



'''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()
SCREAMING_SNAKE_CASE__     : Any				=   logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__     : Any				=   {
    """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,
}

SCREAMING_SNAKE_CASE__     : Tuple				=   list("""ARNDCQEGHILKMFPSTWYV""")

SCREAMING_SNAKE_CASE__     : Dict				=   restypes + ["""X"""]
SCREAMING_SNAKE_CASE__     : int				=   restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]







def  SCREAMING_SNAKE_CASE__ (				):
		with TemporaryDirectory() as tempdir:
				SCREAMING_SNAKE_CASE_						:int										=      '\n'.join(SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      Path(SCREAMING_SNAKE_CASE     ) / 'vocab.txt'
				vocab_file.write_text(SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:Dict										=      EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE     )     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      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     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      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'     ):
				SCREAMING_SNAKE_CASE_						:int										=      MODEL_MAPPING[model]()
		else:
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

		if model.startswith('esmfold'     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      esm.esm.embed_dim
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.esm.num_layers
				SCREAMING_SNAKE_CASE_						:List[Any]										=      esm.esm.attention_heads
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      4 * embed_dim
				SCREAMING_SNAKE_CASE_						:str										=      esm.esm.token_dropout
				SCREAMING_SNAKE_CASE_						:str										=      False  # This code path does not exist in ESM-2
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      'rotary'
				SCREAMING_SNAKE_CASE_						:List[str]										=      True
				SCREAMING_SNAKE_CASE_						:List[str]										=      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
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      esm.args.embed_dim
				SCREAMING_SNAKE_CASE_						:Any										=      esm.args.layers
				SCREAMING_SNAKE_CASE_						:str										=      esm.args.attention_heads
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.args.ffn_embed_dim
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      esm.args.token_dropout
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      True if esm.emb_layer_norm_before else False
				SCREAMING_SNAKE_CASE_						:Dict										=      'absolute'
				SCREAMING_SNAKE_CASE_						:int										=      False
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      None
		else:
				# Indicates an ESM-2 model
				SCREAMING_SNAKE_CASE_						:Any										=      esm.embed_dim
				SCREAMING_SNAKE_CASE_						:List[str]										=      esm.num_layers
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm.attention_heads
				SCREAMING_SNAKE_CASE_						:Any										=      4 * embed_dim  # This is hardcoded in ESM-2
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm.token_dropout
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      False  # This code path does not exist in ESM-2
				SCREAMING_SNAKE_CASE_						:List[Any]										=      'rotary'
				SCREAMING_SNAKE_CASE_						:List[Any]										=      False
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      None

		if is_folding_model:
				SCREAMING_SNAKE_CASE_						:int										=      esm.esm.alphabet
		SCREAMING_SNAKE_CASE_						:List[Any]										=      tuple(alphabet.all_toks     )
		SCREAMING_SNAKE_CASE_						:Dict										=      alphabet.mask_idx
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      alphabet.padding_idx

		if is_folding_model:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.esm
		else:
				SCREAMING_SNAKE_CASE_						:Tuple										=      esm

		SCREAMING_SNAKE_CASE_						:List[Any]										=      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:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.classification_heads['mnli'].out_proj.weight.shape[0]
		print('Our ESM config:' ,		SCREAMING_SNAKE_CASE     )

		if model.startswith('esmfold'     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      EsmForProteinFolding
		elif classification_head:
				SCREAMING_SNAKE_CASE_						:Tuple										=      EsmForSequenceClassification
		else:
				SCREAMING_SNAKE_CASE_						:int										=      EsmForMaskedLM
		SCREAMING_SNAKE_CASE_						:List[Any]										=      model_class(SCREAMING_SNAKE_CASE     )
		model.eval()

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

		if config.emb_layer_norm_before:
				SCREAMING_SNAKE_CASE_						:Tuple										=      original_esm_model.emb_layer_norm_before.weight
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      original_esm_model.emb_layer_norm_before.bias

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      original_esm_model.emb_layer_norm_after.weight
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      original_esm_model.emb_layer_norm_after.bias

