

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   __lowercase				(_UpperCAmelCase		,       unittest.TestCase			):
 _UpperCamelCase        =  PaintByExamplePipeline
 _UpperCamelCase        =  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 _UpperCamelCase        =  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _UpperCamelCase        =  frozenset([]			)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
 def      UpperCamelCase__  (							self ) ->Optional[Any]:



       '''simple docstring'''





       torch.manual_seed(0 )
       __lowerCAmelCase :						Tuple = UNetaDConditionModel(
           block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=9				,							out_channels=4				,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,							cross_attention_dim=32				,							)
       __lowerCAmelCase :						Optional[int] = PNDMScheduler(skip_prk_steps=A_ )
       torch.manual_seed(0 )
       __lowerCAmelCase :						List[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 )
       __lowerCAmelCase :						List[Any] = CLIPVisionConfig(
           hidden_size=32				,							projection_dim=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							image_size=32				,							patch_size=4				,							)
       __lowerCAmelCase :						str = PaintByExampleImageEncoder(A_				,							proj_size=32 )
       __lowerCAmelCase :						List[str] = CLIPImageProcessor(crop_size=32				,							size=32 )

       __lowerCAmelCase :						str = {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''image_encoder''': image_encoder,
           '''safety_checker''': None,
           '''feature_extractor''': feature_extractor,
       }
       return components
 def      UpperCamelCase__  (							self				,							A_ ) ->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase :						int = np.array(image.convert('''RGB''' ) )
       __lowerCAmelCase :						int = image[None].transpose(0				,							3				,							1				,							2 )
       __lowerCAmelCase :						Tuple = torch.from_numpy(A_ ).to(dtype=torch.floataa ) / 127.5 - 1.0
       return image
 def      UpperCamelCase__  (							self				,							A_="cpu"				,							A_=0 ) ->Tuple:



       '''simple docstring'''





       __lowerCAmelCase :						str = floats_tensor((1, 3, 32, 32)				,							rng=random.Random(A_ ) ).to(A_ )
       __lowerCAmelCase :						Any = image.cpu().permute(0				,							2				,							3				,							1 )[0]
       __lowerCAmelCase :						Optional[Any] = Image.fromarray(np.uinta(A_ ) ).convert('''RGB''' ).resize((64, 64) )
       __lowerCAmelCase :						Optional[Any] = Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       __lowerCAmelCase :						Dict = Image.fromarray(np.uinta(A_ ) ).convert('''RGB''' ).resize((32, 32) )

       if str(A_ ).startswith('''mps''' ):
             __lowerCAmelCase :						List[str] = torch.manual_seed(A_ )
       else:
             __lowerCAmelCase :						Dict = torch.Generator(device=A_ ).manual_seed(A_ )
       __lowerCAmelCase :						Optional[int] = {
           '''example_image''': example_image,
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs
 def      UpperCamelCase__  (							self ) ->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase :						Any = self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       __lowerCAmelCase :						Dict = PaintByExamplePipeline(**A_ )
       __lowerCAmelCase :						Optional[int] = pipe.to('''cpu''' )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						Any = self.get_dummy_inputs()
       __lowerCAmelCase :						List[Any] = pipe(**A_ )
       __lowerCAmelCase :						List[Any] = output.images

       __lowerCAmelCase :						Optional[int] = image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       __lowerCAmelCase :						Any = np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       __lowerCAmelCase :						List[Any] = '''cpu'''
       __lowerCAmelCase :						List[str] = self.get_dummy_inputs()
       inputs.pop('''mask_image''' )
       __lowerCAmelCase :						List[Any] = self.convert_to_pt(inputs.pop('''image''' ) )
       __lowerCAmelCase :						Optional[Any] = image.clamp(0				,							1 ) / 2

       # make sure here that pndm scheduler skips prk
       __lowerCAmelCase :						Optional[int] = PaintByExamplePipeline(**self.get_dummy_components() )
       __lowerCAmelCase :						Dict = pipe.to(A_ )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						Union[str, Any] = pipe(image=A_				,							mask_image=mask_image[:, 0]				,							**A_ )
       __lowerCAmelCase :						Tuple = output.images

       __lowerCAmelCase :						Tuple = image.cpu().permute(0				,							2				,							3				,							1 )[0]
       __lowerCAmelCase :						Any = mask_image.cpu().permute(0				,							2				,							3				,							1 )[0]

