
"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowercase				 =					logging.get_logger(__name__)






def    _snake_case					(		snake_case__ :  Dict=None						,   snake_case__ :  Optional[int]=None  ):
	return field(default_factory=lambda: default						,   metadata=snake_case__  )







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     list_field(
						    default=[]		,       metadata={
						        '''help''': (
						            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
						            ''' of all available models'''
						        )
						    }		,       )

						_lowerCamelCase:     List[int]   =     list_field(
						    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}     )

						_lowerCamelCase:     List[int]   =     list_field(
						    default=[8, 32, 128, 512]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Benchmark training of model'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Verbose memory tracing'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
						    }		,       )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Trace memory line by line'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Save result to a CSV file'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Save all print statements in a log file'''}     )
						_lowerCamelCase:     bool   =     field(default=_lowercase		,       metadata={'''help''': '''Whether to print environment information'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': (
						            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
						            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
						            ''' for debugging / testing and on TPU.'''
						        )
						    }		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'inference_time_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'inference_memory_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'train_time_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'train_memory_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'env_info_{round(time()     )}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
						_lowerCamelCase:     str   =     field(
						    default=F'log_{round(time()     )}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
						_lowerCamelCase:     int   =     field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}     )
						_lowerCamelCase:     bool   =     field(
						    default=_lowercase		,       metadata={
						        '''help''': (
						            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
						            ''' model weights.'''
						        )
						    }		,       )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> List[str]:
							warnings.warn(
							    F'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
							    ' are deprecated in general and it is advised to use external Benchmarking libraries '
							    ' to benchmark Transformer models.'	,A_	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							return json.dumps(dataclasses.asdict(self   )	,indent=2   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]   )  -> List[str]:
							if len(self.models   ) <= 0:
								raise ValueError(
								    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
								    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'   )
							return self.models




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[Any]:
							if not self.multi_process:
								return False
							elif self.is_tpu:
								logger.info('Multiprocessing is currently not possible on TPU.'   )
								return False
							else:
								return True

"""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 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     BlenderbotSmallTokenizer
						_lowerCamelCase:     List[Any]   =     False


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							super().setUp()

							A     = ['__start__', 'adapt', 'act', 'ap@@', 'te', '__end__', '__unk__']
							A     = dict(zip(A_	,range(len(A_   )   )   )   )

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

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = 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(A_   ) + '\n'   )
							with open(self.merges_file	,'w'	,encoding='utf-8'   ) as fp:
								fp.write('\n'.join(A_   )   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,**A_  :   Union[str, Any]   )  -> Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]	,A_  :   Tuple   )  -> List[Any]:
							A     = 'adapt act apte'
							A     = 'adapt act apte'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = BlenderbotSmallTokenizer(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'adapt act apte'
							A     = ['adapt', 'act', 'ap@@', 'te']
							A     = tokenizer.tokenize(A_   )
							self.assertListEqual(A_	,A_   )

							A     = [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							A     = [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> Tuple:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							assert tok('sam'   ).input_ids == [1384]
							A     = 'I am a small frog.'
							A     = tok([src_text]	,padding=A_	,truncation=A_   )['input_ids']
							A     = tok.batch_decode(A_	,skip_special_tokens=A_	,clean_up_tokenization_spaces=A_   )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."




						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> int:
							A     = BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'   )
							A     = 'I am a small frog .'
							A     = '.'
							A     = tok(A_   )['input_ids']
							A     = tok(A_   )['input_ids']

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