'''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: Tuple          =   logging.get_logger(__name__)
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 def run_func(UpperCAmelCase     ):
  @wraps(UpperCAmelCase     )
  def run_in_eager_mode(*UpperCAmelCase		,     **UpperCAmelCase     ):
   return func(*UpperCAmelCase		,     **UpperCAmelCase     )

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

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





class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    42
       SCREAMING_SNAKE_CASE							=    "TensorFlow"

       @property
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        return tf.__version__

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  float:
        # initialize GPU on separate process
        lowercase__				:    str  =					self.args.strategy
        if strategy is None:
         raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
        lowercase__				:    int  =					self._prepare_inference_func(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        return self._measure_speed(_inference )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  float:
        lowercase__				:    Dict  =					self.args.strategy
        if strategy is None:
         raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
        lowercase__				:    Optional[int]  =					self._prepare_train_func(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        return self._measure_speed(_train )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  [Memory, Optional[MemorySummary]]:
        # initialize GPU on separate process
        if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]	,   __lowerCAmelCase )
        lowercase__				:    int  =					self.args.strategy
        if strategy is None:
         raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )
        lowercase__				:    Optional[int]  =					self._prepare_inference_func(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        return self._measure_memory(_inference )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  [Memory, Optional[MemorySummary]]:
        if self.args.is_gpu:
         tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]	,   __lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					self.args.strategy
        if strategy is None:
         raise ValueError('''A device strategy has to be initialized before using TensorFlow.''' )

        lowercase__				:    Tuple  =					self._prepare_train_func(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        return self._measure_memory(_train )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Callable[[], None]:
        lowercase__				:    int  =					self.config_dict[model_name]

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

        lowercase__				:    str  =					(
            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:
          lowercase__				:    str  =					'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
          lowercase__				:    Any  =					__import__('''transformers'''	,   fromlist=[model_class] )
          lowercase__				:    Optional[Any]  =					getattr(__lowerCAmelCase	,   __lowerCAmelCase )
          lowercase__				:    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:
         lowercase__				:    Union[str, Any]  =					TF_MODEL_MAPPING[config.__class__](__lowerCAmelCase )

        # encoder-decoder has vocab size saved differently
        lowercase__				:    Union[str, Any]  =					config.vocab_size if hasattr(__lowerCAmelCase	,   '''vocab_size''' ) else config.encoder.vocab_size
        lowercase__				:    Optional[int]  =					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 )

        lowercase__				:    Optional[Any]  =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

        return _inference

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Callable[[], None]:
        lowercase__				:    Dict  =					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.''' )

        lowercase__				:    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:
          lowercase__				:    Tuple  =					'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
          lowercase__				:    Dict  =					__import__('''transformers'''	,   fromlist=[model_class] )
          lowercase__				:    Dict  =					getattr(__lowerCAmelCase	,   __lowerCAmelCase )
          lowercase__				:    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:
         lowercase__				:    Union[str, Any]  =					TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCAmelCase )

        # encoder-decoder has vocab size saved differently
        lowercase__				:    int  =					config.vocab_size if hasattr(__lowerCAmelCase	,   '''vocab_size''' ) else config.encoder.vocab_size
        lowercase__				:    List[str]  =					random_input_ids(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

        @run_with_tf_optimizations(self.args.eager_mode	,   self.args.use_xla )
        def encoder_decoder_train():
         lowercase__				:    Optional[int]  =					model(__lowerCAmelCase	,   decoder_input_ids=__lowerCAmelCase	,   labels=__lowerCAmelCase	,   training=__lowerCAmelCase )[0]
         lowercase__				:    Any  =					tf.gradients(__lowerCAmelCase	,   model.trainable_variables )
         return gradients

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

        lowercase__				:    Any  =					encoder_decoder_train if config.is_encoder_decoder else encoder_train

        return _train

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  float:
        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
          lowercase__				:    Any  =					timeit.repeat(
              __lowerCAmelCase	,   repeat=self.args.repeat	,   number=10	,   )

          return min(__lowerCAmelCase ) / 1_0.0
         except ResourceExhaustedError as e:
          self.print_fn(F"""Doesn't fit on GPU. {e}""" )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  [Memory, MemorySummary]:
        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.''' )
           lowercase__				:    Union[str, Any]  =					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.''' )
            lowercase__				:    Tuple  =					'''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()
            lowercase__				:    Tuple  =					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
            lowercase__				:    str  =					nvml.nvmlDeviceGetMemoryInfo(__lowerCAmelCase )
            lowercase__				:    List[str]  =					meminfo.used
            lowercase__				:    Optional[Any]  =					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.''' )
            lowercase__				:    Dict  =					None
           else:
            lowercase__				:    Dict  =					measure_peak_memory_cpu(__lowerCAmelCase )
            lowercase__				:    Tuple  =					Memory(__lowerCAmelCase ) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase ) else memory_bytes
          if self.args.trace_memory_line_by_line:
           lowercase__				:    List[str]  =					stop_memory_tracing(__lowerCAmelCase )
           if memory is None:
            lowercase__				:    Optional[Any]  =					summary.total
          else:
           lowercase__				:    Union[str, Any]  =					None

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


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import tensorflow as tf

  from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @slow
       def _lowerCAmelCase(						self )  ->  Dict:
        lowercase__				:    List[str]  =					TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' )
        lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''google/mt5-small''' )

        lowercase__				:    Any  =					tokenizer('''Hello there'''	,   return_tensors='''tf''' ).input_ids
        lowercase__				:    List[Any]  =					tokenizer('''Hi I am'''	,   return_tensors='''tf''' ).input_ids

        lowercase__				:    str  =					model(__lowerCAmelCase	,   labels=__lowerCAmelCase ).loss
        lowercase__				:    List[str]  =					-tf.math.reduce_mean(__lowerCAmelCase ).numpy()

        lowercase__				:    str  =					-2_1.2_2_8_1_6_8
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2E-4 )