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

				# self attention
				SCREAMING_SNAKE_CASE_						:EsmSelfAttention										=      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_						:Optional[Any]										=      esm_layer.self_attn.q_proj.weight
				SCREAMING_SNAKE_CASE_						:str										=      esm_layer.self_attn.q_proj.bias
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.self_attn.k_proj.weight
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.self_attn.k_proj.bias
				SCREAMING_SNAKE_CASE_						:List[str]										=      esm_layer.self_attn.v_proj.weight
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      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.
						SCREAMING_SNAKE_CASE_						:Any										=      esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				SCREAMING_SNAKE_CASE_						:List[str]										=      esm_layer.self_attn_layer_norm.weight
				SCREAMING_SNAKE_CASE_						:str										=      esm_layer.self_attn_layer_norm.bias
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      esm_layer.final_layer_norm.weight
				SCREAMING_SNAKE_CASE_						:Any										=      esm_layer.final_layer_norm.bias

				# self-attention output
				SCREAMING_SNAKE_CASE_						:EsmSelfOutput										=      layer.attention.output
				assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm_layer.self_attn.out_proj.weight
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm_layer.self_attn.out_proj.bias

				# intermediate
				SCREAMING_SNAKE_CASE_						:EsmIntermediate										=      layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.fca.weight
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm_layer.fca.bias

				# output
				SCREAMING_SNAKE_CASE_						:EsmOutput										=      layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE_						:List[Any]										=      esm_layer.fca.weight
				SCREAMING_SNAKE_CASE_						:Any										=      esm_layer.fca.bias
				# end of layer

		if is_folding_model:
				SCREAMING_SNAKE_CASE_						:List[Any]										=      esm.esm_s_combine.data
				SCREAMING_SNAKE_CASE_						:List[Any]										=      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_						:Optional[Any]										=      esm.esm.classification_heads['mnli'].dense.weight
				SCREAMING_SNAKE_CASE_						:Tuple										=      esm.classification_heads['mnli'].dense.bias
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      esm.classification_heads['mnli'].out_proj.weight
				SCREAMING_SNAKE_CASE_						:int										=      esm.classification_heads['mnli'].out_proj.bias
		else:
				# LM Head
				SCREAMING_SNAKE_CASE_						:Dict										=      esm.lm_head.dense.weight
				SCREAMING_SNAKE_CASE_						:str										=      esm.lm_head.dense.bias
				SCREAMING_SNAKE_CASE_						:str										=      esm.lm_head.layer_norm.weight
				SCREAMING_SNAKE_CASE_						:Any										=      esm.lm_head.layer_norm.bias
				SCREAMING_SNAKE_CASE_						:Any										=      esm.lm_head.weight
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      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_						:Any										=      SAMPLE_DATA[:2]
		else:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      SAMPLE_DATA

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

				SCREAMING_SNAKE_CASE_						:List[str]										=      hf_tokenizer([row[1] for row in sample_data] ,		return_tensors='pt' ,		padding=SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:int										=      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_						:Any										=      esm.cuda().infer([row[1] for row in sample_data]     )
						SCREAMING_SNAKE_CASE_						:List[Any]										=      model.cuda()(
						    input_ids=hf_tokens['input_ids'].cuda() ,		attention_mask=hf_tokens['attention_mask'].cuda()     )
				else:
						SCREAMING_SNAKE_CASE_						:Tuple										=      model(**SCREAMING_SNAKE_CASE ,		output_hidden_states=SCREAMING_SNAKE_CASE     )
						SCREAMING_SNAKE_CASE_						:str										=      our_output['logits']
						if classification_head:
								SCREAMING_SNAKE_CASE_						:List[str]										=      esm.model.classification_heads['mnli'](esm.extract_features(SCREAMING_SNAKE_CASE     )     )
						else:
								SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm(hf_tokens['input_ids'] ,		repr_layers=list(range(999     )     )     )
								SCREAMING_SNAKE_CASE_						:Optional[int]										=      their_output['logits']

				if is_folding_model:
						SCREAMING_SNAKE_CASE_						:Dict										=      torch.max(torch.abs(our_output['positions'] - their_output['positions']     )     ).item()
						SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      torch.allclose(our_output['positions'] ,		their_output['positions'] ,		atol=1E-5     )
				else:
						SCREAMING_SNAKE_CASE_						:str										=      torch.max(torch.abs(our_output - their_output     )     ).item()
						SCREAMING_SNAKE_CASE_						:str										=      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
						SCREAMING_SNAKE_CASE_						:List[Any]										=      model.predict_contacts(hf_tokens['input_ids'] ,		hf_tokens['attention_mask']     )
						SCREAMING_SNAKE_CASE_						:Tuple										=      esm.predict_contacts(hf_tokens['input_ids']     )
						SCREAMING_SNAKE_CASE_						:int										=      torch.max(torch.abs(our_output - their_output     )     ).item()
						SCREAMING_SNAKE_CASE_						:str										=      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__":
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   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.""")
							SCREAMING_SNAKE_CASE__     : 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 argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


