
"""simple docstring"""






import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


_lowercase							=				logging.get_logger(__name__)



def      lowerCAmelCase__			(		)						->List[str]:
			# Get the sagemaker specific mp parameters from smp_options variable.
			__lowercase       =       os.getenv("SM_HP_MP_PARAMETERS"			,   "{}"      )
			try:
						# Parse it and check the field "partitions" is included, it is required for model parallel.
						__lowercase       =       json.loads(__magic_name__      )
						if "partitions" not in smp_options:
									return False
			except json.JSONDecodeError:
						return False

			# Get the sagemaker specific framework parameters from mpi_options variable.
			__lowercase       =       os.getenv("SM_FRAMEWORK_PARAMS"			,   "{}"      )
			try:
						# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
						__lowercase       =       json.loads(__magic_name__      )
						if not mpi_options.get("sagemaker_mpi_enabled"			,   __magic_name__      ):
									return False
			except json.JSONDecodeError:
						return False
			# Lastly, check if the `smdistributed` module is present.
			return importlib.util.find_spec("smdistributed"      ) is not None


if is_sagemaker_model_parallel_available():
					import smdistributed.modelparallel.torch as smp

					smp.init()







@dataclass
class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			str          =   field(
							    default=""""""					,   metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""}					,   )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										super().__post_init__()
										warnings.warn(
										    "`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use "
										    "`TrainingArguments` instead."  ,    _lowerCamelCase  ,    )
							@cached_property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							"torch.device":




										'''simple docstring'''
										logger.info("PyTorch: setting up devices"       )
										if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
													logger.warning(
													    "torch.distributed process group is initialized, but local_rank == -1. "
													    "In order to use Torch DDP, launch your script with `python -m torch.distributed.launch"       )
										if self.no_cuda:
													__lowercase       =       torch.device("cpu"       )
													__lowercase       =       0
										elif is_sagemaker_model_parallel_available():
													__lowercase       =       smp.local_rank()
													__lowercase       =       torch.device("cuda"  ,    _lowerCamelCase       )
													__lowercase       =       1
										elif is_sagemaker_dp_enabled():
													import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

													torch.distributed.init_process_group(backend="smddp"  ,    timeout=self.ddp_timeout_delta       )
													__lowercase       =       int(os.getenv("SMDATAPARALLEL_LOCAL_RANK"       )       )
													__lowercase       =       torch.device("cuda"  ,    self.local_rank       )
													__lowercase       =       1
										elif self.local_rank == -1:
													# if n_gpu is > 1 we'll use nn.DataParallel.
													# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
													# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
													# trigger an error that a device index is missing. Index 0 takes into account the
													# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
													# will use the first GPU in that env, i.e. GPU#1
													__lowercase       =       torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
													# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
													# the default value.
													__lowercase       =       torch.cuda.device_count()
										else:
													# Here, we'll use torch.distributed.
													# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
													if not torch.distributed.is_initialized():
																torch.distributed.init_process_group(backend="nccl"  ,    timeout=self.ddp_timeout_delta       )
													__lowercase       =       torch.device("cuda"  ,    self.local_rank       )
													__lowercase       =       1

										if device.type == "cuda":
													torch.cuda.set_device(_lowerCamelCase       )

										return device
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										if is_sagemaker_model_parallel_available():
													return smp.dp_size()

										return super().world_size
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Optional[Any]:




										'''simple docstring'''
										return not is_sagemaker_model_parallel_available()





							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										return False







"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowercase							=				{
    '''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'''
    },
}

_lowercase							=				{'''facebook/blenderbot-3B''': 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      lowerCAmelCase__			(		)						->Any:
			__lowercase       =       (
			    list(range(ord("!"      )			,   ord("~"      ) + 1      )      ) + list(range(ord("¡"      )			,   ord("¬"      ) + 1      )      ) + list(range(ord("®"      )			,   ord("ÿ"      ) + 1      )      )
			)
			__lowercase       =       bs[:]
			__lowercase       =       0
			for b in range(2**8      ):
						if b not in bs:
									bs.append(__magic_name__      )
									cs.append(2**8 + n      )
									n += 1
			__lowercase       =       [chr(__magic_name__      ) for n in cs]
			return dict(zip(__magic_name__			,   __magic_name__      )      )



def      lowerCAmelCase__			(		__magic_name__      )						->Tuple:
			__lowercase       =       set()
			__lowercase       =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)      )
						__lowercase       =       char
			return pairs







class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[str]          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			Tuple          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			int          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase					:			Dict          =   ["""input_ids""", """attention_mask"""]
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase="replace"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="</s>"  ,    _lowerCamelCase="<s>"  ,    _lowerCamelCase="<unk>"  ,    _lowerCamelCase="<pad>"  ,    _lowerCamelCase="<mask>"  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							int:




										'''simple docstring'''
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else bos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else eos_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else sep_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else cls_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else unk_token
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										__lowercase       =       AddedToken(_lowerCamelCase  ,    lstrip=_lowerCamelCase  ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase  ,    _lowerCamelCase       ) else mask_token

										super().__init__(
										    errors=_lowerCamelCase  ,    bos_token=_lowerCamelCase  ,    eos_token=_lowerCamelCase  ,    unk_token=_lowerCamelCase  ,    sep_token=_lowerCamelCase  ,    cls_token=_lowerCamelCase  ,    pad_token=_lowerCamelCase  ,    mask_token=_lowerCamelCase  ,    add_prefix_space=_lowerCamelCase  ,    **_lowerCamelCase  ,    )

