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

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

UpperCamelCase     					=     {
    '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,
}

UpperCamelCase     					=     list('ARNDCQEGHILKMFPSTWYV')

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

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

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Dict		,			_SCREAMING_SNAKE_CASE	:Optional[int]  )				-> List[Any]:
						a_							:			Any     		=					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_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:bool		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:str  )				-> Dict:
						if model.startswith("esmfold"  ):
												a_							:			Union[str, Any]     		=					MODEL_MAPPING[model]()
						else:
												a_		,					a_							:			List[Any]     		=					MODEL_MAPPING[model]()
						esm.eval()  # disable dropout

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

						if is_folding_model:
												a_							:			Any     		=					esm.esm.alphabet
						a_							:			Union[str, Any]     		=					tuple(alphabet.all_toks  )
						a_							:			Union[str, Any]     		=					alphabet.mask_idx
						a_							:			List[Any]     		=					alphabet.padding_idx

						if is_folding_model:
												a_							:			Union[str, Any]     		=					esm.esm
						else:
												a_							:			List[Any]     		=					esm

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

						if model.startswith("esmfold"  ):
												a_							:			Optional[Any]     		=					EsmForProteinFolding
						elif classification_head:
												a_							:			Any     		=					EsmForSequenceClassification
						else:
												a_							:			Any     		=					EsmForMaskedLM
						a_							:			Optional[int]     		=					model_class(_SCREAMING_SNAKE_CASE  )
						model.eval()

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

						if config.emb_layer_norm_before:
												a_							:			Optional[Any]     		=					original_esm_model.emb_layer_norm_before.weight
												a_							:			int     		=					original_esm_model.emb_layer_norm_before.bias

						a_							:			Optional[Any]     		=					original_esm_model.emb_layer_norm_after.weight
						a_							:			Optional[Any]     		=					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_							:			Tuple     		=					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_							:			str     		=					esm_layer.self_attn.q_proj.weight
												a_							:			Any     		=					esm_layer.self_attn.q_proj.bias
												a_							:			List[str]     		=					esm_layer.self_attn.k_proj.weight
												a_							:			List[Any]     		=					esm_layer.self_attn.k_proj.bias
												a_							:			List[Any]     		=					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"		,			_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.
																		a_							:			Dict     		=					esm_layer.self_attn.rot_emb.inv_freq

												# LayerNorm changes for pre-activation
												a_							:			Tuple     		=					esm_layer.self_attn_layer_norm.weight
												a_							:			str     		=					esm_layer.self_attn_layer_norm.bias
												a_							:			str     		=					esm_layer.final_layer_norm.weight
												a_							:			Optional[int]     		=					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_							:			Tuple     		=					esm_layer.self_attn.out_proj.weight
												a_							:			Optional[Any]     		=					esm_layer.self_attn.out_proj.bias

												# intermediate
												a_							:			EsmIntermediate     		=					layer.intermediate
												assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
												a_							:			int     		=					esm_layer.fca.weight
												a_							:			Dict     		=					esm_layer.fca.bias

												# output
												a_							:			EsmOutput     		=					layer.output
												assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
												a_							:			Tuple     		=					esm_layer.fca.weight
												a_							:			Tuple     		=					esm_layer.fca.bias
												# end of layer

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

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

												a_							:			Tuple     		=					hf_tokenizer([row[1] for row in sample_data]		,			return_tensors="pt"		,			padding=_SCREAMING_SNAKE_CASE  )
												a_							:			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_							:			str     		=					esm.cuda().infer([row[1] for row in sample_data]  )
																		a_							:			Optional[int]     		=					model.cuda()(
																		    input_ids=hf_tokens["input_ids"].cuda()		,			attention_mask=hf_tokens["attention_mask"].cuda()  )
												else:
																		a_							:			Dict     		=					model(**_SCREAMING_SNAKE_CASE		,			output_hidden_states=_SCREAMING_SNAKE_CASE  )
																		a_							:			Dict     		=					our_output["logits"]
																		if classification_head:
																								a_							:			Union[str, Any]     		=					esm.model.classification_heads["mnli"](esm.extract_features(_SCREAMING_SNAKE_CASE  )  )
																		else:
																								a_							:			Any     		=					esm(hf_tokens["input_ids"]		,			repr_layers=list(range(999  )  )  )
																								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_							:			List[str]     		=					torch.allclose(our_output["positions"]		,			their_output["positions"]		,			atol=1E-5  )
												else:
																		a_							:			List[Any]     		=					torch.max(torch.abs(our_output - their_output  )  ).item()
																		a_							:			Tuple     		=					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
																		a_							:			Any     		=					model.predict_contacts(hf_tokens["input_ids"]		,			hf_tokens["attention_mask"]  )
																		a_							:			Union[str, Any]     		=					esm.predict_contacts(hf_tokens["input_ids"]  )
																		a_							:			int     		=					torch.max(torch.abs(our_output - their_output  )  ).item()
																		a_							:			Tuple     		=					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__":
				UpperCamelCase     					=     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.')
				UpperCamelCase     					=     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

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




