

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Any      =						OpenAIGPTTokenizer
 _lowercase     :					Any      =						OpenAIGPTTokenizerFast
 _lowercase     :					List[str]      =						True
 _lowercase     :					Tuple      =						False

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase   :					Optional[int]        =    [
          "l",
          "o",
          "w",
          "e",
          "r",
          "s",
          "t",
          "i",
          "d",
          "n",
          "w</w>",
          "r</w>",
          "t</w>",
          "lo",
          "low",
          "er</w>",
          "low</w>",
          "lowest</w>",
          "newer</w>",
          "wider</w>",
          "<unk>",
      ]
      _lowerCAmelCase   :					Union[str, Any]        =    dict(zip(A_     ,						range(len(A_       )       )       )       )
      _lowerCAmelCase   :					List[Any]        =    ["#version: 0.2", "l o", "lo w", "e r</w>", ""]

      _lowerCAmelCase   :					Union[str, Any]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					List[Any]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["merges_file"]       )
      with open(self.vocab_file     ,						"w"       ) as fp:
           fp.write(json.dumps(A_       )       )
      with open(self.merges_file     ,						"w"       ) as fp:
           fp.write("\n".join(A_       )       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Any       ):






      '''simple docstring'''




      return "lower newer", "lower newer"

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    OpenAIGPTTokenizer(self.vocab_file     ,						self.merges_file       )

      _lowerCAmelCase   :					int        =    "lower"
      _lowerCAmelCase   :					Tuple        =    ["low", "er</w>"]
      _lowerCAmelCase   :					Dict        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					List[Any]        =    tokens + ["<unk>"]
      _lowerCAmelCase   :					int        =    [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )

 def    __magic_name__       (		self :	str     ,						A_ :	Any=1_5       ):






      '''simple docstring'''




      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                _lowerCAmelCase   :					Union[str, Any]        =    self.rust_tokenizer_class.from_pretrained(A_     ,						**A_       )

                # Simple input
                _lowerCAmelCase   :					int        =    "This is a simple input"
                _lowerCAmelCase   :					Dict        =    ["This is a simple input 1", "This is a simple input 2"]
                _lowerCAmelCase   :					Tuple        =    ("This is a simple input", "This is a pair")
                _lowerCAmelCase   :					Dict        =    [
                    ("This is a simple input 1", "This is a simple input 2"),
                    ("This is a simple pair 1", "This is a simple pair 2"),
                ]

                # Simple input tests
                self.assertRaises(A_     ,						tokenizer_r.encode     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Simple input
                self.assertRaises(A_     ,						tokenizer_r.encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Simple input
                self.assertRaises(
                    A_     ,						tokenizer_r.batch_encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"     ,						)

                # Pair input
                self.assertRaises(A_     ,						tokenizer_r.encode     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Pair input
                self.assertRaises(A_     ,						tokenizer_r.encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"       )

                # Pair input
                self.assertRaises(
                    A_     ,						tokenizer_r.batch_encode_plus     ,						A_     ,						max_length=A_     ,						padding="max_length"     ,						)


 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      pass





@require_ftfy
@require_spacy
@require_tokenizers
class A__    ( A ):



 """simple docstring"""







 pass









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


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					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]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

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

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




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

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''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       )

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

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




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

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




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


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






