'''simple docstring'''

import os
import re
import unicodedata
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


lowercase_  		=			logging.get_logger(__name__)
lowercase_  		=			{'''vocab_file''': '''spiece.model'''}

lowercase_  		=			{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
}

lowercase_  		=			{
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

lowercase_  		=			'''▁'''
class 	SCREAMING_SNAKE_CASE      (    lowerCamelCase_):







		"""simple docstring"""
		lowercase :   str							=				VOCAB_FILES_NAMES
		lowercase :   List[str]							=				PRETRAINED_VOCAB_FILES_MAP
		lowercase :   Optional[int]							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase :   Tuple							=				["input_ids", "token_type_ids"]






		def __init__( self		,    __A		,    __A=False		,    __A=True		,    __A=True		,    __A="<unk>"		,    __A="[SEP]"		,    __A="<pad>"		,    __A="[CLS]"		,    __A="[MASK]"		,    __A = None		,    **__A		,    )			->  str:
								_lowerCAmelCase					    =(
								    AddedToken(__A		,    lstrip=__A		,    rstrip=__A		,    normalized=__A					)
								    if isinstance(__A		,    __A					)
								    else mask_token
								)

								_lowerCAmelCase					    ={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=__A		,    remove_space=__A		,    keep_accents=__A		,    unk_token=__A		,    sep_token=__A		,    pad_token=__A		,    cls_token=__A		,    mask_token=__A		,    sp_model_kwargs=self.sp_model_kwargs		,    **__A		,    )

								_lowerCAmelCase					    =do_lower_case
								_lowerCAmelCase					    =remove_space
								_lowerCAmelCase					    =keep_accents
								_lowerCAmelCase					    =vocab_file

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






		@property
		def 		UpperCamelCase__							( self					)			->  int:
								return len(self.sp_model					)






		def 		UpperCamelCase__							( self					)			->  Tuple:
								_lowerCAmelCase					    ={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					)			->  List[str]:
								_lowerCAmelCase					    =self.__dict__.copy()
								_lowerCAmelCase					    =None
								return state






		def __setstate__( self		,    __A					)			->  List[Any]:
								_lowerCAmelCase					    =d

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

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






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:




								if self.remove_space:
														_lowerCAmelCase					    =''' '''.join(inputs.strip().split()					)
								else:
														_lowerCAmelCase					    =inputs
								_lowerCAmelCase					    =outputs.replace('``'		,    '"'					).replace('\'\''		,    '"'					)

								if not self.keep_accents:
														_lowerCAmelCase					    =unicodedata.normalize('NFKD'		,    __A					)
														_lowerCAmelCase					    =''''''.join([c for c in outputs if not unicodedata.combining(__A					)]					)
								if self.do_lower_case:
														_lowerCAmelCase					    =outputs.lower()

								return outputs






		def 		UpperCamelCase__							( self		,    __A					)			->  List[str]:
								_lowerCAmelCase					    =self.preprocess_text(__A					)
								_lowerCAmelCase					    =self.sp_model.encode(__A		,    out_type=__A					)
								_lowerCAmelCase					    =[]
								for piece in pieces:
														if len(__A					) > 1 and piece[-1] == str(','					) and piece[-2].isdigit():
																				_lowerCAmelCase					    =self.sp_model.EncodeAsPieces(piece[:-1].replace(__A		,    ''					)					)
																				if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																										if len(cur_pieces[0]					) == 1:
																																_lowerCAmelCase					    =cur_pieces[1:]
																										else:
																																_lowerCAmelCase					    =cur_pieces[0][1:]
																				cur_pieces.append(piece[-1]					)
																				new_pieces.extend(__A					)
														else:
																				new_pieces.append(__A					)

								return new_pieces






		def 		UpperCamelCase__							( self		,    __A					)			->  Union[str, Any]:
								return self.sp_model.PieceToId(__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  Optional[int]:
								return self.sp_model.IdToPiece(__A					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =[]
								_lowerCAmelCase					    =''''''
								_lowerCAmelCase					    =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
																				_lowerCAmelCase					    =True
																				_lowerCAmelCase					    =[]
														else:
																				current_sub_tokens.append(__A					)
																				_lowerCAmelCase					    =False
								out_string += self.sp_model.decode(__A					)
								return out_string.strip()






		def 		UpperCamelCase__							( self		,    __A		,    __A = False		,    __A = None		,    __A = True		,    **__A		,    )			->  str:
								_lowerCAmelCase					    =kwargs.pop('use_source_tokenizer'		,    __A					)

