"""simple docstring"""






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

from ..utils import logging


lowerCAmelCase__  :  Union[str, Any]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase=None					,			lowerCamelCase=None       ):
				return field(default_factory=lambda: default					,			metadata=lowerCamelCase       )







@dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = 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"
					        )
					    }      ,    )

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

					snake_case__				    = list_field(
					    default=[8, 32, 1_28, 5_12]      ,    metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}      ,    )

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									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.'    ,__lowerCAmelCase    ,)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return json.dumps(dataclasses.asdict(self			)    ,indent=2			)







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									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   :	Any			):
									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




"""simple docstring"""


from __future__ import annotations






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_failure_array(lowercase						)

  # 2) Step through text searching for pattern
  _UpperCAmelCase       , _UpperCAmelCase         =					0, 0  # index into text, pattern
  while i < len(lowercase						):
    if pattern[j] == text[i]:
      if j == (len(lowercase						) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCAmelCase         =					failure[j - 1]
      continue
    i += 1
  return False






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[0]
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					1
  while j < len(lowercase						):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCAmelCase         =					failure[i - 1]
      continue
    j += 1
    failure.append(lowercase						)
  return failure


if __name__ == "__main__":
    # Test 1)
    UpperCAmelCase__     						=  """abc1abc12"""
    UpperCAmelCase__     						=  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
    UpperCAmelCase__     						=  """alskfjaldsk23adsfabcabc"""
    assert kmp(pattern, texta) and not kmp(pattern, texta)

    # Test 2)
    UpperCAmelCase__     						=  """ABABX"""
    UpperCAmelCase__     						=  """ABABZABABYABABX"""
    assert kmp(pattern, text)

    # Test 3)
    UpperCAmelCase__     						=  """AAAB"""
    UpperCAmelCase__     						=  """ABAAAAAB"""
    assert kmp(pattern, text)

    # Test 4)
    UpperCAmelCase__     						=  """abcdabcy"""
    UpperCAmelCase__     						=  """abcxabcdabxabcdabcdabcy"""
    assert kmp(pattern, text)

    # Test 5)
    UpperCAmelCase__     						=  """aabaabaaa"""
    assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
