



"""simple docstring"""


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

__A   :							int										=   logging.get_logger(__name__)


__A   :							Optional[int]										=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A   :							Any										=   {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__A   :							Tuple										=   {"facebook/blenderbot-3B": 128}
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	VOCAB_FILES_NAMES
			UpperCamelCase__			      =	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__			      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__			      =	["""input_ids""", """attention_mask"""]
			UpperCamelCase__			      =	BlenderbotTokenizer



			def __init__(		self     :    int				,   __UpperCamelCase     :    List[Any]=None				,   __UpperCamelCase     :    Optional[Any]=None				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    Dict="replace"				,   __UpperCamelCase     :    Dict="<s>"				,   __UpperCamelCase     :    str="</s>"				,   __UpperCamelCase     :    Optional[Any]="</s>"				,   __UpperCamelCase     :    int="<s>"				,   __UpperCamelCase     :    Optional[Any]="<unk>"				,   __UpperCamelCase     :    Tuple="<pad>"				,   __UpperCamelCase     :    Dict="<mask>"				,   __UpperCamelCase     :    Union[str, Any]=False				,   __UpperCamelCase     :    Optional[int]=True				,   **__UpperCamelCase     :    Tuple				,   )->Union[str, Any]:
							super().__init__(
							    __UpperCamelCase				,   __UpperCamelCase				,   tokenizer_file=__UpperCamelCase				,   errors=__UpperCamelCase				,   bos_token=__UpperCamelCase				,   eos_token=__UpperCamelCase				,   sep_token=__UpperCamelCase				,   cls_token=__UpperCamelCase				,   unk_token=__UpperCamelCase				,   pad_token=__UpperCamelCase				,   mask_token=__UpperCamelCase				,   add_prefix_space=__UpperCamelCase				,   trim_offsets=__UpperCamelCase				,   **__UpperCamelCase				,   )

							_UpperCAmelCase					=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('''add_prefix_space'''				,   __UpperCamelCase   ) != add_prefix_space:
											_UpperCAmelCase					=    getattr(__UpperCamelCase				,   pre_tok_state.pop('''type'''   )   )
											_UpperCAmelCase					=    add_prefix_space
											_UpperCAmelCase					=    pre_tok_class(**__UpperCamelCase   )

							_UpperCAmelCase					=    add_prefix_space

							_UpperCAmelCase					=    '''post_processor'''
							_UpperCAmelCase					=    getattr(self.backend_tokenizer				,   __UpperCamelCase				,   __UpperCamelCase   )
							if tokenizer_component_instance:
											_UpperCAmelCase					=    json.loads(tokenizer_component_instance.__getstate__()   )

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
															_UpperCAmelCase					=    tuple(state['''sep''']   )
											if "cls" in state:
															_UpperCAmelCase					=    tuple(state['''cls''']   )

											_UpperCAmelCase					=    False

											if state.get('''add_prefix_space'''				,   __UpperCamelCase   ) != add_prefix_space:
															_UpperCAmelCase					=    add_prefix_space
															_UpperCAmelCase					=    True

											if state.get('''trim_offsets'''				,   __UpperCamelCase   ) != trim_offsets:
															_UpperCAmelCase					=    trim_offsets
															_UpperCAmelCase					=    True

											if changes_to_apply:
															_UpperCAmelCase					=    getattr(__UpperCamelCase				,   state.pop('''type'''   )   )
															_UpperCAmelCase					=    component_class(**__UpperCamelCase   )
															setattr(self.backend_tokenizer				,   __UpperCamelCase				,   __UpperCamelCase   )



			@property
			# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
			def  lowercase__						(		self     :    Any   )->str:
							if self._mask_token is None:
											if self.verbose:
															logger.error('''Using mask_token, but it is not set yet.'''   )
											return None
							return str(self._mask_token   )



			@mask_token.setter
			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Dict   )->Optional[int]:
							_UpperCAmelCase					=    AddedToken(__UpperCamelCase				,   lstrip=__UpperCamelCase				,   rstrip=__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else value
							_UpperCAmelCase					=    value



			def  lowercase__						(		self     :    List[str]				,   *__UpperCamelCase     :    Union[str, Any]				,   **__UpperCamelCase     :    Union[str, Any]   )->BatchEncoding:
							_UpperCAmelCase					=    kwargs.get('''is_split_into_words'''				,   __UpperCamelCase   )
							assert self.add_prefix_space or not is_split_into_words, (
							    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[Any]				,   *__UpperCamelCase     :    Tuple				,   **__UpperCamelCase     :    Tuple   )->BatchEncoding:
							_UpperCAmelCase					=    kwargs.get('''is_split_into_words'''				,   __UpperCamelCase   )

