






"""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 							A(    ):
       """simple docstring"""





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






def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





       lowercase__:							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(    snake_case_		,  snake_case_		,  snake_case_		,  snake_case_		,  snake_case_						):
       """simple docstring"""





       if model.startswith("esmfold"						):
              lowercase__:							List[str]         =     MODEL_MAPPING[model]()
       else:
              lowercase__    ,      lowercase__:							str         =     MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

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

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

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

       lowercase__:							Tuple         =     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=1026		,  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:
              lowercase__:							str         =     esm.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our ESM config:"		,  UpperCamelCase_						)

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

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

       if config.emb_layer_norm_before:
              lowercase__:							List[Any]         =     original_esm_model.emb_layer_norm_before.weight
              lowercase__:							Optional[Any]         =     original_esm_model.emb_layer_norm_before.bias

       lowercase__:							List[str]         =     original_esm_model.emb_layer_norm_after.weight
       lowercase__:							List[str]         =     original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              lowercase__:							Dict         =     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)						)
              )

              lowercase__:							Union[str, Any]         =     esm_layer.self_attn.q_proj.weight
              lowercase__:							Union[str, Any]         =     esm_layer.self_attn.q_proj.bias
              lowercase__:							Union[str, Any]         =     esm_layer.self_attn.k_proj.weight
              lowercase__:							List[str]         =     esm_layer.self_attn.k_proj.bias
              lowercase__:							Tuple         =     esm_layer.self_attn.v_proj.weight
              lowercase__:							Any         =     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.
                     lowercase__:							int         =     esm_layer.self_attn.rot_emb.inv_freq

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

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

              # intermediate
              lowercase__:							Tuple         =     layer.intermediate
              assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
              lowercase__:							int         =     esm_layer.fca.weight
              lowercase__:							List[Any]         =     esm_layer.fca.bias

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

       if is_folding_model:
              lowercase__:							Dict         =     esm.esm_s_combine.data
              lowercase__:							List[Any]         =     esm.afa_to_esm.data
              transfer_and_check_weights(esm.embedding		,  model.embedding						)
              transfer_and_check_weights(esm.esm_s_mlp		,  model.esm_s_mlp						)
              transfer_and_check_weights(esm.trunk		,  model.trunk						)
              transfer_and_check_weights(esm.distogram_head		,  model.distogram_head						)
              transfer_and_check_weights(esm.ptm_head		,  model.ptm_head						)
              transfer_and_check_weights(esm.lm_head		,  model.lm_head						)
              transfer_and_check_weights(esm.lddt_head		,  model.lddt_head						)

       elif classification_head:
              lowercase__:							str         =     esm.esm.classification_heads["mnli"].dense.weight
              lowercase__:							Union[str, Any]         =     esm.classification_heads["mnli"].dense.bias
              lowercase__:							Optional[int]         =     esm.classification_heads["mnli"].out_proj.weight
              lowercase__:							Tuple         =     esm.classification_heads["mnli"].out_proj.bias
       else:
              # LM Head
              lowercase__:							str         =     esm.lm_head.dense.weight
              lowercase__:							Tuple         =     esm.lm_head.dense.bias
              lowercase__:							Tuple         =     esm.lm_head.layer_norm.weight
              lowercase__:							Optional[int]         =     esm.lm_head.layer_norm.bias
              lowercase__:							Tuple         =     esm.lm_head.weight
              lowercase__:							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.
              lowercase__:							Optional[Any]         =     SAMPLE_DATA[:2]
       else:
              lowercase__:							Dict         =     SAMPLE_DATA

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

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

              if is_folding_model:
                     lowercase__:							Optional[Any]         =     torch.max(torch.abs(our_output["positions"] - their_output["positions"]						)						).item()
                     lowercase__:							List[Any]         =     torch.allclose(our_output["positions"]		,  their_output["positions"]		,  atol=1E-5						)
              else:
                     lowercase__:							List[Any]         =     torch.max(torch.abs(our_output - their_output						)						).item()
                     lowercase__:							Union[str, Any]         =     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
                     lowercase__:							str         =     model.predict_contacts(hf_tokens["input_ids"]		,  hf_tokens["attention_mask"]						)
                     lowercase__:							Union[str, Any]         =     esm.predict_contacts(hf_tokens["input_ids"]						)
                     lowercase__:							Union[str, Any]         =     torch.max(torch.abs(our_output - their_output						)						).item()
                     lowercase__:							Any         =     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__":
  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
  )













import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase										=					1_6
_UpperCAmelCase										=					3_2




