
'''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()
lowerCAmelCase_      =   logging.get_logger(__name__)

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

lowerCAmelCase_      =   {
    "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,
}

lowerCAmelCase_      =   list("ARNDCQEGHILKMFPSTWYV")

lowerCAmelCase_      =   restypes + ["X"]
lowerCAmelCase_      =   restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]







def   lowerCAmelCase(  ):







       '''simple docstring'''






       with TemporaryDirectory() as tempdir:
              lowerCamelCase__				   =      "\n".join(a__      )
              lowerCamelCase__				   =      Path(a__      ) / "vocab.txt"
              vocab_file.write_text(a__      )
              lowerCamelCase__				   =      EsmTokenizer(vocab_file=str(a__      )      )
       lowerCamelCase__				   =      0  # Overlaps with 'A' but that seems to be what they want
       return hf_tokenizer







def   lowerCAmelCase(  a__    :  Any      , a__    :  Optional[int]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      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   lowerCAmelCase(  a__    :  str      , a__    :  str      , a__    :  bool      , a__    :  str      , a__    :  str      ):







       '''simple docstring'''






       if model.startswith("esmfold"      ):
              lowerCamelCase__				   =      MODEL_MAPPING[model]()
       else:
              lowerCamelCase__   ,		lowerCamelCase__				   =      MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

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

       if is_folding_model:
              lowerCamelCase__				   =      esm.esm.alphabet
       lowerCamelCase__				   =      tuple(alphabet.all_toks      )
       lowerCamelCase__				   =      alphabet.mask_idx
       lowerCamelCase__				   =      alphabet.padding_idx

       if is_folding_model:
              lowerCamelCase__				   =      esm.esm
       else:
              lowerCamelCase__				   =      esm

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

       if model.startswith("esmfold"      ):
              lowerCamelCase__				   =      EsmForProteinFolding
       elif classification_head:
              lowerCamelCase__				   =      EsmForSequenceClassification
       else:
              lowerCamelCase__				   =      EsmForMaskedLM
       lowerCamelCase__				   =      model_class(a__      )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       lowerCamelCase__				   =      original_esm_model.embed_tokens.weight
       if position_embedding_type == "absolute":
              lowerCamelCase__				   =      original_esm_model.embed_positions.weight

       if config.emb_layer_norm_before:
              lowerCamelCase__				   =      original_esm_model.emb_layer_norm_before.weight
              lowerCamelCase__				   =      original_esm_model.emb_layer_norm_before.bias

       lowerCamelCase__				   =      original_esm_model.emb_layer_norm_after.weight
       lowerCamelCase__				   =      original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              lowerCamelCase__				   =      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)      )
              )

              lowerCamelCase__				   =      esm_layer.self_attn.q_proj.weight
              lowerCamelCase__				   =      esm_layer.self_attn.q_proj.bias
              lowerCamelCase__				   =      esm_layer.self_attn.k_proj.weight
              lowerCamelCase__				   =      esm_layer.self_attn.k_proj.bias
              lowerCamelCase__				   =      esm_layer.self_attn.v_proj.weight
              lowerCamelCase__				   =      esm_layer.self_attn.v_proj.bias

              if getattr(esm_layer.self_attn      , "rot_emb"      , a__      ) 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.
                     lowerCamelCase__				   =      esm_layer.self_attn.rot_emb.inv_freq

              # LayerNorm changes for pre-activation
              lowerCamelCase__				   =      esm_layer.self_attn_layer_norm.weight
              lowerCamelCase__				   =      esm_layer.self_attn_layer_norm.bias
              lowerCamelCase__				   =      esm_layer.final_layer_norm.weight
              lowerCamelCase__				   =      esm_layer.final_layer_norm.bias

              # self-attention output
              lowerCamelCase__				   =      layer.attention.output
              assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
              lowerCamelCase__				   =      esm_layer.self_attn.out_proj.weight
              lowerCamelCase__				   =      esm_layer.self_attn.out_proj.bias

              # intermediate
              lowerCamelCase__				   =      layer.intermediate
              assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
              lowerCamelCase__				   =      esm_layer.fca.weight
              lowerCamelCase__				   =      esm_layer.fca.bias

