




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       SCREAMING_SNAKE_CASE			( )						->							Any:


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




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		List[Any] ,  __UpperCamelCase    :		Tuple   )						->							Optional[Any]:
    UpperCAmelCase_											=  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    :		Any ,  __UpperCamelCase    :		Union[str, Any] ,  __UpperCamelCase    :		Tuple ,  __UpperCamelCase    :		Any ,  __UpperCamelCase    :		List[Any]   )						->							str:


    if model.startswith('''esmfold'''   ):
        UpperCAmelCase_											=  MODEL_MAPPING[model]()
    else:
        UpperCAmelCase_											=  MODEL_MAPPING[model]()
    esm.eval()  # disable dropout

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

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

    if is_folding_model:
        UpperCAmelCase_											=  esm.esm
    else:
        UpperCAmelCase_											=  esm

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

    if model.startswith('''esmfold'''   ):
        UpperCAmelCase_											=  EsmForProteinFolding
    elif classification_head:
        UpperCAmelCase_											=  EsmForSequenceClassification
    else:
        UpperCAmelCase_											=  EsmForMaskedLM
    UpperCAmelCase_											=  model_class(SCREAMING_SNAKE_CASE__   )
    model.eval()

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

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

    UpperCAmelCase_											=  original_esm_model.emb_layer_norm_after.weight
    UpperCAmelCase_											=  original_esm_model.emb_layer_norm_after.bias

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

        # self attention
        UpperCAmelCase_											=  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)   )
        )

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

        if getattr(esm_layer.self_attn ,  '''rot_emb''' ,  SCREAMING_SNAKE_CASE__   ) 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.
            UpperCAmelCase_											=  esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

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

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

        del esm  # Free up some memory before continuing

    print(f'Saving tokenizer to {pytorch_dump_folder_path}'   )
    hf_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__   )

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


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
 )











import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_lowerCamelCase    				=		logging.get_logger(__name__)

_lowerCamelCase    				=		{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class     a  (     _A    ):

     '''simple docstring'''





     lowerCAmelCase       : Any     	=						'vit-hybrid'
     def __init__(			self     :    str      ,					__snake_case     :    str=None      ,					__snake_case     :    List[Any]=7_68      ,					__snake_case     :    List[Any]=12      ,					__snake_case     :    Dict=12      ,					__snake_case     :    Optional[int]=30_72      ,					__snake_case     :    Optional[int]="gelu"      ,					__snake_case     :    Union[str, Any]=0.0      ,					__snake_case     :    Any=0.0      ,					__snake_case     :    List[Any]=0.02      ,					__snake_case     :    Union[str, Any]=1E-12      ,					__snake_case     :    Optional[int]=2_24      ,					__snake_case     :    Optional[int]=1      ,					__snake_case     :    Tuple=3      ,					__snake_case     :    Union[str, Any]=[1, 10_24, 24, 24]      ,					__snake_case     :    List[Any]=True      ,					**__snake_case     :    Tuple      ,					):
         super().__init__(**__snake_case       )

         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''       )
             UpperCAmelCase_											=  {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }

         if isinstance(__snake_case      ,					__snake_case       ):
             if "model_type" in backbone_config:
                 UpperCAmelCase_											=  CONFIG_MAPPING[backbone_config['''model_type''']]
             else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''       )
                 UpperCAmelCase_											=  BitConfig
             UpperCAmelCase_											=  backbone_config_class(**__snake_case       )

         UpperCAmelCase_											=  backbone_featmap_shape
         UpperCAmelCase_											=  backbone_config
         UpperCAmelCase_											=  hidden_size
         UpperCAmelCase_											=  num_hidden_layers
         UpperCAmelCase_											=  num_attention_heads
         UpperCAmelCase_											=  intermediate_size
         UpperCAmelCase_											=  hidden_act
         UpperCAmelCase_											=  hidden_dropout_prob
         UpperCAmelCase_											=  attention_probs_dropout_prob
         UpperCAmelCase_											=  initializer_range
         UpperCAmelCase_											=  layer_norm_eps
         UpperCAmelCase_											=  image_size
         UpperCAmelCase_											=  patch_size
         UpperCAmelCase_											=  num_channels
         UpperCAmelCase_											=  qkv_bias




     def 						lowerCamelCase_     (			self     :    Any       ):
         UpperCAmelCase_											=  copy.deepcopy(self.__dict__       )
         UpperCAmelCase_											=  self.backbone_config.to_dict()
         UpperCAmelCase_											=  self.__class__.model_type
         return output