       __lowerCAmelCase :						Optional[Any] = Image.fromarray(np.uinta(A_ ) ).convert('''RGB''' )
       __lowerCAmelCase :						Tuple = Image.fromarray(np.uinta(A_ ) ).convert('''RGB''' )

       __lowerCAmelCase :						Tuple = pipe(**self.get_dummy_inputs() )
       __lowerCAmelCase :						Tuple = output.images

       assert out_a.shape == (1, 64, 64, 3)
       assert np.abs(out_a.flatten() - out_a.flatten() ).max() < 5e-2





 def      UpperCamelCase__  (							self ) ->str:



       '''simple docstring'''





       super().test_inference_batch_single_identical(expected_max_diff=3e-3 )







@slow
@require_torch_gpu
class   __lowercase				(unittest.TestCase			):
 def      UpperCamelCase__  (							self ) ->Tuple:



       '''simple docstring'''





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





 def      UpperCamelCase__  (							self ) ->Any:



       '''simple docstring'''





       __lowerCAmelCase :						Tuple = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/dog_in_bucket.png''' )
       __lowerCAmelCase :						List[Any] = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/mask.png''' )
       __lowerCAmelCase :						Dict = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/panda.jpg''' )

       __lowerCAmelCase :						Tuple = PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example''' )
       __lowerCAmelCase :						int = pipe.to(A_ )
       pipe.set_progress_bar_config(disable=A_ )

       __lowerCAmelCase :						Dict = torch.manual_seed(321 )
       __lowerCAmelCase :						Any = pipe(
           image=A_				,							mask_image=A_				,							example_image=A_				,							generator=A_				,							guidance_scale=5.0				,							num_inference_steps=50				,							output_type='''np'''				,							)

       __lowerCAmelCase :						int = output.images

       __lowerCAmelCase :						Tuple = image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       __lowerCAmelCase :						Tuple = np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374] )

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





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			      =		logging.get_logger(__name__)

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

_UpperCamelCase			      =		{
    "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			      =		list("ARNDCQEGHILKMFPSTWYV")

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







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







def 		_lowercase			(					lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						int = 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 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):
      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Tuple = MODEL_MAPPING[model]()
      else:
            __lowerCAmelCase,							__lowerCAmelCase :						str = MODEL_MAPPING[model]()
      esm.eval()  # disable dropout

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

      if is_folding_model:
            __lowerCAmelCase :						Dict = esm.esm.alphabet
      __lowerCAmelCase :						str = tuple(alphabet.all_toks						)
      __lowerCAmelCase :						Tuple = alphabet.mask_idx
      __lowerCAmelCase :						Optional[int] = alphabet.padding_idx

      if is_folding_model:
            __lowerCAmelCase :						List[Any] = esm.esm
      else:
            __lowerCAmelCase :						Tuple = esm

      __lowerCAmelCase :						Optional[int] = EsmConfig(
          vocab_size=original_esm_model.embed_tokens.num_embeddings       ,     mask_token_id=lowercase__       ,     hidden_size=lowercase__       ,     num_hidden_layers=lowercase__       ,     num_attention_heads=lowercase__       ,     intermediate_size=lowercase__       ,     max_position_embeddings=1_0_2_6       ,     layer_norm_eps=1E-5       ,     attention_probs_dropout_prob=0.0       ,     hidden_dropout_prob=0.0       ,     pad_token_id=lowercase__       ,     emb_layer_norm_before=lowercase__       ,     token_dropout=lowercase__       ,     position_embedding_type=lowercase__       ,     is_folding_model=lowercase__       ,     esmfold_config=lowercase__       ,     vocab_list=lowercase__       ,     )
      if classification_head:
            __lowerCAmelCase :						str = esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
      print('''Our ESM config:'''       ,     lowercase__						)

      if model.startswith('''esmfold'''						):
            __lowerCAmelCase :						Any = EsmForProteinFolding
      elif classification_head:
            __lowerCAmelCase :						Union[str, Any] = EsmForSequenceClassification
      else:
            __lowerCAmelCase :						Any = EsmForMaskedLM
      __lowerCAmelCase :						str = model_class(lowercase__						)
      model.eval()

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

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

      __lowerCAmelCase :						Union[str, Any] = original_esm_model.emb_layer_norm_after.weight
      __lowerCAmelCase :						Any = original_esm_model.emb_layer_norm_after.bias

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

            # self attention
            __lowerCAmelCase :						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)						)
            )

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

            if getattr(esm_layer.self_attn       ,     '''rot_emb'''       ,     lowercase__						) 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 :						str = esm_layer.self_attn.rot_emb.inv_freq

