




"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


A 			=  logging.get_logger(__name__)


A 			=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
A 			=  {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

A 			=  {
    '''allenai/led-base-16384''': 16_384,
}

@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     __A      (			)		->		Tuple:
				__a :						Any			       =  (
				    list(range(ord('''!''')   ,       ord('''~''') + 1)) + list(range(ord('''¡''')   ,       ord('''¬''') + 1)) + list(range(ord('''®''')   ,       ord('''ÿ''') + 1))
				)
				__a :						Union[str, Any]			       =  bs[:]
				__a :						Union[str, Any]			       =  0
				for b in range(2**8):
								if b not in bs:
												bs.append(_lowerCamelCase)
												cs.append(2**8 + n)
												n += 1
				__a :						str			       =  [chr(_lowerCamelCase) for n in cs]
				return dict(zip(_lowerCamelCase   ,       _lowerCamelCase))

def     __A      (			a_						:str)		->		List[str]:
				__a :						int			       =  set()
				__a :						Optional[Any]			       =  word[0]
				for char in word[1:]:
								pairs.add((prev_char, char))
								__a :						Optional[Any]			       =  char
				return pairs






class 				__lowercase			(			a__							):







					'''simple docstring'''




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







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase="replace"	,			_UpperCAmelCase="<s>"	,			_UpperCAmelCase="</s>"	,			_UpperCAmelCase="</s>"	,			_UpperCAmelCase="<s>"	,			_UpperCAmelCase="<unk>"	,			_UpperCAmelCase="<pad>"	,			_UpperCAmelCase="<mask>"	,			_UpperCAmelCase=False	,			**_UpperCAmelCase	,			):
									__a :						Optional[int]			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else bos_token
									__a :						Dict			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else eos_token
									__a :						Union[str, Any]			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else sep_token
									__a :						Any			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else cls_token
									__a :						Tuple			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else unk_token
									__a :						Optional[int]			       =  AddedToken(_A	,			lstrip=_A	,			rstrip=_A     ) if isinstance(_A	,			_A     ) else pad_token

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

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

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

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									__a :						int			       =  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.bart.tokenization_bart.BartTokenizer.vocab_size
					def  _lowerCamelCase		(						self     ):
									return len(self.encoder     )







					def  _lowerCamelCase		(						self     ):
									return dict(self.encoder	,			**self.added_tokens_encoder     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):

									if token in self.cache:
													return self.cache[token]
									__a :						int			       =  tuple(_A     )
									__a :						str			       =  get_pairs(_A     )

									if not pairs:
													return token

									while True:
													__a :						Dict			       =  min(_A	,			key=lambda _UpperCAmelCase     : self.bpe_ranks.get(_A	,			float('''inf'''     )     )     )
													if bigram not in self.bpe_ranks:
																	break
													__a :						List[str]			       =  bigram
													__a :						Tuple			       =  []
													__a :						Tuple			       =  0
													while i < len(_A     ):
																	try:
																					__a :						int			       =  word.index(_A	,			_A     )
																	except ValueError:
																					new_word.extend(word[i:]     )
																					break
																	else:
																					new_word.extend(word[i:j]     )
																					__a :						int			       =  j

																	if word[i] == first and i < len(_A     ) - 1 and word[i + 1] == second:
																					new_word.append(first + second     )
																					i += 2
																	else:
																					new_word.append(word[i]     )
																					i += 1
													__a :						Optional[Any]			       =  tuple(_A     )
													__a :						Tuple			       =  new_word
													if len(_A     ) == 1:
																	break
													else:
																	__a :						List[str]			       =  get_pairs(_A     )
									__a :						Tuple			       =  ' '.join(_A     )
									__a :						List[Any]			       =  word
									return word







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  []
									for token in re.findall(self.pat	,			_A     ):
													__a :						int			       =  ''.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(_A     ).split(''' '''     )     )
									return bpe_tokens







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return self.encoder.get(_A	,			self.encoder.get(self.unk_token     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									return self.decoder.get(_A     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						int			       =  ''.join(_A     )
									__a :						Optional[Any]			       =  bytearray([self.byte_decoder[c] for c in text]     ).decode('''utf-8'''	,			errors=self.errors     )
									return text







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):

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

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

									__a :						Optional[int]			       =  0
									with open(_A	,			'''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 _UpperCAmelCase     : 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!'''     )
																					__a :						Union[str, Any]			       =  token_index
																	writer.write(''' '''.join(_A     ) + '''\n'''     )
																	index += 1

									return vocab_file, merge_file







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):

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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False     ):

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

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







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									__a :						int			       =  [self.sep_token_id]
									__a :						str			       =  [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  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False	,			**_UpperCAmelCase     ):
									__a :						List[str]			       =  kwargs.pop('''add_prefix_space'''	,			self.add_prefix_space     )
									if (is_split_into_words or add_prefix_space) and (len(_A     ) > 0 and not text[0].isspace()):
													__a :						Union[str, Any]			       =  ' ' + text
									return (text, kwargs)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = PaddingStrategy.DO_NOT_PAD	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			):
									__a :						Any			       =  super()._pad(
									    encoded_inputs=_A	,			max_length=_A	,			padding_strategy=_A	,			pad_to_multiple_of=_A	,			return_attention_mask=_A	,			)

									# Load from model defaults
									if return_attention_mask is None:
													__a :						Union[str, Any]			       =  'attention_mask' in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
													__a :						Optional[Any]			       =  encoded_inputs[self.model_input_names[0]]
													# `global_attention_mask` need to have the same length as other (sequential) inputs.
													__a :						Any			       =  len(encoded_inputs['''global_attention_mask''']     ) != len(_A     )

													if needs_to_be_padded:
																	__a :						Optional[Any]			       =  len(_A     ) - len(encoded_inputs['''global_attention_mask''']     )

																	if self.padding_side == "right":
																					# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																					__a :						Optional[Any]			       =  (
																					    encoded_inputs['global_attention_mask'] + [-1] * difference
																					)
																	elif self.padding_side == "left":
																					__a :						List[Any]			       =  [-1] * difference + encoded_inputs[
																					    'global_attention_mask'
																					]
																	else:
																					raise ValueError('''Invalid padding strategy:''' + str(self.padding_side     )     )

									return encoded_inputs






"""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