





'''simple docstring'''



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig




def       _lowerCAmelCase  ( lowerCamelCase_	:     str						,				lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     List[str]=0 ):
			# Format the message.
			if name is None:
						__lowercase   =							None
			else:
						__lowercase   =							'''.''' * max(0						,				spaces - 2 ) + '''# {:''' + str(5_0 - spaces ) + '''s}'''
						__lowercase   =							fmt.format(lowerCamelCase_ )

			# Print and recurse (if needed).
			if isinstance(lowerCamelCase_						,				lowerCamelCase_ ):
						if msg is not None:
									print(lowerCamelCase_ )
						for k in val.keys():
									recursive_print(lowerCamelCase_						,				val[k]						,				spaces + 2 )
			elif isinstance(lowerCamelCase_						,				torch.Tensor ):
						print(lowerCamelCase_						,				''':'''						,				val.size() )
			else:
						print(lowerCamelCase_						,				''':'''						,				lowerCamelCase_ )




def       _lowerCAmelCase  ( lowerCamelCase_	:     Optional[int]						,				lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     List[str]						,				lowerCamelCase_	:     List[str] ):
			# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
			# for compatibility with later versions of NVIDIA Megatron-LM.
			# The inverse operation is performed inside Megatron-LM to read checkpoints:
			# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
			# If param is the weight tensor of the self-attention block, the returned tensor
			# will have to be transposed one more time to be read by HuggingFace BERT.
			__lowercase   =							param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						__lowercase   =							(num_heads, hidden_size, num_splits) + input_shape[1:]
						__lowercase   =							param.view(*lowerCamelCase_ )
						__lowercase   =							param.transpose(0						,				2 )
						__lowercase   =							param.transpose(1						,				2 ).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						__lowercase   =							(num_heads, num_splits, hidden_size) + input_shape[1:]
						__lowercase   =							param.view(*lowerCamelCase_ )
						__lowercase   =							param.transpose(0						,				1 ).contiguous()
			__lowercase   =							param.view(*lowerCamelCase_ )
			return param




def       _lowerCAmelCase  ( lowerCamelCase_	:     Union[str, Any]						,				lowerCamelCase_	:     Optional[Any]						,				lowerCamelCase_	:     Any ):
			# The converted output model.
			__lowercase   =							{}

			# old versions did not store training args
			__lowercase   =							input_state_dict.get('''args'''						,				lowerCamelCase_ )
			if ds_args is not None:
						# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
						# from pprint import pprint
						# pprint(vars(ds_args))

						__lowercase   =							ds_args.tokenizer_type
						__lowercase   =							ds_args.padded_vocab_size
						__lowercase   =							ds_args.max_position_embeddings
						__lowercase   =							ds_args.hidden_size
						__lowercase   =							ds_args.num_layers
						__lowercase   =							ds_args.num_attention_heads
						__lowercase   =							ds_args.ffn_hidden_size if '''ffn_hidden_size''' in ds_args else 4 * ds_args.hidden_size
						# pprint(config)

			# The number of heads.
			__lowercase   =							config.num_attention_heads
			# The hidden_size per head.
			__lowercase   =							config.hidden_size // heads
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						__lowercase   =							input_state_dict['''checkpoint_version''']
			else:
						__lowercase   =							0.0

			# The model.
			__lowercase   =							input_state_dict['''model''']
			# The language model.
			__lowercase   =							model['''language_model''']
			# The embeddings.
			__lowercase   =							lm['''embedding''']

			# The word embeddings.
			__lowercase   =							embeddings['''word_embeddings''']['''weight''']
			# Truncate the embedding table to vocab_size rows.
			__lowercase   =							word_embeddings[: config.vocab_size, :]
			# Store the word embeddings.
			__lowercase   =							word_embeddings

			# The position embeddings.
			__lowercase   =							embeddings['''position_embeddings''']['''weight''']
			assert pos_embeddings.size(0 ) == config.max_position_embeddings and pos_embeddings.size(1 ) == config.hidden_size
			# Store the position embeddings.
			__lowercase   =							pos_embeddings