								_lowerCAmelCase					    =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
								_lowerCAmelCase					    =[]
								_lowerCAmelCase					    =[]
								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					)					)
																										_lowerCAmelCase					    =[]
																				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 after <unk>
								if spaces_between_special_tokens:
														_lowerCAmelCase					    =re.sub(r'(<unk>) '		,    r'\1'		,    ' '.join(__A					)					)
								else:
														_lowerCAmelCase					    =''''''.join(__A					)

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






		def 		UpperCamelCase__							( self		,    __A		,    __A = None					)			->  int:
								_lowerCAmelCase					    =[self.sep_token_id]
								_lowerCAmelCase					    =[self.cls_token_id]
								if token_ids_a is None:
														return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






		def 		UpperCamelCase__							( self		,    __A		,    __A = None		,    __A = False					)			->  Optional[int]:




								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 not None:
														return [1] + ([0] * len(__A					)) + [1] + ([0] * len(__A					)) + [1]
								return [1] + ([0] * len(__A					)) + [1]






		def 		UpperCamelCase__							( self		,    __A		,    __A = None					)			->  Union[str, Any]:
								_lowerCAmelCase					    =[self.sep_token_id]
								_lowerCAmelCase					    =[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]






		def 		UpperCamelCase__							( self		,    __A		,    __A = None					)			->  Tuple:




								if not os.path.isdir(__A					):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
														return
								_lowerCAmelCase					    =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:
																				_lowerCAmelCase					    =self.sp_model.serialized_model_proto()
																				fi.write(__A					)

								return (out_vocab_file,)











'''simple docstring'''






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
      import torch

      from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
      import pyanvml.pyanvml as nvml


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

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):
 __snake_case						:  PyTorchBenchmarkArguments
 __snake_case						:  PretrainedConfig
 __snake_case						:  str       =   "PyTorch"



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return torch.__version__



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		str    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_speed(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		int    =   self._prepare_inference_func(A ,		A ,		A			)
        return self._measure_memory(_inference			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_speed(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self._prepare_train_func(A ,		A ,		A			)
        return self._measure_memory(_train			)



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Any    =   self.config_dict[model_name]

        if self.args.torchscript:
               lowercase	:		Any    =   True

        lowercase	:		Union[str, Any]    =   (
            hasattr(A ,		'''architectures'''			)
            and isinstance(config.architectures ,		A			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      lowercase	:		Any    =   config.architectures[0]
                      lowercase	:		Dict    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		List[str]    =   getattr(A ,		A			)
                      lowercase	:		Dict    =   model_cls(A			)
               except ImportError:
                      raise ImportError(
                          f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                          ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''			)
        else:
               lowercase	:		Optional[int]    =   MODEL_MAPPING[config.__class__](A			)

        model.eval()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		str    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		Union[str, Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)
               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        if self.args.torchscript:
               with torch.no_grad():
                      lowercase	:		int    =   torch.jit.trace(A ,		A			)
        else:
               lowercase	:		Any    =   model

        def encoder_decoder_forward():
               with torch.no_grad():
                      lowercase	:		int    =   inference_model(A ,		decoder_input_ids=A			)
               return outputs

        def encoder_forward():
               with torch.no_grad():
                      lowercase	:		Tuple    =   inference_model(A			)
               return outputs

        lowercase	:		int    =   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward



 def  SCREAMING_SNAKE_CASE(	self ,		A ,		A ,		A			):






