'''simple docstring'''

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

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



def snake_case_      (					__snake_case  :							List[str]	,   __snake_case  :							Dict)			->					Dict:







   def run_func(__snake_case  :							Optional[int]):
      @wraps(__snake_case)
      def run_in_eager_mode(*__snake_case  :							List[str]	,   **__snake_case  :							int):
         return func(*__snake_case	,   **__snake_case)

      @wraps(__snake_case)
      @tf.function(experimental_compile=__snake_case)
      def run_in_graph_mode(*__snake_case  :							Tuple	,   **__snake_case  :							Any):
         return func(*__snake_case	,   **__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 snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							List[str]	,   __snake_case  :							Dict)			->					["tf.Tensor"]:
   lowerCAmelCase_			=      random.Random()
   lowerCAmelCase_			=      [rng.randint(0	,   vocab_size - 1) for i in range(batch_size * sequence_length)]
   return tf.constant(__snake_case	,   shape=(batch_size, sequence_length)	,   dtype=tf.intaa)






class     __UpperCAmelCase       ( lowercase_		):
  __A :				TensorFlowBenchmarkArguments
  __A :				PretrainedConfig
  __A :				str											= "TensorFlow"





  @property
  def 						UpperCAmelCase_	(	self ):
     return tf.__version__





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.args.strategy
     if strategy is None:
        raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
     lowerCAmelCase_			=      self._prepare_inference_func(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     return self._measure_speed(_inference )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.args.strategy
     if strategy is None:
        raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
     lowerCAmelCase_			=      self._prepare_train_func(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     return self._measure_speed(_train )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):







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





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):







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

     lowerCAmelCase_			=      self._prepare_train_func(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     return self._measure_memory(_train )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.config_dict[model_name]

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

     lowerCAmelCase_			=      (
         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:
           lowerCAmelCase_			=      '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
           lowerCAmelCase_			=      __import__('''transformers'''    ,     fromlist=[model_class] )
           lowerCAmelCase_			=      getattr(_lowerCamelCase    ,     _lowerCamelCase )
           lowerCAmelCase_			=      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:
        lowerCAmelCase_			=      TF_MODEL_MAPPING[config.__class__](_lowerCamelCase )

     # encoder-decoder has vocab size saved differently
     lowerCAmelCase_			=      config.vocab_size if hasattr(_lowerCamelCase    ,     '''vocab_size''' ) else config.encoder.vocab_size
     lowerCAmelCase_			=      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 )

     lowerCAmelCase_			=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

     return _inference





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      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.''' )

     lowerCAmelCase_			=      (
         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:
           lowerCAmelCase_			=      '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
           lowerCAmelCase_			=      __import__('''transformers'''    ,     fromlist=[model_class] )
           lowerCAmelCase_			=      getattr(_lowerCamelCase    ,     _lowerCamelCase )
           lowerCAmelCase_			=      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:
        lowerCAmelCase_			=      TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCamelCase )

     # encoder-decoder has vocab size saved differently
     lowerCAmelCase_			=      config.vocab_size if hasattr(_lowerCamelCase    ,     '''vocab_size''' ) else config.encoder.vocab_size
     lowerCAmelCase_			=      random_input_ids(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

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

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

     lowerCAmelCase_			=      encoder_decoder_train if config.is_encoder_decoder else encoder_train

     return _train





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):







     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
           lowerCAmelCase_			=      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    ,     _lowerCamelCase ):
     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.''' )
              lowerCAmelCase_			=      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.''' )
                 lowerCAmelCase_			=      '''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()
                 lowerCAmelCase_			=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
                 lowerCAmelCase_			=      nvml.nvmlDeviceGetMemoryInfo(_lowerCamelCase )
                 lowerCAmelCase_			=      meminfo.used
                 lowerCAmelCase_			=      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.''' )
                 lowerCAmelCase_			=      None
              else:
                 lowerCAmelCase_			=      measure_peak_memory_cpu(_lowerCamelCase )
                 lowerCAmelCase_			=      Memory(_lowerCamelCase ) if isinstance(_lowerCamelCase    ,     _lowerCamelCase ) else memory_bytes
           if self.args.trace_memory_line_by_line:
              lowerCAmelCase_			=      stop_memory_tracing(_lowerCamelCase )
              if memory is None:
                 lowerCAmelCase_			=      summary.total
           else:
              lowerCAmelCase_			=      None

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









'''simple docstring'''

import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class     __lowerCAmelCase		(lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	str       =				RoFormerTokenizer
      lowerCAmelCase__   :	Dict       =				RoFormerTokenizerFast
      lowerCAmelCase__   :	List[str]       =				True
      lowerCAmelCase__   :	List[str]       =				True



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             super().setUp()



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       Dict					):

             '''simple docstring'''







             return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		**UpperCamelCase       :       int					):

             '''simple docstring'''







             return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					'''永和服装饰品有限公司,今天天气非常好'''
             lowercase__      =					'''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
             return input_text, output_text



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					self.get_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)

             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)

             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.get_rust_tokenizer()
             lowercase__     ,lowercase__      =					self.get_chinese_input_output_texts()
             lowercase__      =					tokenizer.tokenize(UpperCamelCase					)
             self.assertListEqual(UpperCamelCase  ,		output_text.split()					)
             lowercase__      =					tokens + [tokenizer.unk_token]
             lowercase__      =					[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase					)  ,		UpperCamelCase					)



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             pass