			# The token-type embeddings.
			__lowercase   =							embeddings['''tokentype_embeddings''']['''weight''']
			# Store the position embeddings.
			__lowercase   =							tokentype_embeddings

			# The transformer.
			__lowercase   =							lm['''transformer'''] if '''transformer''' in lm.keys() else lm['''encoder''']

			# The regex to extract layer names.
			__lowercase   =							re.compile(r'''layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)''' )

			# The simple map of names for "automated" rules.
			__lowercase   =							{
			    '''attention.dense''': '''.attention.output.dense.''',
			    '''self_attention.dense''': '''.attention.output.dense.''',
			    '''mlp.dense_h_to_4h''': '''.intermediate.dense.''',
			    '''mlp.dense_4h_to_h''': '''.output.dense.''',
			}

			# Keep track of the attention/query/value tensor.
			__lowercase   =							None

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						__lowercase   =							layer_re.match(lowerCamelCase_ )

						# Stop if that's not a layer
						if m is None:
									break

						# The index of the layer.
						__lowercase   =							int(m.group(1 ) )
						# The name of the operation.
						__lowercase   =							m.group(2 )
						# Is it a weight or a bias?
						__lowercase   =							m.group(3 )

						# The name of the layer.
						__lowercase   =							f"bert.encoder.layer.{layer_idx}"

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith('''layernorm''' ):
									__lowercase   =							'''attention.ln''' if op_name.startswith('''input''' ) else '''ln'''
									__lowercase   =							val

						# Transpose the QKV matrix.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "weight":
									# Make sure the QKV pointer is nil.
									assert attention_qkv_weight is None, ""

									__lowercase   =							fix_query_key_value_ordering(lowerCamelCase_						,				lowerCamelCase_						,				3						,				lowerCamelCase_						,				lowerCamelCase_ )
									# Store the tensor as we need the bias as well to interleave QKV and biases.
									__lowercase   =							out_val

						# Transpose the bias.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "bias":
									# Make sure we read the weight tensor.
									assert attention_qkv_weight is not None, ""

									# Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
									__lowercase   =							attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
									__lowercase   =							attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
									__lowercase   =							attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

									__lowercase   =							fix_query_key_value_ordering(lowerCamelCase_						,				lowerCamelCase_						,				3						,				lowerCamelCase_						,				lowerCamelCase_ )
									# Split the bias.
									__lowercase   =							out_val[0 * config.hidden_size : 1 * config.hidden_size]
									__lowercase   =							out_val[1 * config.hidden_size : 2 * config.hidden_size]
									__lowercase   =							out_val[2 * config.hidden_size : 3 * config.hidden_size]

									# Store.
									__lowercase   =							q
									__lowercase   =							q_bias
									__lowercase   =							k
									__lowercase   =							k_bias
									__lowercase   =							v
									__lowercase   =							v_bias

									# Clear the stored tensor.
									__lowercase   =							None

						# Copy weights and biases as is.
						elif weight_or_bias in ["weight", "bias"]:
									__lowercase   =							megatron_to_transformers[op_name]
									__lowercase   =							val

    # The final layernorm.
			__lowercase   =							transformer['''final_layernorm.weight''']
			__lowercase   =							transformer['''final_layernorm.bias''']

			# The pooler.
			__lowercase   =							lm['''pooler''']

			# Store the matrix and the bias.
			__lowercase   =							pooler['''dense.weight''']
			__lowercase   =							pooler['''dense.bias''']

			# The LM head from Megatron (for RACE).
			__lowercase   =							model['''lm_head''']

			# The transform matrix.
			__lowercase   =							lm_head['''dense.weight''']
			__lowercase   =							lm_head['''dense.bias''']

			# The transform LN.
			__lowercase   =							lm_head['''layernorm.weight''']
			__lowercase   =							lm_head['''layernorm.bias''']

			# For the decoder, we replicate the weights.
			__lowercase   =							word_embeddings
			__lowercase   =							lm_head['''bias''']

			# The classifier from Megatron (for MLNI).
			__lowercase   =							model['''binary_head''']

			# Store the classifier.
			__lowercase   =							binary_head['''weight''']
			__lowercase   =							binary_head['''bias''']

