





"""simple docstring"""






import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


A 			=  logging.get_logger(__name__)

A 			=  {
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

A 			=  {
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

A 			=  {
    '''jukebox''': 512,
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  VOCAB_FILES_NAMES
					__lowerCAmelCase  =  PRETRAINED_VOCAB_FILES_MAP
					__lowerCAmelCase  =  PRETRAINED_LYRIC_TOKENS_SIZES
					__lowerCAmelCase  =  ['''input_ids''', '''attention_mask''']







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=["v3", "v2", "v2"]	,			_UpperCAmelCase=512	,			_UpperCAmelCase=5	,			_UpperCAmelCase="<|endoftext|>"	,			**_UpperCAmelCase	,			):
									__a :						int			       =  AddedToken(_UpperCAmelCase	,			lstrip=_UpperCAmelCase	,			rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) else unk_token
									super().__init__(
									    unk_token=_UpperCAmelCase	,			n_genres=_UpperCAmelCase	,			version=_UpperCAmelCase	,			max_n_lyric_tokens=_UpperCAmelCase	,			**_UpperCAmelCase	,			)
									__a :						Dict			       =  version
									__a :						Dict			       =  max_n_lyric_tokens
									__a :						Dict			       =  n_genres

									with open(_UpperCAmelCase	,			encoding='''utf-8'''     ) as vocab_handle:
													__a :						Tuple			       =  json.load(_UpperCAmelCase     )

									with open(_UpperCAmelCase	,			encoding='''utf-8'''     ) as vocab_handle:
													__a :						str			       =  json.load(_UpperCAmelCase     )

									with open(_UpperCAmelCase	,			encoding='''utf-8'''     ) as vocab_handle:
													__a :						Tuple			       =  json.load(_UpperCAmelCase     )

									__a :						int			       =  R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''
									# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
									if len(self.lyrics_encoder     ) == 79:
													__a :						Any			       =  oov.replace(R'''\-\''''	,			R'''\-+\''''     )

									__a :						str			       =  regex.compile(_UpperCAmelCase     )
									__a :						int			       =  {v: k for k, v in self.artists_encoder.items()}
									__a :						str			       =  {v: k for k, v in self.genres_encoder.items()}
									__a :						str			       =  {v: k for k, v in self.lyrics_encoder.items()}







					@property
					def  _lowerCamelCase		(						self     ):
									return len(self.artists_encoder     ) + len(self.genres_encoder     ) + len(self.lyrics_encoder     )







					def  _lowerCamelCase		(						self     ):
									return dict(self.artists_encoder	,			self.genres_encoder	,			self.lyrics_encoder     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  [self.artists_encoder.get(_UpperCAmelCase	,			0     ) for artist in list_artists]
									for genres in range(len(_UpperCAmelCase     )     ):
													__a :						int			       =  [self.genres_encoder.get(_UpperCAmelCase	,			0     ) for genre in list_genres[genres]]
													__a :						List[Any]			       =  list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]     ))

									__a :						str			       =  [[self.lyrics_encoder.get(_UpperCAmelCase	,			0     ) for character in list_lyrics[0]], [], []]
									return artists_id, list_genres, lyric_ids







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return list(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a       ,						__a       ,						__a :						str			       =  self.prepare_for_tokenization(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self._tokenize(_UpperCAmelCase     )
									return artist, genre, lyrics







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = False     ):
									for idx in range(len(self.version     )     ):
													if self.version[idx] == "v3":
																	__a :						Any			       =  artists[idx].lower()
																	__a :						Optional[int]			       =  [genres[idx].lower()]
													else:
																	__a :						int			       =  self._normalize(artists[idx]     ) + '''.v2'''
																	__a :						Tuple			       =  [
																	    self._normalize(_UpperCAmelCase     ) + '''.v2''' for genre in genres[idx].split('''_'''     )
																	]  # split is for the full dictionary with combined genres

									if self.version[0] == "v2":
													__a :						Dict			       =  regex.compile(R'''[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'''     )
													__a :						Dict			       =  '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'''
													__a :						List[Any]			       =  {vocab[index]: index + 1 for index in range(len(_UpperCAmelCase     )     )}
													__a :						Optional[Any]			       =  0
													__a :						Union[str, Any]			       =  len(_UpperCAmelCase     ) + 1
													__a :						Union[str, Any]			       =  self.vocab
													__a :						Dict			       =  {v: k for k, v in self.vocab.items()}
													__a :						Tuple			       =  ''''''
									else:
													__a :						int			       =  regex.compile(R'''[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'''     )

