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

from ..utils import logging


SCREAMING_SNAKE_CASE							:List[str]							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_=None		,   a_=None						)		->	Any:







				"""simple docstring"""



				return field(default_factory=lambda: default		,   metadata=a_						)





@dataclass
class 							UpperCAmelCase      :




	'''simple docstring'''





	snake_case_					    =     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"
	        )
	    }			,			)

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

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

	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Use FP16 to accelerate inference."}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Benchmark training of model"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Verbose memory tracing"}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}			,			)
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={
	        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
	    }			,			)
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Trace memory line by line"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Save result to a CSV file"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Save all print statements in a log file"}  )
	snake_case_					    =     field(default=__SCREAMING_SNAKE_CASE			,			metadata={"help": "Whether to print environment information"}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			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."
	        )
	    }			,			)
	snake_case_					    =     field(
	    default=F"""inference_time_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving time results to csv."}			,			)
	snake_case_					    =     field(
	    default=F"""inference_memory_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving memory results to csv."}			,			)
	snake_case_					    =     field(
	    default=F"""train_time_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving time results to csv for training."}			,			)
	snake_case_					    =     field(
	    default=F"""train_memory_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving memory results to csv for training."}			,			)
	snake_case_					    =     field(
	    default=F"""env_info_{round(time()  )}.csv"""			,			metadata={"help": "CSV filename used if saving environment information."}			,			)
	snake_case_					    =     field(
	    default=F"""log_{round(time()  )}.csv"""			,			metadata={"help": "Log filename used if print statements are saved in log."}			,			)
	snake_case_					    =     field(default=3			,			metadata={"help": "Times an experiment will be run."}  )
	snake_case_					    =     field(
	    default=__SCREAMING_SNAKE_CASE			,			metadata={
	        "help": (
	            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
	            " model weights."
	        )
	    }			,			)






	def 				UpperCamelCase_  (       self					: 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 				UpperCamelCase_  (       self					: Tuple ):
					return json.dumps(dataclasses.asdict(self )							,indent=2 )






	@property
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					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 				UpperCamelCase_  (       self					: List[str] ):
					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


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE							:List[Any]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:int							 =				{'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

SCREAMING_SNAKE_CASE							:int							 =				{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]
	snake_case_					    =     []






	def __init__(       self					: Any							,A					: List[str]							,A					: str="<unk>"							,A					: int="<s>"							,A					: Union[str, Any]="</s>"							,A					: List[str]="<pad>"							,A					: int="[SEP]"							,A					: Optional[Any]="[MASK]"							,A					: Tuple="[CLS]"							,A					: Optional[Dict[str, Any]] = None							,**A					: Any							,):
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else bos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else eos_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else unk_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else pad_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else cls_token
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else sep_token

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

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,pad_token=A							,sep_token=A							,mask_token=A							,cls_token=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)

					__A   			= vocab_file

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(A )






	@property
	def 				UpperCamelCase_  (       self					: List[str] ):
					return self.sp_model.get_piece_size()






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def __getstate__(       self					: Optional[int] ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: str							,A					: Optional[Any] ):
					__A   			= d

					# for backward compatibility
					if not hasattr(self							,"sp_model_kwargs" ):
									__A   			= {}

					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(self.vocab_file )






	def 				UpperCamelCase_  (       self					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Tuple ):
					return self.sp_model.piece_to_id(A )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Tuple ):
					__A   			= self.sp_model.IdToPiece(A )
					return token






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: int ):
					__A   			= []
					__A   			= ""
					__A   			= False
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													if not prev_is_special:
																	out_string += " "
													out_string += self.sp_model.decode(A ) + token
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: List[int]							,A					: bool = False							,A					: bool = None							,A					: bool = True							,**A					: Union[str, Any]							,):
					__A   			= kwargs.pop("use_source_tokenizer"							,A )

					__A   			= self.convert_ids_to_tokens(A							,skip_special_tokens=A )

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					__A   			= []
					__A   			= []
					for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
													continue
									if token in self.added_tokens_encoder:
													if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(A ) )
																	__A   			= []
													sub_texts.append(A )
									else:
													current_sub_text.append(A )
					if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A ) )

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
									__A   			= re.sub(R" (\[(MASK|SEP)\])"							,R"\1"							," ".join(A ) )
					else:
									__A   			= "".join(A )

					__A   			= (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
									__A   			= self.clean_up_tokenization(A )
									return clean_text
					else:
									return text






	def 				UpperCamelCase_  (       self					: str							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
									copyfile(self.vocab_file							,A )
					elif not os.path.isfile(self.vocab_file ):
									with open(A							,"wb" ) as fi:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None ):
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__A   			= [self.cls_token_id]
					__A   			= [self.sep_token_id]
					return cls + token_ids_a + sep + token_ids_a + sep






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A							,token_ids_a=A							,already_has_special_tokens=A )

					if token_ids_a is None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1]







	def 				UpperCamelCase_  (       self					: Any							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 ) * [0] + len(token_ids_a + sep ) * [1]
