
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__				:				Any						=      logging.get_logger(__name__)

lowercase__				:				str						=      [
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

lowercase__				:				Tuple						=      {
    "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__				:				Any						=      list("ARNDCQEGHILKMFPSTWYV")

lowercase__				:				str						=      restypes + ["X"]
lowercase__				:				Union[str, Any]						=      restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]




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



						'''simple docstring'''

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




def 						A_			(				snake_case				: int			,   snake_case				: Optional[Any]			)   ->     str:



						'''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 						A_			(				snake_case				: str			,   snake_case				: str			,   snake_case				: bool			,   snake_case				: str			,   snake_case				: str			)   ->     List[Any]:



						'''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(snake_case			,   snake_case			) and key != "trunk":
																								setattr(snake_case			,   snake_case			,   snake_case			)
												for key, val in esm.cfg.trunk.items():
																		if hasattr(esmfold_config.trunk			,   snake_case			) and key != "structure_module":
																								setattr(esmfold_config.trunk			,   snake_case			,   snake_case			)
												for key, val in esm.cfg.trunk.structure_module.items():
																		if hasattr(esmfold_config.trunk.structure_module			,   snake_case			):
																								setattr(esmfold_config.trunk.structure_module			,   snake_case			,   snake_case			)
						elif hasattr(snake_case			,   '''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=snake_case			,   hidden_size=snake_case			,   num_hidden_layers=snake_case			,   num_attention_heads=snake_case			,   intermediate_size=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=snake_case			,   emb_layer_norm_before=snake_case			,   token_dropout=snake_case			,   position_embedding_type=snake_case			,   is_folding_model=snake_case			,   esmfold_config=snake_case			,   vocab_list=snake_case			,   )
						if classification_head:
												__UpperCamelCase  	=      esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
						print('''Our ESM config:'''			,   snake_case			)

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

												__UpperCamelCase  	=      hf_tokenizer([row[1] for row in sample_data]			,   return_tensors='''pt'''			,   padding=snake_case			)
												__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(**snake_case			,   output_hidden_states=snake_case			)
																		__UpperCamelCase  	=      our_output['''logits''']
																		if classification_head:
																								__UpperCamelCase  	=      esm.model.classification_heads['''mnli'''](esm.extract_features(snake_case			)			)
																		else:
																								__UpperCamelCase  	=      esm(hf_tokens['''input_ids''']			,   repr_layers=list(range(999			)			)			)
																								__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(snake_case			,   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
																		__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(snake_case			,   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(snake_case			).mkdir(parents=snake_case			,   exist_ok=snake_case			)
												print(f"Saving model to {pytorch_dump_folder_path}"			)
												model.save_pretrained(snake_case			)

												del esm  # Free up some memory before continuing

						print(f"Saving tokenizer to {pytorch_dump_folder_path}"			)
						hf_tokenizer.save_pretrained(snake_case			)

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


if __name__ == "__main__":
				lowercase__				:				Tuple						=      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__				:				Any						=      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 bsa
import requests




def 							_SCREAMING_SNAKE_CASE	(					A	:							bsa.element.ResultSet	)     ->			dict[str, str]:







   """simple docstring"""
   return {
       "name": soup.ha.a.text,
       "genre": soup.find('span'	,		class_='genre'	).text.strip(),
       "rating": soup.strong.text,
       "page_link": F"""https://www.imdb.com{soup.a.get("href"	)}""",
   }




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 5	)     ->			tuple:







   """simple docstring"""
   __snake_case					:   Optional[int]  				=			int(float(A	)	)
   if num_movies < 1:
      return ()
   __snake_case					:   Union[str, Any]  				=			(
       'https://www.imdb.com/search/title?title_type='
       F"""feature&sort=num_votes,desc&count={num_movies}"""
   )
   __snake_case					:   List[Any]  				=			bsa.BeautifulSoup(requests.get(A	).content	,		'html.parser'	)
   return tuple(
       get_movie_data_from_soup(A	)
       for movie in source.find_all('div'	,		class_='lister-item mode-advanced'	)	)


if __name__ == "__main__":
      import json

      __A     						= int(input('''How many movies would you like to see? '''))
      print(
          ''', '''.join(
              json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
          )
      )