


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

def 			_a  (				SCREAMING_SNAKE_CASE	:				bool    , SCREAMING_SNAKE_CASE	:				bool     ):







 """simple docstring"""



 def run_func(SCREAMING_SNAKE_CASE	:				List[Any]     ):
  @wraps(SCREAMING_SNAKE_CASE     )
  def run_in_eager_mode(*SCREAMING_SNAKE_CASE	:				Optional[Any]    , **SCREAMING_SNAKE_CASE	:				Dict     ):
   return func(*SCREAMING_SNAKE_CASE    , **SCREAMING_SNAKE_CASE     )

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

  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 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   random.Random()
 UpperCamelCase__					:    List[str]          =   [rng.randint(0    , vocab_size - 1     ) for i in range(batch_size * sequence_length     )]
 return tf.constant(SCREAMING_SNAKE_CASE    , shape=(batch_size, sequence_length)    , dtype=tf.intaa     )






class       __magic_name__	(     __lowerCAmelCase):
     A:		TensorFlowBenchmarkArguments
     A:		PretrainedConfig
     A:		str			=      "TensorFlow"


     @property
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      List[str]:





      '''simple docstring'''






      return tf.__version__


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      float:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.args.strategy
      if strategy is None:
       raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
      UpperCamelCase__					:    Tuple          =   self._prepare_inference_func(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)
      return self._measure_speed(_inference			)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      float:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.args.strategy
      if strategy is None:
       raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
      UpperCamelCase__					:    str          =   self._prepare_train_func(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)
      return self._measure_speed(_train			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     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]							,	lowerCamelCase__			)
      UpperCamelCase__					:    int          =   self.args.strategy
      if strategy is None:
       raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)
      UpperCamelCase__					:    Optional[Any]          =   self._prepare_inference_func(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)
      return self._measure_memory(_inference			)


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     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]							,	lowerCamelCase__			)
      UpperCamelCase__					:    Tuple          =   self.args.strategy
      if strategy is None:
       raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''			)

      UpperCamelCase__					:    Optional[Any]          =   self._prepare_train_func(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)
      return self._measure_memory(_train			)


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      Callable[[], None]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.config_dict[model_name]

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

      UpperCamelCase__					:    int          =   (
          hasattr(lowerCamelCase__							,	'''architectures'''			)
          and isinstance(config.architectures							,	lowerCamelCase__			)
          and len(config.architectures			) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
       try:
        UpperCamelCase__					:    List[Any]          =   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
        UpperCamelCase__					:    int          =   __import__('''transformers'''							,	fromlist=[model_class]			)
        UpperCamelCase__					:    Optional[Any]          =   getattr(lowerCamelCase__							,	lowerCamelCase__			)
        UpperCamelCase__					:    Optional[int]          =   model_cls(lowerCamelCase__			)
       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__					:    Union[str, Any]          =   TF_MODEL_MAPPING[config.__class__](lowerCamelCase__			)

      # encoder-decoder has vocab size saved differently
      UpperCamelCase__					:    int          =   config.vocab_size if hasattr(lowerCamelCase__							,	'''vocab_size'''			) else config.encoder.vocab_size
      UpperCamelCase__					:    str          =   random_input_ids(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)

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

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

      UpperCamelCase__					:    List[str]          =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

      return _inference


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     int			)  ->      Callable[[], None]:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   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__					:    Optional[Any]          =   (
          hasattr(lowerCamelCase__							,	'''architectures'''			)
          and isinstance(config.architectures							,	lowerCamelCase__			)
          and len(config.architectures			) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
       try:
        UpperCamelCase__					:    Union[str, Any]          =   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
        UpperCamelCase__					:    Any          =   __import__('''transformers'''							,	fromlist=[model_class]			)
        UpperCamelCase__					:    Any          =   getattr(lowerCamelCase__							,	lowerCamelCase__			)
        UpperCamelCase__					:    List[str]          =   model_cls(lowerCamelCase__			)
       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__					:    List[Any]          =   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__			)

      # encoder-decoder has vocab size saved differently
      UpperCamelCase__					:    Any          =   config.vocab_size if hasattr(lowerCamelCase__							,	'''vocab_size'''			) else config.encoder.vocab_size
      UpperCamelCase__					:    str          =   random_input_ids(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__			)

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

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

      UpperCamelCase__					:    Union[str, Any]          =   encoder_decoder_train if config.is_encoder_decoder else encoder_train

      return _train


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Union[str, Any]			)  ->      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(lowerCamelCase__							,	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__					:    Optional[Any]          =   timeit.repeat(
            lowerCamelCase__							,	repeat=self.args.repeat							,	number=10							,	)

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


     def      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     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__					:    List[str]          =   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__					:    Any          =   '''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__					:    List[str]          =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
          UpperCamelCase__					:    Tuple          =   nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__			)
          UpperCamelCase__					:    Dict          =   meminfo.used
          UpperCamelCase__					:    int          =   Memory(lowerCamelCase__			)
          # 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__					:    Optional[Any]          =   None
         else:
          UpperCamelCase__					:    Union[str, Any]          =   measure_peak_memory_cpu(lowerCamelCase__			)
          UpperCamelCase__					:    Dict          =   Memory(lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else memory_bytes
        if self.args.trace_memory_line_by_line:
         UpperCamelCase__					:    Tuple          =   stop_memory_tracing(lowerCamelCase__			)
         if memory is None:
          UpperCamelCase__					:    Dict          =   summary.total
        else:
         UpperCamelCase__					:    Tuple          =   None

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






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

 for i in range(len(SCREAMING_SNAKE_CASE     )     ):
  curr_string += data_bits[i]
  if curr_string not in lexicon:
   continue

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

 while curr_string != "" and curr_string not in lexicon:
  curr_string += "0"

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   os.path.getsize(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   bin(SCREAMING_SNAKE_CASE     )[2:]
 UpperCamelCase__					:    Tuple          =   len(SCREAMING_SNAKE_CASE     )

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_SNAKE_CASE     )
   ]

   if len(result_byte_array[-1]     ) % byte_length == 0:
    result_byte_array.append('''10000000'''     )
   else:
    result_byte_array[-1] += "1" + "0" * (
        byte_length - len(result_byte_array[-1]     ) - 1
    )

   for elem in result_byte_array:
    opened_file.write(int(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])