        '''simple docstring'''




        lowercase	:		Optional[int]    =   self.config_dict[model_name]

        lowercase	:		Optional[Any]    =   (
            hasattr(A ,		'''architectures'''			)
            and isinstance(config.architectures ,		A			)
            and len(config.architectures			) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      lowercase	:		str    =   config.architectures[0]
                      lowercase	:		List[str]    =   __import__('''transformers''' ,		fromlist=[model_class]			)
                      lowercase	:		int    =   getattr(A ,		A			)
                      lowercase	:		Any    =   model_cls(A			)
               except ImportError:
                      raise ImportError(
                          f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
                          ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''			)
        else:
               lowercase	:		Union[str, Any]    =   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A			)

        if self.args.torchscript:
               raise NotImplementedError('''Training for torchscript is currently not implemented'''			)
        else:
               lowercase	:		Dict    =   model

        model.train()
        model.to(self.args.device			)

        # encoder-decoder has vocab size saved differently
        lowercase	:		Union[str, Any]    =   config.vocab_size if hasattr(A ,		'''vocab_size'''			) else config.encoder.vocab_size
        lowercase	:		List[Any]    =   torch.randint(A ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device			)

        if self.args.fpaa:
               logger.info('''Running training in Mixed Precision...'''			)
               if not self.args.is_gpu:
                      raise ValueError('''Mixed precision is possible only for GPU.'''			)

               # amp seems to have memory leaks so that memory usage
               # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
               model.half()

        def compute_loss_and_backprob_encoder():
               lowercase	:		Optional[Any]    =   train_model(A ,		labels=A			)[0]
               loss.backward()
               return loss

        def compute_loss_and_backprob_encoder_decoder():
               lowercase	:		List[Any]    =   train_model(A ,		decoder_input_ids=A ,		labels=A			)[0]
               loss.backward()
               return loss

        lowercase	:		Any    =   (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.is_tpu or self.args.torchscript:
                      # run additional 10 times to stabilize compilation for tpu and torchscript
                      logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''			)
                      timeit.repeat(
                          A ,		repeat=1 ,		number=5 ,		)

               # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
               lowercase	:		str    =   timeit.repeat(
                   A ,		repeat=self.args.repeat ,		number=10 ,		)

               if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                      import torch_xla.debug.metrics as met

                      self.print_fn(met.metrics_report()			)

               return min(A			) / 1_0.0
        except RuntimeError as e:
               self.print_fn(f"""Doesn't fit on GPU. {e}"""			)
               return "N/A"





 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        try:
               if self.args.trace_memory_line_by_line:
                      lowercase	:		int    =   start_memory_tracing('''transformers'''			)

               if self.args.is_tpu:
                      # tpu
                      raise NotImplementedError(
                          '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
                          ''' `--no-memory` or `args.memory=False`'''			)
               elif self.args.is_gpu:
                      if not is_pyanvml_available():
                             logger.warning(
                                 '''py3nvml not installed, we won\'t log GPU memory usage. '''
                                 '''Install py3nvml (pip install py3nvml) to log information about GPU.'''			)
                             lowercase	:		str    =   '''N/A'''
                      else:
                             logger.info(
                                 '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
                                 ''' on the same GPU.'''			)
                             # init nvml
                             nvml.nvmlInit()
                             func()
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
                             lowercase	:		List[Any]    =   nvml.nvmlDeviceGetMemoryInfo(A			)
                             lowercase	:		int    =   meminfo.used
                             lowercase	:		Dict    =   Memory(A			)
                             # shutdown nvml
                             nvml.nvmlShutdown()
               else:
                      # cpu
                      lowercase	:		Optional[Any]    =   measure_peak_memory_cpu(A			)
                      lowercase	:		Union[str, Any]    =   Memory(A			) if isinstance(A ,		A			) else memory_bytes

               if self.args.trace_memory_line_by_line:
                      lowercase	:		Tuple    =   stop_memory_tracing(A			)
               else:
                      lowercase	:		int    =   None

               return memory, summary
        except RuntimeError as e:
               self.print_fn(f"""Doesn't fit on GPU. {e}"""			)
               return "N/A", None




