




"""simple docstring"""


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 experiment tracking 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
#
########################################################################

_A													=      1_6
_A													=      3_2



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase = 16				)						->    Optional[Any]:
      lowerCAmelCase__      :			List[Any]   =	AutoTokenizer.from_pretrained("""bert-base-cased"""				)
      lowerCAmelCase__      :			Dict   =	load_dataset("""glue"""    ,		"""mrpc"""				)

      def tokenize_function(__UpperCAmelCase				):
            # max_length=None => use the model max length (it's actually the default)
            lowerCAmelCase__      :			str   =	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():
            lowerCAmelCase__      :			List[Any]   =	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
      lowerCAmelCase__      :			Any   =	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.
            lowerCAmelCase__      :			int   =	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__      :			Optional[int]   =	16
            elif accelerator.mixed_precision != "no":
                  lowerCAmelCase__      :			int   =	8
            else:
                  lowerCAmelCase__      :			Optional[Any]   =	None

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

      # Instantiate dataloaders.
      lowerCAmelCase__      :			Union[str, Any]   =	DataLoader(
          tokenized_datasets["""train"""]    ,		shuffle=__UpperCAmelCase    ,		collate_fn=__UpperCAmelCase    ,		batch_size=__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	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

  _A													=      mocked_dataloaders  # noqa: F811



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[str]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""    ,		__UpperCAmelCase				) == "1":
            lowerCAmelCase__      :			List[str]   =	2
      # Initialize Accelerator

      # New Code #
      # We pass in "all" to `log_with` to grab all available trackers in the environment
      # Note: If using a custom `Tracker` class, should be passed in here such as:
      # >>> log_with = ["all", MyCustomTrackerClassInstance()]
      if args.with_tracking:
            lowerCAmelCase__      :			List[str]   =	Accelerator(
                cpu=args.cpu    ,		mixed_precision=args.mixed_precision    ,		log_with="""all"""    ,		project_dir=args.project_dir				)
      else:
            lowerCAmelCase__      :			str   =	Accelerator(cpu=args.cpu    ,		mixed_precision=args.mixed_precision				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCAmelCase__      :			Optional[int]   =	config["""lr"""]
      lowerCAmelCase__      :			List[Any]   =	int(config["""num_epochs"""]				)
      lowerCAmelCase__      :			str   =	int(config["""seed"""]				)
      lowerCAmelCase__      :			List[str]   =	int(config["""batch_size"""]				)
      set_seed(__UpperCAmelCase				)

      lowerCAmelCase__ ,   lowerCAmelCase__      :			Tuple   =	get_dataloaders(__UpperCAmelCase    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Union[str, Any]   =	evaluate.load("""glue"""    ,		"""mrpc"""				)

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

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

      # Instantiate optimizer
      lowerCAmelCase__      :			Dict   =	AdamW(params=model.parameters()    ,		lr=__UpperCAmelCase				)

      # Instantiate scheduler
      lowerCAmelCase__      :			List[str]   =	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.
      lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	accelerator.prepare(
          __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # New Code #
      # We need to initialize the trackers we use. Overall configurations can also be stored
      if args.with_tracking:
            lowerCAmelCase__      :			Tuple   =	os.path.split(__UpperCAmelCase				)[-1].split("""."""				)[0]
            accelerator.init_trackers(__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Now we train the model
      for epoch in range(__UpperCAmelCase				):
            model.train()
            # New Code #
            # For our tracking example, we will log the total loss of each epoch
            if args.with_tracking:
                  lowerCAmelCase__      :			Tuple   =	0
            for step, batch in enumerate(__UpperCAmelCase				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  lowerCAmelCase__      :			int   =	model(**__UpperCAmelCase				)
                  lowerCAmelCase__      :			Optional[Any]   =	outputs.loss
                  # New Code #
                  if args.with_tracking:
                        total_loss += loss.detach().float()
                  lowerCAmelCase__      :			Union[str, Any]   =	loss / gradient_accumulation_steps
                  accelerator.backward(__UpperCAmelCase				)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            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():
                        lowerCAmelCase__      :			Union[str, Any]   =	model(**__UpperCAmelCase				)
                  lowerCAmelCase__      :			int   =	outputs.logits.argmax(dim=-1				)
                  lowerCAmelCase__ ,   lowerCAmelCase__      :			str   =	accelerator.gather_for_metrics((predictions, batch["""labels"""])				)
                  metric.add_batch(
                      predictions=__UpperCAmelCase    ,		references=__UpperCAmelCase    ,		)

            lowerCAmelCase__      :			Union[str, Any]   =	metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"""epoch {epoch}:"""    ,		__UpperCAmelCase				)

            # New Code #
            # To actually log, we call `Accelerator.log`
            # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
            if args.with_tracking:
                  accelerator.log(
                      {
                          """accuracy""": eval_metric["""accuracy"""],
                          """f1""": eval_metric["""f1"""],
                          """train_loss""": total_loss.item() / len(__UpperCAmelCase				),
                          """epoch""": epoch,
                      }    ,		step=__UpperCAmelCase    ,		)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
      if args.with_tracking:
            accelerator.end_training()



def 	lowercase_				(			)						->    Any:
      lowerCAmelCase__      :			Tuple   =	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(
          """--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(
          """--project_dir"""    ,		type=__UpperCAmelCase    ,		default="""logs"""    ,		help="""Location on where to store experiment tracking logs` and relevent project information"""    ,		)
      lowerCAmelCase__      :			Optional[Any]   =	parser.parse_args()
      lowerCAmelCase__      :			Tuple   =	{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__UpperCAmelCase    ,		__UpperCAmelCase				)


if __name__ == "__main__":
  main()











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  lowerCAmelCase__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

    # print entropy
      print(f"""{round(-1 * my_fir_sum				):.1f}"""				)

      # two len string
      lowerCAmelCase__      :			Tuple   =	sum(two_char_strings.values()				)
      lowerCAmelCase__      :			str   =	0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  lowerCAmelCase__      :			Optional[int]   =	cha + cha
                  if sequence in two_char_strings:
                        lowerCAmelCase__      :			int   =	two_char_strings[sequence]
                        lowerCAmelCase__      :			str   =	int(__UpperCAmelCase				) / all_sum
                        my_sec_sum += prob * math.loga(__UpperCAmelCase				)

    # print second entropy
      print(f"""{round(-1 * my_sec_sum				):.1f}"""				)

      # print the difference between them
      print(f"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum)				):.1f}"""				)



def 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0    ,		len(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    Any:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
  main()





