
"""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		:    Tuple			=       logging.get_logger(__name__)

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

lowercase		:    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,
}

lowercase		:    str			=       list('ARNDCQEGHILKMFPSTWYV')

lowercase		:    Optional[int]			=       restypes + ['X']
lowercase		:    str			=       restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']


def      lowerCamelCase_     (						)  ->      Tuple:


					'''simple docstring'''
					with TemporaryDirectory() as tempdir:
										__a:    Tuple		     =       """\n""".join(lowerCamelCase_			)
										__a:    Dict		     =       Path(lowerCamelCase_			) / """vocab.txt"""
										vocab_file.write_text(lowerCamelCase_			)
										__a:    str		     =       EsmTokenizer(vocab_file=str(lowerCamelCase_			)			)
					__a:    List[Any]		     =       0  # Overlaps with 'A' but that seems to be what they want
					return hf_tokenizer


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_			)  ->      Optional[int]:


					'''simple docstring'''
					__a:    Optional[int]		     =       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_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)  ->      Optional[Any]:


					'''simple docstring'''
					if model.startswith("""esmfold"""			):
										__a:    int		     =       MODEL_MAPPING[model]()
					else:
										__a     ,					__a:    int		     =       MODEL_MAPPING[model]()
					esm.eval()  # disable dropout

					if model.startswith("""esmfold"""			):
										__a:    Optional[Any]		     =       esm.esm.embed_dim
										__a:    str		     =       esm.esm.num_layers
										__a:    Optional[Any]		     =       esm.esm.attention_heads
										__a:    Dict		     =       4 * embed_dim
										__a:    Tuple		     =       esm.esm.token_dropout
										__a:    Any		     =       False  # This code path does not exist in ESM-2
										__a:    Dict		     =       """rotary"""
										__a:    Any		     =       True
										__a:    Tuple		     =       EsmFoldConfig()
										for key, val in esm.cfg.items():
															if hasattr(lowerCamelCase_						,					lowerCamelCase_			) and key != "trunk":
																				setattr(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_			)
										for key, val in esm.cfg.trunk.items():
															if hasattr(esmfold_config.trunk						,					lowerCamelCase_			) and key != "structure_module":
																				setattr(esmfold_config.trunk						,					lowerCamelCase_						,					lowerCamelCase_			)
										for key, val in esm.cfg.trunk.structure_module.items():
															if hasattr(esmfold_config.trunk.structure_module						,					lowerCamelCase_			):
																				setattr(esmfold_config.trunk.structure_module						,					lowerCamelCase_						,					lowerCamelCase_			)
					elif hasattr(lowerCamelCase_						,					"""args"""			):
										# Indicates an ESM-1b or ESM-1v model
										__a:    List[str]		     =       esm.args.embed_dim
										__a:    List[str]		     =       esm.args.layers
										__a:    str		     =       esm.args.attention_heads
										__a:    int		     =       esm.args.ffn_embed_dim
										__a:    Tuple		     =       esm.args.token_dropout
										__a:    List[str]		     =       True if esm.emb_layer_norm_before else False
										__a:    str		     =       """absolute"""
										__a:    List[str]		     =       False
										__a:    Optional[int]		     =       None
					else:
										# Indicates an ESM-2 model
										__a:    Union[str, Any]		     =       esm.embed_dim
										__a:    Tuple		     =       esm.num_layers
										__a:    Dict		     =       esm.attention_heads
										__a:    Optional[Any]		     =       4 * embed_dim  # This is hardcoded in ESM-2
										__a:    str		     =       esm.token_dropout
										__a:    Tuple		     =       False  # This code path does not exist in ESM-2
										__a:    List[str]		     =       """rotary"""
										__a:    Dict		     =       False
										__a:    Dict		     =       None

					if is_folding_model:
										__a:    Optional[Any]		     =       esm.esm.alphabet
					__a:    Dict		     =       tuple(alphabet.all_toks			)
					__a:    Optional[Any]		     =       alphabet.mask_idx
					__a:    str		     =       alphabet.padding_idx

					if is_folding_model:
										__a:    Tuple		     =       esm.esm
					else:
										__a:    str		     =       esm

					__a:    Any		     =       EsmConfig(
					    vocab_size=original_esm_model.embed_tokens.num_embeddings						,					mask_token_id=lowerCamelCase_						,					hidden_size=lowerCamelCase_						,					num_hidden_layers=lowerCamelCase_						,					num_attention_heads=lowerCamelCase_						,					intermediate_size=lowerCamelCase_						,					max_position_embeddings=10_26						,					layer_norm_eps=1e-5						,					attention_probs_dropout_prob=0.0						,					hidden_dropout_prob=0.0						,					pad_token_id=lowerCamelCase_						,					emb_layer_norm_before=lowerCamelCase_						,					token_dropout=lowerCamelCase_						,					position_embedding_type=lowerCamelCase_						,					is_folding_model=lowerCamelCase_						,					esmfold_config=lowerCamelCase_						,					vocab_list=lowerCamelCase_						,					)
					if classification_head:
										__a:    Optional[int]		     =       esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
					print("""Our ESM config:"""						,					lowerCamelCase_			)

					if model.startswith("""esmfold"""			):
										__a:    str		     =       EsmForProteinFolding
					elif classification_head:
										__a:    str		     =       EsmForSequenceClassification
					else:
										__a:    List[Any]		     =       EsmForMaskedLM
					__a:    int		     =       model_class(lowerCamelCase_			)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					__a:    List[Any]		     =       original_esm_model.embed_tokens.weight
					if position_embedding_type == "absolute":
										__a:    Dict		     =       original_esm_model.embed_positions.weight

					if config.emb_layer_norm_before:
										__a:    Dict		     =       original_esm_model.emb_layer_norm_before.weight
										__a:    List[str]		     =       original_esm_model.emb_layer_norm_before.bias