              # output
              lowerCamelCase__				   =      layer.output
              assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
              lowerCamelCase__				   =      esm_layer.fca.weight
              lowerCamelCase__				   =      esm_layer.fca.bias
              # end of layer

       if is_folding_model:
              lowerCamelCase__				   =      esm.esm_s_combine.data
              lowerCamelCase__				   =      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:
              lowerCamelCase__				   =      esm.esm.classification_heads["mnli"].dense.weight
              lowerCamelCase__				   =      esm.classification_heads["mnli"].dense.bias
              lowerCamelCase__				   =      esm.classification_heads["mnli"].out_proj.weight
              lowerCamelCase__				   =      esm.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              lowerCamelCase__				   =      esm.lm_head.dense.weight
              lowerCamelCase__				   =      esm.lm_head.dense.bias
              lowerCamelCase__				   =      esm.lm_head.layer_norm.weight
              lowerCamelCase__				   =      esm.lm_head.layer_norm.bias
              lowerCamelCase__				   =      esm.lm_head.weight
              lowerCamelCase__				   =      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.
              lowerCamelCase__				   =      SAMPLE_DATA[:2]
       else:
              lowerCamelCase__				   =      SAMPLE_DATA

       if is_folding_model:
              lowerCamelCase__				   =      get_esmfold_tokenizer()
              lowerCamelCase__				   =      hf_tokenizer(
                  [row[1] for row in sample_data]      , return_tensors="pt"      , padding=a__      , add_special_tokens=a__      )
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      esmfold_encode_sequences([row[1] for row in sample_data]      )
              lowerCamelCase__				   =      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.
              lowerCamelCase__				   =      alphabet.get_batch_converter()
              lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__				   =      batch_converter(a__      )
              # Prepare tokenizer and make sure it matches
              with TemporaryDirectory() as tempdir:
                     lowerCamelCase__				   =      "\n".join(alphabet.all_toks      )
                     lowerCamelCase__				   =      Path(a__      ) / "vocab.txt"
                     vocab_file.write_text(a__      )
                     lowerCamelCase__				   =      EsmTokenizer(vocab_file=str(a__      )      )

              lowerCamelCase__				   =      hf_tokenizer([row[1] for row in sample_data]      , return_tensors="pt"      , padding=a__      )
              lowerCamelCase__				   =      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.
                     lowerCamelCase__				   =      esm.cuda().infer([row[1] for row in sample_data]      )
                     lowerCamelCase__				   =      model.cuda()(
                         input_ids=hf_tokens["input_ids"].cuda()      , attention_mask=hf_tokens["attention_mask"].cuda()      )
              else:
                     lowerCamelCase__				   =      model(**a__      , output_hidden_states=a__      )
                     lowerCamelCase__				   =      our_output["logits"]
                     if classification_head:
                            lowerCamelCase__				   =      esm.model.classification_heads["mnli"](esm.extract_features(a__      )      )
                     else:
                            lowerCamelCase__				   =      esm(hf_tokens["input_ids"]      , repr_layers=list(range(999      )      )      )
                            lowerCamelCase__				   =      their_output["logits"]

              if is_folding_model:
                     lowerCamelCase__				   =      torch.max(torch.abs(our_output["positions"] - their_output["positions"]      )      ).item()
                     lowerCamelCase__				   =      torch.allclose(our_output["positions"]      , their_output["positions"]      , atol=1E-5      )
              else:
                     lowerCamelCase__				   =      torch.max(torch.abs(our_output - their_output      )      ).item()
                     lowerCamelCase__				   =      torch.allclose(a__      , a__      , 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
                     lowerCamelCase__				   =      model.predict_contacts(hf_tokens["input_ids"]      , hf_tokens["attention_mask"]      )
                     lowerCamelCase__				   =      esm.predict_contacts(hf_tokens["input_ids"]      )
                     lowerCamelCase__				   =      torch.max(torch.abs(our_output - their_output      )      ).item()
                     lowerCamelCase__				   =      torch.allclose(a__      , a__      , 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(a__      ).mkdir(parents=a__      , exist_ok=a__      )
              print(f"""Saving model to {pytorch_dump_folder_path}"""      )
              model.save_pretrained(a__      )

              del esm  # Free up some memory before continuing

       print(f"""Saving tokenizer to {pytorch_dump_folder_path}"""      )
       hf_tokenizer.save_pretrained(a__      )

