



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

from ..utils import logging


__snake_case						:Optional[Any]      		=     logging.get_logger(__name__)





def      __snake_case	(			_UpperCAmelCase=None   , _UpperCAmelCase=None				):
    return field(default_factory=lambda: default   , metadata=_UpperCAmelCase				)

@dataclass
class     _A       :
       UpperCamelCase__  :    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'''
               )
           }      ,)

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

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

       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}      ,)
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}      ,)
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Use FP16 to accelerate inference.'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Benchmark training of model'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Verbose memory tracing'''}    )
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}      ,)
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={
               '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
           }      ,)
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Trace memory line by line'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Save result to a CSV file'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Save all print statements in a log file'''}    )
       UpperCamelCase__  :    bool											=						field(default=__UpperCAmelCase      ,metadata={'''help''': '''Whether to print environment information'''}    )
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,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.'''
               )
           }      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''inference_time_{round(time()    )}.csv'''      ,metadata={'''help''': '''CSV filename used if saving time results to csv.'''}      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''inference_memory_{round(time()    )}.csv'''      ,metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''train_time_{round(time()    )}.csv'''      ,metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''train_memory_{round(time()    )}.csv'''      ,metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''env_info_{round(time()    )}.csv'''      ,metadata={'''help''': '''CSV filename used if saving environment information.'''}      ,)
       UpperCamelCase__  :    str											=						field(
           default=F'''log_{round(time()    )}.csv'''      ,metadata={'''help''': '''Log filename used if print statements are saved in log.'''}      ,)
       UpperCamelCase__  :    int											=						field(default=3      ,metadata={'''help''': '''Times an experiment will be run.'''}    )
       UpperCamelCase__  :    bool											=						field(
           default=__UpperCAmelCase      ,metadata={
               '''help''': (
                   '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
                   ''' model weights.'''
               )
           }      ,)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''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.'''     ,		__SCREAMING_SNAKE_CASE     ,		)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           return json.dumps(dataclasses.asdict(self)     ,		indent=2)



       @property
       def 		_lowerCamelCase			(		self							:     str):


           '''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 		_lowerCamelCase			(		self							:     int):


           '''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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length, 2)   , _UpperCAmelCase				)
    else:
        __a						       =				np.full((len(_UpperCAmelCase				), sequence_length)   , _UpperCAmelCase				)

    for i, tensor in enumerate(_UpperCAmelCase				):
        if padding_side == "right":
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]
        else:
            if isinstance(_UpperCAmelCase   , _UpperCAmelCase				):
                __a						       =				tensor[:sequence_length]
            else:
                __a						       =				tensor[:sequence_length]

    return out_tensor.tolist()





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				ord(_UpperCAmelCase				)
    if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
        return True
    __a						       =				unicodedata.category(_UpperCAmelCase				)
    if cat.startswith('''P'''				):
        return True
    return False

@dataclass
class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    PreTrainedTokenizerBase
       UpperCamelCase__  :    Union[bool, str, PaddingStrategy]											=						True
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    Optional[int]											=						None
       UpperCamelCase__  :    int											=						-100
       UpperCamelCase__  :    str											=						"pt"



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]):


           '''simple docstring'''

           import torch

           __a						       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
           __a						       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
           __a						       =				self.tokenizer.pad(
               __SCREAMING_SNAKE_CASE     ,		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

           __a						       =				torch.tensor(batch['''entity_ids''']).shape[1]
           __a						       =				self.tokenizer.padding_side
           if padding_side == "right":
               __a						       =				[
                   list(__SCREAMING_SNAKE_CASE) + [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) for label in labels
               ]
           else:
               __a						       =				[
                   [self.label_pad_token_id] * (sequence_length - len(__SCREAMING_SNAKE_CASE)) + list(__SCREAMING_SNAKE_CASE) for label in labels
               ]

           __a						       =				[feature['''ner_tags'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		-1     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				[feature['''original_entity_spans'''] for feature in features]
           __a						       =				padding_tensor(__SCREAMING_SNAKE_CASE     ,		(-1, -1)     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				{k: torch.tensor(__SCREAMING_SNAKE_CASE     ,		dtype=torch.intaa) for k, v in batch.items()}

           return batch





