=UpperCamelCase_				,     **UpperCamelCase_				,     )

														__UpperCAmelCase  :				List[str]       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
														self.sp_model.Load(str(UpperCamelCase_      )      )
														__UpperCAmelCase  :				Optional[Any]       				=		vocab_file
														# Original fairseq vocab and spm vocab must be "aligned":
														# Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
														# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
														# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
														# spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

														# Mimic fairseq token-to-id alignment for the first 4 token
														__UpperCAmelCase  :				List[Any]       				=		{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

														# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
														__UpperCAmelCase  :				str       				=		1

														__UpperCAmelCase  :				Union[str, Any]       				=		len(self.sp_model      )
														__UpperCAmelCase  :				List[Any]       				=		{
														    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase_      )
														}
														__UpperCAmelCase  :				Optional[Any]       				=		{v: k for k, v in self.lang_code_to_id.items()}
														__UpperCAmelCase  :				Optional[int]       				=		len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

														self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
														__UpperCAmelCase  :				Union[str, Any]       				=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
														__UpperCAmelCase  :				Any       				=		list(self.lang_code_to_id.keys()      )

														if additional_special_tokens is not None:
																					# Only add those special tokens if they are not already there.
																					self._additional_special_tokens.extend(
																					    [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

														__UpperCAmelCase  :				Optional[Any]       				=		src_lang if src_lang is not None else "eng_Latn"
														__UpperCAmelCase  :				Optional[int]       				=		self.lang_code_to_id[self._src_lang]
														__UpperCAmelCase  :				Tuple       				=		tgt_lang
														self.set_src_lang_special_tokens(self._src_lang      )






							def __getstate__(      self      ):
														__UpperCAmelCase  :				int       				=		self.__dict__.copy()
														__UpperCAmelCase  :				str       				=		None
														__UpperCAmelCase  :				Any       				=		self.sp_model.serialized_model_proto()
														return state






							def __setstate__(      self				,     UpperCamelCase_      ):
														__UpperCAmelCase  :				Optional[Any]       				=		d

														# for backward compatibility
														if not hasattr(self				,     "sp_model_kwargs"      ):
																					__UpperCAmelCase  :				Dict       				=		{}

														__UpperCAmelCase  :				List[Any]       				=		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






							@property
							def 			_snake_case						(      self      ):
														return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token






							@property
							def 			_snake_case						(      self      ):
														return self._src_lang






							@src_lang.setter
							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														__UpperCAmelCase  :				List[Any]       				=		new_src_lang
														self.set_src_lang_special_tokens(self._src_lang      )






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None				,     UpperCamelCase_ = False      ):

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

														__UpperCAmelCase  :				Optional[int]       				=		[1] * len(self.prefix_tokens      )
														__UpperCAmelCase  :				str       				=		[1] * len(self.suffix_tokens      )
														if token_ids_a is None:
																					return prefix_ones + ([0] * len(UpperCamelCase_      )) + suffix_ones
														return prefix_ones + ([0] * len(UpperCamelCase_      )) + ([0] * len(UpperCamelCase_      )) + suffix_ones






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
														if token_ids_a is None:
																					return self.prefix_tokens + token_ids_a + self.suffix_tokens
														# We don't expect to process pairs, but leave the pair logic for API consistency
														return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
														__UpperCAmelCase  :				Optional[Any]       				=		[self.sep_token_id]
														__UpperCAmelCase  :				Dict       				=		[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 + sep + token_ids_a + sep      ) * [0]






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      ):
														if src_lang is None or tgt_lang is None:
																					raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"      )
														__UpperCAmelCase  :				Union[str, Any]       				=		src_lang
														__UpperCAmelCase  :				str       				=		self(UpperCamelCase_				,     add_special_tokens=UpperCamelCase_				,     return_tensors=UpperCamelCase_				,     **UpperCamelCase_      )
														__UpperCAmelCase  :				Union[str, Any]       				=		self.convert_tokens_to_ids(UpperCamelCase_      )
														__UpperCAmelCase  :				Optional[int]       				=		tgt_lang_id
														return inputs






							def 			_snake_case						(      self      ):
														__UpperCAmelCase  :				Optional[Any]       				=		{self.convert_ids_to_tokens(UpperCamelCase_      ): i for i in range(self.vocab_size      )}
														vocab.update(self.added_tokens_encoder      )
														return vocab






							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														return self.sp_model.encode(UpperCamelCase_				,     out_type=UpperCamelCase_      )






							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														__UpperCAmelCase  :				Union[str, Any]       				=		self.sp_model.PieceToId(UpperCamelCase_      )

