





"""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


snake_case__		:     Optional[int]    				=				logging.get_logger(__name__)


def      _snake_case						(						_snake_case				:			Any=None ,   _snake_case				:			str=None			):
      return field(default_factory=lambda: default ,   metadata=SCREAMING_SNAKE_CASE_			)






@dataclass
class 		snake_case_:
     __UpperCamelCase  =					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'''
             )
         }							,					)

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

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

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






     def      lowerCamelCase__     (	self  :					Optional[int]				):
           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.'''					,				__lowercase					,				)






     def      lowerCamelCase__     (	self  :					Any				):
           return json.dumps(dataclasses.asdict(self				)					,				indent=2				)






     @property
     def      lowerCamelCase__     (	self  :					Dict				):
           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      lowerCamelCase__     (	self  :					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
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


snake_case__		:     Optional[Any]    				=				logging.get_logger(__name__)


snake_case__		:     Any    				=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
snake_case__		:     Optional[Any]    				=				{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

snake_case__		:     List[Any]    				=				{
    '''allenai/led-base-16384''': 16_384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def      _snake_case						(						):
      lowerCAmelCase					:	Optional[int]       =   (
          list(range(ord('''!'''			) ,   ord('''~'''			) + 1			)			) + list(range(ord('''¡'''			) ,   ord('''¬'''			) + 1			)			) + list(range(ord('''®'''			) ,   ord('''ÿ'''			) + 1			)			)
      )
      lowerCAmelCase					:	str       =   bs[:]
      lowerCAmelCase					:	Optional[int]       =   0
      for b in range(2**8			):
            if b not in bs:
                  bs.append(_snake_case			)
                  cs.append(2**8 + n			)
                  n += 1
      lowerCAmelCase					:	int       =   [chr(_snake_case			) for n in cs]
      return dict(zip(_snake_case ,   _snake_case			)			)


def      _snake_case						(						_snake_case				:			List[Any]			):
      lowerCAmelCase					:	List[str]       =   set()
      lowerCAmelCase					:	Any       =   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)			)
            lowerCAmelCase					:	Optional[Any]       =   char
      return pairs






class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']






     def __init__(	self  :					Tuple					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Dict					,				UpperCamelCase_  :					Tuple="replace"					,				UpperCamelCase_  :					Union[str, Any]="<s>"					,				UpperCamelCase_  :					List[str]="</s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					int="<s>"					,				UpperCamelCase_  :					int="<unk>"					,				UpperCamelCase_  :					Union[str, Any]="<pad>"					,				UpperCamelCase_  :					Tuple="<mask>"					,				UpperCamelCase_  :					Optional[int]=False					,				**UpperCamelCase_  :					Tuple					,				):
           lowerCAmelCase					:	Any       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else bos_token
           lowerCAmelCase					:	Union[str, Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else eos_token
           lowerCAmelCase					:	Optional[int]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else sep_token
           lowerCAmelCase					:	int       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else cls_token
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else unk_token
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else pad_token

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

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

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

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           lowerCAmelCase					:	Dict       =   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.bart.tokenization_bart.BartTokenizer.vocab_size
     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           return len(self.encoder				)






     def      lowerCamelCase__     (	self  :					Union[str, Any]				):
           return dict(self.encoder					,				**self.added_tokens_encoder				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					int				):
           if token in self.cache:
                 return self.cache[token]
           lowerCAmelCase					:	List[str]       =   tuple(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[Any]       =   get_pairs(UpperCamelCase_				)

           if not pairs:
                 return token

           while True:
                 lowerCAmelCase					:	List[Any]       =   min(UpperCamelCase_					,				key=lambda UpperCamelCase_				: self.bpe_ranks.get(UpperCamelCase_					,				float('''inf'''				)				)				)
                 if bigram not in self.bpe_ranks:
                       break
                 lowerCAmelCase,      lowerCAmelCase					:	Any       =   bigram
                 lowerCAmelCase					:	Tuple       =   []
                 lowerCAmelCase					:	Any       =   0
                 while i < len(UpperCamelCase_				):
                       try:
                             lowerCAmelCase					:	int       =   word.index(UpperCamelCase_					,				UpperCamelCase_				)
                       except ValueError:
                             new_word.extend(word[i:]				)
                             break
                       else:
                             new_word.extend(word[i:j]				)
                             lowerCAmelCase					:	int       =   j

