




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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__lowerCAmelCase		:    Union[str, Any]	      =		16
__lowerCAmelCase		:    Optional[Any]	      =		32




def       __magic_name__    (			A		: str,  A		: Union[str, Any]       ):






   '''simple docstring'''







   if args.with_tracking:
      a   =      Accelerator(
          cpu=args.cpu,  mixed_precision=args.mixed_precision,  log_with="all",  project_dir=args.project_dir       )
   else:
      a   =      Accelerator(cpu=args.cpu,  mixed_precision=args.mixed_precision       )

   if hasattr(args.checkpointing_steps,  "isdigit"       ):
      if args.checkpointing_steps == "epoch":
         a   =      args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         a   =      int(args.checkpointing_steps       )
      else:
         raise ValueError(
             F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
   else:
      a   =      None
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   a   =      config["lr"]
   a   =      int(config["num_epochs"]       )
   a   =      int(config["seed"]       )
   a   =      int(config["batch_size"]       )

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      a   =      os.path.split(__A       )[-1].split("."       )[0]
      accelerator.init_trackers(__A,  __A       )

   a   =      AutoTokenizer.from_pretrained("bert-base-cased"       )
   a   =      load_dataset("glue",  "mrpc"       )
   a   =      evaluate.load("glue",  "mrpc"       )

   def tokenize_function(A		: Any       ):
      # max_length=None => use the model max length (it's actually the default)
      a   =      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():
      a   =      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
   a   =      tokenized_datasets.rename_column("label",  "labels"       )

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

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

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

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

   set_seed(__A       )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   a   =      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).
   a   =      model.to(accelerator.device       )

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

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

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

   # Potentially load in the weights and states from a previous save
   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       )
         a   =      os.path.basename(args.resume_from_checkpoint       )
      else:
         # Get the most recent checkpoint
         a   =      [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
         dirs.sort(key=os.path.getctime       )
         a   =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      a   =      os.path.splitext(__A       )[0]

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

    # Now we train the model
   for epoch in range(__A,  __A       ):
      model.train()
      if args.with_tracking:
         a   =      0
      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
         a   =      accelerator.skip_first_batches(__A,  __A       )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         a   =      train_dataloader
      for step, batch in enumerate(__A       ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device       )
         a   =      model(**__A       )
         a   =      outputs.loss
         a   =      loss / gradient_accumulation_steps
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(__A       )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1

         if isinstance(__A,  __A       ):
            a   =      F"""step_{overall_step}"""
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  a   =      os.path.join(args.output_dir,  __A       )
               accelerator.save_state(__A       )

      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():
            a   =      model(**__A       )
         a   =      outputs.logits.argmax(dim=-1       )
         a   ,		a   =      accelerator.gather_for_metrics((predictions, batch["labels"])       )
         metric.add_batch(
             predictions=__A,  references=__A,  )

      a   =      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:""",  __A       )
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": eval_metric["accuracy"],
                 "f1": eval_metric["f1"],
                 "train_loss": total_loss.item() / len(__A       ),
                 "epoch": epoch,
             },  step=__A,  )

      if checkpointing_steps == "epoch":
         a   =      F"""epoch_{epoch}"""
         if args.output_dir is not None:
            a   =      os.path.join(args.output_dir,  __A       )
         accelerator.save_state(__A       )

   if args.with_tracking:
      accelerator.end_training()




def       __magic_name__    (			):






   '''simple docstring'''







   a   =      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.",  )
   parser.add_argument("--cpu",  action="store_true",  help="If passed, will train on the CPU."       )
   parser.add_argument(
       "--checkpointing_steps",  type=__A,  default=__A,  help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.",  )
   parser.add_argument(
       "--resume_from_checkpoint",  type=__A,  default=__A,  help="If the training should continue from a checkpoint folder.",  )
   parser.add_argument(
       "--with_tracking",  action="store_true",  help="Whether to load in all available experiment trackers from the environment and use them for logging.",  )
   parser.add_argument(
       "--output_dir",  type=__A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
   parser.add_argument(
       "--project_dir",  type=__A,  default="logs",  help="Location on where to store experiment tracking logs` and relevent project information",  )
   a   =      parser.parse_args()
   a   =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(__A,  __A       )


if __name__ == "__main__":
  main()












from typing import Any

import numpy as np




class    snake_case__							:
				"""simple docstring"""



				def __init__(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int							) ->					None:
							# Check if RHS is negative
							if np.any(tableau[:, -1] ,				where=tableau[:, -1] < 0							):
										raise ValueError("RHS must be > 0"							)

							a   =      tableau
							a   ,		a   =      tableau.shape

							# Number of decision variables x1, x2, x3...
							a   =      n_vars

							# Number of artificial variables to be minimised
							a   =      len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)

							# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
							a   =      (self.n_art_vars > 0) + 1

							# Number of slack variables added to make inequalities into equalities
							a   =      self.n_rows - self.n_stages

							# Objectives for each stage
							a   =      ["max"]

							# In two stage simplex, first minimise then maximise
							if self.n_art_vars:
										self.objectives.append("min"							)

							a   =      [""]