			# It should be done!
			return output_state_dict




def       _lowerCAmelCase  ( ):
			# Create the argument parser.
			__lowercase   =							argparse.ArgumentParser()
			parser.add_argument('''--print-checkpoint-structure'''						,				action='''store_true''' )
			parser.add_argument('''path_to_checkpoint'''						,				type=lowerCamelCase_						,				help='''Path to the ZIP file containing the checkpoint''' )
			parser.add_argument(
			    '''--config_file'''						,				default=''''''						,				type=lowerCamelCase_						,				help='''An optional config json file describing the pre-trained model.'''						,				)
			__lowercase   =							parser.parse_args()

			# Extract the basename.
			__lowercase   =							os.path.dirname(args.path_to_checkpoint )

			# Load the model.
			# the .zip is very optional, let's keep it for backward compatibility
			print(f"Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\"" )
			if args.path_to_checkpoint.endswith('''.zip''' ):
						with zipfile.ZipFile(args.path_to_checkpoint						,				'''r''' ) as checkpoint:
									with checkpoint.open('''release/mp_rank_00/model_optim_rng.pt''' ) as pytorch_dict:
												__lowercase   =							torch.load(lowerCamelCase_						,				map_location='''cpu''' )
			else:
						__lowercase   =							torch.load(args.path_to_checkpoint						,				map_location='''cpu''' )

			if args.config_file == "":
						# Default config of megatron-bert 345m
						__lowercase   =							MegatronBertConfig()

						# different megatron-bert-*-345m models have different vocab sizes, so override the default
						# config (which is for megatron-bert-cased-345m) with the actual vocab dimension
						__lowercase   =							input_state_dict['''model''']['''lm_head''']['''bias'''].numel()
			else:
						__lowercase   =							MegatronBertConfig.from_json_file(args.config_file )

			# Convert.
			print('''Converting''' )
			__lowercase   =							convert_megatron_checkpoint(lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_ )

			# Print the structure of converted state dict.
			if args.print_checkpoint_structure:
						recursive_print(lowerCamelCase_						,				lowerCamelCase_ )

			# Store the config to file.
			print('''Saving config''' )
			config.save_pretrained(lowerCamelCase_ )

			# Store the state_dict to file.
			__lowercase   =							os.path.join(lowerCamelCase_						,				'''pytorch_model.bin''' )
			print(f"Saving checkpoint to \"{output_checkpoint_file}\"" )
			torch.save(lowerCamelCase_						,				lowerCamelCase_ )


####################################################################################################

if __name__ == "__main__":
	main()

####################################################################################################












'''simple docstring'''



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






@skip_mps
class 							__lowercase				(					lowerCAmelCase__       ,					lowerCAmelCase__       ,					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: str      =				StableDiffusionModelEditingPipeline
	a			: int      =				TEXT_TO_IMAGE_PARAMS
	a			: List[Any]      =				TEXT_TO_IMAGE_BATCH_PARAMS
	a			: Tuple      =				TEXT_TO_IMAGE_IMAGE_PARAMS
	a			: Any      =				TEXT_TO_IMAGE_IMAGE_PARAMS


	def   _UpperCAmelCase   (self       )							->							Dict:







				'''simple docstring'''





				torch.manual_seed(0       )
				__lowercase   =							UNetaDConditionModel(
				    block_out_channels=(32, 64)   ,layers_per_block=2   ,sample_size=32   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=32   ,)
				__lowercase   =							DDIMScheduler()
				torch.manual_seed(0       )
				__lowercase   =							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       )
				__lowercase   =							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   ,)
				__lowercase   =							CLIPTextModel(_lowerCamelCase       )
				__lowercase   =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

				__lowercase   =							{
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''safety_checker''': None,
				    '''feature_extractor''': None,
				}
				return components