										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as vocab_handle:
													__lowercase       =       json.load(_lowerCamelCase       )
										__lowercase       =       {v: k for k, v in self.encoder.items()}
										__lowercase       =       errors  # how to handle errors in decoding
										__lowercase       =       bytes_to_unicode()
										__lowercase       =       {v: k for k, v in self.byte_encoder.items()}
										with open(_lowerCamelCase  ,    encoding="utf-8"       ) as merges_handle:
													__lowercase       =       merges_handle.read().split("\n"       )[1:-1]
										__lowercase       =       [tuple(merge.split()       ) for merge in bpe_merges]
										__lowercase       =       dict(zip(_lowerCamelCase  ,    range(len(_lowerCamelCase       )       )       )       )
										__lowercase       =       {}
										__lowercase       =       add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										__lowercase       =       re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )
							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Tuple:




										'''simple docstring'''
										return len(self.encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Dict:




										'''simple docstring'''
										return dict(self.encoder  ,    **self.added_tokens_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							int:




										'''simple docstring'''
										if token in self.cache:
													return self.cache[token]
										__lowercase       =       tuple(_lowerCamelCase       )
										__lowercase       =       get_pairs(_lowerCamelCase       )

										if not pairs:
													return token

										while True:
													__lowercase       =       min(_lowerCamelCase  ,    key=lambda _lowerCamelCase       : self.bpe_ranks.get(_lowerCamelCase  ,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																break
													__lowercase   ,					__lowercase       =       bigram
													__lowercase       =       []
													__lowercase       =       0
													while i < len(_lowerCamelCase       ):
																try:
																			__lowercase       =       word.index(_lowerCamelCase  ,    _lowerCamelCase       )
																except ValueError:
																			new_word.extend(word[i:]       )
																			break
																else:
																			new_word.extend(word[i:j]       )
																			__lowercase       =       j

																if word[i] == first and i < len(_lowerCamelCase       ) - 1 and word[i + 1] == second:
																			new_word.append(first + second       )
																			i += 2
																else:
																			new_word.append(word[i]       )
																			i += 1
													__lowercase       =       tuple(_lowerCamelCase       )
													__lowercase       =       new_word
													if len(_lowerCamelCase       ) == 1:
																break
													else:
																__lowercase       =       get_pairs(_lowerCamelCase       )
										__lowercase       =       " ".join(_lowerCamelCase       )
										__lowercase       =       word
										return word
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       []
										for token in re.findall(self.pat  ,    _lowerCamelCase       ):
													__lowercase       =       "".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase       ).split(" "       )       )
										return bpe_tokens
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										return self.encoder.get(_lowerCamelCase  ,    self.encoder.get(self.unk_token       )       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return self.decoder.get(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										__lowercase       =       "".join(_lowerCamelCase       )
										__lowercase       =       bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"  ,    errors=self.errors       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




										'''simple docstring'''
										if not os.path.isdir(_lowerCamelCase       ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
													return
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
										__lowercase       =       os.path.join(
										    _lowerCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

										with open(_lowerCamelCase  ,    "w"  ,    encoding="utf-8"       ) as f:
													f.write(json.dumps(self.encoder  ,    indent=2  ,    sort_keys=_lowerCamelCase  ,    ensure_ascii=_lowerCamelCase       ) + "\n"       )

										__lowercase       =       0
										with open(_lowerCamelCase  ,    "w"  ,    encoding="utf-8"       ) as writer:
													writer.write("#version: 0.2\n"       )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,    key=lambda _lowerCamelCase       : kv[1]       ):
																if index != token_index:
																			logger.warning(
																			    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																			    " Please check that the tokenizer is not corrupted!"       )
																			__lowercase       =       token_index
																writer.write(" ".join(_lowerCamelCase       ) + "\n"       )
																index += 1

										return vocab_file, merge_file
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							List[int]:




										'''simple docstring'''
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_lowerCamelCase  ,    token_ids_a=_lowerCamelCase  ,    already_has_special_tokens=_lowerCamelCase       )

										if token_ids_a is None:
													return [1] + ([0] * len(_lowerCamelCase       )) + [1]
										return [1] + ([0] * len(_lowerCamelCase       )) + [1, 1] + ([0] * len(_lowerCamelCase       )) + [1]
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       [self.sep_token_id]
										__lowercase       =       [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   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase=False  ,    **_lowerCamelCase       )			->							int:




										'''simple docstring'''
										__lowercase       =       kwargs.pop("add_prefix_space"  ,    self.add_prefix_space       )
										if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase       ) > 0 and not text[0].isspace()):
													__lowercase       =       " " + text
										return (text, kwargs)
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple:




										'''simple docstring'''
										return token_ids_a + [self.eos_token_id]





							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[int]:




										'''simple docstring'''
										__lowercase       =       []
										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(_lowerCamelCase       )

										__lowercase       =       "  ".join(_lowerCamelCase       )
										__lowercase       =       self.encode(_lowerCamelCase       )
										if len(_lowerCamelCase       ) > self.model_max_length:
													__lowercase       =       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





