


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()
__lowerCamelCase   	=  logging.get_logger(__name__)

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

__lowerCamelCase   	=  {
    '''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,
}

__lowerCamelCase   	=  list('''ARNDCQEGHILKMFPSTWYV''')

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



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



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[str]				      =     our_module.load_state_dict(original_module.state_dict()			)
     if status.missing_keys:
          raise ValueError(F'''Missing keys: {status.missing_keys}'''			)
     if status.unexpected_keys:
          raise ValueError(F'''Unexpected keys: {status.unexpected_keys}'''			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     if model.startswith("""esmfold"""			):
          a_     :  Optional[Any]				      =     MODEL_MAPPING[model]()
     else:
          a_      ,      a_     :  Optional[int]				      =     MODEL_MAPPING[model]()
     esm.eval()  # disable dropout

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

     if is_folding_model:
          a_     :  Any				      =     esm.esm.alphabet
     a_     :  int				      =     tuple(alphabet.all_toks			)
     a_     :  Tuple				      =     alphabet.mask_idx
     a_     :  int				      =     alphabet.padding_idx

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

     a_     :  Dict				      =     EsmConfig(
         vocab_size=original_esm_model.embed_tokens.num_embeddings	,	mask_token_id=__UpperCamelCase	,	hidden_size=__UpperCamelCase	,	num_hidden_layers=__UpperCamelCase	,	num_attention_heads=__UpperCamelCase	,	intermediate_size=__UpperCamelCase	,	max_position_embeddings=1_0_2_6	,	layer_norm_eps=1E-5	,	attention_probs_dropout_prob=0.0	,	hidden_dropout_prob=0.0	,	pad_token_id=__UpperCamelCase	,	emb_layer_norm_before=__UpperCamelCase	,	token_dropout=__UpperCamelCase	,	position_embedding_type=__UpperCamelCase	,	is_folding_model=__UpperCamelCase	,	esmfold_config=__UpperCamelCase	,	vocab_list=__UpperCamelCase	,	)
     if classification_head:
          a_     :  Any				      =     esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
     print("""Our ESM config:"""	,	__UpperCamelCase			)

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

     # Now let's copy all the weights.
     # Embeddings
     a_     :  Union[str, Any]				      =     original_esm_model.embed_tokens.weight
     if position_embedding_type == "absolute":
          a_     :  str				      =     original_esm_model.embed_positions.weight

     if config.emb_layer_norm_before:
          a_     :  List[Any]				      =     original_esm_model.emb_layer_norm_before.weight
          a_     :  Union[str, Any]				      =     original_esm_model.emb_layer_norm_before.bias

     a_     :  Optional[int]				      =     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_     :  Dict				      =     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_     :  Union[str, Any]				      =     esm_layer.self_attn.q_proj.weight
          a_     :  int				      =     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_     :  Union[str, Any]				      =     esm_layer.self_attn.v_proj.weight
          a_     :  Dict				      =     esm_layer.self_attn.v_proj.bias

          if getattr(esm_layer.self_attn	,	"""rot_emb"""	,	__UpperCamelCase			) 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_     :  int				      =     esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

          a_     :  List[str]				      =     hf_tokenizer([row[1] for row in sample_data]	,	return_tensors="""pt"""	,	padding=__UpperCamelCase			)
          a_     :  Optional[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_     :  Dict				      =     esm.cuda().infer([row[1] for row in sample_data]			)
               a_     :  Union[str, Any]				      =     model.cuda()(
                   input_ids=hf_tokens["""input_ids"""].cuda()	,	attention_mask=hf_tokens["""attention_mask"""].cuda()			)
          else:
               a_     :  Dict				      =     model(**__UpperCamelCase	,	output_hidden_states=__UpperCamelCase			)
               a_     :  Tuple				      =     our_output["""logits"""]
               if classification_head:
                    a_     :  Optional[int]				      =     esm.model.classification_heads["""mnli"""](esm.extract_features(__UpperCamelCase			)			)
               else:
                    a_     :  Union[str, Any]				      =     esm(hf_tokens["""input_ids"""]	,	repr_layers=list(range(9_9_9			)			)			)
                    a_     :  Optional[Any]				      =     their_output["""logits"""]

          if is_folding_model:
               a_     :  List[str]				      =     torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(our_output["""positions"""]	,	their_output["""positions"""]	,	atol=1E-5			)
          else:
               a_     :  Optional[int]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  str				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	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_     :  List[Any]				      =     esm.predict_contacts(hf_tokens["""input_ids"""]			)
               a_     :  Optional[Any]				      =     torch.max(torch.abs(our_output - their_output			)			).item()
               a_     :  Optional[int]				      =     torch.allclose(__UpperCamelCase	,	__UpperCamelCase	,	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(__UpperCamelCase			).mkdir(parents=__UpperCamelCase	,	exist_ok=__UpperCamelCase			)
          print(F'''Saving model to {pytorch_dump_folder_path}'''			)
          model.save_pretrained(__UpperCamelCase			)

          del esm  # Free up some memory before continuing

     print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
     hf_tokenizer.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
       __lowerCamelCase   	=  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.''')
       __lowerCamelCase   	=  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
       )







