


import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


UpperCamelCase__   : Optional[int]					  = Mapping[str, np.ndarray]
UpperCamelCase__   : Any					  = Mapping[str, Any]  # Is a nested dict.
UpperCamelCase__   : int					  = 0.01







@dataclasses.dataclass(frozen=A_  )
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     np.ndarray  # [num_res, num_atom_type, 3]

					# Amino-acid type for each residue represented as an integer between 0 and
					# 20, where 20 is 'X'.
					lowerCamelCase     :     np.ndarray  # [num_res]

					# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
					# is present and 0.0 if not. This should be used for loss masking.
					lowerCamelCase     :     np.ndarray  # [num_res, num_atom_type]

					# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
					lowerCamelCase     :     np.ndarray  # [num_res]

					# B-factors, or temperature factors, of each residue (in sq. angstroms units),
					# representing the displacement of the residue from its ground truth mean
					# value.
					lowerCamelCase     :     np.ndarray  # [num_res, num_atom_type]

					# Chain indices for multi-chain predictions
					lowerCamelCase     :     Optional[np.ndarray]			    =       None

					# Optional remark about the protein. Included as a comment in output PDB
					# files
					lowerCamelCase     :     Optional[str]			    =       None

					# Templates used to generate this protein (prediction-only)
					lowerCamelCase     :     Optional[Sequence[str]]			    =       None

					# Chain corresponding to each parent
					lowerCamelCase     :     Optional[Sequence[int]]			    =       None




def    A_(			A   ):
	UpperCAmelCase_								=     R"""(\[[A-Z]+\]\n)"""
	UpperCAmelCase_								=     [tag.strip() for tag in re.split(A				,      A   ) if len(A   ) > 0]
	UpperCAmelCase_								=     zip(tags[0::2]				,      [l.split("""\n"""   ) for l in tags[1::2]]   )

	UpperCAmelCase_								=     ["""N""", """CA""", """C"""]
	UpperCAmelCase_								=     None
	UpperCAmelCase_								=     None
	UpperCAmelCase_								=     None
	for g in groups:
		if "[PRIMARY]" == g[0]:
			UpperCAmelCase_								=     g[1][0].strip()
			for i in range(len(A   )   ):
				if seq[i] not in residue_constants.restypes:
					UpperCAmelCase_								=     """X"""  # FIXME: strings are immutable
			UpperCAmelCase_								=     np.array(
			    [residue_constants.restype_order.get(A				,      residue_constants.restype_num   ) for res_symbol in seq]   )
		elif "[TERTIARY]" == g[0]:
			UpperCAmelCase_								=     []
			for axis in range(3   ):
				tertiary.append(list(map(A				,      g[1][axis].split()   )   )   )
			UpperCAmelCase_								=     np.array(A   )
			UpperCAmelCase_								=     np.zeros((len(tertiary[0]   ) // 3, residue_constants.atom_type_num, 3)   ).astype(np.floataa   )
			for i, atom in enumerate(A   ):
				UpperCAmelCase_								=     np.transpose(tertiary_np[:, i::3]   )
			atom_positions *= PICO_TO_ANGSTROM
		elif "[MASK]" == g[0]:
			UpperCAmelCase_								=     np.array(list(map({"""-""": 0, """+""": 1}.get				,      g[1][0].strip()   )   )   )
			UpperCAmelCase_								=     np.zeros(
			    (
			        len(A   ),
			        residue_constants.atom_type_num,
			    )   ).astype(np.floataa   )
			for i, atom in enumerate(A   ):
				UpperCAmelCase_								=     1
			atom_mask *= mask[..., None]

	assert aatype is not None

	return Protein(
	    atom_positions=A				,      atom_mask=A				,      aatype=A				,      residue_index=np.arange(len(A   )   )				,      b_factors=A				,      )




def    A_(			A				,      A = 0   ):
	UpperCAmelCase_								=     []

	UpperCAmelCase_								=     prot.remark
	if remark is not None:
		pdb_headers.append(f"""REMARK {remark}"""   )

