


'''simple docstring'''

import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
 from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
 raise ImportError('''You need to install the version of fairseq from the Mega repo!''')







class     _lowercase					(    nn.Module      ):


 '''simple docstring'''






 def __init__(				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     str							)  ->   List[Any]:
  super().__init__()
  __lowerCAmelCase    							=						mega_args
  __lowerCAmelCase    							=						nn.Embedding(__SCREAMING_SNAKE_CASE				,	self.mega_args.encoder_embed_dim							)
  __lowerCAmelCase    							=						nn.ModuleList([MegaEncoderLayer(self.mega_args							) for _ in range(__SCREAMING_SNAKE_CASE							)]							)
  __lowerCAmelCase    							=						depth





 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Dict=True				,	SCREAMING_SNAKE_CASE__       :     List[str]=0							)  ->   Any:

  # Mega expects embeddings to be (time, batch, embedding size), but
  # Hugging Face returns tokens as (batch, time)
  if batch_first:
   __lowerCAmelCase    							=						input_ids.T

  # to make things more confusing, Mega expects the attention mask to
  # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
  # which is the opposite of what HF returns
  if ignore_mask_value == 0:
   __lowerCAmelCase    							=						1 - attention_mask

  # get token embeddings from IDs
  __lowerCAmelCase    							=						self.embedding_layer(__SCREAMING_SNAKE_CASE							)

  # pass through the Mega layers
  # input is (time, batch, encoder dim) and output is the same
  for encoder in self.encoders:
   __lowerCAmelCase    							=						encoder(__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE							)

  # return according to the shape specified
  if batch_first:
   # (T, B, H) --> (B, T, H)
   return torch.transpose(__SCREAMING_SNAKE_CASE				,	0				,	1							)
  else:
   return embeds







class     _lowercase					(    nn.Module      ):


 '''simple docstring'''






 def __init__(				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     Optional[int]							)  ->   str:
  super().__init__()
  __lowerCAmelCase    							=						MegaLM(__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase    							=						nn.Linear(mega_args.encoder_embed_dim				,	__SCREAMING_SNAKE_CASE							)
  __lowerCAmelCase    							=						nn.Dropout(p=0.1							)





 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Any				,	SCREAMING_SNAKE_CASE__       :     Tuple=True				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=0							)  ->   str:
  __lowerCAmelCase    							=						self.mega(__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE				,	__SCREAMING_SNAKE_CASE							)
  return self.mlm_head(self.dropout(__SCREAMING_SNAKE_CASE							)							)







def 					UpperCamelCase_						( snake_case_	:	int						,		snake_case_	:	str						,		snake_case_	:	Dict				) ->					List[str]:






 '''simple docstring'''

 with open(os.path.join(snake_case__						,		"""model_args.pkl"""				)						,		"""rb"""				) as f:
  __lowerCAmelCase    							=						pkl.load(snake_case__				)

 # load the original encoder
 __lowerCAmelCase    							=						OriginalMegaForMaskedLM(**snake_case__				).eval()

 # load its weights
 print(
     """Original Mega encoder:"""						,		original_mlm.mega.load_state_dict(
         torch.load(os.path.join(snake_case__						,		"""encoder_weights.pt"""				)						,		map_location="""cpu"""				)				)						,		)
 print(
     """Original Mega MLM layer:"""						,		original_mlm.mlm_head.load_state_dict(
         torch.load(os.path.join(snake_case__						,		"""mlm_head_weights.pt"""				)						,		map_location="""cpu"""				)				)						,		)

 # create a new config from the old one
 __lowerCAmelCase    							=						MegaConfig(
     num_hidden_layers=mega_original_args["""depth"""]						,		vocab_size=mega_original_args["""vocab_size"""]						,		hidden_size=mega_original_args["""mega_args"""].encoder_embed_dim						,		shared_representation_size=mega_original_args["""mega_args"""].encoder_z_dim						,		intermediate_size=mega_original_args["""mega_args"""].encoder_hidden_dim						,		ema_projection_size=mega_original_args["""mega_args"""].encoder_n_dim						,		dropout_prob=mega_original_args["""mega_args"""].dropout						,		attention_probs_dropout_prob=mega_original_args["""mega_args"""].attention_dropout						,		hidden_dropout_prob=mega_original_args["""mega_args"""].hidden_dropout						,		activation=mega_original_args["""mega_args"""].activation_fn						,		attention_activation=mega_original_args["""mega_args"""].attention_activation_fn						,		bidirectional=mega_original_args["""mega_args"""].bidirectional						,		use_chunking=mega_original_args["""mega_args"""].encoder_chunk_size > 0						,		chunk_size=mega_original_args["""mega_args"""].encoder_chunk_size						,		truncation=mega_original_args["""mega_args"""].truncation_length						,		normalization_type=mega_original_args["""mega_args"""].normalization_type						,		normalize_before_mega=snake_case__						,		norm_affine=snake_case__						,		use_feature_dropout=mega_original_args["""mega_args"""].feature_dropout						,		relative_positional_bias=mega_original_args["""mega_args"""].rel_pos_bias						,		max_positions=mega_original_args["""mega_args"""].max_source_positions						,		nffn_hidden_size=mega_original_args["""mega_args"""].encoder_ffn_embed_dim						,		normalize_before_ffn=mega_original_args["""mega_args"""].normalize_before						,		nffn_activation_dropout_prob=0.0						,		add_token_type_embeddings=snake_case__						,		add_lm_hidden_dense_layer=snake_case__						,		)

