




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



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		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    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case				):
							if model.startswith('''esmfold'''				):
														lowerCAmelCase =		MODEL_MAPPING[model]()
							else:
														lowerCAmelCase =		MODEL_MAPPING[model]()
							esm.eval()  # disable dropout

							if model.startswith('''esmfold'''				):
														lowerCAmelCase =		esm.esm.embed_dim
														lowerCAmelCase =		esm.esm.num_layers
														lowerCAmelCase =		esm.esm.attention_heads
														lowerCAmelCase =		4 * embed_dim
														lowerCAmelCase =		esm.esm.token_dropout
														lowerCAmelCase =		False  # This code path does not exist in ESM-2
														lowerCAmelCase =		'''rotary'''
														lowerCAmelCase =		True
														lowerCAmelCase =		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
														lowerCAmelCase =		esm.args.embed_dim
														lowerCAmelCase =		esm.args.layers
														lowerCAmelCase =		esm.args.attention_heads
														lowerCAmelCase =		esm.args.ffn_embed_dim
														lowerCAmelCase =		esm.args.token_dropout
														lowerCAmelCase =		True if esm.emb_layer_norm_before else False
														lowerCAmelCase =		'''absolute'''
														lowerCAmelCase =		False
														lowerCAmelCase =		None
							else:
														# Indicates an ESM-2 model
														lowerCAmelCase =		esm.embed_dim
														lowerCAmelCase =		esm.num_layers
														lowerCAmelCase =		esm.attention_heads
														lowerCAmelCase =		4 * embed_dim  # This is hardcoded in ESM-2
														lowerCAmelCase =		esm.token_dropout
														lowerCAmelCase =		False  # This code path does not exist in ESM-2
														lowerCAmelCase =		'''rotary'''
														lowerCAmelCase =		False
														lowerCAmelCase =		None

							if is_folding_model:
														lowerCAmelCase =		esm.esm.alphabet
							lowerCAmelCase =		tuple(alphabet.all_toks				)
							lowerCAmelCase =		alphabet.mask_idx
							lowerCAmelCase =		alphabet.padding_idx

							if is_folding_model:
														lowerCAmelCase =		esm.esm
							else:
														lowerCAmelCase =		esm

							lowerCAmelCase =		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=1026    ,			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:
														lowerCAmelCase =		esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
							print('''Our ESM config:'''    ,			__lowerCamelCase				)

							if model.startswith('''esmfold'''				):
														lowerCAmelCase =		EsmForProteinFolding
							elif classification_head:
														lowerCAmelCase =		EsmForSequenceClassification
							else:
														lowerCAmelCase =		EsmForMaskedLM
							lowerCAmelCase =		model_class(__lowerCamelCase				)
							model.eval()

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

							if config.emb_layer_norm_before:
														lowerCAmelCase =		original_esm_model.emb_layer_norm_before.weight
														lowerCAmelCase =		original_esm_model.emb_layer_norm_before.bias

							lowerCAmelCase =		original_esm_model.emb_layer_norm_after.weight
							lowerCAmelCase =		original_esm_model.emb_layer_norm_after.bias

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

														# self attention
														lowerCAmelCase =		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)				)
														)

														lowerCAmelCase =		esm_layer.self_attn.q_proj.weight
														lowerCAmelCase =		esm_layer.self_attn.q_proj.bias
														lowerCAmelCase =		esm_layer.self_attn.k_proj.weight
														lowerCAmelCase =		esm_layer.self_attn.k_proj.bias
														lowerCAmelCase =		esm_layer.self_attn.v_proj.weight
														lowerCAmelCase =		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.
																					lowerCAmelCase =		esm_layer.self_attn.rot_emb.inv_freq

														# LayerNorm changes for pre-activation
														lowerCAmelCase =		esm_layer.self_attn_layer_norm.weight
														lowerCAmelCase =		esm_layer.self_attn_layer_norm.bias
														lowerCAmelCase =		esm_layer.final_layer_norm.weight
														lowerCAmelCase =		esm_layer.final_layer_norm.bias

														# self-attention output
														lowerCAmelCase =		layer.attention.output
														assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
														lowerCAmelCase =		esm_layer.self_attn.out_proj.weight
														lowerCAmelCase =		esm_layer.self_attn.out_proj.bias

														# intermediate
														lowerCAmelCase =		layer.intermediate
														assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
														lowerCAmelCase =		esm_layer.fca.weight
														lowerCAmelCase =		esm_layer.fca.bias