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


__lowerCamelCase   	=  logging.get_logger(__name__)


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  PyTorchBenchmarkArguments
       lowerCamelCase__:  PretrainedConfig
       lowerCamelCase__:  str				     =  "PyTorch"

       @property
       def     UpperCAmelCase( self   :  str     ):
            return torch.__version__

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Tuple				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_inference     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     self._prepare_inference_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_inference     )

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  str				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_speed(_train     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  List[Any]				      =     self._prepare_train_func(lowerCamelCase_   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            return self._measure_memory(_train     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Union[str, Any]				      =     self.config_dict[model_name]

            if self.args.torchscript:
                 a_     :  Optional[Any]				      =     True

            a_     :  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:
                      a_     :  Union[str, Any]				      =     config.architectures[0]
                      a_     :  Any				      =     __import__("""transformers"""   ,				fromlist=[model_class]     )
                      a_     :  List[str]				      =     getattr(lowerCamelCase_   ,				lowerCamelCase_     )
                      a_     :  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:
                 a_     :  str				      =     MODEL_MAPPING[config.__class__](lowerCamelCase_     )

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

            # encoder-decoder has vocab size saved differently
            a_     :  Union[str, Any]				      =     config.vocab_size if hasattr(lowerCamelCase_   ,				"""vocab_size"""     ) else config.encoder.vocab_size
            a_     :  Dict				      =     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():
                      a_     :  Optional[int]				      =     torch.jit.trace(lowerCamelCase_   ,				lowerCamelCase_     )
            else:
                 a_     :  Optional[Any]				      =     model

            def encoder_decoder_forward():
                 with torch.no_grad():
                      a_     :  Any				      =     inference_model(lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_     )
                 return outputs

            def encoder_forward():
                 with torch.no_grad():
                      a_     :  Dict				      =     inference_model(lowerCamelCase_     )
                 return outputs

            a_     :  Union[str, Any]				      =     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
            return _forward

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            a_     :  Optional[int]				      =     self.config_dict[model_name]

            a_     :  str				      =     (
                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:
                      a_     :  Optional[int]				      =     config.architectures[0]
                      a_     :  List[Any]				      =     __import__("""transformers"""   ,				fromlist=[model_class]     )
                      a_     :  List[str]				      =     getattr(lowerCamelCase_   ,				lowerCamelCase_     )
                      a_     :  Any				      =     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:
                 a_     :  Union[str, Any]				      =     MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase_     )

            if self.args.torchscript:
                 raise NotImplementedError("""Training for torchscript is currently not implemented"""     )
            else:
                 a_     :  Dict				      =     model

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

            # encoder-decoder has vocab size saved differently
            a_     :  Any				      =     config.vocab_size if hasattr(lowerCamelCase_   ,				"""vocab_size"""     ) else config.encoder.vocab_size
            a_     :  Tuple				      =     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():
                 a_     :  Dict				      =     train_model(lowerCamelCase_   ,				labels=lowerCamelCase_     )[0]
                 loss.backward()
                 return loss

            def compute_loss_and_backprob_encoder_decoder():
                 a_     :  Optional[int]				      =     train_model(lowerCamelCase_   ,				decoder_input_ids=lowerCamelCase_   ,				labels=lowerCamelCase_     )[0]
                 loss.backward()
                 return loss

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

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[int]     ):
            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
                 a_     :  List[str]				      =     timeit.repeat(
                     lowerCamelCase_   ,				repeat=self.args.repeat   ,				number=1_0   ,				)

                 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_     ) / 1_0.0
            except RuntimeError as e:
                 self.print_fn(F'''Doesn\'t fit on GPU. {e}'''     )
                 return "N/A"

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  Callable[[], None]     ):
            try:
                 if self.args.trace_memory_line_by_line:
                      a_     :  Dict				      =     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."""     )
                           a_     :  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()
                           a_     :  List[Any]				      =     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx     )
                           a_     :  Tuple				      =     nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase_     )
                           a_     :  Any				      =     meminfo.used
                           a_     :  Any				      =     Memory(lowerCamelCase_     )
                           # shutdown nvml
                           nvml.nvmlShutdown()
                 else:
                      # cpu
                      a_     :  Union[str, Any]				      =     measure_peak_memory_cpu(lowerCamelCase_     )
                      a_     :  Optional[Any]				      =     Memory(lowerCamelCase_     ) if isinstance(lowerCamelCase_   ,				lowerCamelCase_     ) else memory_bytes

                 if self.args.trace_memory_line_by_line:
                      a_     :  Dict				      =     stop_memory_tracing(lowerCamelCase_     )
                 else:
                      a_     :  Any				      =     None

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