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    '''height''': None,
				    '''width''': None,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 6.0,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							sd_pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_5_5, 0.5_1_3_2, 0.4_9_7_6, 0.3_9_0_4, 0.3_5_5_4, 0.4_7_6_5, 0.5_1_3_9, 0.5_1_5_8, 0.4_8_8_9]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				__lowercase   =							'''french fries'''
				__lowercase   =							sd_pipe(**_lowerCamelCase   ,negative_prompt=_lowerCamelCase       )
				__lowercase   =							output.images
				__lowercase   =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_9_9_2, 0.5_1_0_1, 0.5_0_0_4, 0.3_9_4_9, 0.3_6_0_4, 0.4_7_3_5, 0.5_2_1_6, 0.5_2_0_4, 0.4_9_1_3]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Tuple:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							EulerAncestralDiscreteScheduler(
				    beta_start=0.0_0_0_8_5   ,beta_end=0.0_1_2   ,beta_schedule='''scaled_linear'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

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

				assert image.shape == (1, 64, 64, 3)

				__lowercase   =							np.array([0.4_7_4_7, 0.5_3_7_2, 0.4_7_7_9, 0.4_9_8_2, 0.5_5_4_3, 0.4_8_1_6, 0.5_2_3_8, 0.4_9_0_4, 0.5_0_2_7]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2


	def   _UpperCAmelCase   (self       )							->							Optional[int]:







				'''simple docstring'''





				__lowercase   =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				__lowercase   =							self.get_dummy_components()
				__lowercase   =							PNDMScheduler()
				__lowercase   =							StableDiffusionModelEditingPipeline(**_lowerCamelCase       )
				__lowercase   =							sd_pipe.to(_lowerCamelCase       )
				sd_pipe.set_progress_bar_config(disable=_lowerCamelCase       )

				__lowercase   =							self.get_dummy_inputs(_lowerCamelCase       )
				# the pipeline does not expect pndm so test if it raises error.
				with self.assertRaises(_lowerCamelCase       ):
							__lowercase   =							sd_pipe(**_lowerCamelCase       ).images


	def   _UpperCAmelCase   (self       )							->							int:







				'''simple docstring'''





				super().test_inference_batch_single_identical(expected_max_diff=5E-3       )


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				super().test_attention_slicing_forward_pass(expected_max_diff=5E-3       )






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







	'''simple docstring'''

	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase=0       )							->							Any:







				'''simple docstring'''





				__lowercase   =							torch.manual_seed(_lowerCamelCase       )
				__lowercase   =							{
				    '''prompt''': '''A field of roses''',
				    '''generator''': generator,
				    '''num_inference_steps''': 3,
				    '''guidance_scale''': 7.5,
				    '''output_type''': '''numpy''',
				}
				return inputs


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				__lowercase   =							np.array(
				    [0.6_7_4_9_4_9_6, 0.6_3_8_6_4_5_3, 0.5_1_4_4_3_2_6_7, 0.6_6_0_9_4_9_0_5, 0.6_1_9_2_1_2_1_5, 0.5_4_9_1_3_3_2, 0.5_7_4_4_4_1_7, 0.5_8_0_7_5_1_0_6, 0.5_1_7_4_6_5_8]       )

				assert np.abs(expected_slice - image_slice       ).max() < 1E-2

				# make sure image changes after editing
				pipe.edit_model('''A pack of roses'''   ,'''A pack of blue roses'''       )

				__lowercase   =							pipe(**_lowerCamelCase       ).images
				__lowercase   =							image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(expected_slice - image_slice       ).max() > 1E-1


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





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

				__lowercase   =							'''CompVis/stable-diffusion-v1-4'''
				__lowercase   =							DDIMScheduler.from_pretrained(_lowerCamelCase   ,subfolder='''scheduler'''       )
				__lowercase   =							StableDiffusionModelEditingPipeline.from_pretrained(
				    _lowerCamelCase   ,scheduler=_lowerCamelCase   ,safety_checker=_lowerCamelCase       )
				__lowercase   =							pipe.to(_lowerCamelCase       )
				pipe.set_progress_bar_config(disable=_lowerCamelCase       )
				pipe.enable_attention_slicing(1       )
				pipe.enable_sequential_cpu_offload()

				__lowercase   =							self.get_inputs()
				__lowercase   =							pipe(**_lowerCamelCase       )

				__lowercase   =							torch.cuda.max_memory_allocated()
				# make sure that less than 4.4 GB is allocated
				assert mem_bytes < 4.4 * 10**9