 __lowerCAmelCase    							=						MegaForMaskedLM(snake_case__				).eval()

 # the originl checkpoint just uses nn.Embedding for the word embeddings
 # we use a wrapper module for embeddings to add support for positional embeddings
 __lowerCAmelCase    							=						original_mlm.mega.embedding_layer.weight

 # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
 # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
 # also renaming previously confusing parameter names
 __lowerCAmelCase    							=						original_mlm.mega.encoders.state_dict()
 __lowerCAmelCase    							=						{}
 for module_name in original_state_dict.keys():
  __lowerCAmelCase    							=						None
  # have to handle gamma, beta, and alpha differently due to their use
  # in multiple modules within the original repository;
  # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
  # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
  if "beta" in module_name:
   # EMA sub-layers were always called "move" in the original repo
   if "move.beta" in module_name:
    __lowerCAmelCase    							=						module_name.replace("""move.beta"""						,		"""ema_gate.ema_expansion_matrix"""				)
   elif "mega_layer.beta" in module_name:
    __lowerCAmelCase    							=						module_name.replace("""beta"""						,		"""qk_bias"""				)
   else:
    __lowerCAmelCase    							=						module_name.replace("""beta"""						,		"""b_param"""				)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
  elif "gamma" in module_name:
   if "move.gamma" in module_name:
    __lowerCAmelCase    							=						module_name.replace("""move.gamma"""						,		"""ema_gate.kernel_projection_matrix"""				)
   elif "mega_layer.gamma" in module_name:
    __lowerCAmelCase    							=						module_name.replace("""gamma"""						,		"""qk_weight"""				)
   else:
    __lowerCAmelCase    							=						module_name.replace("""gamma"""						,		"""g_param"""				)
        # alpha is used in EMA and positional bias; renaming to improve readability
  elif "move.alpha" in module_name:
   __lowerCAmelCase    							=						module_name.replace("""move.alpha"""						,		"""ema_gate.decay_factor"""				)
  # delta is only used in EMA; renaming to improve readability
  elif "move.delta" in module_name:
   __lowerCAmelCase    							=						module_name.replace("""move.delta"""						,		"""ema_gate.damping_factor"""				)
  # omega is only used in EMA; renaming to improve readability
  elif "omega" in module_name:
   __lowerCAmelCase    							=						module_name.replace("""move.omega"""						,		"""ema_gate.residual_weight"""				)

  if new_module_name:
   __lowerCAmelCase    							=						new_module_name

 if len(snake_case__				) != 0:
  print(f"""Renaming these keys: {updated_keys.keys()}"""				)
 else:
  print("""No need to rename state dict entries"""				)
 for old, new in updated_keys.items():
  __lowerCAmelCase    							=						original_state_dict.pop(snake_case__				)

 # now attempt to load the state dictionary with updated names
 # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
 print("""HF Mega encoder:"""						,		hf_mlm.mega.layers.load_state_dict(snake_case__				)				)

 # load the MLM head weights directly
 print(
     """HF Mega MLM layer:"""						,		hf_mlm.mlm_head.load_state_dict(
         torch.load(os.path.join(snake_case__						,		"""mlm_head_weights.pt"""				)						,		map_location="""cpu"""				)				)						,		)

 # test on a randomly generated input sequence
 __lowerCAmelCase    							=						torch.randint(0						,		hf_config.vocab_size						,		size=(4, 2_56)				)
 __lowerCAmelCase    							=						torch.ones_like(snake_case__				)
 # mask a few tokens to make sure masking is applied appropriately :)
 __lowerCAmelCase    							=						0

 # run forward passes
 __lowerCAmelCase    							=						original_mlm(snake_case__						,		snake_case__						,		batch_first=snake_case__						,		ignore_mask_value=0				)
 __lowerCAmelCase    							=						hf_mlm(snake_case__						,		snake_case__				)[0]