														# output
														lowerCAmelCase =		layer.output
														assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
														lowerCAmelCase =		esm_layer.fca.weight
														lowerCAmelCase =		esm_layer.fca.bias
														# end of layer

							if is_folding_model:
														lowerCAmelCase =		esm.esm_s_combine.data
														lowerCAmelCase =		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:
														lowerCAmelCase =		esm.esm.classification_heads['''mnli'''].dense.weight
														lowerCAmelCase =		esm.classification_heads['''mnli'''].dense.bias
														lowerCAmelCase =		esm.classification_heads['''mnli'''].out_proj.weight
														lowerCAmelCase =		esm.classification_heads['''mnli'''].out_proj.bias
							else:
														# LM Head
														lowerCAmelCase =		esm.lm_head.dense.weight
														lowerCAmelCase =		esm.lm_head.dense.bias
														lowerCAmelCase =		esm.lm_head.layer_norm.weight
														lowerCAmelCase =		esm.lm_head.layer_norm.bias
														lowerCAmelCase =		esm.lm_head.weight
														lowerCAmelCase =		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.
														lowerCAmelCase =		SAMPLE_DATA[:2]
							else:
														lowerCAmelCase =		SAMPLE_DATA

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

														lowerCAmelCase =		hf_tokenizer([row[1] for row in sample_data]    ,			return_tensors='''pt'''    ,			padding=__lowerCamelCase				)
														lowerCAmelCase =		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.
																					lowerCAmelCase =		esm.cuda().infer([row[1] for row in sample_data]				)
																					lowerCAmelCase =		model.cuda()(
																					    input_ids=hf_tokens['''input_ids'''].cuda()    ,			attention_mask=hf_tokens['''attention_mask'''].cuda()				)
														else:
																					lowerCAmelCase =		model(**__lowerCamelCase    ,			output_hidden_states=__lowerCamelCase				)
																					lowerCAmelCase =		our_output['''logits''']
																					if classification_head:
																												lowerCAmelCase =		esm.model.classification_heads['''mnli'''](esm.extract_features(__lowerCamelCase				)				)
																					else:
																												lowerCAmelCase =		esm(hf_tokens['''input_ids''']    ,			repr_layers=list(range(999				)				)				)
																												lowerCAmelCase =		their_output['''logits''']

														if is_folding_model:
																					lowerCAmelCase =		torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']				)				).item()
																					lowerCAmelCase =		torch.allclose(our_output['''positions''']    ,			their_output['''positions''']    ,			atol=1E-5				)
														else:
																					lowerCAmelCase =		torch.max(torch.abs(our_output - their_output				)				).item()
																					lowerCAmelCase =		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
																					lowerCAmelCase =		model.predict_contacts(hf_tokens['''input_ids''']    ,			hf_tokens['''attention_mask''']				)
																					lowerCAmelCase =		esm.predict_contacts(hf_tokens['''input_ids''']				)
																					lowerCAmelCase =		torch.max(torch.abs(our_output - their_output				)				).item()
																					lowerCAmelCase =		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__":
		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 timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


_lowercase					:					Optional[Any] 		=		logging.get_logger(__name__)

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				"PyTorch"



					@property
					def 						snake_case			(			self				:    Dict	)->   List[str]:
								return torch.__version__



					def 						snake_case			(			self				:    Optional[Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_inference	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						List[Any]						      =self._prepare_inference_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_inference	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   float:
								lowerCamelCase__						:						int						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_speed(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   [Memory, Optional[MemorySummary]]:
								lowerCamelCase__						:						str						      =self._prepare_train_func(lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								return self._measure_memory(_train	)



					def 						snake_case			(			self				:    List[str],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						List[str]						      =self.config_dict[model_name]

								if self.args.torchscript:
											lowerCamelCase__						:						int						      =True

								lowerCamelCase__						:						Optional[Any]						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						int						      =config.architectures[0]
														lowerCamelCase__						:						Dict						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						str						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Tuple						      =model_cls(lowerCamelCase	)
											except ImportError:
														raise ImportError(
														    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
								else:
											lowerCamelCase__						:						List[str]						      =MODEL_MAPPING[config.__class__](lowerCamelCase	)

								model.eval()
								model.to(self.args.device	)

