




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


_snake_case     :   Optional[int]            = logging.get_logger(__name__)







@dataclass
class 		lowercase							(     a__	):
     _a											=		[
         "no_inference",
         "no_cuda",
         "no_tpu",
         "no_speed",
         "no_memory",
         "no_env_print",
         "no_multi_process",
     ]

     def __init__(						self	,  **_a       )		->							List[Any]:

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

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

     @cached_property
     def       a__			(						self       )		->							Dict:
           requires_backends(self	,  ["""tf"""]       )
           _A							:  Optional[Any]    =		None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             _A							:  Tuple    =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                       else:
                             _A							:  List[str]    =		tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       _A							:  Union[str, Any]    =		None
           return tpu

     @cached_property
     def       a__			(						self       )		->							Dict:
           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       )

                 _A							:  Any    =		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"""       )
                       _A							:  str    =		tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
                 else:
                       tf.config.set_visible_devices([]	,  """GPU"""       )  # disable GPU
                       _A							:  List[str]    =		tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

           return strategy

     @property
     def       a__			(						self       )		->							List[Any]:
           requires_backends(self	,  ["""tf"""]       )
           return self._setup_tpu is not None

     @property
     def       a__			(						self       )		->							Union[str, Any]:
           requires_backends(self	,  ["""tf"""]       )
           return self._setup_strategy

     @property
     def       a__			(						self       )		->							Optional[Any]:
           requires_backends(self	,  ["""tf"""]       )
           return tf.config.list_physical_devices("""GPU"""       )

     @property
     def       a__			(						self       )		->							str:
           requires_backends(self	,  ["""tf"""]       )
           if self.cuda:
                 return len(self.gpu_list       )
           return 0



     @property
     def       a__			(						self       )		->							Optional[Any]:
           return self.n_gpu > 0







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

