


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


def __lowercase  (   lowerCamelCase_	:   List[Any]    ,       lowerCamelCase_	:   List[Any]	):
				SCREAMING_SNAKE_CASE__  				=					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  (   lowerCamelCase_	:   str    ,       lowerCamelCase_	:   str    ,       lowerCamelCase_	:   bool    ,       lowerCamelCase_	:   str    ,       lowerCamelCase_	:   str	):
				if model.startswith("esmfold"	):
								SCREAMING_SNAKE_CASE__  				=					MODEL_MAPPING[model]()
				else:
								SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

				if model.startswith("esmfold"	):
								SCREAMING_SNAKE_CASE__  				=					esm.esm.embed_dim
								SCREAMING_SNAKE_CASE__  				=					esm.esm.num_layers
								SCREAMING_SNAKE_CASE__  				=					esm.esm.attention_heads
								SCREAMING_SNAKE_CASE__  				=					4 * embed_dim
								SCREAMING_SNAKE_CASE__  				=					esm.esm.token_dropout
								SCREAMING_SNAKE_CASE__  				=					False  # This code path does not exist in ESM-2
								SCREAMING_SNAKE_CASE__  				=					"rotary"
								SCREAMING_SNAKE_CASE__  				=					True
								SCREAMING_SNAKE_CASE__  				=					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
								SCREAMING_SNAKE_CASE__  				=					esm.args.embed_dim
								SCREAMING_SNAKE_CASE__  				=					esm.args.layers
								SCREAMING_SNAKE_CASE__  				=					esm.args.attention_heads
								SCREAMING_SNAKE_CASE__  				=					esm.args.ffn_embed_dim
								SCREAMING_SNAKE_CASE__  				=					esm.args.token_dropout
								SCREAMING_SNAKE_CASE__  				=					True if esm.emb_layer_norm_before else False
								SCREAMING_SNAKE_CASE__  				=					"absolute"
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					None
				else:
								# Indicates an ESM-2 model
								SCREAMING_SNAKE_CASE__  				=					esm.embed_dim
								SCREAMING_SNAKE_CASE__  				=					esm.num_layers
								SCREAMING_SNAKE_CASE__  				=					esm.attention_heads
								SCREAMING_SNAKE_CASE__  				=					4 * embed_dim  # This is hardcoded in ESM-2
								SCREAMING_SNAKE_CASE__  				=					esm.token_dropout
								SCREAMING_SNAKE_CASE__  				=					False  # This code path does not exist in ESM-2
								SCREAMING_SNAKE_CASE__  				=					"rotary"
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					None

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

				if is_folding_model:
								SCREAMING_SNAKE_CASE__  				=					esm.esm
				else:
								SCREAMING_SNAKE_CASE__  				=					esm

				SCREAMING_SNAKE_CASE__  				=					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:
								SCREAMING_SNAKE_CASE__  				=					esm.classification_heads["mnli"].out_proj.weight.shape[0]
				print("Our ESM config:"    ,       lowerCamelCase_	)

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

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

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

				SCREAMING_SNAKE_CASE__  				=					original_esm_model.emb_layer_norm_after.weight
				SCREAMING_SNAKE_CASE__  				=					original_esm_model.emb_layer_norm_after.bias

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

								# self attention
								SCREAMING_SNAKE_CASE__  				=					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)	)
								)

								SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.q_proj.weight
								SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.q_proj.bias
								SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.k_proj.weight
								SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.k_proj.bias
								SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.v_proj.weight
								SCREAMING_SNAKE_CASE__  				=					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.
												SCREAMING_SNAKE_CASE__  				=					esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

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

								if is_folding_model:
												SCREAMING_SNAKE_CASE__  				=					torch.max(torch.abs(our_output["positions"] - their_output["positions"]	)	).item()
												SCREAMING_SNAKE_CASE__  				=					torch.allclose(our_output["positions"]    ,       their_output["positions"]    ,       atol=1e-5	)
								else:
												SCREAMING_SNAKE_CASE__  				=					torch.max(torch.abs(our_output - their_output	)	).item()
												SCREAMING_SNAKE_CASE__  				=					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
												SCREAMING_SNAKE_CASE__  				=					model.predict_contacts(hf_tokens["input_ids"]    ,       hf_tokens["attention_mask"]	)
												SCREAMING_SNAKE_CASE__  				=					esm.predict_contacts(hf_tokens["input_ids"]	)
												SCREAMING_SNAKE_CASE__  				=					torch.max(torch.abs(our_output - their_output	)	).item()
												SCREAMING_SNAKE_CASE__  				=					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  =	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"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