A							     = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


A							     = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
def 	snake_case_							(				_a  :				str      ):
      snake_case_    							=				BertTokenizer.from_pretrained("bert-base-uncased"			,       do_lower_case=_a      )
      snake_case_    							=				BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"      )
      model.to(args.device      )
      model.eval()

      snake_case_    							=				{
          "BOS": tokenizer.vocab["[unused0]"],
          "EOS": tokenizer.vocab["[unused1]"],
          "PAD": tokenizer.vocab["[PAD]"],
      }

      if args.compute_rouge:
            snake_case_    							=				[]
            snake_case_    							=				[]

            import nltk
            import rouge

            nltk.download("punkt"      )
            snake_case_    							=				rouge.Rouge(
                metrics=["rouge-n", "rouge-l"]			,       max_n=2			,       limit_length=_a			,       length_limit=args.beam_size			,       length_limit_type="words"			,       apply_avg=_a			,       apply_best=_a			,       alpha=0.5			,       weight_factor=1.2			,       stemming=_a			,       )

      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      snake_case_    							=				""
      snake_case_    							=				""

      snake_case_    							=				build_data_iterator(_a			,       _a      )
      snake_case_    							=				build_predictor(_a			,       _a			,       _a			,       _a      )

      logger.info("***** Running evaluation *****"      )
      logger.info("  Number examples = %d"			,       len(data_iterator.dataset      )      )
      logger.info("  Batch size = %d"			,       args.batch_size      )
      logger.info(""      )
      logger.info("***** Beam Search parameters *****"      )
      logger.info("  Beam size = %d"			,       args.beam_size      )
      logger.info("  Minimum length = %d"			,       args.min_length      )
      logger.info("  Maximum length = %d"			,       args.max_length      )
      logger.info("  Alpha (length penalty) = %.2f"			,       args.alpha      )
      logger.info("  Trigrams %s be blocked"			,       ("will" if args.block_trigram else "will NOT")      )

      for batch in tqdm(_a      ):
            snake_case_    							=				predictor.translate_batch(_a      )
            snake_case_    							=				predictor.from_batch(_a      )
            snake_case_    							=				[format_summary(_a      ) for t in translations]
            save_summaries(_a			,       args.summaries_output_dir			,       batch.document_names      )

            if args.compute_rouge:
                  reference_summaries += batch.tgt_str
                  generated_summaries += summaries

      if args.compute_rouge:
            snake_case_    							=				rouge_evaluator.get_scores(_a			,       _a      )
            snake_case_    							=				format_rouge_scores(_a      )
            save_rouge_scores(_a      )
            print(_a      )
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				List[str]			,       _a  :				Any      ):
      for summary, document_name in zip(_a			,       _a      ):
            # Prepare the summary file's name
            if "." in document_name:
                  snake_case_    							=				".".join(document_name.split("."      )[:-1]      )
                  snake_case_    							=				document_name.split("."      )[-1]
                  snake_case_    							=				bare_document_name + "_summary." + extension
            else:
                  snake_case_    							=				document_name + "_summary"

            snake_case_    							=				os.path.join(_a			,       _a      )
            with open(_a			,       "w"      ) as output:
                  output.write(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				translation
      snake_case_    							=				(
          raw_summary.replace("[unused0]"			,       ""      )
          .replace("[unused3]"			,       ""      )
          .replace("[PAD]"			,       ""      )
          .replace("[unused1]"			,       ""      )
          .replace(R" +"			,       " "      )
          .replace(" [unused2] "			,       ". "      )
          .replace("[unused2]"			,       ""      )
          .strip()
      )

