'''simple docstring'''

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
  import torch

if is_torch_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm


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



@dataclass
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    [
           "no_inference",
           "no_cuda",
           "no_tpu",
           "no_speed",
           "no_memory",
           "no_env_print",
           "no_multi_process",
       ]

       def __init__(						self	,   **__lowerCAmelCase )  ->  Tuple:
        for deprecated_arg in self.deprecated_args:
         if deprecated_arg in kwargs:
          lowercase__				:    List[Any]  =					deprecated_arg[3:]
          setattr(self	,   __lowerCAmelCase	,   not kwargs.pop(__lowerCAmelCase ) )
          logger.warning(
              F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
              F""" {positive_arg}={kwargs[positive_arg]}""" )

        lowercase__				:    Dict  =					kwargs.pop('''torchscript'''	,   self.torchscript )
        lowercase__				:    Dict  =					kwargs.pop('''torch_xla_tpu_print_metrics'''	,   self.torch_xla_tpu_print_metrics )
        lowercase__				:    Dict  =					kwargs.pop('''fp16_opt_level'''	,   self.fpaa_opt_level )
        super().__init__(**__lowerCAmelCase )

       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Trace the models using torchscript"}					)
       SCREAMING_SNAKE_CASE							=    field(default=a__    ,						metadata={"help": "Print Xla/PyTorch tpu metrics"}					)
       SCREAMING_SNAKE_CASE							=    field(
           default="O1"    ,						metadata={
               "help": (
                   "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                   "See details at https://nvidia.github.io/apex/amp.html"
               )
           }    ,						)

       @cached_property
       def _lowerCAmelCase(						self )  ->  Tuple["torch.device", int]:
        requires_backends(self	,   ['''torch'''] )
        logger.info('''PyTorch: setting up devices''' )
        if not self.cuda:
         lowercase__				:    int  =					torch.device('''cpu''' )
         lowercase__				:    Union[str, Any]  =					0
        elif is_torch_tpu_available():
         lowercase__				:    str  =					xm.xla_device()
         lowercase__				:    Tuple  =					0
        else:
         lowercase__				:    Any  =					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
         lowercase__				:    Any  =					torch.cuda.device_count()
        return device, n_gpu

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        return is_torch_tpu_available() and self.tpu

       @property
       def _lowerCAmelCase(						self )  ->  int:
        requires_backends(self	,   ['''torch'''] )
        # TODO(PVP): currently only single GPU is supported
        return torch.cuda.current_device()

       @property
       def _lowerCAmelCase(						self )  ->  "torch.device":
        requires_backends(self	,   ['''torch'''] )
        return self._setup_devices[0]

       @property
       def _lowerCAmelCase(						self )  ->  Tuple:
        requires_backends(self	,   ['''torch'''] )
        return self._setup_devices[1]


       @property
       def _lowerCAmelCase(						self )  ->  str:
        return self.n_gpu > 0


'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