	UpperCAmelCase_								=     prot.parents
	UpperCAmelCase_								=     prot.parents_chain_index
	if parents is not None and parents_chain_index is not None:
		UpperCAmelCase_								=     [p for i, p in zip(A				,      A   ) if i == chain_id]

	if parents is None or len(A   ) == 0:
		UpperCAmelCase_								=     ["""N/A"""]

	pdb_headers.append(f"""PARENT {' '.join(A   )}"""   )

	return pdb_headers




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     pdb_str.split("""\n"""   )

	UpperCAmelCase_								=     prot.remark
	if remark is not None:
		out_pdb_lines.append(f"""REMARK {remark}"""   )

	UpperCAmelCase_								=     42
	if prot.parents is not None and len(prot.parents   ) > 0:
		UpperCAmelCase_								=     []
		if prot.parents_chain_index is not None:
			UpperCAmelCase_								=     {}
			for p, i in zip(prot.parents				,      prot.parents_chain_index   ):
				parent_dict.setdefault(str(A   )				,      []   )
				parent_dict[str(A   )].append(A   )

			UpperCAmelCase_								=     max([int(A   ) for chain_idx in parent_dict]   )
			for i in range(max_idx + 1   ):
				UpperCAmelCase_								=     parent_dict.get(str(A   )				,      ["""N/A"""]   )
				parents_per_chain.append(A   )
		else:
			parents_per_chain.append(list(prot.parents   )   )
	else:
		UpperCAmelCase_								=     [["""N/A"""]]

	def make_parent_line(A   ) -> str:
		return f"""PARENT {' '.join(A   )}"""

	out_pdb_lines.append(make_parent_line(parents_per_chain[0]   )   )

	UpperCAmelCase_								=     0
	for i, l in enumerate(A   ):
		if "PARENT" not in l and "REMARK" not in l:
			out_pdb_lines.append(A   )
		if "TER" in l and "END" not in lines[i + 1]:
			chain_counter += 1
			if not chain_counter >= len(A   ):
				UpperCAmelCase_								=     parents_per_chain[chain_counter]
			else:
				UpperCAmelCase_								=     ["""N/A"""]

			out_pdb_lines.append(make_parent_line(A   )   )

	return "\n".join(A   )




def    A_(			A   ):
	UpperCAmelCase_								=     residue_constants.restypes + ["""X"""]

	def res_atoa(A   ) -> str:
		return residue_constants.restype_atoa.get(restypes[r]				,      """UNK"""   )

	UpperCAmelCase_								=     residue_constants.atom_types

	UpperCAmelCase_								=     []

	UpperCAmelCase_								=     prot.atom_mask
	UpperCAmelCase_								=     prot.aatype
	UpperCAmelCase_								=     prot.atom_positions
	UpperCAmelCase_								=     prot.residue_index.astype(np.intaa   )
	UpperCAmelCase_								=     prot.b_factors
	UpperCAmelCase_								=     prot.chain_index

	if np.any(aatype > residue_constants.restype_num   ):
		raise ValueError("""Invalid aatypes."""   )

	UpperCAmelCase_								=     get_pdb_headers(A   )
	if len(A   ) > 0:
		pdb_lines.extend(A   )

	UpperCAmelCase_								=     aatype.shape[0]
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     string.ascii_uppercase
	UpperCAmelCase_								=     None
	# Add all atom sites.
	for i in range(A   ):
		UpperCAmelCase_								=     res_atoa(aatype[i]   )
		for atom_name, pos, mask, b_factor in zip(A				,      atom_positions[i]				,      atom_mask[i]				,      b_factors[i]   ):
			if mask < 0.5:
				continue

			UpperCAmelCase_								=     """ATOM"""
			UpperCAmelCase_								=     atom_name if len(A   ) == 4 else f""" {atom_name}"""
			UpperCAmelCase_								=     """"""
			UpperCAmelCase_								=     """"""
			UpperCAmelCase_								=     1.00
			UpperCAmelCase_								=     atom_name[0]  # Protein supports only C, N, O, S, this works.
			UpperCAmelCase_								=     """"""