      return summary
def 	snake_case_							(				_a  :				str      ):
      return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
          scores["rouge-1"]["f"]			,       scores["rouge-1"]["p"]			,       scores["rouge-1"]["r"]			,       scores["rouge-2"]["f"]			,       scores["rouge-2"]["p"]			,       scores["rouge-2"]["r"]			,       scores["rouge-l"]["f"]			,       scores["rouge-l"]["p"]			,       scores["rouge-l"]["r"]			,       )
def 	snake_case_							(				_a  :				str      ):
      with open("rouge_scores.txt"			,       "w"      ) as output:
            output.write(_a      )
def 	snake_case_							(				_a  :				Tuple			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				load_and_cache_examples(_a			,       _a      )
      snake_case_    							=				SequentialSampler(_a      )

      def collate_fn(_a  :				Tuple      ):
            return collate(_a			,       _a			,       block_size=512			,       device=args.device      )

      snake_case_    							=				DataLoader(
          _a			,       sampler=_a			,       batch_size=args.batch_size			,       collate_fn=_a			,       )

      return iterator
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
      snake_case_    							=				CNNDMDataset(args.documents_dir      )
      return dataset
def 	snake_case_							(				_a  :				int			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				[x for x in data if not len(x[1]      ) == 0]  # remove empty_files
      snake_case_    							=				[name for name, _, _ in data]
      snake_case_    							=				[" ".join(_a      ) for _, _, summary_list in data]

      snake_case_    							=				[encode_for_summarization(_a			,       _a			,       _a      ) for _, story, summary in data]
      snake_case_    							=				torch.tensor(
          [truncate_or_pad(_a			,       _a			,       tokenizer.pad_token_id      ) for story, _ in encoded_text]      )
      snake_case_    							=				compute_token_type_ids(_a			,       tokenizer.cls_token_id      )
      snake_case_    							=				build_mask(_a			,       tokenizer.pad_token_id      )

      snake_case_    							=				Batch(
          document_names=_a			,       batch_size=len(_a      )			,       src=encoded_stories.to(_a      )			,       segs=encoder_token_type_ids.to(_a      )			,       mask_src=encoder_mask.to(_a      )			,       tgt_str=_a			,       )

      return batch
def 	snake_case_							(				_a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				summary_tokens.to("cpu"      ).numpy()
      snake_case_    							=				tokenizer.decode(_a      )
      snake_case_    							=				summary.split("."      )
      snake_case_    							=				[s + "." for s in sentences]
      return sentences
def 	snake_case_							(				):
      snake_case_    							=				argparse.ArgumentParser()
      parser.add_argument(
          "--documents_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder where the documents to summarize are located."			,       )
      parser.add_argument(
          "--summaries_output_dir"			,       default=_a			,       type=_a			,       required=_a			,       help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"			,       )
      parser.add_argument(
          "--compute_rouge"			,       default=_a			,       type=_a			,       required=_a			,       help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."			,       )
      # EVALUATION options
      parser.add_argument(
          "--no_cuda"			,       default=_a			,       type=_a			,       help="Whether to force the execution on CPU."			,       )
      parser.add_argument(
          "--batch_size"			,       default=4			,       type=_a			,       help="Batch size per GPU/CPU for training."			,       )
      # BEAM SEARCH arguments
      parser.add_argument(
          "--min_length"			,       default=50			,       type=_a			,       help="Minimum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--max_length"			,       default=200			,       type=_a			,       help="Maixmum number of tokens for the summaries."			,       )
      parser.add_argument(
          "--beam_size"			,       default=5			,       type=_a			,       help="The number of beams to start with for each example."			,       )
      parser.add_argument(
          "--alpha"			,       default=0.9_5			,       type=_a			,       help="The value of alpha for the length penalty in the beam search."			,       )
      parser.add_argument(
          "--block_trigram"			,       default=_a			,       type=_a			,       help="Whether to block the existence of repeating trigrams in the text generated by beam search."			,       )
      snake_case_    							=				parser.parse_args()

      # Select device (distibuted not available)
      snake_case_    							=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )

      # Check the existence of directories
      if not args.summaries_output_dir:
            snake_case_    							=				args.documents_dir

      if not documents_dir_is_valid(args.documents_dir      ):
            raise FileNotFoundError(
                "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
                " specify a valid path."      )
      os.makedirs(args.summaries_output_dir			,       exist_ok=_a      )

      evaluate(_a      )
def 	snake_case_							(				_a  :				Optional[int]      ):
      if not os.path.exists(_a      ):
            return False

      snake_case_    							=				os.listdir(_a      )
      if len(_a      ) == 0:
            return False

      return True


if __name__ == "__main__":
     main()

