import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
      import tensorflow as tf
      from tensorflow.python.framework.errors_impl import ResourceExhaustedError

      from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
      import pyanvml.pyanvml as nvml

UpperCamelCase__											=					logging.get_logger(__name__)




def    lowerCAmelCase_ (     __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      def run_func(__A							):
            @wraps(__A							)
            def run_in_eager_mode(*__A,  **__A							):
                  return func(*__A,  **__A							)

            @wraps(__A							)
            @tf.function(experimental_compile=__A							)
            def run_in_graph_mode(*__A,  **__A							):
                  return func(*__A,  **__A							)

            if do_eager_mode is True:
                  if use_xla is not False:
                        raise ValueError(
                            "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."							)
                  return run_in_eager_mode
            else:
                  return run_in_graph_mode

      return run_func




def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				["tf.Tensor"]:







      '''simple docstring'''


      UpperCAmelCase__								=  random.Random()
      UpperCAmelCase__								=  [rng.randint(0,  vocab_size - 1							) for i in range(batch_size * sequence_length							)]
      return tf.constant(__A,  shape=(batch_size, sequence_length),  dtype=tf.intaa							)







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : TensorFlowBenchmarkArguments
   __UpperCAmelCase : PretrainedConfig
   __UpperCAmelCase : str      =			"TensorFlow"


   @property
   def 			lowercase_      (self     :						Any		)						->				str:
         """simple docstring"""



         return tf.__version__


   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				float:
         """simple docstring"""



         UpperCAmelCase__								=  self.args.strategy
         if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
         UpperCAmelCase__								=  self._prepare_inference_func(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
         return self._measure_speed(_inference		)


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				float:
         """simple docstring"""



         UpperCAmelCase__								=  self.args.strategy
         if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
         UpperCAmelCase__								=  self._prepare_train_func(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
         return self._measure_speed(_train		)


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				[Memory, Optional[MemorySummary]]:
         """simple docstring"""



         if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  self.args.strategy
         if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."		)
         UpperCAmelCase__								=  self._prepare_inference_func(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
         return self._measure_memory(_inference		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				[Memory, Optional[MemorySummary]]:
         """simple docstring"""



         if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  self.args.strategy
         if strategy is None:
               raise ValueError("A device strategy has to be initialized before using TensorFlow."		)

         UpperCAmelCase__								=  self._prepare_train_func(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)
         return self._measure_memory(_train		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				Callable[[], None]:
         """simple docstring"""



         UpperCAmelCase__								=  self.config_dict[model_name]

         if self.args.fpaa:
               raise NotImplementedError("Mixed precision is currently not supported."		)

         UpperCAmelCase__								=  (
             hasattr(__UpperCAmelCase       ,  "architectures"		)
             and isinstance(config.architectures       ,  __UpperCAmelCase		)
             and len(config.architectures		) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                     UpperCAmelCase__								=  "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                     UpperCAmelCase__								=  __import__("transformers"       ,  fromlist=[model_class]		)
                     UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     UpperCAmelCase__								=  model_cls(__UpperCAmelCase		)
               except ImportError:
                     raise ImportError(
                         f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                         " set `--only_pretrain_model` or `args.only_pretrain_model=True`."		)
         else:
               UpperCAmelCase__								=  TF_MODEL_MAPPING[config.__class__](__UpperCAmelCase		)

         # encoder-decoder has vocab size saved differently
         UpperCAmelCase__								=  config.vocab_size if hasattr(__UpperCAmelCase       ,  "vocab_size"		) else config.encoder.vocab_size
         UpperCAmelCase__								=  random_input_ids(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)

         @run_with_tf_optimizations(self.args.eager_mode       ,  self.args.use_xla		)
         def encoder_decoder_forward():
               return model(__UpperCAmelCase       ,  decoder_input_ids=__UpperCAmelCase       ,  training=__UpperCAmelCase		)

         @run_with_tf_optimizations(self.args.eager_mode       ,  self.args.use_xla		)
         def encoder_forward():
               return model(__UpperCAmelCase       ,  training=__UpperCAmelCase		)

         UpperCAmelCase__								=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

         return _inference


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int		)						->				Callable[[], None]:
         """simple docstring"""



         UpperCAmelCase__								=  self.config_dict[model_name]

         if self.args.eager_mode is not False:
               raise ValueError("Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."		)

         if self.args.fpaa:
               raise NotImplementedError("Mixed precision is currently not supported."		)

         UpperCAmelCase__								=  (
             hasattr(__UpperCAmelCase       ,  "architectures"		)
             and isinstance(config.architectures       ,  __UpperCAmelCase		)
             and len(config.architectures		) > 0
         )
         if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                     UpperCAmelCase__								=  "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                     UpperCAmelCase__								=  __import__("transformers"       ,  fromlist=[model_class]		)
                     UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     UpperCAmelCase__								=  model_cls(__UpperCAmelCase		)
               except ImportError:
                     raise ImportError(
                         f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                         " set `--only_pretrain_model` or `args.only_pretrain_model=True`."		)
         else:
               UpperCAmelCase__								=  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCAmelCase		)

         # encoder-decoder has vocab size saved differently
         UpperCAmelCase__								=  config.vocab_size if hasattr(__UpperCAmelCase       ,  "vocab_size"		) else config.encoder.vocab_size
         UpperCAmelCase__								=  random_input_ids(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase		)