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_ = 16	)  ->		Dict:
       UpperCamelCase_ 							=	AutoTokenizer.from_pretrained("bert-base-cased"	)
       UpperCamelCase_ 							=	load_dataset("glue"       ,		"mrpc"	)

       def tokenize_function(UpperCamelCase_	):
              # max_length=None => use the model max length (it's actually the default)
              UpperCamelCase_ 							=	tokenizer(examples["sentence1"]       ,		examples["sentence2"]       ,		truncation=UpperCamelCase_       ,		max_length=UpperCamelCase_	)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              UpperCamelCase_ 							=	datasets.map(
                  UpperCamelCase_       ,		batched=UpperCamelCase_       ,		remove_columns=["idx", "sentence1", "sentence2"]       ,		)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       UpperCamelCase_ 							=	tokenized_datasets.rename_column("label"       ,		"labels"	)

       def collate_fn(UpperCamelCase_	):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              UpperCamelCase_ 							=	128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     UpperCamelCase_ 							=	16
              elif accelerator.mixed_precision != "no":
                     UpperCamelCase_ 							=	8
              else:
                     UpperCamelCase_ 							=	None

              return tokenizer.pad(
                  UpperCamelCase_       ,		padding="longest"       ,		max_length=UpperCamelCase_       ,		pad_to_multiple_of=UpperCamelCase_       ,		return_tensors="pt"       ,		)

       # Instantiate dataloaders.
       UpperCamelCase_ 							=	DataLoader(
           tokenized_datasets["train"]       ,		shuffle=UpperCamelCase_       ,		collate_fn=UpperCamelCase_       ,		batch_size=UpperCamelCase_	)
       UpperCamelCase_ 							=	DataLoader(
           tokenized_datasets["validation"]       ,		shuffle=UpperCamelCase_       ,		collate_fn=UpperCamelCase_       ,		batch_size=UpperCamelCase_	)

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
     from accelerate.test_utils.training import mocked_dataloaders

     _UpperCAmelCase										=					mocked_dataloaders  # noqa: F811




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,		UpperCamelCase_	) == "1":
              UpperCamelCase_ 							=	2
       # Initialize accelerator
       UpperCamelCase_ 							=	Accelerator(cpu=args.cpu       ,		mixed_precision=args.mixed_precision	)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       UpperCamelCase_ 							=	config["lr"]
       UpperCamelCase_ 							=	int(config["num_epochs"]	)
       UpperCamelCase_ 							=	int(config["seed"]	)
       UpperCamelCase_ 							=	int(config["batch_size"]	)

       # New Code #
       # Parse out whether we are saving every epoch or after a certain number of batches
       if hasattr(args.checkpointing_steps       ,		"isdigit"	):
              if args.checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCamelCase_ 							=	int(args.checkpointing_steps	)
              else:
                     raise ValueError(
                         F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
       else:
              UpperCamelCase_ 							=	None

       set_seed(UpperCamelCase_	)

       UpperCamelCase_							,			UpperCamelCase_ 							=	get_dataloaders(UpperCamelCase_       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	evaluate.load("glue"       ,		"mrpc"	)

       # If the batch size is too big we use gradient accumulation
       UpperCamelCase_ 							=	1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              UpperCamelCase_ 							=	batch_size // MAX_GPU_BATCH_SIZE
              UpperCamelCase_ 							=	MAX_GPU_BATCH_SIZE

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       UpperCamelCase_ 							=	AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,		return_dict=UpperCamelCase_	)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       UpperCamelCase_ 							=	model.to(accelerator.device	)

       # Instantiate optimizer
       UpperCamelCase_ 							=	AdamW(params=model.parameters()       ,		lr=UpperCamelCase_	)

       # Instantiate scheduler
       UpperCamelCase_ 							=	get_linear_schedule_with_warmup(
           optimizer=UpperCamelCase_       ,		num_warmup_steps=100       ,		num_training_steps=(len(UpperCamelCase_	) * num_epochs) // gradient_accumulation_steps       ,		)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.prepare(
           UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       UpperCamelCase_ 							=	0
       # We also need to keep track of the stating epoch so files are named properly
       UpperCamelCase_ 							=	0

