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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_								=							{
    '''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'''
        ),
    },
}

UpperCAmelCase_								=							{
    '''Salesforce/codegen-350M-mono''': 2_048,
}





class      lowerCAmelCase						(					__a			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE     :     Tuple		=PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE     :     Tuple		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE     :     Any		=["""input_ids""", """attention_mask"""]
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=CodeGenTokenizer



     def __init__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__="<|endoftext|>"				,      lowerCAmelCase__="<|endoftext|>"				,      lowerCAmelCase__="<|endoftext|>"				,      lowerCAmelCase__=False				,      **lowerCAmelCase__				,      ):
            super().__init__(
                a_				,      a_				,      tokenizer_file=a_				,      unk_token=a_				,      bos_token=a_				,      eos_token=a_				,      add_prefix_space=a_				,      **a_				,      )

            if kwargs.pop('add_bos_token'				,      a_       ):
                   _A=	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.'       )

            _A=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
            if pre_tok_state.get('add_prefix_space'				,      a_       ) != add_prefix_space:
                   _A=	getattr(a_				,      pre_tok_state.pop('type'       )       )
                   _A=	add_prefix_space
                   _A=	pre_tok_class(**a_       )

            _A=	add_prefix_space



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A=	kwargs.get('is_split_into_words'				,      a_       )
            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(*a_				,      **a_       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            _A=	kwargs.get('is_split_into_words'				,      a_       )

            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(*a_				,      **a_       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = None       ):
            _A=	self._tokenizer.model.save(a_				,      name=a_       )
            return tuple(a_       )



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__ = False				,      lowerCAmelCase__ = None				,      lowerCAmelCase__ = None				,      **lowerCAmelCase__				,      ):
            _A=	super().decode(
                token_ids=a_				,      skip_special_tokens=a_				,      clean_up_tokenization_spaces=a_				,      **a_				,      )

            if truncate_before_pattern is not None and len(a_       ) > 0:
                   _A=	self.truncate(a_				,      a_       )

            return decoded_text





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):



            def find_re(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	pattern.search(a_				,      a_       )
                   return m.start() if m else -1

            _A=	[re.compile(a_				,      re.MULTILINE       ) for pattern in truncate_before_pattern]

            _A=	list(re.finditer('^print'				,      a_				,      re.MULTILINE       )       )

            if len(a_       ) > 1:
                   _A=	completion[: prints[1].start()]

            _A=	list(re.finditer('^def'				,      a_				,      re.MULTILINE       )       )

            if len(a_       ) > 1:
                   _A=	completion[: defs[1].start()]

            _A=	0

            _A=	[
                pos for pos in [find_re(a_				,      a_				,      a_       ) for terminal in terminals] if pos != -1
            ]

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







#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist

def 							UpperCamelCase				(    *lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       with open(lowerCAmelCase_       ,					'r'   ) as fh:
              fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_EX   )
              try:
                     print(*lowerCAmelCase_   )
              finally:
                     fcntl.flock(lowerCAmelCase_       ,					fcntl.LOCK_UN   )


UpperCAmelCase_								=							int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
UpperCAmelCase_								=							torch.device('''cuda''', local_rank)
UpperCAmelCase_								=							socket.gethostname()

UpperCAmelCase_								=							F"[{hostname}-{local_rank}]"

try:
      # test distributed
      dist.init_process_group('''nccl''')
      dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
      dist.barrier()

      # test cuda is available and can allocate memory
      torch.cuda.is_available()
      torch.ones(1).cuda(local_rank)

      # global rank
      UpperCAmelCase_								=							dist.get_rank()
      UpperCAmelCase_								=							dist.get_world_size()

      printflock(F"{gpu} is OK (global rank: {rank}/{world_size})")

      dist.barrier()
      if rank == 0:
            printflock(F"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}")

except Exception:
      printflock(F"{gpu} is broken")
      raise