									__a :						str			       =  self._run_strip_accents(_UpperCAmelCase     )
									__a :						str			       =  lyrics.replace('''\\'''	,			'''\n'''     )
									__a :						Dict			       =  self.out_of_vocab.sub(''''''	,			_UpperCAmelCase     ), [], []
									return artists, genres, lyrics







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						str			       =  unicodedata.normalize('''NFD'''	,			_UpperCAmelCase     )
									__a :						Dict			       =  []
									for char in text:
													__a :						Dict			       =  unicodedata.category(_UpperCAmelCase     )
													if cat == "Mn":
																	continue
													output.append(_UpperCAmelCase     )
									return "".join(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  (
									    [chr(_UpperCAmelCase     ) for i in range(ord('''a'''     )	,			ord('''z'''     ) + 1     )]
									    + [chr(_UpperCAmelCase     ) for i in range(ord('''A'''     )	,			ord('''Z'''     ) + 1     )]
									    + [chr(_UpperCAmelCase     ) for i in range(ord('''0'''     )	,			ord('''9'''     ) + 1     )]
									    + ['''.''']
									)
									__a :						Optional[Any]			       =  frozenset(_UpperCAmelCase     )
									__a :						Any			       =  re.compile(R'''_+'''     )
									__a :						Dict			       =  ''''''.join([c if c in accepted else '''_''' for c in text.lower()]     )
									__a :						Optional[int]			       =  pattern.sub('''_'''	,			_UpperCAmelCase     ).strip('''_'''     )
									return text







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return " ".join(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False     ):
									# Convert to TensorType
									if not isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
													__a :						Dict			       =  TensorType(_UpperCAmelCase     )

									# Get a function reference for the correct framework
									if tensor_type == TensorType.TENSORFLOW:
													if not is_tf_available():
																	raise ImportError(
																	    '''Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'''     )
													import tensorflow as tf

													__a :						int			       =  tf.constant
													__a :						str			       =  tf.is_tensor
									elif tensor_type == TensorType.PYTORCH:
													if not is_torch_available():
																	raise ImportError('''Unable to convert output to PyTorch tensors format, PyTorch is not installed.'''     )
													import torch

													__a :						List[Any]			       =  torch.tensor
													__a :						Any			       =  torch.is_tensor
									elif tensor_type == TensorType.JAX:
													if not is_flax_available():
																	raise ImportError('''Unable to convert output to JAX tensors format, JAX is not installed.'''     )
													import jax.numpy as jnp  # noqa: F811

													__a :						Dict			       =  jnp.array
													__a :						Tuple			       =  _is_jax
									else:
													__a :						Union[str, Any]			       =  np.asarray
													__a :						List[Any]			       =  _is_numpy

									# Do the tensor conversion in batch

									try:
													if prepend_batch_axis:
																	__a :						Union[str, Any]			       =  [inputs]

													if not is_tensor(_UpperCAmelCase     ):
																	__a :						Optional[Any]			       =  as_tensor(_UpperCAmelCase     )
									except:  # noqa E722
													raise ValueError(
													    '''Unable to create tensor, you should probably activate truncation and/or padding '''
													    '''with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'''     )

									return inputs







					def __call__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase=""	,			_UpperCAmelCase="pt"     ):
									__a :						List[Any]			       =  [0, 0, 0]
									__a :						Union[str, Any]			       =  [artist] * len(self.version     )
									__a :						List[str]			       =  [genres] * len(self.version     )

									__a       ,						__a       ,						__a :						Any			       =  self.tokenize(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )
									__a       ,						__a       ,						__a :						int			       =  self._convert_token_to_id(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     )

									__a :						List[Any]			       =  [-INFINITY] * len(full_tokens[-1]     )
									__a :						str			       =  [
									    self.convert_to_tensors(
									        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]	,			tensor_type=_UpperCAmelCase     )
									    for i in range(len(self.version     )     )
									]
									return BatchEncoding({'''input_ids''': input_ids, '''attention_masks''': attention_masks}     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									if not os.path.isdir(_UpperCAmelCase     ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
													return

									__a :						Any			       =  os.path.join(
									    _UpperCAmelCase	,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''artists_file''']     )
									with open(_UpperCAmelCase	,			'''w'''	,			encoding='''utf-8'''     ) as f:
													f.write(json.dumps(self.artists_encoder	,			ensure_ascii=_UpperCAmelCase     )     )

									__a :						int			       =  os.path.join(
									    _UpperCAmelCase	,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''genres_file''']     )
									with open(_UpperCAmelCase	,			'''w'''	,			encoding='''utf-8'''     ) as f:
													f.write(json.dumps(self.genres_encoder	,			ensure_ascii=_UpperCAmelCase     )     )

