


from dataclasses import asdict, dataclass
from typing import Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__      =						logging.get_logger(__name__)

# TODO Update this
snake_case__      =						{
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'esm'




						def __init__(	self						:   Optional[Any]   ,     __A						:   Optional[int]=None   ,     __A						:   Union[str, Any]=None   ,     __A						:   str=None   ,     __A						:   List[str]=768   ,     __A						:   Tuple=12   ,     __A						:   Optional[Any]=12   ,     __A						:   int=3072   ,     __A						:   Optional[Any]=0.1   ,     __A						:   str=0.1   ,     __A						:   Optional[int]=1026   ,     __A						:   List[str]=0.02   ,     __A						:   str=1E-12   ,     __A						:   Optional[int]="absolute"   ,     __A						:   Optional[int]=True   ,     __A						:   Dict=None   ,     __A						:   Dict=False   ,     __A						:   Dict=False   ,     __A						:   int=None   ,     __A						:   Any=None   ,     **__A						:   Dict   ,     )      ->Optional[Any]:






									"""simple docstring"""






									super().__init__(pad_token_id=__A   ,     mask_token_id=__A   ,     **__A						)

									a__ :Union[str, Any]						  =    vocab_size
									a__ :Dict						  =    hidden_size
									a__ :Tuple						  =    num_hidden_layers
									a__ :Optional[int]						  =    num_attention_heads
									a__ :Optional[Any]						  =    intermediate_size
									a__ :Dict						  =    hidden_dropout_prob
									a__ :Optional[Any]						  =    attention_probs_dropout_prob
									a__ :Any						  =    max_position_embeddings
									a__ :str						  =    initializer_range
									a__ :str						  =    layer_norm_eps
									a__ :Union[str, Any]						  =    position_embedding_type
									a__ :Dict						  =    use_cache
									a__ :Optional[Any]						  =    emb_layer_norm_before
									a__ :Dict						  =    token_dropout
									a__ :str						  =    is_folding_model
									if is_folding_model:
												if esmfold_config is None:
															logger.info("No esmfold_config supplied for folding model, using default values."						)
															a__ :List[Any]						  =    EsmFoldConfig()
												elif isinstance(__A   ,     __A						):
															a__ :int						  =    EsmFoldConfig(**__A						)
												a__ :Tuple						  =    esmfold_config
												if vocab_list is None:
															logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"						)
															a__ :str						  =    get_default_vocab_list()
												else:
															a__ :Union[str, Any]						  =    vocab_list
									else:
												a__ :Union[str, Any]						  =    None
												a__ :Union[str, Any]						  =    None
									if self.esmfold_config is not None and getattr(self.esmfold_config   ,     "use_esm_attn_map"   ,     __A						):
												raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"						)






						def  _snake_case					(	self						:   Dict						)      ->str:






									"""simple docstring"""






									a__ :Any						  =    super().to_dict()
									if isinstance(self.esmfold_config   ,     __A						):
												a__ :Tuple						  =    self.esmfold_config.to_dict()
									return output






@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     None
						lowerCamelCase_         =     True
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False
						lowerCamelCase_         =     0

						lowerCamelCase_         =     True
						lowerCamelCase_         =     False

						lowerCamelCase_         =     128
						lowerCamelCase_         =     None




						def  _snake_case					(	self						:   Dict						)      ->int:






									"""simple docstring"""






									if self.trunk is None:
												a__ :str						  =    TrunkConfig()
									elif isinstance(self.trunk   ,     __A						):
												a__ :Any						  =    TrunkConfig(**self.trunk						)






						def  _snake_case					(	self						:   Any						)      ->Any:






									"""simple docstring"""






									a__ :Tuple						  =    asdict(self						)
									a__ :str						  =    self.trunk.to_dict()
									return output






@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     48
						lowerCamelCase_         =     1_024
						lowerCamelCase_         =     128
						lowerCamelCase_         =     32
						lowerCamelCase_         =     32
						lowerCamelCase_         =     32
						lowerCamelCase_         =     0
						lowerCamelCase_         =     0
						lowerCamelCase_         =     False
						lowerCamelCase_         =     4
						lowerCamelCase_         =     128
						lowerCamelCase_         =     None




						def  _snake_case					(	self						:   Optional[Any]						)      ->Dict:






									"""simple docstring"""






									if self.structure_module is None:
												a__ :int						  =    StructureModuleConfig()
									elif isinstance(self.structure_module   ,     __A						):
												a__ :Any						  =    StructureModuleConfig(**self.structure_module						)

									if self.max_recycles <= 0:
												raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''						)
									if self.sequence_state_dim % self.sequence_state_dim != 0:
												raise ValueError(
												    "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
												    F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''						)
									if self.pairwise_state_dim % self.pairwise_state_dim != 0:
												raise ValueError(
												    "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
												    F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''						)

									a__ :Dict						  =    self.sequence_state_dim // self.sequence_head_width
									a__ :str						  =    self.pairwise_state_dim // self.pairwise_head_width

									if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
												raise ValueError(
												    "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
												    F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''						)
									if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
												raise ValueError(
												    "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
												    F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''						)
									if self.pairwise_state_dim % 2 != 0:
												raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''						)

									if self.dropout >= 0.4:
												raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''						)






						def  _snake_case					(	self						:   int						)      ->Dict:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    asdict(self						)
									a__ :List[str]						  =    self.structure_module.to_dict()
									return output






@dataclass
class 					lowerCAmelCase_ :
						lowerCamelCase_         =     384
						lowerCamelCase_         =     128
						lowerCamelCase_         =     16
						lowerCamelCase_         =     128
						lowerCamelCase_         =     12
						lowerCamelCase_         =     4
						lowerCamelCase_         =     8
						lowerCamelCase_         =     0.1
						lowerCamelCase_         =     8
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     7
						lowerCamelCase_         =     10
						lowerCamelCase_         =     1e-8
						lowerCamelCase_         =     1e5




						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return asdict(self						)






def 				lowerCamelCase__	(      )    ->	Union[str, Any]:





			"""simple docstring"""
			return (
			    "<cls>",
			    "<pad>",
			    "<eos>",
			    "<unk>",
			    "L",
			    "A",
			    "G",
			    "V",
			    "S",
			    "E",
			    "R",
			    "T",
			    "I",
			    "D",
			    "P",
			    "K",
			    "Q",
			    "N",
			    "F",
			    "Y",
			    "M",
			    "H",
			    "W",
			    "C",
			    "X",
			    "B",
			    "U",
			    "Z",
			    "O",
			    ".",
			    "-",
			    "<null_1>",
			    "<mask>",
			)






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

snake_case__			 =						ort.SessionOptions()
snake_case__			 =						ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case__			 =						['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case__			 =						ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case__			 =						ort.RunOptions()

snake_case__			 =						128
snake_case__			 =						1
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)
snake_case__			 =						np.ones((batch, sequence), dtype=np.intaa)

print('''Warm up phase...''')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('''Start inference...''')
snake_case__			 =						time.time()
snake_case__			 =						2000
snake_case__			 =						{}
for iter in range(max_iters):
		snake_case__			 =						sess.run(
		    None,
		    {
		        sess.get_inputs()[0].name: input_ids,
		        sess.get_inputs()[1].name: attention_mask,
		        sess.get_inputs()[2].name: token_type_ids,
		    },
		    run_options=run_opt,
		)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1000 / max_iters))