														# Need to return unknown token if the SP model returned 0
														return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														if index in self.fairseq_ids_to_tokens:
																					return self.fairseq_ids_to_tokens[index]
														return self.sp_model.IdToPiece(index - self.fairseq_offset      )






							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														__UpperCAmelCase  :				str       				=		"".join(UpperCamelCase_      ).replace(UpperCamelCase_				,     " "      ).strip()
														return out_string






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
														if not os.path.isdir(UpperCamelCase_      ):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																					return
														__UpperCAmelCase  :				List[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:
																												__UpperCAmelCase  :				Dict       				=		self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase_      )

														return (out_vocab_file,)






							def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = "eng_Latn"				,     UpperCamelCase_ = None				,     UpperCamelCase_ = "fra_Latn"				,     **UpperCamelCase_				,     ):
														__UpperCAmelCase  :				Tuple       				=		src_lang
														__UpperCAmelCase  :				Dict       				=		tgt_lang
														return super().prepare_seqaseq_batch(UpperCamelCase_				,     UpperCamelCase_				,     **UpperCamelCase_      )






							def 			_snake_case						(      self      ):
														return self.set_src_lang_special_tokens(self.src_lang      )






							def 			_snake_case						(      self      ):
														return self.set_tgt_lang_special_tokens(self.tgt_lang      )






							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														__UpperCAmelCase  :				int       				=		self.lang_code_to_id[src_lang]
														if self.legacy_behaviour:
																					__UpperCAmelCase  :				Union[str, Any]       				=		[]
																					__UpperCAmelCase  :				Optional[int]       				=		[self.eos_token_id, self.cur_lang_code]
														else:
																					__UpperCAmelCase  :				Dict       				=		[self.cur_lang_code]
																					__UpperCAmelCase  :				Any       				=		[self.eos_token_id]







							def 			_snake_case						(      self				,     UpperCamelCase_      ):
														__UpperCAmelCase  :				int       				=		self.lang_code_to_id[lang]
														if self.legacy_behaviour:
																					__UpperCAmelCase  :				Union[str, Any]       				=		[]
																					__UpperCAmelCase  :				Optional[int]       				=		[self.eos_token_id, self.cur_lang_code]
														else:
																					__UpperCAmelCase  :				Tuple       				=		[self.cur_lang_code]
																					__UpperCAmelCase  :				Any       				=		[self.eos_token_id]







'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def _lowercase	(      lowerCamelCase__						)					->       Union[str, Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Dict       				=		[tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]						)






class 		__A       (__magic_name__ ,					__magic_name__ ,					__magic_name__ ,					unittest.TestCase   ):
       snake_case       :Union[str, Any]				    =					StableDiffusionLatentUpscalePipeline
       snake_case       :Optional[int]				    =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       snake_case       :List[str]				    =					PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       snake_case       :Optional[Any]				    =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       snake_case       :Optional[Any]				    =					frozenset(
           []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       snake_case       :Any				    =					frozenset([]   )

       snake_case       :Optional[int]				    =					True






       @property
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		1
              __UpperCAmelCase  :				Dict       				=		4
              __UpperCAmelCase  :				List[str]       				=		(16, 16)

              __UpperCAmelCase  :				Dict       				=		floats_tensor((batch_size, num_channels) + sizes				,     rng=random.Random(0      )      ).to(UpperCamelCase_      )
              return image






       def 			_snake_case						(      self      ):
              torch.manual_seed(0      )
              __UpperCAmelCase  :				List[str]       				=		UNetaDConditionModel(
                  act_fn="gelu"				,     attention_head_dim=8				,     norm_num_groups=UpperCamelCase_				,     block_out_channels=[32, 32, 64, 64]				,     time_cond_proj_dim=1_60				,     conv_in_kernel=1				,     conv_out_kernel=1				,     cross_attention_dim=32				,     down_block_types=(
                      "KDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                      "KCrossAttnDownBlock2D",
                  )				,     in_channels=8				,     mid_block_type=UpperCamelCase_				,     only_cross_attention=UpperCamelCase_				,     out_channels=5				,     resnet_time_scale_shift="scale_shift"				,     time_embedding_type="fourier"				,     timestep_post_act="gelu"				,     up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D")				,     )
              __UpperCAmelCase  :				int       				=		AutoencoderKL(
                  block_out_channels=[32, 32, 64, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=[
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                      "DownEncoderBlock2D",
                  ]				,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]				,     latent_channels=4				,     )
              __UpperCAmelCase  :				Optional[int]       				=		EulerDiscreteScheduler(prediction_type="sample"      )
              __UpperCAmelCase  :				int       				=		CLIPTextConfig(
                  bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     hidden_act="quick_gelu"				,     projection_dim=5_12				,     )
              __UpperCAmelCase  :				List[str]       				=		CLIPTextModel(UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		{
                  "unet": model.eval(),
                  "vae": vae.eval(),
                  "scheduler": scheduler,
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
              }