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


if __name__ == "__main__":
    lowerCAmelCase_      =   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.")
    lowerCAmelCase_      =   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
    )



'''simple docstring'''







import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowerCAmelCase_      =   logging.get_logger(__name__)







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  bool = True      ):







       '''simple docstring'''






       lowerCamelCase__				   =      {}
       for name in model.attn_processors.keys():
              lowerCamelCase__				   =      name.endswith("attn2.processor"      ) and not (
                  name.startswith("transformer_in"      ) or "temp_attentions" in name.split("."      )
              )
              lowerCamelCase__				   =      model.config.cross_attention_dim if has_cross_attention else None
              if name.startswith("mid_block"      ):
                     lowerCamelCase__				   =      model.config.block_out_channels[-1]
              elif name.startswith("up_blocks"      ):
                     lowerCamelCase__				   =      int(name[len("up_blocks."      )]      )
                     lowerCamelCase__				   =      list(reversed(model.config.block_out_channels      )      )[block_id]
              elif name.startswith("down_blocks"      ):
                     lowerCamelCase__				   =      int(name[len("down_blocks."      )]      )
                     lowerCamelCase__				   =      model.config.block_out_channels[block_id]
              elif name.startswith("transformer_in"      ):
                     # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
                     lowerCamelCase__				   =      8 * model.config.attention_head_dim

              lowerCamelCase__				   =      LoRAAttnProcessor(hidden_size=a__      , cross_attention_dim=a__      )
              lowerCamelCase__				   =      lora_attn_procs[name].to(model.device      )

              if mock_weights:
                     # add 1 to weights to mock trained weights
                     with torch.no_grad():
                            lora_attn_procs[name].to_q_lora.up.weight += 1
                            lora_attn_procs[name].to_k_lora.up.weight += 1
                            lora_attn_procs[name].to_v_lora.up.weight += 1
                            lora_attn_procs[name].to_out_lora.up.weight += 1

       return lora_attn_procs







@skip_mps
class 		snake_case_       (						A__		,	A__		,	unittest.TestCase						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[int]								=UNetaDConditionModel
   __lowerCAmelCase	:							int								='''sample'''







   @property
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      4
          lowerCamelCase__				   =      (32, 32)

          lowerCamelCase__				   =      floats_tensor((batch_size, num_channels, num_frames) + sizes).to(UpperCamelCase)
          lowerCamelCase__				   =      torch.tensor([10]).to(UpperCamelCase)
          lowerCamelCase__				   =      floats_tensor((batch_size, 4, 32)).to(UpperCamelCase)

          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







   @property
   def     __UpperCAmelCase      ( self):
          return (4, 4, 32, 32)







   @property
   def     __UpperCAmelCase      ( self):
          return (4, 4, 32, 32)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__				   =      {
              "block_out_channels": (32, 64),
              "down_block_types": (
                  "CrossAttnDownBlock3D",
                  "DownBlock3D",
              ),
              "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
              "cross_attention_dim": 32,
              "attention_head_dim": 8,
              "out_channels": 4,
              "in_channels": 4,
              "layers_per_block": 1,
              "sample_size": 32,
          }
          lowerCamelCase__				   =      self.dummy_input
          return init_dict, inputs_dict







   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()			,   reason="XFormers attention is only available with CUDA and `xformers` installed"			,   )
   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)

          model.enable_xformers_memory_efficient_attention()

          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      32

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)

                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      output.sample

          self.assertIsNotNone(UpperCamelCase)
          lowerCamelCase__				   =      inputs_dict["sample"].shape
          self.assertEqual(output.shape			,   UpperCamelCase			,   "Input and output shapes do not match")







