



"""simple docstring"""







import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase:  Tuple			     =				logging.get_logger(__name__)

UpperCAmelCase:  str			     =				[
    (
        """protein1""",
        """MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA""",
    ),
    ("""protein2""", """MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"""),
    ("""protein3""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"""),
    ("""protein4""", """MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"""),
]

UpperCAmelCase:  Union[str, Any]			     =				{
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

UpperCAmelCase:  Tuple			     =				list("""ARNDCQEGHILKMFPSTWYV""")

UpperCAmelCase:  List[str]			     =				restypes + ["""X"""]
UpperCAmelCase:  List[Any]			     =				restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]


def  __SCREAMING_SNAKE_CASE (  ):
		with TemporaryDirectory() as tempdir:
				_lowercase				: List[str]     =   """\n""".join(__UpperCAmelCase      )
				_lowercase				: Any     =   Path(__UpperCAmelCase      ) / """vocab.txt"""
				vocab_file.write_text(__UpperCAmelCase      )
				_lowercase				: Dict     =   EsmTokenizer(vocab_file=str(__UpperCAmelCase      )      )
		_lowercase				: Optional[Any]     =   0  # Overlaps with 'A' but that seems to be what they want
		return hf_tokenizer


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: Any     =   our_module.load_state_dict(original_module.state_dict()      )
		if status.missing_keys:
				raise ValueError(F"""Missing keys: {status.missing_keys}"""      )
		if status.unexpected_keys:
				raise ValueError(F"""Unexpected keys: {status.unexpected_keys}"""      )


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		if model.startswith("""esmfold"""      ):
				_lowercase				: str     =   MODEL_MAPPING[model]()
		else:
				_lowercase     ,  _lowercase				: int     =   MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

		if model.startswith("""esmfold"""      ):
				_lowercase				: Dict     =   esm.esm.embed_dim
				_lowercase				: Any     =   esm.esm.num_layers
				_lowercase				: Optional[int]     =   esm.esm.attention_heads
				_lowercase				: List[str]     =   4 * embed_dim
				_lowercase				: Union[str, Any]     =   esm.esm.token_dropout
				_lowercase				: Optional[Any]     =   False  # This code path does not exist in ESM-2
				_lowercase				: Optional[int]     =   """rotary"""
				_lowercase				: Dict     =   True
				_lowercase				: Optional[int]     =   EsmFoldConfig()
				for key, val in esm.cfg.items():
						if hasattr(__UpperCAmelCase   ,				__UpperCAmelCase      ) and key != "trunk":
								setattr(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )
				for key, val in esm.cfg.trunk.items():
						if hasattr(esmfold_config.trunk   ,				__UpperCAmelCase      ) and key != "structure_module":
								setattr(esmfold_config.trunk   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )
				for key, val in esm.cfg.trunk.structure_module.items():
						if hasattr(esmfold_config.trunk.structure_module   ,				__UpperCAmelCase      ):
								setattr(esmfold_config.trunk.structure_module   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )
		elif hasattr(__UpperCAmelCase   ,				"""args"""      ):
				# Indicates an ESM-1b or ESM-1v model
				_lowercase				: Optional[int]     =   esm.args.embed_dim
				_lowercase				: Union[str, Any]     =   esm.args.layers
				_lowercase				: Optional[Any]     =   esm.args.attention_heads
				_lowercase				: Optional[int]     =   esm.args.ffn_embed_dim
				_lowercase				: str     =   esm.args.token_dropout
				_lowercase				: Dict     =   True if esm.emb_layer_norm_before else False
				_lowercase				: List[Any]     =   """absolute"""
				_lowercase				: Optional[int]     =   False
				_lowercase				: Union[str, Any]     =   None
		else:
				# Indicates an ESM-2 model
				_lowercase				: int     =   esm.embed_dim
				_lowercase				: Any     =   esm.num_layers
				_lowercase				: Tuple     =   esm.attention_heads
				_lowercase				: List[Any]     =   4 * embed_dim  # This is hardcoded in ESM-2
				_lowercase				: List[str]     =   esm.token_dropout
				_lowercase				: str     =   False  # This code path does not exist in ESM-2
				_lowercase				: Optional[int]     =   """rotary"""
				_lowercase				: Tuple     =   False
				_lowercase				: List[Any]     =   None

		if is_folding_model:
				_lowercase				: List[Any]     =   esm.esm.alphabet
		_lowercase				: List[Any]     =   tuple(alphabet.all_toks      )
		_lowercase				: Union[str, Any]     =   alphabet.mask_idx
		_lowercase				: Optional[int]     =   alphabet.padding_idx

		if is_folding_model:
				_lowercase				: List[Any]     =   esm.esm
		else:
				_lowercase				: Optional[Any]     =   esm