         @run_with_tf_optimizations(self.args.eager_mode       ,  self.args.use_xla		)
         def encoder_decoder_train():
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  decoder_input_ids=__UpperCAmelCase       ,  labels=__UpperCAmelCase       ,  training=__UpperCAmelCase		)[0]
               UpperCAmelCase__								=  tf.gradients(__UpperCAmelCase       ,  model.trainable_variables		)
               return gradients

         @run_with_tf_optimizations(self.args.eager_mode       ,  self.args.use_xla		)
         def encoder_train():
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  labels=__UpperCAmelCase       ,  training=__UpperCAmelCase		)[0]
               UpperCAmelCase__								=  tf.gradients(__UpperCAmelCase       ,  model.trainable_variables		)
               return gradients

         UpperCAmelCase__								=  encoder_decoder_train if config.is_encoder_decoder else encoder_train

         return _train


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						Dict		)						->				float:
         """simple docstring"""



         with self.args.strategy.scope():
               try:
                     if self.args.is_tpu or self.args.use_xla:
                           # run additional 10 times to stabilize compilation for tpu
                           logger.info("Do inference on TPU. Running model 5 times to stabilize compilation"		)
                           timeit.repeat(__UpperCAmelCase       ,  repeat=1       ,  number=5		)

                     # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                     UpperCAmelCase__								=  timeit.repeat(
                         __UpperCAmelCase       ,  repeat=self.args.repeat       ,  number=1_0       ,  )

                     return min(__UpperCAmelCase		) / 10.0
               except ResourceExhaustedError as e:
                     self.print_fn(f"""Doesn't fit on GPU. {e}"""		)





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Callable[[], None]		)						->				[Memory, MemorySummary]:
         """simple docstring"""



         logger.info(
             "Note that TensorFlow allocates more memory than "
             "it might need to speed up computation. "
             "The memory reported here corresponds to the memory "
             "reported by `nvidia-smi`, which can vary depending "
             "on total available memory on the GPU that is used."		)
         with self.args.strategy.scope():
               try:
                     if self.args.trace_memory_line_by_line:
                           if not self.args.eager_mode:
                                 raise ValueError(
                                     "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"
                                     " consumption line by line."		)
                           UpperCAmelCase__								=  start_memory_tracing("transformers"		)

                     if self.args.is_tpu:
                           # tpu
                           raise NotImplementedError(
                               "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"
                               " with `args.memory=False`"		)
                     elif self.args.is_gpu:
                           # gpu
                           if not is_pyanvml_available():
                                 logger.warning(
                                     "py3nvml not installed, we won't log GPU memory usage. "
                                     "Install py3nvml (pip install py3nvml) to log information about GPU."		)
                                 UpperCAmelCase__								=  "N/A"
                           else:
                                 logger.info(
                                     "Measuring total GPU usage on GPU device. Make sure to not have additional processes"
                                     " running on the same GPU."		)
                                 # init nvml
                                 nvml.nvmlInit()
                                 func()
                                 UpperCAmelCase__								=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
                                 UpperCAmelCase__								=  nvml.nvmlDeviceGetMemoryInfo(__UpperCAmelCase		)
                                 UpperCAmelCase__								=  meminfo.used
                                 UpperCAmelCase__								=  Memory(__UpperCAmelCase		)
                                 # shutdown nvml
                                 nvml.nvmlShutdown()
                     else:
                           # cpu
                           if self.args.trace_memory_line_by_line:
                                 logger.info(
                                     "When enabling line by line tracing, the max peak memory for CPU is inaccurate in"
                                     " TensorFlow."		)
                                 UpperCAmelCase__								=  None
                           else:
                                 UpperCAmelCase__								=  measure_peak_memory_cpu(__UpperCAmelCase		)
                                 UpperCAmelCase__								=  Memory(__UpperCAmelCase		) if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) else memory_bytes
                     if self.args.trace_memory_line_by_line:
                           UpperCAmelCase__								=  stop_memory_tracing(__UpperCAmelCase		)
                           if memory is None:
                                 UpperCAmelCase__								=  summary.total
                     else:
                           UpperCAmelCase__								=  None

                     return memory, summary
               except ResourceExhaustedError as e:
                     self.print_fn(f"""Doesn't fit on GPU. {e}"""		)
                     return "N/A", None




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      def get_matched_characters(__A,  __A							) -> str:
            UpperCAmelCase__								=  []
            UpperCAmelCase__								=  min(len(_stra							),  len(_stra							)							) // 2
            for i, l in enumerate(_stra							):
                  UpperCAmelCase__								=  int(max(0,  i - limit							)							)
                  UpperCAmelCase__								=  int(min(i + limit + 1,  len(_stra							)							)							)
                  if l in _stra[left:right]:
                        matched.append(__A							)
                        UpperCAmelCase__								=  f"""{_stra[0:_stra.index(__A							)]} {_stra[_stra.index(__A							) + 1:]}"""

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

      # transposition
      UpperCAmelCase__								=  (
          len([(ca, ca) for ca, ca in zip(__A,  __A							) if ca != ca]							) // 2
      )

      if not match_count:
            UpperCAmelCase__								=  0.0
      else:
            UpperCAmelCase__								=  (
                1
                / 3
                * (
                    match_count / len(__A							)
                    + match_count / len(__A							)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      UpperCAmelCase__								=  0
      for ca, ca in zip(stra[:4],  stra[:4]							):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler('hello', 'world'))