							assert self.add_prefix_space or not is_split_into_words, (
							    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Optional[str] = None   )->Tuple[str]:
							_UpperCAmelCase					=    self._tokenizer.model.save(__UpperCamelCase				,   name=__UpperCamelCase   )
							return tuple(__UpperCamelCase   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->List[int]:
							_UpperCAmelCase					=    [self.sep_token_id]
							_UpperCAmelCase					=    [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    List[int]				,   __UpperCamelCase     :    Optional[List[int]] = None   )->Optional[int]:
							return token_ids_a + [self.eos_token_id]



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    "Conversation"   )->List[int]:
							_UpperCAmelCase					=    []
							for is_user, text in conversation.iter_texts():
											if is_user:
															# We need to space prefix as it's being done within blenderbot
															inputs.append(''' ''' + text   )
											else:
															# Generated responses should contain them already.
															inputs.append(__UpperCamelCase   )

							_UpperCAmelCase					=    '''  '''.join(__UpperCamelCase   )
							_UpperCAmelCase					=    self.encode(__UpperCamelCase   )
							if len(__UpperCamelCase   ) > self.model_max_length:
											_UpperCAmelCase					=    input_ids[-self.model_max_length :]
											logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'   )
							return input_ids








"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    str   )->Tuple:
							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs''']				,   model_result['''ss''']   ):
															_UpperCAmelCase					=    model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Optional[Any]:
							_UpperCAmelCase					=    '''sgugger/tiny-distilbert-classification'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   only_pretrain_model=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   torchscript=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Cant do half precision'''   )
			def  lowercase__						(		self     :    Any   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   fpaa=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							# set architectures equal to `None`
							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[Any]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			@unittest.skipIf(torch_device == '''cpu'''				,   '''Can\'t do half precision'''   )
			def  lowercase__						(		self     :    List[str]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   fpaa=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    str   )->Dict:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    Optional[int]   )->Optional[Any]:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_inference_result   )
							self.check_results_dict_not_empty(results.memory_inference_result   )



			def  lowercase__						(		self     :    List[str]   )->str:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[int]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tinier_bart'''
							_UpperCAmelCase					=    AutoConfig.from_pretrained(__UpperCamelCase   )
							_UpperCAmelCase					=    PyTorchBenchmarkArguments(
							    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   multi_process=__UpperCamelCase				,   )
							_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase				,   configs=[config]   )
							_UpperCAmelCase					=    benchmark.run()
							self.check_results_dict_not_empty(results.time_train_result   )
							self.check_results_dict_not_empty(results.memory_train_result   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   save_to_csv=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   inference_time_csv_file=os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )				,   train_memory_csv_file=os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )				,   inference_memory_csv_file=os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )				,   train_time_csv_file=os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )				,   env_info_csv_file=os.path.join(__UpperCamelCase				,   '''env.csv'''   )				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											benchmark.run()
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''inf_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''train_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''env.csv'''   )   ).exists()   )



			def  lowercase__						(		self     :    Tuple   )->List[Any]:
							_UpperCAmelCase					=    '''sshleifer/tiny-gpt2'''

							def _check_summary_is_not_empty(__UpperCamelCase     :    str   ):
											self.assertTrue(hasattr(__UpperCamelCase				,   '''sequential'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''cumulative'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''current'''   )   )
											self.assertTrue(hasattr(__UpperCamelCase				,   '''total'''   )   )

							with tempfile.TemporaryDirectory() as tmp_dir:
											_UpperCAmelCase					=    PyTorchBenchmarkArguments(
											    models=[MODEL_ID]				,   training=__UpperCamelCase				,   inference=__UpperCamelCase				,   sequence_lengths=[8]				,   batch_sizes=[1]				,   log_filename=os.path.join(__UpperCamelCase				,   '''log.txt'''   )				,   log_print=__UpperCamelCase				,   trace_memory_line_by_line=__UpperCamelCase				,   multi_process=__UpperCamelCase				,   )
											_UpperCAmelCase					=    PyTorchBenchmark(__UpperCamelCase   )
											_UpperCAmelCase					=    benchmark.run()
											_check_summary_is_not_empty(result.inference_summary   )
											_check_summary_is_not_empty(result.train_summary   )
											self.assertTrue(Path(os.path.join(__UpperCamelCase				,   '''log.txt'''   )   ).exists()   )