									__a :						List[Any]			       =  os.path.join(
									    _UpperCAmelCase	,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''lyrics_file''']     )
									with open(_UpperCAmelCase	,			'''w'''	,			encoding='''utf-8'''     ) as f:
													f.write(json.dumps(self.lyrics_encoder	,			ensure_ascii=_UpperCAmelCase     )     )

									return (artists_file, genres_file, lyrics_file)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						str			       =  self.artists_decoder.get(_UpperCAmelCase     )
									__a :						List[Any]			       =  [self.genres_decoder.get(_UpperCAmelCase     ) for genre in genres_index]
									__a :						Optional[Any]			       =  [self.lyrics_decoder.get(_UpperCAmelCase     ) for character in lyric_index]
									return artist, genres, lyrics






"""simple docstring"""






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 				__lowercase			(			_UpperCamelCase   ,      _UpperCamelCase   ,      _UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  StableDiffusionInstructPixaPixPipeline
					__lowerCAmelCase  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					__lowerCAmelCase  =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__lowerCAmelCase  =  IMAGE_TO_IMAGE_IMAGE_PARAMS
					__lowerCAmelCase  =  IMAGE_TO_IMAGE_IMAGE_PARAMS







					def  _lowerCamelCase		(						self     ):
									torch.manual_seed(0     )
									__a :						Optional[Any]			       =  UNetaDConditionModel(
									    block_out_channels=(32, 64)	,			layers_per_block=2	,			sample_size=32	,			in_channels=8	,			out_channels=4	,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,			cross_attention_dim=32	,			)
									__a :						List[str]			       =  PNDMScheduler(skip_prk_steps=_UpperCAmelCase     )
									torch.manual_seed(0     )
									__a :						int			       =  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     )
									__a :						str			       =  CLIPTextConfig(
									    bos_token_id=0	,			eos_token_id=2	,			hidden_size=32	,			intermediate_size=37	,			layer_norm_eps=1e-0_5	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1000	,			)
									__a :						Optional[int]			       =  CLIPTextModel(_UpperCAmelCase     )
									__a :						str			       =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

									__a :						Union[str, Any]			       =  {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=0     ):
									__a :						Any			       =  floats_tensor((1, 3, 32, 32)	,			rng=random.Random(_UpperCAmelCase     )     ).to(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  image.cpu().permute(0	,			2	,			3	,			1     )[0]
									__a :						Tuple			       =  Image.fromarray(np.uinta(_UpperCAmelCase     )     ).convert('''RGB'''     )
									if str(_UpperCAmelCase     ).startswith('''mps'''     ):
													__a :						Union[str, Any]			       =  torch.manual_seed(_UpperCAmelCase     )
									else:
													__a :						Union[str, Any]			       =  torch.Generator(device=_UpperCAmelCase     ).manual_seed(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  {
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 6.0,
									    '''image_guidance_scale''': 1,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						int			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						str			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						List[Any]			       =  image[0, -3:, -3:, -1]
									assert image.shape == (1, 32, 32, 3)
									__a :						Optional[int]			       =  np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Optional[Any]			       =  self.get_dummy_components()
									__a :						Dict			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[Any]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Dict			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  '''french fries'''
									__a :						str			       =  sd_pipe(**_UpperCAmelCase	,			negative_prompt=_UpperCAmelCase     )
									__a :						Dict			       =  output.images
									__a :						Optional[int]			       =  image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									__a :						Tuple			       =  np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						Dict			       =  self.get_dummy_components()
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  self.get_dummy_inputs(_UpperCAmelCase     )
									__a :						List[str]			       =  [inputs['''prompt''']] * 2

									__a :						Optional[Any]			       =  np.array(inputs['''image''']     ).astype(np.floataa     ) / 2_5_5.0
									__a :						Optional[Any]			       =  torch.from_numpy(_UpperCAmelCase     ).unsqueeze(0     ).to(_UpperCAmelCase     )
									__a :						Tuple			       =  image / 2 + 0.5
									__a :						str			       =  image.permute(0	,			3	,			1	,			2     )
									__a :						List[str]			       =  image.repeat(2	,			1	,			1	,			1     )

									__a :						int			       =  sd_pipe(**_UpperCAmelCase     ).images
									__a :						Optional[Any]			       =  image[-1, -3:, -3:, -1]

									assert image.shape == (2, 32, 32, 3)
									__a :						List[str]			       =  np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
									__a :						List[str]			       =  self.get_dummy_components()
									__a :						Union[str, Any]			       =  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule='''scaled_linear'''     )
									__a :						str			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						List[str]			       =  sd_pipe.to(_UpperCAmelCase     )
									sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

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

									__a :						Optional[int]			       =  [round(_UpperCAmelCase	,			4     ) for x in image_slice.flatten().tolist()]
									print(''','''.join([str(_UpperCAmelCase     ) for x in slice]     )     )

									assert image.shape == (1, 32, 32, 3)
									__a :						int			       =  np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]     )

