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
#
########################################################################

lowercase_  : str								=					1_6
lowercase_  : List[Any]								=					3_2







def       A__   (						snake_case_			:		Accelerator							,					snake_case_			:		int = 16   ):
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   AutoTokenizer.from_pretrained('''bert-base-cased'''   )
 SCREAMING_SNAKE_CASE__: str=   load_dataset('''glue'''							,					'''mrpc'''   )

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

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

  return tokenizer.pad(
      snake_case_							,					padding='''longest'''							,					max_length=snake_case_							,					pad_to_multiple_of=snake_case_							,					return_tensors='''pt'''							,					)

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE__: Tuple=   DataLoader(
     tokenized_datasets['''train''']							,					shuffle=snake_case_							,					collate_fn=snake_case_							,					batch_size=snake_case_   )
 SCREAMING_SNAKE_CASE__: Tuple=   DataLoader(
     tokenized_datasets['''validation''']							,					shuffle=snake_case_							,					collate_fn=snake_case_							,					batch_size=snake_case_   )

 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

      lowercase_  : int								=					mocked_dataloaders  # noqa: F811







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		int   ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,					snake_case_   ) == "1":
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   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:
  SCREAMING_SNAKE_CASE__: int=   Accelerator(
      cpu=args.cpu							,					mixed_precision=args.mixed_precision							,					log_with='''all'''							,					project_dir=args.project_dir   )
 else:
  SCREAMING_SNAKE_CASE__: List[str]=   Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision   )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 SCREAMING_SNAKE_CASE__: List[str]=   config['''lr''']
 SCREAMING_SNAKE_CASE__: Optional[int]=   int(config['''num_epochs''']   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   int(config['''seed''']   )
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   int(config['''batch_size''']   )
 set_seed(snake_case_   )

 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   get_dataloaders(snake_case_							,					snake_case_   )
 SCREAMING_SNAKE_CASE__: int=   evaluate.load('''glue'''							,					'''mrpc'''   )

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

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 SCREAMING_SNAKE_CASE__: Optional[Any]=   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''							,					return_dict=snake_case_   )

 # 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).
 SCREAMING_SNAKE_CASE__: Optional[int]=   model.to(accelerator.device   )

 # Instantiate optimizer
 SCREAMING_SNAKE_CASE__: Tuple=   AdamW(params=model.parameters()							,					lr=snake_case_   )

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   get_linear_schedule_with_warmup(
     optimizer=snake_case_							,					num_warmup_steps=100							,					num_training_steps=(len(snake_case_   ) * 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.
 SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: List[Any]=   accelerator.prepare(
     snake_case_							,					snake_case_							,					snake_case_							,					snake_case_							,					snake_case_   )

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

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

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

  SCREAMING_SNAKE_CASE__: Optional[Any]=   metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'epoch {epoch}:'							,					snake_case_   )

  # 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(snake_case_   ),
           '''epoch''': epoch,
       }							,					step=snake_case_							,					)

    # 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       A__   (						):
 SCREAMING_SNAKE_CASE__: Any=   argparse.ArgumentParser(description='''Simple example of training script.'''   )
 parser.add_argument(
     '''--mixed_precision'''							,					type=snake_case_							,					default=snake_case_							,					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=snake_case_							,					default='''logs'''							,					help='''Location on where to store experiment tracking logs` and relevent project information'''							,					)
 SCREAMING_SNAKE_CASE__: Optional[int]=   parser.parse_args()
 SCREAMING_SNAKE_CASE__: int=   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(snake_case_							,					snake_case_   )


if __name__ == "__main__":
      main()





'''simple docstring'''







import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


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

lowerCAmelCase     :  Union[str, Any]											=    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase     :  Optional[Any]											=    {
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase     :  List[str]											=    {
    """Salesforce/codegen-350M-mono""": 20_48,
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     VOCAB_FILES_NAMES
     __magic_name__           =     PRETRAINED_VOCAB_FILES_MAP
     __magic_name__           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__           =     ["input_ids", "attention_mask"]
     __magic_name__           =     CodeGenTokenizer







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__="<|endoftext|>"   ,						snake_case__=False   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                snake_case__   ,						snake_case__   ,						tokenizer_file=snake_case__   ,						unk_token=snake_case__   ,						bos_token=snake_case__   ,						eos_token=snake_case__   ,						add_prefix_space=snake_case__   ,						**snake_case__   ,						)

            if kwargs.pop('add_bos_token'   ,						snake_case__ ):
                   _lowerCAmelCase     :				Tuple       =							kwargs.pop('name_or_path'   ,						'' )
                   raise ValueError(
                       'Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'
                       'Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'
                       F'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
                       F'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
                       'This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'
                       ' so that the fast tokenizer works correctly.' )

            _lowerCAmelCase     :				List[str]       =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get('add_prefix_space'   ,						snake_case__ ) != add_prefix_space:
                   _lowerCAmelCase     :				List[Any]       =							getattr(snake_case__   ,						pre_tok_state.pop('type' ) )
                   _lowerCAmelCase     :				Dict       =							add_prefix_space
                   _lowerCAmelCase     :				List[Any]       =							pre_tok_class(**snake_case__ )

            _lowerCAmelCase     :				Tuple       =							add_prefix_space







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )
            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						*snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							kwargs.get('is_split_into_words'   ,						snake_case__ )

            assert self.add_prefix_space or not is_split_into_words, (
                F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*snake_case__   ,						**snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self._tokenizer.model.save(snake_case__   ,						name=snake_case__ )
            return tuple(snake_case__ )







     def a						(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							super().decode(
                token_ids=snake_case__   ,						skip_special_tokens=snake_case__   ,						clean_up_tokenization_spaces=snake_case__   ,						**snake_case__   ,						)

            if truncate_before_pattern is not None and len(snake_case__ ) > 0:
                   _lowerCAmelCase     :				Optional[int]       =							self.truncate(snake_case__   ,						snake_case__ )

            return decoded_text







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            def find_re(snake_case__   ,						snake_case__   ,						snake_case__ ):
                   _lowerCAmelCase     :				List[Any]       =							pattern.search(snake_case__   ,						snake_case__ )
                   return m.start() if m else -1

            _lowerCAmelCase     :				Any       =							[re.compile(snake_case__   ,						re.MULTILINE ) for pattern in truncate_before_pattern]

            _lowerCAmelCase     :				Dict       =							list(re.finditer('^print'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[int]       =							completion[: prints[1].start()]

            _lowerCAmelCase     :				Optional[int]       =							list(re.finditer('^def'   ,						snake_case__   ,						re.MULTILINE ) )

            if len(snake_case__ ) > 1:
                   _lowerCAmelCase     :				Optional[Any]       =							completion[: defs[1].start()]

            _lowerCAmelCase     :				int       =							0

            _lowerCAmelCase     :				Tuple       =							[
                pos for pos in [find_re(snake_case__   ,						snake_case__   ,						snake_case__ ) for terminal in terminals] if pos != -1
            ]

            if len(snake_case__ ) > 0:
                   return completion[: min(snake_case__ )]
            else:
                   return completion