							# Index of current pivot row and column
							a   =      None
							a   =      None

							# Does objective row only contain (non)-negative values?
							a   =      False





				@staticmethod
				def 							__UpperCAmelCase							(				*__lowerCamelCase    :					int							) ->					list[str]:
							if len(__lowerCamelCase							) != 3:
										raise ValueError("Must provide n_vars, n_slack, and n_art_vars"							)

							if not all(x >= 0 and isinstance(__lowerCamelCase ,				__lowerCamelCase							) for x in args							):
										raise ValueError("All arguments must be non-negative integers"							)

							# decision | slack | artificial
							a   =      ["x", "s", "a"]
							a   =      []
							for i in range(3							):
										for j in range(args[i]							):
													titles.append(string_starts[i] + str(j + 1							)							)
							titles.append("RHS"							)
							return titles





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					tuple[Any, Any]:
							a   =      self.objectives[-1]

							# Find entries of highest magnitude in objective rows
							a   =      (objective == "min") - (objective == "max")
							a   =      np.argmax(sign * tableau[0, : self.n_vars]							)

							# Choice is only valid if below 0 for maximise, and above for minimise
							if sign * self.tableau[0, col_idx] <= 0:
										a   =      True
										return 0, 0

							# Pivot row is chosen as having the lowest quotient when elements of
							# the pivot column divide the right-hand side

							# Slice excluding the objective rows
							a   =      slice(self.n_stages ,				self.n_rows							)

							# RHS
							a   =      tableau[s, -1]

							# Elements of pivot column within slice
							a   =      tableau[s, col_idx]

							# Array filled with nans
							a   =      np.full(self.n_rows - self.n_stages ,				np.nan							)

							# If element in pivot column is greater than zeron_stages, return
							# quotient or nan otherwise
							a   =      np.divide(__lowerCamelCase ,				__lowerCamelCase ,				out=__lowerCamelCase ,				where=divisor > 0							)

							# Arg of minimum quotient excluding the nan values. n_stages is added
							# to compensate for earlier exclusion of objective columns
							a   =      np.nanargmin(__lowerCamelCase							) + self.n_stages
							return row_idx, col_idx





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					int							) ->					np.ndarray:
							a   =      tableau[row_idx].copy()

							a   =      piv_row[col_idx]

							# Entry becomes 1
							piv_row *= 1 / piv_val

							# Variable in pivot column becomes basic, ie the only non-zero entry
							for idx, coeff in enumerate(tableau[:, col_idx]							):
										tableau[idx] += -coeff * piv_row
							a   =      piv_row
							return tableau





				def 							__UpperCAmelCase							(				self    :					Tuple ,				__lowerCamelCase    :					np.ndarray							) ->					np.ndarray:
							self.objectives.pop()

							if not self.objectives:
										return tableau

							# Slice containing ids for artificial columns
							a   =      slice(-self.n_art_vars - 1 ,				-1							)

							# Delete the artificial variable columns
							a   =      np.delete(__lowerCamelCase ,				__lowerCamelCase ,				axis=1							)

							# Delete the objective row of the first stage
							a   =      np.delete(__lowerCamelCase ,				0 ,				axis=0							)

							a   =      1
							self.n_rows -= 1
							a   =      0
							a   =      False
							return tableau





				def 							__UpperCAmelCase							(				self    :					Any							) ->					dict[Any, Any]:
							# Stop simplex algorithm from cycling.
							for _ in range(1_00							):
										# Completion of each stage removes an objective. If both stages
										# are complete, then no objectives are left
										if not self.objectives:
													a   =      self.generate_col_titles(
													    self.n_vars ,				self.n_slack ,				self.n_art_vars							)

													# Find the values of each variable at optimal solution
													return self.interpret_tableau(self.tableau ,				self.col_titles							)

										a   ,		a   =      self.find_pivot(self.tableau							)

										# If there are no more negative values in objective row
										if self.stop_iter:
													# Delete artificial variable columns and rows. Update attributes
													a   =      self.change_stage(self.tableau							)
										else:
													a   =      self.pivot(self.tableau ,				__lowerCamelCase ,				__lowerCamelCase							)
							return {}





				def 							__UpperCAmelCase							(				self    :					int ,				__lowerCamelCase    :					np.ndarray ,				__lowerCamelCase    :					list[str]							) ->					dict[str, float]:
							a   =      {"P": abs(tableau[0, -1]							)}

							for i in range(self.n_vars							):
										# Gives ids of nonzero entries in the ith column
										a   =      np.nonzero(tableau[:, i]							)
										a   =      len(nonzero[0]							)

										# First entry in the nonzero ids
										a   =      nonzero[0][0]
										a   =      tableau[nonzero_rowidx, i]

										# If there is only one nonzero value in column, which is one
										if n_nonzero == nonzero_val == 1:
													a   =      tableau[nonzero_rowidx, -1]
													a   =      rhs_val

        # Check for basic variables
							for title in col_titles:
										# Don't add RHS or slack variables to output dict
										if title[0] not in "R-s-a":
													output_dict.setdefault(__lowerCamelCase ,				0							)
							return output_dict


if __name__ == "__main__":
		import doctest

		doctest.testmod()