			UpperCAmelCase_								=     """A"""
			if chain_index is not None:
				UpperCAmelCase_								=     chain_tags[chain_index[i]]

			# PDB is a columnar format, every space matters here!
			UpperCAmelCase_								=     (
			    f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
			    f"""{res_name_a:>3} {chain_tag:>1}"""
			    f"""{residue_index[i]:>4}{insertion_code:>1}   """
			    f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
			    f"""{occupancy:>6.2f}{b_factor:>6.2f}          """
			    f"""{element:>2}{charge:>2}"""
			)
			pdb_lines.append(A   )
			atom_index += 1

		UpperCAmelCase_								=     i == n - 1
		if chain_index is not None:
			if i != n - 1 and chain_index[i + 1] != prev_chain_index:
				UpperCAmelCase_								=     True
				UpperCAmelCase_								=     chain_index[i + 1]

		if should_terminate:
			# Close the chain.
			UpperCAmelCase_								=     """TER"""
			UpperCAmelCase_								=     (
			    f"""{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]   ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
			)
			pdb_lines.append(A   )
			atom_index += 1

			if i != n - 1:
				# "prev" is a misnomer here. This happens at the beginning of
				# each new chain.
				pdb_lines.extend(get_pdb_headers(A				,      A   )   )

	pdb_lines.append("""END"""   )
	pdb_lines.append(""""""   )
	return "\n".join(A   )




def    A_(			A   ):
	return residue_constants.STANDARD_ATOM_MASK[prot.aatype]




def    A_(			A				,      A				,      A = None				,      A = None				,      A = None				,      A = None				,      A = None				,      ):
	return Protein(
	    aatype=features["""aatype"""]				,      atom_positions=result["""final_atom_positions"""]				,      atom_mask=result["""final_atom_mask"""]				,      residue_index=features["""residue_index"""] + 1				,      b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]   )				,      chain_index=A				,      remark=A				,      parents=A				,      parents_chain_index=A				,      )









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
# and perform gradient accumulation
#
# 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 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__   : Union[str, Any]					  = 16
UpperCamelCase__   : Tuple					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

	def tokenize_function(A   ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_								=     tokenizer(examples["""sentence1"""]				,      examples["""sentence2"""]				,      truncation=A				,      max_length=A   )
		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(
		    A				,      batched=A				,      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(A   ):
		# 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(
		    A				,      padding="""longest"""				,      max_length=A				,      pad_to_multiple_of=A				,      return_tensors="""pt"""				,      )

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

	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__   : str					  = mocked_dataloaders  # noqa: F811




def    A_(			A				,      A   ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,      A   ) == "1":
		UpperCAmelCase_								=     2
	# New Code #
	UpperCAmelCase_								=     int(args.gradient_accumulation_steps   )
	# Initialize accelerator
	UpperCAmelCase_								=     Accelerator(
	    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=A   )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""   )
	# 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"""]   )

	UpperCAmelCase_								=     evaluate.load("""glue"""				,      """mrpc"""   )

	set_seed(A   )
	UpperCAmelCase_						,	UpperCAmelCase_								=     get_dataloaders(A				,      A   )
	# 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=A   )

	# 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=A   )

	# Instantiate scheduler
	UpperCAmelCase_								=     get_linear_schedule_with_warmup(
	    optimizer=A				,      num_warmup_steps=100				,      num_training_steps=(len(A   ) * num_epochs)				,      )

	# 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(
	    A				,      A				,      A				,      A				,      A   )

	# Now we train the model
	for epoch in range(A   ):
		model.train()
		for step, batch in enumerate(A   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(A   ):
				UpperCAmelCase_								=     model(**A   )
				UpperCAmelCase_								=     output.loss
				accelerator.backward(A   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		UpperCAmelCase_								=     metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""				,      A   )




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""				,      type=A				,      default=A				,      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."""				,      )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""				,      type=A				,      default=1				,      help="""The number of minibatches to be ran before gradients are accumulated."""				,      )
	parser.add_argument("""--cpu"""				,      action="""store_true"""				,      help="""If passed, will train on the CPU."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(A				,      A   )


if __name__ == "__main__":
				main()