 # print shapes and diff
 print(f"""original output {original_output.shape}"""				)
 print(f"""hf output {hf_output.shape}"""				)
 print(f"""max diff: {(original_output - hf_output).max()}"""				)  # 0.0
 __lowerCAmelCase    							=						torch.allclose(snake_case__						,		snake_case__						,		atol=1E-3				)

 if success:
  print("""Yay!"""				)
  hf_mlm.save_pretrained(snake_case__				)
 else:
  raise RuntimeError(f"""Something's broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}"""				)

 if includes_tokenizer:
  print("""Transferring tokenizer"""				)
  __lowerCAmelCase    							=						AutoTokenizer.from_pretrained(snake_case__				)
  tokenizer.save_pretrained(snake_case__				)


if __name__ == "__main__":
 _A      :				Dict 				=    argparse.ArgumentParser()

 parser.add_argument(
     '''--pretrained_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Point to the directory containing your model weights using the official Mega repo''',
 )

 parser.add_argument(
     '''--output_path''', default=None, type=str, required=True, help='''Location to save the Hugging Face version'''
 )

 parser.add_argument(
     '''--includes_tokenizer''',
     action='''store_true''',
     help='''Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo''',
 )

 _A      :				Optional[int] 				=    parser.parse_args()

 convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)







'''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					(    UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							unittest.TestCase      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	int				     = StableDiffusionModelEditingPipeline
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = TEXT_TO_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE					:	Tuple				     = TEXT_TO_IMAGE_BATCH_PARAMS
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = TEXT_TO_IMAGE_IMAGE_PARAMS
 _SCREAMING_SNAKE_CASE					:	Optional[int]				     = TEXT_TO_IMAGE_IMAGE_PARAMS
 def a (				self       :     Tuple							)  ->   Any:
  torch.manual_seed(0							)
  __lowerCAmelCase    							=						UNetaDConditionModel(
      block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=4				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
  __lowerCAmelCase    							=						DDIMScheduler()
  torch.manual_seed(0							)
  __lowerCAmelCase    							=						AutoencoderKL(
      block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
  torch.manual_seed(0							)
  __lowerCAmelCase    							=						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=10_00				,	)
  __lowerCAmelCase    							=						CLIPTextModel(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

  __lowerCAmelCase    							=						{
      """unet""": unet,
      """scheduler""": scheduler,
      """vae""": vae,
      """text_encoder""": text_encoder,
      """tokenizer""": tokenizer,
      """safety_checker""": None,
      """feature_extractor""": None,
  }
  return components
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     Dict=0							)  ->   Tuple:
  __lowerCAmelCase    							=						torch.manual_seed(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{
      """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 a (				self       :     int							)  ->   Optional[int]:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase    							=						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 a (				self       :     Dict							)  ->   Tuple:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						"""french fries"""
  __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__				,	negative_prompt=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						output.images
  __lowerCAmelCase    							=						image[0, -3:, -3:, -1]

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

  __lowerCAmelCase    							=						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 a (				self       :     Union[str, Any]							)  ->   int:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						EulerAncestralDiscreteScheduler(
      beta_start=0.0_0_0_8_5				,	beta_end=0.0_1_2				,	beta_schedule="""scaled_linear"""							)
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

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

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

  __lowerCAmelCase    							=						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 a (				self       :     Optional[int]							)  ->   Optional[int]:
  __lowerCAmelCase    							=						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
  __lowerCAmelCase    							=						self.get_dummy_components()
  __lowerCAmelCase    							=						PNDMScheduler()
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						sd_pipe.to(SCREAMING_SNAKE_CASE__							)
  sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)
  # the pipeline does not expect pndm so test if it raises error.
  with self.assertRaises(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						sd_pipe(**SCREAMING_SNAKE_CASE__							).images
 def a (				self       :     Tuple							)  ->   List[str]:
  super().test_inference_batch_single_identical(expected_max_diff=5e-3							)





 def a (				self       :     Optional[Any]							)  ->   List[str]:
  super().test_attention_slicing_forward_pass(expected_max_diff=5e-3							)







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


 '''simple docstring'''






 def a (				self       :     Optional[int]							)  ->   List[Any]:
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     Dict=0							)  ->   int:
  __lowerCAmelCase    							=						torch.manual_seed(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{
      """prompt""": """A field of roses""",
      """generator""": generator,
      """num_inference_steps""": 3,
      """guidance_scale""": 7.5,
      """output_type""": """numpy""",
  }
  return inputs
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						"""CompVis/stable-diffusion-v1-4"""
  __lowerCAmelCase    							=						StableDiffusionModelEditingPipeline.from_pretrained(SCREAMING_SNAKE_CASE__				,	safety_checker=SCREAMING_SNAKE_CASE__							)
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)
  pipe.enable_attention_slicing()

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

  assert image.shape == (1, 5_12, 5_12, 3)

  __lowerCAmelCase    							=						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"""							)

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

  assert image.shape == (1, 5_12, 5_12, 3)

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





 def a (				self       :     Tuple							)  ->   Optional[Any]:
  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()

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

  __lowerCAmelCase    							=						self.get_inputs()
  __lowerCAmelCase    							=						pipe(**SCREAMING_SNAKE_CASE__							)

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