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







					def  _lowerCamelCase		(						self     ):
									super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  self.get_dummy_components()
									__a :						Optional[Any]			       =  StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase     )
									__a :						Any			       =  VaeImageProcessor(do_resize=_UpperCAmelCase	,			do_normalize=_UpperCAmelCase     )
									__a :						Dict			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )

									__a :						str			       =  pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase	,			input_image_type='''pt'''     )     )[0]

									__a :						List[Any]			       =  components['''vae''']
									__a :						List[Any]			       =  self.get_dummy_inputs_by_type(_UpperCAmelCase	,			input_image_type='''pt'''     )

									for image_param in self.image_latents_params:
													if image_param in inputs.keys():
																	__a :						Optional[Any]			       =  vae.encode(inputs[image_param]     ).latent_dist.mode()

									__a :						str			       =  pipe(**_UpperCAmelCase     )[0]

									__a :						Union[str, Any]			       =  np.abs(out - out_latents_inputs     ).max()
									self.assertLess(_UpperCAmelCase	,			1e-4	,			'''passing latents as image input generate different result from passing image'''     )






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







					'''simple docstring'''




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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=0     ):
									__a :						List[str]			       =  torch.manual_seed(_UpperCAmelCase     )
									__a :						str			       =  load_image(
									    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''     )
									__a :						List[str]			       =  {
									    '''prompt''': '''turn him into a cyborg''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 3,
									    '''guidance_scale''': 7.5,
									    '''image_guidance_scale''': 1.0,
									    '''output_type''': '''numpy''',
									}
									return inputs







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									__a :						Optional[Any]			       =  pipe(**_UpperCAmelCase     ).images
									__a :						List[str]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[str]			       =  self.get_inputs()
									__a :						Optional[int]			       =  pipe(**_UpperCAmelCase     ).images
									__a :						Optional[Any]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Tuple			       =  np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase     )
									__a :						Optional[int]			       =  DDIMScheduler.from_config(pipe.scheduler.config     )
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[str]			       =  self.get_inputs()
									__a :						str			       =  pipe(**_UpperCAmelCase     ).images
									__a :						Optional[int]			       =  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									__a :						Any			       =  np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Dict			       =  0

									def callback_fn(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ) -> None:
													__a :						Optional[Any]			       =  True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																	__a :						Optional[int]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						int			       =  latents[0, -3:, -3:, -1]
																	__a :						int			       =  np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]     )

																	assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
													elif step == 2:
																	__a :						Union[str, Any]			       =  latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	__a :						List[str]			       =  latents[0, -3:, -3:, -1]
																	__a :						Tuple			       =  np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]     )

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

									__a :						Union[str, Any]			       =  False

									__a :						Optional[int]			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						str			       =  self.get_inputs()
									pipe(**_UpperCAmelCase	,			callback=_UpperCAmelCase	,			callback_steps=1     )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







					def  _lowerCamelCase		(						self     ):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    '''timbrooks/instruct-pix2pix'''	,			safety_checker=_UpperCAmelCase	,			torch_dtype=torch.floataa     )
									__a :						Optional[int]			       =  pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing(1     )
									pipe.enable_sequential_cpu_offload()

									__a :						List[Any]			       =  self.get_inputs()
									__a :						Tuple			       =  pipe(**_UpperCAmelCase     )

									__a :						List[str]			       =  torch.cuda.max_memory_allocated()
									# make sure that less than 2.2 GB is allocated
									assert mem_bytes < 2.2 * 10**9







					def  _lowerCamelCase		(						self     ):
									__a :						List[Any]			       =  self.get_inputs()
									# resize to resolution that is divisible by 8 but not 16 or 32
									__a :						str			       =  inputs['''image'''].resize((504, 504)     )

									__a :						Tuple			       =  '''timbrooks/instruct-pix2pix'''
									__a :						Any			       =  StableDiffusionInstructPixaPixPipeline.from_pretrained(
									    _UpperCAmelCase	,			safety_checker=_UpperCAmelCase	,			)
									pipe.to(_UpperCAmelCase     )
									pipe.set_progress_bar_config(disable=_UpperCAmelCase     )
									pipe.enable_attention_slicing()

									__a :						List[Any]			       =  pipe(**_UpperCAmelCase     )
									__a :						int			       =  output.images[0]

									__a :						Optional[int]			       =  image[255:258, 383:386, -1]

									assert image.shape == (504, 504, 3)
									__a :						Union[str, Any]			       =  np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]     )

									assert np.abs(image_slice.flatten() - expected_slice     ).max() < 5e-3