



"""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__    :  Any							    =	logging.get_logger(__name__)




def __lowercase			(							_a=None		,			_a=None    ):
    return field(default_factory=lambda: default		,			metadata=_a    )






@dataclass
class        _UpperCAmelCase       :
 _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, 3_2, 1_2_8, 5_1_2]    ,     metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}    ,     )

 _lowerCAmelCase   :			bool							=		field(
     default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}    ,     )
 _lowerCAmelCase   :			bool							=		field(
     default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}    ,     )
 _lowerCAmelCase   :			bool							=		field(
     default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Use FP16 to accelerate inference."""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Benchmark training of model"""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Verbose memory tracing"""})
 _lowerCAmelCase   :			bool							=		field(
     default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}    ,     )
 _lowerCAmelCase   :			bool							=		field(
     default=_SCREAMING_SNAKE_CASE    ,     metadata={
         """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
     }    ,     )
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Trace memory line by line"""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Save result to a CSV file"""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Save all print statements in a log file"""})
 _lowerCAmelCase   :			bool							=		field(default=_SCREAMING_SNAKE_CASE    ,     metadata={"""help""": """Whether to print environment information"""})
 _lowerCAmelCase   :			bool							=		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."""
         )
     }    ,     )
 _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=_SCREAMING_SNAKE_CASE    ,     metadata={
         """help""": (
             """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
             """ model weights."""
         )
     }    ,     )

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

 @property
 def 						_snake_case					(     self		:       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 						_snake_case					(     self		:       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







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


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

_UpperCAmelCase					: Dict      =       {"vocab_file": "spiece.model"}

_UpperCAmelCase					: Dict      =       {
    "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"
        ),
    }
}

_UpperCAmelCase					: int      =       {
    "google/bigbird-roberta-base": 4_096,
    "google/bigbird-roberta-large": 4_096,
    "google/bigbird-base-trivia-itc": 4_096,
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				VOCAB_FILES_NAMES
		__lowercase       :       int	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       List[Any]	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       List[Any]	     =				["input_ids", "attention_mask"]
		__lowercase       :       List[int]	     =				[]



		def __init__( self				,      A_				,      A_="<unk>"				,      A_="<s>"				,      A_="</s>"				,      A_="<pad>"				,      A_="[SEP]"				,      A_="[MASK]"				,      A_="[CLS]"				,      A_ = None				,      **A_				,      )  ->					None:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else bos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else eos_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else unk_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else pad_token
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else cls_token
				UpperCamelCase		=     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
				UpperCamelCase		=     AddedToken(A_				,      lstrip=A_				,      rstrip=A_       ) if isinstance(A_				,      A_       ) else mask_token

				UpperCamelCase		=     {} 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_				,      )

				UpperCamelCase		=     vocab_file

				UpperCamelCase		=     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
				self.sp_model.Load(A_       )



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.sp_model.get_piece_size()



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {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]:
				"""simple docstring"""
				UpperCamelCase		=     self.__dict__.copy()
				UpperCamelCase		=     None
				return state



		def __setstate__( self				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     d

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

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



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				return self.sp_model.encode(A_				,      out_type=A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return self.sp_model.piece_to_id(A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.sp_model.IdToPiece(A_       )
				return token



		def __UpperCamelCase ( self				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     []
				UpperCamelCase		=     ''
				UpperCamelCase		=     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
								UpperCamelCase		=     True
								UpperCamelCase		=     []
						else:
								current_sub_tokens.append(A_       )
								UpperCamelCase		=     False
				out_string += self.sp_model.decode(A_       )
				return out_string.strip()



		def __UpperCamelCase ( self				,      A_				,      A_ = False				,      A_ = None				,      A_ = True				,      **A_				,      )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     kwargs.pop('use_source_tokenizer'				,      A_       )

				UpperCamelCase		=     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
				UpperCamelCase		=     []
				UpperCamelCase		=     []
				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_       )       )
										UpperCamelCase		=     []
								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:
						UpperCamelCase		=     re.sub(r' (\[(MASK|SEP)\])'				,      r'\1'				,      ' '.join(A_       )       )
				else:
						UpperCamelCase		=     ''.join(A_       )

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



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     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:
								UpperCamelCase		=     self.sp_model.serialized_model_proto()
								fi.write(A_       )

				return (out_vocab_file,)



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				UpperCamelCase		=     [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep



		def __UpperCamelCase ( self				,      A_				,      A_ = None				,      A_ = False       )  ->					List[int]:
				"""simple docstring"""
				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				,      A_				,      A_ = None       )  ->					List[int]:
				"""simple docstring"""
				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       ) * [0] + len(token_ids_a + sep       ) * [1]