		_lowercase				: List[Any]     =   EsmConfig(
		    vocab_size=original_esm_model.embed_tokens.num_embeddings   ,				mask_token_id=__UpperCAmelCase   ,				hidden_size=__UpperCAmelCase   ,				num_hidden_layers=__UpperCAmelCase   ,				num_attention_heads=__UpperCAmelCase   ,				intermediate_size=__UpperCAmelCase   ,				max_position_embeddings=1026   ,				layer_norm_eps=1E-5   ,				attention_probs_dropout_prob=0.0   ,				hidden_dropout_prob=0.0   ,				pad_token_id=__UpperCAmelCase   ,				emb_layer_norm_before=__UpperCAmelCase   ,				token_dropout=__UpperCAmelCase   ,				position_embedding_type=__UpperCAmelCase   ,				is_folding_model=__UpperCAmelCase   ,				esmfold_config=__UpperCAmelCase   ,				vocab_list=__UpperCAmelCase   ,				)
		if classification_head:
				_lowercase				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
		print("""Our ESM config:"""   ,				__UpperCAmelCase      )

		if model.startswith("""esmfold"""      ):
				_lowercase				: Union[str, Any]     =   EsmForProteinFolding
		elif classification_head:
				_lowercase				: Optional[Any]     =   EsmForSequenceClassification
		else:
				_lowercase				: str     =   EsmForMaskedLM
		_lowercase				: List[str]     =   model_class(__UpperCAmelCase      )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		_lowercase				: Union[str, Any]     =   original_esm_model.embed_tokens.weight
		if position_embedding_type == "absolute":
				_lowercase				: Optional[int]     =   original_esm_model.embed_positions.weight

		if config.emb_layer_norm_before:
				_lowercase				: Optional[int]     =   original_esm_model.emb_layer_norm_before.weight
				_lowercase				: int     =   original_esm_model.emb_layer_norm_before.bias

		_lowercase				: List[Any]     =   original_esm_model.emb_layer_norm_after.weight
		_lowercase				: Optional[Any]     =   original_esm_model.emb_layer_norm_after.bias

		for i in range(config.num_hidden_layers      ):
				# Encoder: start of layer
				_lowercase				: EsmLayer     =   model.esm.encoder.layer[i]
				# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
				_lowercase				: Optional[Any]     =   original_esm_model.layers[i]

				# self attention
				_lowercase				: EsmSelfAttention     =   layer.attention.self
				assert (
				    esm_layer.self_attn.k_proj.weight.data.shape
				    == esm_layer.self_attn.q_proj.weight.data.shape
				    == esm_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size)      )
				)

				_lowercase				: Any     =   esm_layer.self_attn.q_proj.weight
				_lowercase				: Tuple     =   esm_layer.self_attn.q_proj.bias
				_lowercase				: Tuple     =   esm_layer.self_attn.k_proj.weight
				_lowercase				: Dict     =   esm_layer.self_attn.k_proj.bias
				_lowercase				: Any     =   esm_layer.self_attn.v_proj.weight
				_lowercase				: Optional[int]     =   esm_layer.self_attn.v_proj.bias

				if getattr(esm_layer.self_attn   ,				"""rot_emb"""   ,				__UpperCAmelCase      ) is not None:
						# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
						# During the training of ESM-2 the model was converted to float16 precision, which also converts
						# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
						# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
						# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
						# we make sure the new model copies the data from the old inv_freq.
						_lowercase				: List[Any]     =   esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				_lowercase				: Optional[int]     =   esm_layer.self_attn_layer_norm.weight
				_lowercase				: Any     =   esm_layer.self_attn_layer_norm.bias
				_lowercase				: Any     =   esm_layer.final_layer_norm.weight
				_lowercase				: str     =   esm_layer.final_layer_norm.bias

