"""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()
__lowercase    =				logging.get_logger(__name__)

__lowercase    =				[
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

__lowercase    =				{
    '''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,
}

__lowercase    =				list('''ARNDCQEGHILKMFPSTWYV''')

__lowercase    =				restypes + ['''X''']
__lowercase    =				restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']


def    lowerCAmelCase			():
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    Any							,					__UpperCamelCase							:    Tuple			):
							"""simple docstring"""


							__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    lowerCAmelCase			(__UpperCamelCase							:    str							,					__UpperCamelCase							:    str							,					__UpperCamelCase							:    bool							,					__UpperCamelCase							:    str							,					__UpperCamelCase							:    str			):
							"""simple docstring"""


							if model.startswith('''esmfold'''			):
														__UpperCamelCase							=MODEL_MAPPING[model]()
							else:
														__UpperCamelCase	,      __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(__UpperCamelCase							,					__UpperCamelCase			) and key != "trunk":
																												setattr(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase			)
														for key, val in esm.cfg.trunk.items():
																					if hasattr(esmfold_config.trunk							,					__UpperCamelCase			) and key != "structure_module":
																												setattr(esmfold_config.trunk							,					__UpperCamelCase							,					__UpperCamelCase			)
														for key, val in esm.cfg.trunk.structure_module.items():
																					if hasattr(esmfold_config.trunk.structure_module							,					__UpperCamelCase			):
																												setattr(esmfold_config.trunk.structure_module							,					__UpperCamelCase							,					__UpperCamelCase			)
							elif hasattr(__UpperCamelCase							,					'''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=__UpperCamelCase							,					hidden_size=__UpperCamelCase							,					num_hidden_layers=__UpperCamelCase							,					num_attention_heads=__UpperCamelCase							,					intermediate_size=__UpperCamelCase							,					max_position_embeddings=1_0_2_6							,					layer_norm_eps=1E-5							,					attention_probs_dropout_prob=0.0							,					hidden_dropout_prob=0.0							,					pad_token_id=__UpperCamelCase							,					emb_layer_norm_before=__UpperCamelCase							,					token_dropout=__UpperCamelCase							,					position_embedding_type=__UpperCamelCase							,					is_folding_model=__UpperCamelCase							,					esmfold_config=__UpperCamelCase							,					vocab_list=__UpperCamelCase							,					)
							if classification_head:
														__UpperCamelCase							=esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
							print('''Our ESM config:'''							,					__UpperCamelCase			)

							if model.startswith('''esmfold'''			):
														__UpperCamelCase							=EsmForProteinFolding
							elif classification_head:
														__UpperCamelCase							=EsmForSequenceClassification
							else:
														__UpperCamelCase							=EsmForMaskedLM
							__UpperCamelCase							=model_class(__UpperCamelCase			)
							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'''							,					__UpperCamelCase			) 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=__UpperCamelCase							,					add_special_tokens=__UpperCamelCase			)
														__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __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	,      __UpperCamelCase	,      __UpperCamelCase							=batch_converter(__UpperCamelCase			)
														# Prepare tokenizer and make sure it matches
														with TemporaryDirectory() as tempdir:
																					__UpperCamelCase							='''\n'''.join(alphabet.all_toks			)
																					__UpperCamelCase							=Path(__UpperCamelCase			) / '''vocab.txt'''
																					vocab_file.write_text(__UpperCamelCase			)
																					__UpperCamelCase							=EsmTokenizer(vocab_file=str(__UpperCamelCase			)			)

														__UpperCamelCase							=hf_tokenizer([row[1] for row in sample_data]							,					return_tensors='''pt'''							,					padding=__UpperCamelCase			)
														__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(**__UpperCamelCase							,					output_hidden_states=__UpperCamelCase			)
																					__UpperCamelCase							=our_output['''logits''']
																					if classification_head:
																												__UpperCamelCase							=esm.model.classification_heads['''mnli'''](esm.extract_features(__UpperCamelCase			)			)
																					else:
																												__UpperCamelCase							=esm(hf_tokens['''input_ids''']							,					repr_layers=list(range(9_9_9			)			)			)
																												__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(__UpperCamelCase							,					__UpperCamelCase							,					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(__UpperCamelCase							,					__UpperCamelCase							,					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(__UpperCamelCase			).mkdir(parents=__UpperCamelCase							,					exist_ok=__UpperCamelCase			)
														print(F"""Saving model to {pytorch_dump_folder_path}"""			)
														model.save_pretrained(__UpperCamelCase			)

														del esm  # Free up some memory before continuing

							print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
							hf_tokenizer.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
				__lowercase    =				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.''')
				__lowercase    =				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, is_vision_available


__lowercase    =				{
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				[
				    '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''VideoMAEForPreTraining''',
				    '''VideoMAEModel''',
				    '''VideoMAEPreTrainedModel''',
				    '''VideoMAEForVideoClassification''',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowercase    =				['''VideoMAEFeatureExtractor''']
				__lowercase    =				['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
				from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_videomae import (
								    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VideoMAEForPreTraining,
								    VideoMAEForVideoClassification,
								    VideoMAEModel,
								    VideoMAEPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_videomae import VideoMAEFeatureExtractor
								from .image_processing_videomae import VideoMAEImageProcessor

else:
				import sys

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