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          with torch.no_grad():
                 # Warmup pass when using mps (see #372)
                 if torch_device == "mps" and isinstance(UpperCamelCase			,   UpperCamelCase):
                        model(**self.dummy_input)

                 lowerCamelCase__				   =      model(**UpperCamelCase)
                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      first.sample

                 lowerCamelCase__				   =      model(**UpperCamelCase)
                 if isinstance(UpperCamelCase			,   UpperCamelCase):
                        lowerCamelCase__				   =      second.sample

          lowerCamelCase__				   =      first.cpu().numpy()
          lowerCamelCase__				   =      second.cpu().numpy()
          lowerCamelCase__				   =      out_a[~np.isnan(UpperCamelCase)]
          lowerCamelCase__				   =      out_a[~np.isnan(UpperCamelCase)]
          lowerCamelCase__				   =      np.amax(np.abs(out_a - out_a))
          self.assertLessEqual(UpperCamelCase			,   1E-5)







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          model.set_attention_slice("auto")
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)
          assert output is not None

          model.set_attention_slice("max")
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)
          assert output is not None

          model.set_attention_slice(2)
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)
          assert output is not None







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)

          # make sure we can set a list of attention processors
          model.set_attn_processor(UpperCamelCase)
          model.to(UpperCamelCase)

          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.0}).sample
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample

          assert (samplea - samplea).abs().max() < 3E-3
          assert (samplea - samplea).abs().max() < 3E-3

          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3E-3







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(UpperCamelCase)
                 self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase			,   "pytorch_lora_weights.bin")))
                 torch.manual_seed(0)
                 lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
                 new_model.to(UpperCamelCase)
                 new_model.load_attn_procs(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      new_model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample

          assert (sample - new_sample).abs().max() < 1E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(UpperCamelCase			,   safe_serialization=UpperCamelCase)
                 self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase			,   "pytorch_lora_weights.safetensors")))
                 torch.manual_seed(0)
                 lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
                 new_model.to(UpperCamelCase)
                 new_model.load_attn_procs(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      new_model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.5}).sample

          assert (sample - new_sample).abs().max() < 3E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







   def     __UpperCAmelCase      ( self):
          # enable deterministic behavior for gradient checkpointing
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase			,   mock_weights=UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(UpperCamelCase)
                 self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase			,   "pytorch_lora_weights.bin")))
                 torch.manual_seed(0)
                 lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
                 new_model.to(UpperCamelCase)
                 new_model.load_attn_procs(UpperCamelCase			,   weight_name="pytorch_lora_weights.bin")







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase			,   mock_weights=UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(UpperCamelCase)
                 self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase			,   "pytorch_lora_weights.bin")))
                 torch.manual_seed(0)
                 lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
                 new_model.to(UpperCamelCase)
                 with self.assertRaises(UpperCamelCase) as e:
                        new_model.load_attn_procs(UpperCamelCase			,   use_safetensors=UpperCamelCase)
                 self.assertIn("Error no file named pytorch_lora_weights.safetensors"			,   str(e.exception))







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      8

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase			,   cross_attention_kwargs={"scale": 0.0}).sample

          model.set_attn_processor(AttnProcessor())

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          assert (sample - new_sample).abs().max() < 1E-4
          assert (sample - old_sample).abs().max() < 3E-3







   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()			,   reason="XFormers attention is only available with CUDA and `xformers` installed"			,   )
   def     __UpperCAmelCase      ( self):
          # enable deterministic behavior for gradient checkpointing
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()

          lowerCamelCase__				   =      4

          torch.manual_seed(0)
          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          lowerCamelCase__				   =      create_lora_layers(UpperCamelCase)
          model.set_attn_processor(UpperCamelCase)

          # default
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

                 model.enable_xformers_memory_efficient_attention()
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

                 model.disable_xformers_memory_efficient_attention()
                 lowerCamelCase__				   =      model(**UpperCamelCase).sample

          assert (sample - on_sample).abs().max() < 1E-4
          assert (sample - off_sample).abs().max() < 1E-4







   def     __UpperCAmelCase      ( self):
          lowerCamelCase__   ,		lowerCamelCase__				   =      self.prepare_init_args_and_inputs_for_common()
          lowerCamelCase__				   =      32

          lowerCamelCase__				   =      self.model_class(**UpperCamelCase)
          model.to(UpperCamelCase)
          model.eval()

          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
                 lowerCamelCase__				   =      model(**UpperCamelCase)[0]

          self.assertEqual(output.shape			,   output_a.shape			,   "Shape doesn't match")
          assert np.abs(output.cpu() - output_a.cpu()).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.