				# self-attention output
				_lowercase				: EsmSelfOutput     =   layer.attention.output
				assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
				_lowercase				: Any     =   esm_layer.self_attn.out_proj.weight
				_lowercase				: Any     =   esm_layer.self_attn.out_proj.bias

				# intermediate
				_lowercase				: EsmIntermediate     =   layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				_lowercase				: Optional[int]     =   esm_layer.fca.weight
				_lowercase				: str     =   esm_layer.fca.bias

				# output
				_lowercase				: EsmOutput     =   layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				_lowercase				: Optional[Any]     =   esm_layer.fca.weight
				_lowercase				: str     =   esm_layer.fca.bias
				# end of layer

		if is_folding_model:
				_lowercase				: List[str]     =   esm.esm_s_combine.data
				_lowercase				: Union[str, Any]     =   esm.afa_to_esm.data
				transfer_and_check_weights(esm.embedding   ,				model.embedding      )
				transfer_and_check_weights(esm.esm_s_mlp   ,				model.esm_s_mlp      )
				transfer_and_check_weights(esm.trunk   ,				model.trunk      )
				transfer_and_check_weights(esm.distogram_head   ,				model.distogram_head      )
				transfer_and_check_weights(esm.ptm_head   ,				model.ptm_head      )
				transfer_and_check_weights(esm.lm_head   ,				model.lm_head      )
				transfer_and_check_weights(esm.lddt_head   ,				model.lddt_head      )

		elif classification_head:
				_lowercase				: Union[str, Any]     =   esm.esm.classification_heads["""mnli"""].dense.weight
				_lowercase				: Dict     =   esm.classification_heads["""mnli"""].dense.bias
				_lowercase				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.weight
				_lowercase				: Optional[int]     =   esm.classification_heads["""mnli"""].out_proj.bias
		else:
				# LM Head
				_lowercase				: Any     =   esm.lm_head.dense.weight
				_lowercase				: Union[str, Any]     =   esm.lm_head.dense.bias
				_lowercase				: int     =   esm.lm_head.layer_norm.weight
				_lowercase				: str     =   esm.lm_head.layer_norm.bias
				_lowercase				: Dict     =   esm.lm_head.weight
				_lowercase				: Dict     =   esm.lm_head.bias

		# Contact prediction head
		transfer_and_check_weights(esm.contact_head   ,				model.esm.contact_head      )

		# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
		if is_folding_model:
				# Folding models aren't trained on masked inputs and don't like mask tokens.
				_lowercase				: Any     =   SAMPLE_DATA[:2]
		else:
				_lowercase				: Tuple     =   SAMPLE_DATA

		if is_folding_model:
				_lowercase				: Optional[Any]     =   get_esmfold_tokenizer()
				_lowercase				: List[str]     =   hf_tokenizer(
				    [row[1] for row in sample_data]   ,				return_tensors="""pt"""   ,				padding=__UpperCAmelCase   ,				add_special_tokens=__UpperCAmelCase      )
				_lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase     ,  _lowercase				: Union[str, Any]     =   esmfold_encode_sequences([row[1] for row in sample_data]      )
				_lowercase				: List[Any]     =   torch.all(hf_tokens["""input_ids"""] == esmfold_aas      ) and torch.all(
				    hf_tokens["""attention_mask"""] == esmfold_mask      )
		else:
				# Let's check that we get the same results.
				_lowercase				: Dict     =   alphabet.get_batch_converter()
				_lowercase     ,  _lowercase     ,  _lowercase				: Optional[int]     =   batch_converter(__UpperCAmelCase      )
				# Prepare tokenizer and make sure it matches
				with TemporaryDirectory() as tempdir:
						_lowercase				: Optional[int]     =   """\n""".join(alphabet.all_toks      )
						_lowercase				: List[str]     =   Path(__UpperCAmelCase      ) / """vocab.txt"""
						vocab_file.write_text(__UpperCAmelCase      )
						_lowercase				: List[Any]     =   EsmTokenizer(vocab_file=str(__UpperCAmelCase      )      )