                       if word[i] == first and i < len(UpperCamelCase_				) - 1 and word[i + 1] == second:
                             new_word.append(first + second				)
                             i += 2
                       else:
                             new_word.append(word[i]				)
                             i += 1
                 lowerCAmelCase					:	Tuple       =   tuple(UpperCamelCase_				)
                 lowerCAmelCase					:	Tuple       =   new_word
                 if len(UpperCamelCase_				) == 1:
                       break
                 else:
                       lowerCAmelCase					:	Optional[Any]       =   get_pairs(UpperCamelCase_				)
           lowerCAmelCase					:	Union[str, Any]       =   ''' '''.join(UpperCamelCase_				)
           lowerCAmelCase					:	List[str]       =   word
           return word






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	Dict       =   []
           for token in re.findall(self.pat					,				UpperCamelCase_				):
                 lowerCAmelCase					:	Union[str, Any]       =   ''''''.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(UpperCamelCase_				).split(''' '''				)				)
           return bpe_tokens






     def      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					str				):
           return self.encoder.get(UpperCamelCase_					,				self.encoder.get(self.unk_token				)				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Union[str, Any]				):
           return self.decoder.get(UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					List[str]				):
           lowerCAmelCase					:	Optional[int]       =   ''''''.join(UpperCamelCase_				)
           lowerCAmelCase					:	Optional[int]       =   bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''					,				errors=self.errors				)
           return text






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           if not os.path.isdir(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	int       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
           lowerCAmelCase					:	Optional[Any]       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

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

           lowerCAmelCase					:	Optional[int]       =   0
           with open(UpperCamelCase_					,				'''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 UpperCamelCase_				: 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!'''				)
                             lowerCAmelCase					:	Tuple       =   token_index
                       writer.write(''' '''.join(UpperCamelCase_				) + '''\n'''				)
                       index += 1

           return vocab_file, merge_file






     def      lowerCamelCase__     (	self  :					Optional[Any]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           lowerCAmelCase					:	Any       =   [self.cls_token_id]
           lowerCAmelCase					:	str       =   [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep






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

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






     def      lowerCamelCase__     (	self  :					List[str]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	Optional[Any]       =   [self.sep_token_id]
           lowerCAmelCase					:	Optional[int]       =   [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      lowerCamelCase__     (	self  :					int					,				UpperCamelCase_  :					Any					,				UpperCamelCase_  :					Dict=False					,				**UpperCamelCase_  :					Tuple				):
           lowerCAmelCase					:	Union[str, Any]       =   kwargs.pop('''add_prefix_space'''					,				self.add_prefix_space				)
           if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase_				) > 0 and not text[0].isspace()):
                 lowerCAmelCase					:	List[Any]       =   ''' ''' + text
           return (text, kwargs)






     def      lowerCamelCase__     (	self  :					str					,				UpperCamelCase_  :					Union[Dict[str, EncodedInput], BatchEncoding]					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,				UpperCamelCase_  :					Optional[int] = None					,				UpperCamelCase_  :					Optional[bool] = None					,				):
           lowerCAmelCase					:	Dict       =   super()._pad(
               encoded_inputs=UpperCamelCase_					,				max_length=UpperCamelCase_					,				padding_strategy=UpperCamelCase_					,				pad_to_multiple_of=UpperCamelCase_					,				return_attention_mask=UpperCamelCase_					,				)

           # Load from model defaults
           if return_attention_mask is None:
                 lowerCAmelCase					:	Tuple       =   '''attention_mask''' in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                 lowerCAmelCase					:	Dict       =   encoded_inputs[self.model_input_names[0]]
                 # `global_attention_mask` need to have the same length as other (sequential) inputs.
                 lowerCAmelCase					:	List[Any]       =   len(encoded_inputs['''global_attention_mask''']				) != len(UpperCamelCase_				)

                 if needs_to_be_padded:
                       lowerCAmelCase					:	int       =   len(UpperCamelCase_				) - len(encoded_inputs['''global_attention_mask''']				)

                       if self.padding_side == "right":
                             # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                             lowerCAmelCase					:	Dict       =   (
                                 encoded_inputs['''global_attention_mask'''] + [-1] * difference
                             )
                       elif self.padding_side == "left":
                             lowerCAmelCase					:	int       =   [-1] * difference + encoded_inputs[
                                 '''global_attention_mask'''
                             ]
                       else:
                             raise ValueError('''Invalid padding strategy:''' + str(self.padding_side				)				)

           return encoded_inputs


