

"""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__			:					Any							  =					logging.get_logger(__name__)

lowerCamelCase__			:					Union[str, Any]							  =					[
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

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

lowerCamelCase__			:					Optional[Any]							  =					list('''ARNDCQEGHILKMFPSTWYV''')

lowerCamelCase__			:					int							  =					restypes + ['''X''']
lowerCamelCase__			:					str							  =					restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']
def  UpperCamelCase   (     )			->     Optional[int]:
	with TemporaryDirectory() as tempdir:
		_UpperCAmelCase      :							str       =   """\n""".join(_lowerCAmelCase	)
		_UpperCAmelCase      :							Optional[Any]       =   Path(_lowerCAmelCase	) / """vocab.txt"""
		vocab_file.write_text(_lowerCAmelCase	)
		_UpperCAmelCase      :							Union[str, Any]       =   EsmTokenizer(vocab_file=str(_lowerCAmelCase	)	)
	_UpperCAmelCase      :							int       =   0  # Overlaps with 'A' but that seems to be what they want
	return hf_tokenizer
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  List[Any]	)			->     Dict:
	_UpperCAmelCase      :							Optional[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  UpperCamelCase   (     _lowerCAmelCase       :  str,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  bool,					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  str	)			->     Optional[int]:
	if model.startswith("""esmfold"""	):
		_UpperCAmelCase      :							List[str]       =   MODEL_MAPPING[model]()
	else:
		_UpperCAmelCase      :							Tuple       =   MODEL_MAPPING[model]()
	esm.eval()  # disable dropout

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

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

	if is_folding_model:
		_UpperCAmelCase      :							Optional[Any]       =   esm.esm
	else:
		_UpperCAmelCase      :							Any       =   esm

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

	if model.startswith("""esmfold"""	):
		_UpperCAmelCase      :							Dict       =   EsmForProteinFolding
	elif classification_head:
		_UpperCAmelCase      :							List[str]       =   EsmForSequenceClassification
	else:
		_UpperCAmelCase      :							Optional[int]       =   EsmForMaskedLM
	_UpperCAmelCase      :							Optional[int]       =   model_class(_lowerCAmelCase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_UpperCAmelCase      :							List[Any]       =   original_esm_model.embed_tokens.weight
	if position_embedding_type == "absolute":
		_UpperCAmelCase      :							Dict       =   original_esm_model.embed_positions.weight

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

	_UpperCAmelCase      :							Any       =   original_esm_model.emb_layer_norm_after.weight
	_UpperCAmelCase      :							int       =   original_esm_model.emb_layer_norm_after.bias

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

		# self attention
		_UpperCAmelCase      :							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)	)
		)

		_UpperCAmelCase      :							str       =   esm_layer.self_attn.q_proj.weight
		_UpperCAmelCase      :							int       =   esm_layer.self_attn.q_proj.bias
		_UpperCAmelCase      :							Tuple       =   esm_layer.self_attn.k_proj.weight
		_UpperCAmelCase      :							str       =   esm_layer.self_attn.k_proj.bias
		_UpperCAmelCase      :							List[str]       =   esm_layer.self_attn.v_proj.weight
		_UpperCAmelCase      :							Dict       =   esm_layer.self_attn.v_proj.bias

		if getattr(esm_layer.self_attn,					"""rot_emb""",					_lowerCAmelCase	) 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      :							List[Any]       =   esm_layer.self_attn.rot_emb.inv_freq

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

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

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

		# output
		_UpperCAmelCase      :							EsmOutput       =   layer.output
		assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
		_UpperCAmelCase      :							int       =   esm_layer.fca.weight
		_UpperCAmelCase      :							int       =   esm_layer.fca.bias
		# end of layer

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

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

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

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

		del esm  # Free up some memory before continuing

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

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


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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						lowerCamelCase__			:					Optional[Any]							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





