





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






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							if model.startswith("""esmfold"""   ):
														_snake_case     =						MODEL_MAPPING[model]()
							else:
														_snake_case,							_snake_case     =						MODEL_MAPPING[model]()
							esm.eval()  # disable dropout

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

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

							if is_folding_model:
														_snake_case     =						esm.esm
							else:
														_snake_case     =						esm

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

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

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

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

							_snake_case     =						original_esm_model.emb_layer_norm_after.weight
							_snake_case     =						original_esm_model.emb_layer_norm_after.bias

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

														# self attention
														_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)   )
														)

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

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

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

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

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

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

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

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

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

'''simple docstring'''
import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


__lowerCAmelCase  							= logging.get_logger(__name__)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Any:
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("""1.12.0"""						):
																					raise ImportError(
																					    f"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
																					    """PytorchGELUTanh. Please upgrade torch."""						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return nn.functional.gelu(UpperCAmelCase						,						approximate="""tanh"""						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.04_4715 * torch.pow(UpperCAmelCase						,						3.0						))						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase = False						)  ->     Optional[Any]:
														super().__init__()
														if use_gelu_python:
																					_snake_case     =						self._gelu_python
														else:
																					_snake_case     =						nn.functional.gelu

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return self.act(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * torch.sigmoid(1.702 * input						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						,						UpperCAmelCase						,						UpperCAmelCase						)  ->     List[Any]:
														if min > max:
																					raise ValueError(f"""min should be < max (got min: {min}, max: {max})"""						)

														super().__init__()
														_snake_case     =						min
														_snake_case     =						max





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return torch.clip(gelu(UpperCAmelCase						)						,						self.min						,						self.max						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Tuple:
														super().__init__()
														_snake_case     =						math.sqrt(2 / math.pi						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(UpperCAmelCase						,						3						))						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return nn.functional.silu(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def __init__(self						)  ->     Any:
														super().__init__()
														if version.parse(torch.__version__						) < version.parse("""1.9.0"""						):
																					_snake_case     =						self._mish_python
														else:
																					_snake_case     =						nn.functional.mish

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input * torch.tanh(nn.functional.softplus(UpperCAmelCase						)						)





							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return self.act(UpperCAmelCase						)


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Tensor:
														return input


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase=0.70_7107						,						UpperCAmelCase=0.28_2095						)  ->     List[str]:
														_snake_case     =						(input - mu).div(sigma * math.sqrt(2.0						)						)
														return 0.5 * (1.0 + torch.erf(UpperCAmelCase						))


class     _lowerCAmelCase					(					nn.Module   ):







							'''simple docstring'''

							def  lowercase							(self						,						UpperCAmelCase						)  ->     Any:
														_snake_case     =						nn.functional.relu(UpperCAmelCase						)
														_snake_case     =						torch.square(UpperCAmelCase						)
														return squared


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							def __getitem__(self						,						UpperCAmelCase						)  ->     Tuple:
														_snake_case     =						super().__getitem__(UpperCAmelCase						)
														_snake_case,							_snake_case     =						content if isinstance(UpperCAmelCase						,						UpperCAmelCase						) else (content, {})
														return cls(**UpperCAmelCase						)
__lowerCAmelCase  							= {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
__lowerCAmelCase  							= ClassInstantier(ACTaCLS)






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							if activation_string in ACTaFN:
														return ACTaFN[activation_string]
							else:
														raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()   )}"""   )


# For backwards compatibility with: from activations import gelu_python
__lowerCAmelCase  							= get_activation('gelu_python')
__lowerCAmelCase  							= get_activation('gelu_new')
__lowerCAmelCase  							= get_activation('gelu')
__lowerCAmelCase  							= get_activation('gelu_fast')
__lowerCAmelCase  							= get_activation('quick_gelu')
__lowerCAmelCase  							= get_activation('silu')
__lowerCAmelCase  							= get_activation('mish')

__lowerCAmelCase  							= get_activation('linear')