					__a:    List[Any]		     =       original_esm_model.emb_layer_norm_after.weight
					__a:    Optional[int]		     =       original_esm_model.emb_layer_norm_after.bias

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

										# self attention
										__a:    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)			)
										)

										__a:    List[str]		     =       esm_layer.self_attn.q_proj.weight
										__a:    Tuple		     =       esm_layer.self_attn.q_proj.bias
										__a:    Dict		     =       esm_layer.self_attn.k_proj.weight
										__a:    Any		     =       esm_layer.self_attn.k_proj.bias
										__a:    Dict		     =       esm_layer.self_attn.v_proj.weight
										__a:    List[Any]		     =       esm_layer.self_attn.v_proj.bias

										if getattr(esm_layer.self_attn						,					"""rot_emb"""						,					lowerCamelCase_			) 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.
															__a:    Dict		     =       esm_layer.self_attn.rot_emb.inv_freq

										# LayerNorm changes for pre-activation
										__a:    Dict		     =       esm_layer.self_attn_layer_norm.weight
										__a:    Dict		     =       esm_layer.self_attn_layer_norm.bias
										__a:    Dict		     =       esm_layer.final_layer_norm.weight
										__a:    Tuple		     =       esm_layer.final_layer_norm.bias

										# self-attention output
										__a:    EsmSelfOutput		     =       layer.attention.output
										assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
										__a:    List[str]		     =       esm_layer.self_attn.out_proj.weight
										__a:    str		     =       esm_layer.self_attn.out_proj.bias

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

										# output
										__a:    EsmOutput		     =       layer.output
										assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
										__a:    int		     =       esm_layer.fca.weight
										__a:    str		     =       esm_layer.fca.bias
										# end of layer

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

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

										__a:    Dict		     =       hf_tokenizer([row[1] for row in sample_data]						,					return_tensors="""pt"""						,					padding=lowerCamelCase_			)
										__a:    List[Any]		     =       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.
															__a:    Tuple		     =       esm.cuda().infer([row[1] for row in sample_data]			)
															__a:    Dict		     =       model.cuda()(
															    input_ids=hf_tokens["""input_ids"""].cuda()						,					attention_mask=hf_tokens["""attention_mask"""].cuda()			)
										else:
															__a:    int		     =       model(**lowerCamelCase_						,					output_hidden_states=lowerCamelCase_			)
															__a:    Optional[Any]		     =       our_output["""logits"""]
															if classification_head:
																				__a:    Optional[int]		     =       esm.model.classification_heads["""mnli"""](esm.extract_features(lowerCamelCase_			)			)
															else:
																				__a:    Any		     =       esm(hf_tokens["""input_ids"""]						,					repr_layers=list(range(9_99			)			)			)
																				__a:    Tuple		     =       their_output["""logits"""]

										if is_folding_model:
															__a:    Union[str, Any]		     =       torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]			)			).item()
															__a:    Union[str, Any]		     =       torch.allclose(our_output["""positions"""]						,					their_output["""positions"""]						,					atol=1e-5			)
										else:
															__a:    Dict		     =       torch.max(torch.abs(our_output - their_output			)			).item()
															__a:    Optional[Any]		     =       torch.allclose(lowerCamelCase_						,					lowerCamelCase_						,					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
															__a:    int		     =       model.predict_contacts(hf_tokens["""input_ids"""]						,					hf_tokens["""attention_mask"""]			)
															__a:    Optional[int]		     =       esm.predict_contacts(hf_tokens["""input_ids"""]			)
															__a:    Union[str, Any]		     =       torch.max(torch.abs(our_output - their_output			)			).item()
															__a:    int		     =       torch.allclose(lowerCamelCase_						,					lowerCamelCase_						,					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(lowerCamelCase_			).mkdir(parents=lowerCamelCase_						,					exist_ok=lowerCamelCase_			)
										print(f"""Saving model to {pytorch_dump_folder_path}"""			)
										model.save_pretrained(lowerCamelCase_			)

										del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
			lowercase		:    Union[str, 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.')
			lowercase		:    Optional[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 unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class  _snake_case	(    unittest.TestCase							):






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""     )






							@cached_property
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       self.tokenizer_ab
												__a:    Union[str, Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Optional[Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    str		     =       self.rust_tokenizer_ab
												__a:    Optional[Any]		     =       """ I am a small frog."""
												__a:    str		     =       tok([src_text]    ,			padding=snake_case__    ,			truncation=snake_case__     )["""input_ids"""]
												__a:    Union[str, Any]		     =       tok.batch_decode(snake_case__    ,			skip_special_tokens=snake_case__    ,			clean_up_tokenization_spaces=snake_case__     )[0]
												assert src_text == decoded






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												assert self.tokenizer_ab.add_prefix_space
												assert self.tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												assert self.rust_tokenizer_ab.add_prefix_space
												assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]     ).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]