				_lowercase				: Optional[Any]     =   hf_tokenizer([row[1] for row in sample_data]   ,				return_tensors="""pt"""   ,				padding=__UpperCAmelCase      )
				_lowercase				: Union[str, Any]     =   torch.all(hf_tokens["""input_ids"""] == batch_tokens      )

		print("""Do both models tokenizers output the same tokens?"""   ,				"""🔥""" if success else """💩"""      )
		if not success:
				raise Exception("""Tokenization does not match!"""      )

		with torch.no_grad():
				if is_folding_model:
						# Let's test the model in parts
						# ESMFold always converts the ESM stem to float16, which requires float16 ops
						# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
						# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
						# original and the converted model on the GPU at the same time.
						_lowercase				: List[str]     =   esm.cuda().infer([row[1] for row in sample_data]      )
						_lowercase				: Tuple     =   model.cuda()(
						    input_ids=hf_tokens["""input_ids"""].cuda()   ,				attention_mask=hf_tokens["""attention_mask"""].cuda()      )
				else:
						_lowercase				: Union[str, Any]     =   model(**__UpperCAmelCase   ,				output_hidden_states=__UpperCAmelCase      )
						_lowercase				: Tuple     =   our_output["""logits"""]
						if classification_head:
								_lowercase				: int     =   esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCAmelCase      )      )
						else:
								_lowercase				: List[str]     =   esm(hf_tokens["""input_ids"""]   ,				repr_layers=list(range(999      )      )      )
								_lowercase				: str     =   their_output["""logits"""]

				if is_folding_model:
						_lowercase				: str     =   torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]      )      ).item()
						_lowercase				: str     =   torch.allclose(our_output["""positions"""]   ,				their_output["""positions"""]   ,				atol=1E-5      )
				else:
						_lowercase				: int     =   torch.max(torch.abs(our_output - their_output      )      ).item()
						_lowercase				: Tuple     =   torch.allclose(__UpperCAmelCase   ,				__UpperCAmelCase   ,				atol=1E-5      )

				print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-5
				print("""Do both models output the same tensors?"""   ,				"""🔥""" if success else """💩"""      )

				if not success:
						raise Exception("""Something went wRoNg"""      )

				if not is_folding_model:
						# Let's check contact prediction too
						_lowercase				: Tuple     =   model.predict_contacts(hf_tokens["""input_ids"""]   ,				hf_tokens["""attention_mask"""]      )
						_lowercase				: List[str]     =   esm.predict_contacts(hf_tokens["""input_ids"""]      )
						_lowercase				: Union[str, Any]     =   torch.max(torch.abs(our_output - their_output      )      ).item()
						_lowercase				: Dict     =   torch.allclose(__UpperCAmelCase   ,				__UpperCAmelCase   ,				atol=1E-5      )

						print("""Contact prediction testing:"""      )
						print(F"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-5
						print("""Do both models output the same tensors?"""   ,				"""🔥""" if success else """💩"""      )

						if not success:
								raise Exception("""Something went wRoNg"""      )

				pathlib.Path(__UpperCAmelCase      ).mkdir(parents=__UpperCAmelCase   ,				exist_ok=__UpperCAmelCase      )
				print(F"""Saving model to {pytorch_dump_folder_path}"""      )
				model.save_pretrained(__UpperCAmelCase      )

				del esm  # Free up some memory before continuing

		print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""      )
		hf_tokenizer.save_pretrained(__UpperCAmelCase      )

		if push_to_repo:
				model.push_to_hub(repo_id=__UpperCAmelCase   ,				use_auth_token=__UpperCAmelCase      )
				hf_tokenizer.push_to_hub(repo_id=__UpperCAmelCase   ,				use_auth_token=__UpperCAmelCase      )


if __name__ == "__main__":
		UpperCAmelCase:  Any			     =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--pytorch_dump_folder_path""", type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		parser.add_argument("""--model""", default=None, type=str, required=True, help="""Name of model to convert.""")
		parser.add_argument("""--push_to_repo""", type=str, help="""Repo to upload to (including username!).""")
		parser.add_argument("""--auth_token""", type=str, help="""HuggingFace auth token.""")
		UpperCAmelCase:  Tuple			     =				parser.parse_args()
		convert_esm_checkpoint_to_pytorch(
		    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
		)