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Optional[Any]						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (batch_size, sequence_length),    dtype=torch.long,    device=self.args.device	)

								if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''	)
											if not self.args.is_gpu:
														raise ValueError('''Mixed precision is possible only for GPU.'''	)
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

								if self.args.torchscript:
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =torch.jit.trace(lowerCamelCase,    lowerCamelCase	)
								else:
											lowerCamelCase__						:						Tuple						      =model

								def encoder_decoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Optional[Any]						      =inference_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase	)
											return outputs

								def encoder_forward():
											with torch.no_grad():
														lowerCamelCase__						:						Any						      =inference_model(lowerCamelCase	)
											return outputs

								lowerCamelCase__						:						Optional[int]						      =encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
								return _forward



					def 						snake_case			(			self				:    Union[str, Any],    lowerCamelCase				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    int	)->   Callable[[], None]:
								lowerCamelCase__						:						Any						      =self.config_dict[model_name]

								lowerCamelCase__						:						Tuple						      =(
								    hasattr(lowerCamelCase,    '''architectures'''	)
								    and isinstance(config.architectures,    lowerCamelCase	)
								    and len(config.architectures	) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														lowerCamelCase__						:						List[Any]						      =config.architectures[0]
														lowerCamelCase__						:						int						      =__import__('''transformers''',    fromlist=[model_class]	)
														lowerCamelCase__						:						int						      =getattr(lowerCamelCase,    lowerCamelCase	)
														lowerCamelCase__						:						Dict						      =model_cls(lowerCamelCase	)
											except ImportError:
														raise ImportError(
														    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
								else:
											lowerCamelCase__						:						List[str]						      =MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase	)

								if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
								else:
											lowerCamelCase__						:						str						      =model

								model.train()
								model.to(self.args.device	)

								# encoder-decoder has vocab size saved differently
								lowerCamelCase__						:						Dict						      =config.vocab_size if hasattr(lowerCamelCase,    '''vocab_size'''	) else config.encoder.vocab_size
								lowerCamelCase__						:						int						      =torch.randint(lowerCamelCase,    (batch_size, sequence_length),    dtype=torch.long,    device=self.args.device	)

								if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''	)
											if not self.args.is_gpu:
														raise ValueError('''Mixed precision is possible only for GPU.'''	)

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

								def compute_loss_and_backprob_encoder():
											lowerCamelCase__						:						Optional[int]						      =train_model(lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								def compute_loss_and_backprob_encoder_decoder():
											lowerCamelCase__						:						Dict						      =train_model(lowerCamelCase,    decoder_input_ids=lowerCamelCase,    labels=lowerCamelCase	)[0]
											loss.backward()
											return loss

								lowerCamelCase__						:						Optional[Any]						      =(
								    compute_loss_and_backprob_encoder_decoder
								    if config.is_encoder_decoder
								    else compute_loss_and_backprob_encoder
								)
								return _train



					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Optional[int]	)->   float:
								try:
											if self.args.is_tpu or self.args.torchscript:
														# run additional 10 times to stabilize compilation for tpu and torchscript
														logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''	)
														timeit.repeat(
														    lowerCamelCase,    repeat=1,    number=5,    )

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											lowerCamelCase__						:						List[str]						      =timeit.repeat(
											    lowerCamelCase,    repeat=self.args.repeat,    number=10,    )

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
														import torch_xla.debug.metrics as met

														self.print_fn(met.metrics_report()	)

											return min(lowerCamelCase	) / 10.0
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A"




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    Callable[[], None]	)->   [Memory, MemorySummary]:
								try:
											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						Optional[int]						      =start_memory_tracing('''transformers'''	)

											if self.args.is_tpu:
														# tpu
														raise NotImplementedError(
														    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
														    ''' `--no-memory` or `args.memory=False`'''	)
											elif self.args.is_gpu:
														if not is_pyanvml_available():
																	logger.warning(
																	    '''py3nvml not installed, we won\'t log GPU memory usage. '''
																	    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''	)
																	lowerCamelCase__						:						str						      ='''N/A'''
														else:
																	logger.info(
																	    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
																	    ''' on the same GPU.'''	)
																	# init nvml
																	nvml.nvmlInit()
																	func()
																	lowerCamelCase__						:						int						      =nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
																	lowerCamelCase__						:						Optional[Any]						      =nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
																	lowerCamelCase__						:						str						      =meminfo.used
																	lowerCamelCase__						:						List[str]						      =Memory(lowerCamelCase	)
																	# shutdown nvml
																	nvml.nvmlShutdown()
											else:
														# cpu
														lowerCamelCase__						:						List[Any]						      =measure_peak_memory_cpu(lowerCamelCase	)
														lowerCamelCase__						:						List[Any]						      =Memory(lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else memory_bytes

											if self.args.trace_memory_line_by_line:
														lowerCamelCase__						:						List[str]						      =stop_memory_tracing(lowerCamelCase	)
											else:
														lowerCamelCase__						:						Optional[int]						      =None

											return memory, summary
								except RuntimeError as e:
											self.print_fn(F'''Doesn\'t fit on GPU. {e}'''	)
											return "N/A", None




