




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

__lowerCAmelCase		:    Optional[int]	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    List[Any]	      =		{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__lowerCAmelCase		:    Optional[Any]	      =		{
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

__lowerCAmelCase		:    Any	      =		{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}




@lru_cache()
def       __magic_name__    (			):






			'''simple docstring'''







			a   =      (
			    list(range(ord("!"       ),  ord("~"       ) + 1       )       ) + list(range(ord("¡"       ),  ord("¬"       ) + 1       )       ) + list(range(ord("®"       ),  ord("ÿ"       ) + 1       )       )
			)
			a   =      bs[:]
			a   =      0
			for b in range(2**8       ):
						if b not in bs:
									bs.append(A       )
									cs.append(2**8 + n       )
									n += 1
			a   =      [chr(A       ) for n in cs]
			return dict(zip(A,  A       )       )




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






			'''simple docstring'''







			a   =      set()
			a   =      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)       )
						a   =      char
			return pairs






class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							Dict										= VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_	:							List[Any]										= PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_	:							Any										= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_	:							Optional[Any]										= ["""input_ids""", """attention_mask"""]





				def __init__(				self    :					Dict ,				__lowerCamelCase    :					int ,				__lowerCamelCase    :					Union[str, Any] ,				__lowerCamelCase    :					Union[str, Any]="replace" ,				__lowerCamelCase    :					int="<|endoftext|>" ,				__lowerCamelCase    :					Tuple="<|endoftext|>" ,				__lowerCamelCase    :					int="<|endoftext|>" ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					Optional[int]=False ,				__lowerCamelCase    :					str=False ,				**__lowerCamelCase    :					Tuple ,				) ->					Optional[int]:
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else bos_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else eos_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else unk_token
							a   =      AddedToken(__lowerCamelCase ,				lstrip=__lowerCamelCase ,				rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase ,				__lowerCamelCase							) else pad_token
							super().__init__(
							    errors=__lowerCamelCase ,				unk_token=__lowerCamelCase ,				bos_token=__lowerCamelCase ,				eos_token=__lowerCamelCase ,				pad_token=__lowerCamelCase ,				add_prefix_space=__lowerCamelCase ,				add_bos_token=__lowerCamelCase ,				**__lowerCamelCase ,				)
							a   =      add_bos_token

							with open(__lowerCamelCase ,				encoding="utf-8"							) as vocab_handle:
										a   =      json.load(__lowerCamelCase							)
							a   =      {v: k for k, v in self.encoder.items()}
							a   =      errors  # how to handle errors in decoding
							a   =      bytes_to_unicode()
							a   =      {v: k for k, v in self.byte_encoder.items()}
							with open(__lowerCamelCase ,				encoding="utf-8"							) as merges_handle:
										a   =      merges_handle.read().split("\n"							)[1:-1]
							a   =      [tuple(merge.split()							) for merge in bpe_merges]
							a   =      dict(zip(__lowerCamelCase ,				range(len(__lowerCamelCase							)							)							)							)
							a   =      {}
							a   =      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							a   =      re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Union[str, Any]:
							return len(self.encoder							)





				def 							__UpperCAmelCase							(				self    :					Tuple							) ->					str:
							return dict(self.encoder ,				**self.added_tokens_encoder							)





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Dict							) ->					Optional[int]:
							if token in self.cache:
										return self.cache[token]
							a   =      tuple(__lowerCamelCase							)
							a   =      get_pairs(__lowerCamelCase							)

							if not pairs:
										return token

							while True:
										a   =      min(__lowerCamelCase ,				key=lambda __lowerCamelCase							: self.bpe_ranks.get(__lowerCamelCase ,				float("inf"							)							)							)
										if bigram not in self.bpe_ranks:
													break
										a   ,		a   =      bigram
										a   =      []
										a   =      0
										while i < len(__lowerCamelCase							):
													try:
																a   =      word.index(__lowerCamelCase ,				__lowerCamelCase							)
													except ValueError:
																new_word.extend(word[i:]							)
																break
													else:
																new_word.extend(word[i:j]							)
																a   =      j

													if word[i] == first and i < len(__lowerCamelCase							) - 1 and word[i + 1] == second:
																new_word.append(first + second							)
																i += 2
													else:
																new_word.append(word[i]							)
																i += 1
										a   =      tuple(__lowerCamelCase							)
										a   =      new_word
										if len(__lowerCamelCase							) == 1:
													break
										else:
													a   =      get_pairs(__lowerCamelCase							)
							a   =      " ".join(__lowerCamelCase							)
							a   =      word
							return word