       # We need to load the checkpoint back in before training here with `load_state`
       # The total number of epochs is adjusted based on where the state is being loaded from,
       # as we assume continuation of the same training script
       if args.resume_from_checkpoint:
              if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                     accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''	)
                     accelerator.load_state(args.resume_from_checkpoint	)
                     UpperCamelCase_ 							=	os.path.basename(args.resume_from_checkpoint	)
              else:
                     # Get the most recent checkpoint
                     UpperCamelCase_ 							=	[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
                     dirs.sort(key=os.path.getctime	)
                     UpperCamelCase_ 							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCamelCase_ 							=	os.path.splitext(UpperCamelCase_	)[0]

              if "epoch" in training_difference:
                     UpperCamelCase_ 							=	int(training_difference.replace("epoch_"       ,		""	)	) + 1
                     UpperCamelCase_ 							=	None
              else:
                     UpperCamelCase_ 							=	int(training_difference.replace("step_"       ,		""	)	)
                     UpperCamelCase_ 							=	resume_step // len(UpperCamelCase_	)
                     resume_step -= starting_epoch * len(UpperCamelCase_	)

    # Now we train the model
       for epoch in range(UpperCamelCase_       ,		UpperCamelCase_	):
              model.train()
              # New Code #
              if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                     # We need to skip steps until we reach the resumed step
                     UpperCamelCase_ 							=	accelerator.skip_first_batches(UpperCamelCase_       ,		UpperCamelCase_	)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCamelCase_ 							=	train_dataloader
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device	)
                     UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.loss
                     UpperCamelCase_ 							=	loss / gradient_accumulation_steps
                     accelerator.backward(UpperCamelCase_	)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()
                     # New Code #
                     overall_step += 1

                     # New Code #
                     # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                     # These are saved to folders named `step_{overall_step}`
                     # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                     # If mixed precision was used, will also save a "scalar.bin" file
                     if isinstance(UpperCamelCase_       ,		UpperCamelCase_	):
                            UpperCamelCase_ 							=	F'''step_{overall_step}'''
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                                   accelerator.save_state(UpperCamelCase_	)

              model.eval()
              for step, batch in enumerate(UpperCamelCase_	):
                     # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                     batch.to(accelerator.device	)
                     with torch.no_grad():
                            UpperCamelCase_ 							=	model(**UpperCamelCase_	)
                     UpperCamelCase_ 							=	outputs.logits.argmax(dim=-1	)
                     UpperCamelCase_							,			UpperCamelCase_ 							=	accelerator.gather_for_metrics((predictions, batch["labels"])	)
                     metric.add_batch(
                         predictions=UpperCamelCase_       ,		references=UpperCamelCase_       ,		)

              UpperCamelCase_ 							=	metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'''epoch {epoch}:'''       ,		UpperCamelCase_	)

              # New Code #
              # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
              # These are saved to folders named `epoch_{epoch}`
              # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
              # If mixed precision was used, will also save a "scalar.bin" file
              if checkpointing_steps == "epoch":
                     UpperCamelCase_ 							=	F'''epoch_{epoch}'''
                     if args.output_dir is not None:
                            UpperCamelCase_ 							=	os.path.join(args.output_dir       ,		UpperCamelCase_	)
                     accelerator.save_state(UpperCamelCase_	)




def 			lowerCAmelCase_			(				)  ->		Dict:
       UpperCamelCase_ 							=	argparse.ArgumentParser(description="Simple example of training script."	)
       parser.add_argument(
           "--mixed_precision"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		choices=["no", "fp16", "bf16", "fp8"]       ,		help="Whether to use mixed precision. Choose"
           "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
           "and an Nvidia Ampere GPU."       ,		)
       parser.add_argument("--cpu"       ,		action="store_true"       ,		help="If passed, will train on the CPU."	)
       parser.add_argument(
           "--checkpointing_steps"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."       ,		)
       parser.add_argument(
           "--output_dir"       ,		type=UpperCamelCase_       ,		default="."       ,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."       ,		)
       parser.add_argument(
           "--resume_from_checkpoint"       ,		type=UpperCamelCase_       ,		default=UpperCamelCase_       ,		help="If the training should continue from a checkpoint folder."       ,		)
       UpperCamelCase_ 							=	parser.parse_args()
       UpperCamelCase_ 							=	{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(UpperCamelCase_       ,		UpperCamelCase_	)


if __name__ == "__main__":
     main()