              return components






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=0      ):
              if str(UpperCamelCase_      ).startswith("mps"      ):
                     __UpperCAmelCase  :				str       				=		torch.manual_seed(UpperCamelCase_      )
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{
                  "prompt": "A painting of a squirrel eating a burger",
                  "image": self.dummy_image.cpu(),
                  "generator": generator,
                  "num_inference_steps": 2,
                  "output_type": "numpy",
              }
              return inputs






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		"cpu"

              __UpperCAmelCase  :				List[str]       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Tuple       				=		self.pipeline_class(**UpperCamelCase_      )
              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )

              __UpperCAmelCase  :				Any       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				int       				=		pipe(**UpperCamelCase_      ).images
              __UpperCAmelCase  :				Any       				=		image[0, -3:, -3:, -1]

              self.assertEqual(image.shape				,     (1, 2_56, 2_56, 3)      )
              __UpperCAmelCase  :				Tuple       				=		np.array(
                  [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]      )
              __UpperCAmelCase  :				List[str]       				=		np.abs(image_slice.flatten() - expected_slice      ).max()
              self.assertLessEqual(UpperCamelCase_				,     1E-3      )






       def 			_snake_case						(      self      ):
              super().test_attention_slicing_forward_pass(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_cpu_offload_forward_pass(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_inference_batch_single_identical(expected_max_diff=7E-3      )






       def 			_snake_case						(      self      ):
              super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_local(expected_max_difference=3E-3      )






       def 			_snake_case						(      self      ):
              super().test_save_load_optional_components(expected_max_difference=3E-3      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		[
                  "DDIMScheduler",
                  "DDPMScheduler",
                  "PNDMScheduler",
                  "HeunDiscreteScheduler",
                  "EulerAncestralDiscreteScheduler",
                  "KDPM2DiscreteScheduler",
                  "KDPM2AncestralDiscreteScheduler",
                  "DPMSolverSDEScheduler",
              ]
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_components()
              __UpperCAmelCase  :				Union[str, Any]       				=		self.pipeline_class(**UpperCamelCase_      )

              # make sure that PNDM does not need warm-up
              pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_      )

              pipe.to(UpperCamelCase_      )
              pipe.set_progress_bar_config(disable=UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		self.get_dummy_inputs(UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		2

              __UpperCAmelCase  :				List[str]       				=		[]
              for scheduler_enum in KarrasDiffusionSchedulers:
                     if scheduler_enum.name in skip_schedulers:
                            # no sigma schedulers are not supported
                            # no schedulers
                            continue

                     __UpperCAmelCase  :				Optional[int]       				=		getattr(UpperCamelCase_				,     scheduler_enum.name      )
                     __UpperCAmelCase  :				List[str]       				=		scheduler_cls.from_config(pipe.scheduler.config      )
                     __UpperCAmelCase  :				Optional[int]       				=		pipe(**UpperCamelCase_      )[0]
                     outputs.append(UpperCamelCase_      )

              assert check_same_shape(UpperCamelCase_      )






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






       def 			_snake_case						(      self      ):
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				str       				=		StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"				,     torch_dtype=torch.floataa      )
              pipe.to("cuda"      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[int]       				=		"a photo of an astronaut high resolution, unreal engine, ultra realistic"

              __UpperCAmelCase  :				Any       				=		pipe(UpperCamelCase_				,     generator=UpperCamelCase_				,     output_type="latent"      ).images

              __UpperCAmelCase  :				int       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Optional[Any]       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"      )
              assert np.abs((expected_image - image).mean()      ) < 5E-2







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		torch.manual_seed(33      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StableDiffusionLatentUpscalePipeline.from_pretrained(
                  "stabilityai/sd-x2-latent-upscaler"				,     torch_dtype=torch.floataa      )
              upscaler.to("cuda"      )

              __UpperCAmelCase  :				Optional[Any]       				=		"the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

              __UpperCAmelCase  :				str       				=		load_image(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"      )

              __UpperCAmelCase  :				Dict       				=		upscaler(
                  prompt=UpperCamelCase_				,     image=UpperCamelCase_				,     num_inference_steps=20				,     guidance_scale=0				,     generator=UpperCamelCase_				,     output_type="np"				,     ).images[0]

              __UpperCAmelCase  :				Tuple       				=		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"      )
              assert np.abs((expected_image - image).max()      ) < 5E-2