            # LayerNorm changes for pre-activation
            __lowerCAmelCase :						Union[str, Any] = esm_layer.self_attn_layer_norm.weight
            __lowerCAmelCase :						List[Any] = esm_layer.self_attn_layer_norm.bias
            __lowerCAmelCase :						List[Any] = esm_layer.final_layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm_layer.final_layer_norm.bias

            # self-attention output
            __lowerCAmelCase :						EsmSelfOutput = layer.attention.output
            assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
            __lowerCAmelCase :						str = esm_layer.self_attn.out_proj.weight
            __lowerCAmelCase :						List[str] = esm_layer.self_attn.out_proj.bias

            # intermediate
            __lowerCAmelCase :						EsmIntermediate = layer.intermediate
            assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						str = esm_layer.fca.weight
            __lowerCAmelCase :						int = esm_layer.fca.bias

            # output
            __lowerCAmelCase :						EsmOutput = layer.output
            assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
            __lowerCAmelCase :						Tuple = esm_layer.fca.weight
            __lowerCAmelCase :						List[Any] = esm_layer.fca.bias
            # end of layer

      if is_folding_model:
            __lowerCAmelCase :						Union[str, Any] = esm.esm_s_combine.data
            __lowerCAmelCase :						Optional[int] = 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 :						List[str] = esm.esm.classification_heads['''mnli'''].dense.weight
            __lowerCAmelCase :						List[str] = esm.classification_heads['''mnli'''].dense.bias
            __lowerCAmelCase :						Optional[int] = esm.classification_heads['''mnli'''].out_proj.weight
            __lowerCAmelCase :						List[str] = esm.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            __lowerCAmelCase :						int = esm.lm_head.dense.weight
            __lowerCAmelCase :						Optional[int] = esm.lm_head.dense.bias
            __lowerCAmelCase :						Optional[int] = esm.lm_head.layer_norm.weight
            __lowerCAmelCase :						Union[str, Any] = esm.lm_head.layer_norm.bias
            __lowerCAmelCase :						int = esm.lm_head.weight
            __lowerCAmelCase :						str = 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 :						str = SAMPLE_DATA[:2]
      else:
            __lowerCAmelCase :						int = SAMPLE_DATA

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

            __lowerCAmelCase :						Any = hf_tokenizer([row[1] for row in sample_data]       ,     return_tensors='''pt'''       ,     padding=lowercase__						)
            __lowerCAmelCase :						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.
                  __lowerCAmelCase :						int = esm.cuda().infer([row[1] for row in sample_data]						)
                  __lowerCAmelCase :						Union[str, Any] = model.cuda()(
                      input_ids=hf_tokens['''input_ids'''].cuda()       ,     attention_mask=hf_tokens['''attention_mask'''].cuda()						)
            else:
                  __lowerCAmelCase :						List[Any] = model(**lowercase__       ,     output_hidden_states=lowercase__						)
                  __lowerCAmelCase :						Optional[int] = our_output['''logits''']
                  if classification_head:
                        __lowerCAmelCase :						Optional[int] = esm.model.classification_heads['''mnli'''](esm.extract_features(lowercase__						)						)
                  else:
                        __lowerCAmelCase :						Any = esm(hf_tokens['''input_ids''']       ,     repr_layers=list(range(9_9_9						)						)						)
                        __lowerCAmelCase :						Optional[Any] = their_output['''logits''']

            if is_folding_model:
                  __lowerCAmelCase :						Dict = torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']						)						).item()
                  __lowerCAmelCase :						Dict = torch.allclose(our_output['''positions''']       ,     their_output['''positions''']       ,     atol=1E-5						)
            else:
                  __lowerCAmelCase :						Optional[Any] = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     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 :						Tuple = model.predict_contacts(hf_tokens['''input_ids''']       ,     hf_tokens['''attention_mask''']						)
                  __lowerCAmelCase :						Optional[int] = esm.predict_contacts(hf_tokens['''input_ids''']						)
                  __lowerCAmelCase :						str = torch.max(torch.abs(our_output - their_output						)						).item()
                  __lowerCAmelCase :						Union[str, Any] = torch.allclose(lowercase__       ,     lowercase__       ,     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(lowercase__						).mkdir(parents=lowercase__       ,     exist_ok=lowercase__						)
            print(f"""Saving model to {pytorch_dump_folder_path}"""						)
            model.save_pretrained(lowercase__						)

            del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
 _UpperCamelCase			      =		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			      =		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
 )


