

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def      snake_case_				(__A					:						str ,       __A					:						Dict ,       __A					:						str ,       __A					:						str					)      ->     int:
   if isinstance(__A ,       __A					):
      __lowerCAmelCase  :      Optional[int]    =						np.full((len(__A					), sequence_length, 2) ,       __A					)
   else:
      __lowerCAmelCase  :      Dict    =						np.full((len(__A					), sequence_length) ,       __A					)

   for i, tensor in enumerate(__A					):
      if padding_side == "right":
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      List[str]    =						tensor[:sequence_length]
         else:
            __lowerCAmelCase  :      Optional[int]    =						tensor[:sequence_length]
      else:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      Dict    =						tensor[:sequence_length]
         else:
            __lowerCAmelCase  :      Optional[int]    =						tensor[:sequence_length]

   return out_tensor.tolist()





def      snake_case_				(__A					:						Dict					)      ->     Optional[Any]:
   __lowerCAmelCase  :      List[Any]    =						ord(__A					)
   if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
      return True
   __lowerCAmelCase  :      Optional[Any]    =						unicodedata.category(__A					)
   if cat.startswith("""P"""					):
      return True
   return False



@dataclass
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      PreTrainedTokenizerBase
  lowerCamelCase   :      Union[bool, str, PaddingStrategy]										=True
  lowerCamelCase   :      Optional[int]										=None
  lowerCamelCase   :      Optional[int]										=None
  lowerCamelCase   :      int										=-100
  lowerCamelCase   :      str										="pt"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   List[Any]   )							->     str:






     """simple docstring"""







     import torch

     __lowerCAmelCase  :      Any    =						"""label""" if """label""" in features[0].keys() else """labels"""
     __lowerCAmelCase  :      List[Any]    =						[feature[label_name] for feature in features] if label_name in features[0].keys() else None
     __lowerCAmelCase  :      Optional[int]    =						self.tokenizer.pad(
         lowerCAmelCase	,							padding=self.padding	,							max_length=self.max_length	,							pad_to_multiple_of=self.pad_to_multiple_of	,							return_tensors="""pt""" if labels is None else None	,							)

     if labels is None:
        return batch

     __lowerCAmelCase  :      Any    =						torch.tensor(batch["""entity_ids"""]   ).shape[1]
     __lowerCAmelCase  :      int    =						self.tokenizer.padding_side
     if padding_side == "right":
        __lowerCAmelCase  :      Tuple    =						[
            list(lowerCAmelCase   ) + [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase   )) for label in labels
        ]
     else:
        __lowerCAmelCase  :      int    =						[
            [self.label_pad_token_id] * (sequence_length - len(lowerCAmelCase   )) + list(lowerCAmelCase   ) for label in labels
        ]

     __lowerCAmelCase  :      Dict    =						[feature["""ner_tags"""] for feature in features]
     __lowerCAmelCase  :      Tuple    =						padding_tensor(lowerCAmelCase	,							-1	,							lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						[feature["""original_entity_spans"""] for feature in features]
     __lowerCAmelCase  :      Dict    =						padding_tensor(lowerCAmelCase	,							(-1, -1)	,							lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      Optional[Any]    =						{k: torch.tensor(lowerCAmelCase	,							dtype=torch.intaa   ) for k, v in batch.items()}

     return batch





import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				()      ->     str:
   # Get the sagemaker specific mp parameters from smp_options variable.
   __lowerCAmelCase  :      List[Any]    =						os.getenv("""SM_HP_MP_PARAMETERS""" ,       """{}"""					)
   try:
      # Parse it and check the field "partitions" is included, it is required for model parallel.
      __lowerCAmelCase  :      Any    =						json.loads(__A					)
      if "partitions" not in smp_options:
         return False
   except json.JSONDecodeError:
      return False

   # Get the sagemaker specific framework parameters from mpi_options variable.
   __lowerCAmelCase  :      Any    =						os.getenv("""SM_FRAMEWORK_PARAMS""" ,       """{}"""					)
   try:
      # Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
      __lowerCAmelCase  :      Union[str, Any]    =						json.loads(__A					)
      if not mpi_options.get("""sagemaker_mpi_enabled""" ,       __A					):
         return False
   except json.JSONDecodeError:
      return False
   # Lastly, check if the `smdistributed` module is present.
   return importlib.util.find_spec("""smdistributed"""					) is not None


if is_sagemaker_model_parallel_available():
   import smdistributed.modelparallel.torch as smp

   smp.init()



@dataclass
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(
      default=""       ,							metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}       ,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






     """simple docstring"""







     super().__post_init__()
     warnings.warn(
         """`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
         """`TrainingArguments` instead."""	,							lowerCAmelCase	,							)







  @cached_property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     "torch.device":






     """simple docstring"""







     logger.info("""PyTorch: setting up devices"""   )
     if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
        logger.warning(
            """torch.distributed process group is initialized, but local_rank == -1. """
            """In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"""   )
     if self.no_cuda:
        __lowerCAmelCase  :      Optional[Any]    =						torch.device("""cpu"""   )
        __lowerCAmelCase  :      Union[str, Any]    =						0
     elif is_sagemaker_model_parallel_available():
        __lowerCAmelCase  :      int    =						smp.local_rank()
        __lowerCAmelCase  :      str    =						torch.device("""cuda"""	,							lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						1
     elif is_sagemaker_dp_enabled():
        import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

        torch.distributed.init_process_group(backend="""smddp"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK"""   )   )
        __lowerCAmelCase  :      Tuple    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      Optional[int]    =						1
     elif self.local_rank == -1:
        # if n_gpu is > 1 we'll use nn.DataParallel.
        # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
        # Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
        # trigger an error that a device index is missing. Index 0 takes into account the
        # GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
        # will use the first GPU in that env, i.e. GPU#1
        __lowerCAmelCase  :      List[Any]    =						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
        # Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
        # the default value.
        __lowerCAmelCase  :      Optional[int]    =						torch.cuda.device_count()
     else:
        # Here, we'll use torch.distributed.
        # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
        if not torch.distributed.is_initialized():
           torch.distributed.init_process_group(backend="""nccl"""	,							timeout=self.ddp_timeout_delta   )
        __lowerCAmelCase  :      List[str]    =						torch.device("""cuda"""	,							self.local_rank   )
        __lowerCAmelCase  :      int    =						1

     if device.type == "cuda":
        torch.cuda.set_device(lowerCAmelCase   )

     return device







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Any:






     """simple docstring"""







     if is_sagemaker_model_parallel_available():
        return smp.dp_size()

     return super().world_size







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     return not is_sagemaker_model_parallel_available()







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Tuple:






     """simple docstring"""







     return False


