





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


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



@dataclass
class  A__       (	__snake_case      ):
      _UpperCAmelCase     :Any						   =       [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]





      def __init__(			self	,							**A_       ):
       '''simple docstring'''




       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         UpperCamelCase     : List[str]	     =       deprecated_arg[3:]
         UpperCamelCase     : Union[str, Any]	     =       not kwargs.pop(A_       )
         logger.warning(
             F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
             F""" {positive_arg}={kwargs[positive_arg]}"""       )
       UpperCamelCase     : Dict	     =       kwargs.pop("tpu_name"	,							self.tpu_name       )
       UpperCamelCase     : str	     =       kwargs.pop("device_idx"	,							self.device_idx       )
       UpperCamelCase     : List[str]	     =       kwargs.pop("eager_mode"	,							self.eager_mode       )
       UpperCamelCase     : List[str]	     =       kwargs.pop("use_xla"	,							self.use_xla       )
       super().__init__(**A_       )

      _UpperCAmelCase     :str						   =       field(
          default=__snake_case	,       metadata={'help': 'Name of TPU'}	,       )
      _UpperCAmelCase     :int						   =       field(
          default=0	,       metadata={'help': 'CPU / GPU device index. Defaults to 0.'}	,       )
      _UpperCAmelCase     :bool						   =       field(default=__snake_case	,       metadata={'help': 'Benchmark models in eager model.'}      )
      _UpperCAmelCase     :bool						   =       field(
          default=__snake_case	,       metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }	,       )





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       UpperCamelCase     : Union[str, Any]	     =       None
       if self.tpu:
        try:
         if self.tpu_name:
          UpperCamelCase     : str	     =       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
         else:
          UpperCamelCase     : List[Any]	     =       tf.distribute.cluster_resolver.TPUClusterResolver()
        except ValueError:
         UpperCamelCase     : Tuple	     =       None
       return tpu





      @cached_property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       if self.is_tpu:
        tf.config.experimental_connect_to_cluster(self._setup_tpu       )
        tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

        UpperCamelCase     : int	     =       tf.distribute.TPUStrategy(self._setup_tpu       )
       else:
        # currently no multi gpu is allowed
        if self.is_gpu:
         # TODO: Currently only single GPU is supported
         tf.config.set_visible_devices(self.gpu_list[self.device_idx]	,							"GPU"       )
         UpperCamelCase     : Any	     =       tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""       )
        else:
         tf.config.set_visible_devices([]	,							"GPU"       )  # disable GPU
         UpperCamelCase     : Union[str, Any]	     =       tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""       )

       return strategy





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       return self._setup_tpu is not None





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       return self._setup_strategy





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       return tf.config.list_physical_devices("GPU"       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       requires_backends(self	,							["tf"]       )
       if self.cuda:
        return len(self.gpu_list       )
       return 0





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.n_gpu > 0







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  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