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Optional[Any] ,				__lowerCamelCase    :					Union[str, Any]=None							) ->					Union[str, Any]:
							if self.add_bos_token:
										a   =      [self.bos_token_id]
							else:
										a   =      []

							a   =      bos_token_ids + token_ids_a

							if token_ids_a is None:
										return output

							return output + bos_token_ids + token_ids_a





				def 							__UpperCAmelCase							(				self    :					List[Any] ,				__lowerCamelCase    :					List[int] ,				__lowerCamelCase    :					Optional[List[int]] = None ,				__lowerCamelCase    :					bool = False							) ->					List[int]:
							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__lowerCamelCase ,				token_ids_a=__lowerCamelCase ,				already_has_special_tokens=__lowerCamelCase							)

							if not self.add_bos_token:
										return super().get_special_tokens_mask(
										    token_ids_a=__lowerCamelCase ,				token_ids_a=__lowerCamelCase ,				already_has_special_tokens=__lowerCamelCase							)

							if token_ids_a is None:
										return [1] + ([0] * len(__lowerCamelCase							))
							return [1] + ([0] * len(__lowerCamelCase							)) + [1] + ([0] * len(__lowerCamelCase							))





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Optional[int]							) ->					str:
							a   =      []
							for token in re.findall(self.pat ,				__lowerCamelCase							):
										a   =      "".join(
										    self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
										bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase							).split(" "							)							)
							return bpe_tokens





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					str							) ->					Union[str, Any]:
							return self.encoder.get(__lowerCamelCase ,				self.encoder.get(self.unk_token							)							)





				def 							__UpperCAmelCase							(				self    :					List[str] ,				__lowerCamelCase    :					Any							) ->					Any:
							return self.decoder.get(__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Union[str, Any]							) ->					Tuple:
							a   =      "".join(__lowerCamelCase							)
							a   =      bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8" ,				errors=self.errors							)
							return text





				def 							__UpperCAmelCase							(				self    :					Dict ,				__lowerCamelCase    :					str ,				__lowerCamelCase    :					Optional[str] = None							) ->					Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a   =      os.path.join(
							    __lowerCamelCase ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
							a   =      os.path.join(
							    __lowerCamelCase ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

							with open(__lowerCamelCase ,				"w" ,				encoding="utf-8"							) as f:
										f.write(json.dumps(self.encoder ,				indent=2 ,				sort_keys=__lowerCamelCase ,				ensure_ascii=__lowerCamelCase							) + "\n"							)

							a   =      0
							with open(__lowerCamelCase ,				"w" ,				encoding="utf-8"							) as writer:
										writer.write("#version: 0.2\n"							)
										for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,				key=lambda __lowerCamelCase							: kv[1]							):
													if index != token_index:
																logger.warning(
																    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																    " Please check that the tokenizer is not corrupted!"							)
																a   =      token_index
													writer.write(" ".join(__lowerCamelCase							) + "\n"							)
													index += 1

							return vocab_file, merge_file





				def 							__UpperCAmelCase							(				self    :					str ,				__lowerCamelCase    :					Tuple ,				__lowerCamelCase    :					Optional[Any]=False ,				**__lowerCamelCase    :					str							) ->					List[str]:
							a   =      kwargs.pop("add_prefix_space" ,				self.add_prefix_space							)
							if is_split_into_words or add_prefix_space:
										a   =      " " + text
							return (text, kwargs)





				def 							__UpperCAmelCase							(				self    :					Optional[int] ,				__lowerCamelCase    :					"Conversation"							) ->					List[int]:
							a   =      []
							for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(__lowerCamelCase ,				add_special_tokens=__lowerCamelCase							) + [self.eos_token_id]							)
							if len(__lowerCamelCase							) > self.model_max_length:
										a   =      input_ids[-self.model_max_length :]
							return input_ids












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		:    List[Any]	      =		16
__lowerCAmelCase		:    List[Any]	      =		32




def       __magic_name__    (			A		: List[Any],  A		: List[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		: Tuple       ):
						# 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		: Tuple       ):
						# 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()