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     a_       (			a__				,    a__				,    a__				,    unittest.TestCase							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  StableDiffusionModelEditingPipeline
       __SCREAMING_SNAKE_CASE       :					Optional[int]						=  TEXT_TO_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					int						=  TEXT_TO_IMAGE_BATCH_PARAMS
       __SCREAMING_SNAKE_CASE       :					Any						=  TEXT_TO_IMAGE_IMAGE_PARAMS
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  TEXT_TO_IMAGE_IMAGE_PARAMS






       def 	__lowerCAmelCase     (		self   )					->int:
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Dict				      = 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				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = DDIMScheduler()
           torch.manual_seed(0   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = 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   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = CLIPTextConfig(
               bos_token_id=0				,       eos_token_id=2				,       hidden_size=32				,       intermediate_size=37				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=1000				,       )
           SCREAMING_SNAKE_CASE				:				Dict				      = CLIPTextModel(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Tuple				      = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''unet''': unet,
               '''scheduler''': scheduler,
               '''vae''': vae,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''safety_checker''': None,
               '''feature_extractor''': None,
           }
           return components






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase				,       _lowerCamelCase=0   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Any				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = {
               '''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 	__lowerCAmelCase     (		self   )					->Optional[int]:
           SCREAMING_SNAKE_CASE				:				Tuple				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

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

           SCREAMING_SNAKE_CASE				:				List[str]				      = 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 	__lowerCAmelCase     (		self   )					->List[Any]:
           SCREAMING_SNAKE_CASE				:				Dict				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				Tuple				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				str				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[str]				      = '''french fries'''
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe(**_lowerCamelCase				,       negative_prompt=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Any				      = output.images
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image[0, -3:, -3:, -1]

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

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

           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = sd_pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image[0, -3:, -3:, -1]

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

           SCREAMING_SNAKE_CASE				:				str				      = 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 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_dummy_components()
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = PNDMScheduler()
           SCREAMING_SNAKE_CASE				:				Tuple				      = StableDiffusionModelEditingPipeline(**_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = sd_pipe.to(_lowerCamelCase   )
           sd_pipe.set_progress_bar_config(disable=_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.get_dummy_inputs(_lowerCamelCase   )
           # the pipeline does not expect pndm so test if it raises error.
           with self.assertRaises(_lowerCamelCase   ):
               SCREAMING_SNAKE_CASE				:				Dict				      = sd_pipe(**_lowerCamelCase   ).images






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().test_inference_batch_single_identical(expected_max_diff=5e-3   )






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






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



       """simple docstring"""






       def 	__lowerCAmelCase     (		self   )					->Dict:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






       def 	__lowerCAmelCase     (		self				,       _lowerCamelCase=0   )					->Dict:
           SCREAMING_SNAKE_CASE				:				str				      = torch.manual_seed(_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				List[Any]				      = {
               '''prompt''': '''A field of roses''',
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs






       def 	__lowerCAmelCase     (		self   )					->List[str]:
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				List[str]				      = StableDiffusionModelEditingPipeline.from_pretrained(_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing()

           SCREAMING_SNAKE_CASE				:				Any				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image[0, -3:, -3:, -1].flatten()

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

           SCREAMING_SNAKE_CASE				:				str				      = 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'''   )

           SCREAMING_SNAKE_CASE				:				Any				      = pipe(**_lowerCamelCase   ).images
           SCREAMING_SNAKE_CASE				:				List[str]				      = image[0, -3:, -3:, -1].flatten()

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

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






       def 	__lowerCAmelCase     (		self   )					->str:
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           SCREAMING_SNAKE_CASE				:				Optional[int]				      = '''CompVis/stable-diffusion-v1-4'''
           SCREAMING_SNAKE_CASE				:				Any				      = DDIMScheduler.from_pretrained(_lowerCamelCase				,       subfolder='''scheduler'''   )
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = StableDiffusionModelEditingPipeline.from_pretrained(
               _lowerCamelCase				,       scheduler=_lowerCamelCase				,       safety_checker=_lowerCamelCase   )
           SCREAMING_SNAKE_CASE				:				Dict				      = pipe.to(_lowerCamelCase   )
           pipe.set_progress_bar_config(disable=_lowerCamelCase   )
           pipe.enable_attention_slicing(1   )
           pipe.enable_sequential_cpu_offload()

           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.get_inputs()
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = pipe(**_lowerCamelCase   )

           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.cuda.max_memory_allocated()
           # make sure that less than 4.4 GB is allocated
           assert mem_bytes < 4.4 * 10**9
