
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase  :					int						    =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=None				) ->    int:
       return field(default_factory=lambda: default					,	metadata=SCREAMING_SNAKE_CASE__				)




@dataclass
class __snake_case		:
    _a		:						List[str]=				list_field(
        default=[]					,	metadata={
            "help": (
                "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                " of all available models"
            )
        }					,	)

    _a		:						List[int]=				list_field(
        default=[8]					,	metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}     )

    _a		:						List[int]=				list_field(
        default=[8, 32, 128, 512]					,	metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}					,	)

    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Use FP16 to accelerate inference."}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Benchmark training of model"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Verbose memory tracing"}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}					,	)
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
        }					,	)
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Trace memory line by line"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Save result to a CSV file"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Save all print statements in a log file"}     )
    _a		:						bool=				field(default=lowerCAmelCase					,	metadata={"help": "Whether to print environment information"}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": (
                "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                " for debugging / testing and on TPU."
            )
        }					,	)
    _a		:						str=				field(
        default=f"""inference_time_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving time results to csv."}					,	)
    _a		:						str=				field(
        default=f"""inference_memory_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving memory results to csv."}					,	)
    _a		:						str=				field(
        default=f"""train_time_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving time results to csv for training."}					,	)
    _a		:						str=				field(
        default=f"""train_memory_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving memory results to csv for training."}					,	)
    _a		:						str=				field(
        default=f"""env_info_{round(time()     )}.csv"""					,	metadata={"help": "CSV filename used if saving environment information."}					,	)
    _a		:						str=				field(
        default=f"""log_{round(time()     )}.csv"""					,	metadata={"help": "Log filename used if print statements are saved in log."}					,	)
    _a		:						int=				field(default=3					,	metadata={"help": "Times an experiment will be run."}     )
    _a		:						bool=				field(
        default=lowerCAmelCase					,	metadata={
            "help": (
                "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                " model weights."
            )
        }					,	)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           warnings.warn(
               f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""		,snake_case		,)
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return json.dumps(dataclasses.asdict(self      )		,indent=2      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if len(self.models      ) <= 0:
                  raise ValueError(
                      """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                      """ bert-base-cased` or `args.models = ['bert-base-cased']."""      )
           return self.models
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if not self.multi_process:
                  return False
           elif self.is_tpu:
                  logger.info("""Multiprocessing is currently not possible on TPU."""      )
                  return False
           else:
                  return True



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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      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					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							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					:						Dict					=					torch.tensor(batch['''entity_ids''']					).shape[1]
       __lowerCAmelCase					:						Optional[int]					=					self.tokenizer.padding_side
       if padding_side == "right":
             __lowerCAmelCase					:						Any					=					[
                 list(A_					) + [self.label_pad_token_id] * (sequence_length - len(A_					)) for label in labels
             ]
       else:
             __lowerCAmelCase					:						Optional[int]					=					[
                 [self.label_pad_token_id] * (sequence_length - len(A_					)) + list(A_					) for label in labels
             ]

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


