


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()
SCREAMING_SNAKE_CASE      :						List[Any]    =		logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE      :						List[str]    =		{
    "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,
}

SCREAMING_SNAKE_CASE      :						Optional[Any]    =		list("ARNDCQEGHILKMFPSTWYV")

SCREAMING_SNAKE_CASE      :						int    =		restypes + ["X"]
SCREAMING_SNAKE_CASE      :						Tuple    =		restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			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   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	List[str]			):





			'''simple docstring'''




			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   __lowerCamelCase	(       lowerCamelCase__		:	Optional[Any]     ,       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	str     ,       lowerCamelCase__		:	int			):





			'''simple docstring'''




			if model.startswith("""esmfold"""			):
						lowerCamelCase								=     MODEL_MAPPING[model]()
			else:
						lowerCamelCase		,		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		,		lowerCamelCase		,		lowerCamelCase		,		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		,		lowerCamelCase		,		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__":
		SCREAMING_SNAKE_CASE      :						Dict    =		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.")
		SCREAMING_SNAKE_CASE      :						List[str]    =		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      :						str    =		16
UpperCAmelCase      :						Union[str, Any]    =		32






def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):





			'''simple docstring'''




			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)

			def tokenize_function(lowerCamelCase__		:	List[str]			):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase								=     tokenizer(examples["""sentence1"""]     ,       examples["""sentence2"""]     ,       truncation=lowerCamelCase__     ,       max_length=lowerCamelCase__			)
						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():
						lowerCamelCase								=     datasets.map(
						    lowerCamelCase__     ,       batched=lowerCamelCase__     ,       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
			lowerCamelCase								=     tokenized_datasets.rename_column("""label"""     ,       """labels"""			)

			def collate_fn(lowerCamelCase__		:	Optional[Any]			):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						lowerCamelCase								=     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":
									lowerCamelCase								=     16
						elif accelerator.mixed_precision != "no":
									lowerCamelCase								=     8
						else:
									lowerCamelCase								=     None

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

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

			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      :						Tuple    =		mocked_dataloaders  # noqa: F811






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Optional[int]			):





			'''simple docstring'''




			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "1":
						lowerCamelCase								=     2
			# Initialize accelerator
			lowerCamelCase								=     Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase								=     config["""lr"""]
			lowerCamelCase								=     int(config["""num_epochs"""]			)
			lowerCamelCase								=     int(config["""seed"""]			)
			lowerCamelCase								=     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":
									lowerCamelCase								=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase								=     int(args.checkpointing_steps			)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
			else:
						lowerCamelCase								=     None

			set_seed(lowerCamelCase__			)

			lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
			lowerCamelCase								=     evaluate.load("""glue"""     ,       """mrpc"""			)

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

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

			# 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).
			lowerCamelCase								=     model.to(accelerator.device			)

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

			# Instantiate scheduler
			lowerCamelCase								=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * 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.
			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
			    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			lowerCamelCase								=     0
			# We also need to keep track of the stating epoch so files are named properly
			lowerCamelCase								=     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			)
									lowerCamelCase								=     os.path.basename(args.resume_from_checkpoint			)
						else:
									# Get the most recent checkpoint
									lowerCamelCase								=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
									dirs.sort(key=os.path.getctime			)
									lowerCamelCase								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCamelCase								=     os.path.splitext(lowerCamelCase__			)[0]

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

    # Now we train the model
			for epoch in range(lowerCamelCase__     ,       lowerCamelCase__			):
						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
									lowerCamelCase								=     accelerator.skip_first_batches(lowerCamelCase__     ,       lowerCamelCase__			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCamelCase								=     train_dataloader
						for step, batch in enumerate(lowerCamelCase__			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device			)
									lowerCamelCase								=     model(**lowerCamelCase__			)
									lowerCamelCase								=     outputs.loss
									lowerCamelCase								=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__			)
									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(lowerCamelCase__     ,       lowerCamelCase__			):
												lowerCamelCase								=     f'step_{overall_step}'
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
															accelerator.save_state(lowerCamelCase__			)

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

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

						# 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":
									lowerCamelCase								=     f'epoch_{epoch}'
									if args.output_dir is not None:
												lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
									accelerator.save_state(lowerCamelCase__			)






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     argparse.ArgumentParser(description="""Simple example of training script."""			)
			parser.add_argument(
			    """--mixed_precision"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       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=lowerCamelCase__     ,       default=lowerCamelCase__     ,       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=lowerCamelCase__     ,       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=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""If the training should continue from a checkpoint folder."""     ,       )
			lowerCamelCase								=     parser.parse_args()
			lowerCamelCase								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__     ,       lowerCamelCase__			)


if __name__ == "__main__":
		main()

