






import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           Adafactor,
           AdamW,
           get_constant_schedule,
           get_constant_schedule_with_warmup,
           get_cosine_schedule_with_warmup,
           get_cosine_with_hard_restarts_schedule_with_warmup,
           get_inverse_sqrt_schedule,
           get_linear_schedule_with_warmup,
           get_polynomial_decay_schedule_with_warmup,
       )







def 			A__							(      _a  :					List[Any]		,  _a  :					Optional[int]=10	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =[]
 for _ in range(lowerCamelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs







def 			A__							(      _a  :					str		,  _a  :					Optional[Any]=10	):





 '''simple docstring'''







 snake_case__  :    str							       =[]
 for step in range(lowerCamelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    snake_case__  :    Optional[Any]							       =os.path.join(lowerCamelCase_		,  """schedule.bin"""	)
    torch.save(scheduler.state_dict()		,  lowerCamelCase_	)

    snake_case__  :    int							       =torch.load(lowerCamelCase_	)
    scheduler.load_state_dict(lowerCamelCase_	)
 return lrs







@require_torch
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self				,			a				,			a				,			a   ):
       self.assertEqual(len(__lowerCamelCase   )				,			len(__lowerCamelCase   )   )
       for a, b in zip(__lowerCamelCase				,			__lowerCamelCase   ):
        self.assertAlmostEqual(__lowerCamelCase				,			__lowerCamelCase				,			delta=__lowerCamelCase   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =torch.tensor([0.1, -0.2, -0.1]				,			requires_grad=__lowerCamelCase   )
       snake_case__  :    Optional[int]							       =torch.tensor([0.4, 0.2, -0.5]   )
       snake_case__  :    List[str]							       =nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       snake_case__  :    Dict							       =AdamW(params=[w]				,			lr=2e-1				,			weight_decay=0.0   )
       for _ in range(1_0_0   ):
        snake_case__  :    Optional[int]							       =criterion(__lowerCamelCase				,			__lowerCamelCase   )
        loss.backward()
        optimizer.step()
        w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
        w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()				,			[0.4, 0.2, -0.5]				,			tol=1e-2   )



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =torch.tensor([0.1, -0.2, -0.1]				,			requires_grad=__lowerCamelCase   )
       snake_case__  :    Optional[Any]							       =torch.tensor([0.4, 0.2, -0.5]   )
       snake_case__  :    List[str]							       =nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       snake_case__  :    List[str]							       =Adafactor(
           params=[w]				,			lr=1e-2				,			eps=(1e-30, 1e-3)				,			clip_threshold=1.0				,			decay_rate=-0.8				,			betaa=__lowerCamelCase				,			weight_decay=0.0				,			relative_step=__lowerCamelCase				,			scale_parameter=__lowerCamelCase				,			warmup_init=__lowerCamelCase				,			)
       for _ in range(1_0_0_0   ):
        snake_case__  :    Tuple							       =criterion(__lowerCamelCase				,			__lowerCamelCase   )
        loss.backward()
        optimizer.step()
        w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
        w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()				,			[0.4, 0.2, -0.5]				,			tol=1e-2   )







@require_torch
class  _lowercase   (   unittest.TestCase						):
      _a							:						Any										=							nn.Linear(50						,				50						) if is_torch_available() else None
      _a							:						Union[str, Any]										=							AdamW(m.parameters()						,				lr=10.0						) if is_torch_available() else None
      _a							:						Optional[int]										=							10
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a=None   ):
       self.assertEqual(len(__lowerCamelCase   )				,			len(__lowerCamelCase   )   )
       for a, b in zip(__lowerCamelCase				,			__lowerCamelCase   ):
        self.assertAlmostEqual(__lowerCamelCase				,			__lowerCamelCase				,			delta=__lowerCamelCase				,			msg=__lowerCamelCase   )



      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       ={'''num_warmup_steps''': 2, '''num_training_steps''': 1_0}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       snake_case__  :    Optional[Any]							       ={
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
        snake_case__  :    List[Any]							       =data

        snake_case__  :    str							       =scheduler_func(self.optimizer				,			**__lowerCamelCase   )
        self.assertEqual(len([scheduler.get_lr()[0]]   )				,			1   )
        snake_case__  :    Union[str, Any]							       =unwrap_schedule(__lowerCamelCase				,			self.num_steps   )
        self.assertListAlmostEqual(
            __lowerCamelCase				,			__lowerCamelCase				,			tol=1e-2				,			msg=F"failed for {scheduler_func} in normal scheduler"				,			)

        snake_case__  :    Union[str, Any]							       =scheduler_func(self.optimizer				,			**__lowerCamelCase   )
        if scheduler_func.__name__ != "get_constant_schedule":
         LambdaScheduleWrapper.wrap_scheduler(__lowerCamelCase   )  # wrap to test picklability of the schedule
        snake_case__  :    Optional[int]							       =unwrap_and_save_reload_schedule(__lowerCamelCase				,			self.num_steps   )
        self.assertListEqual(__lowerCamelCase				,			__lowerCamelCase				,			msg=F"failed for {scheduler_func} in save and reload"   )







class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Any							       =fn
      def __call__(     self				,			*a				,			**a   ):
       return self.fn(*__lowerCamelCase				,			**__lowerCamelCase   )



      @classmethod
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Union[str, Any]							       =list(map(self				,			scheduler.lr_lambdas   )   )













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


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		Optional[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"""
        ),
    }
}

__lowerCamelCase	:		List[str]										=   {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}







class  _lowercase   (   _A						):
      _a							:						int										=							VOCAB_FILES_NAMES
      _a							:						List[str]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Any										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      _a							:						List[int]										=							[]
      def __init__(     self				,			a				,			a="<unk>"				,			a="<s>"				,			a="</s>"				,			a="<pad>"				,			a="[SEP]"				,			a="[MASK]"				,			a="[CLS]"				,			a = None				,			**a				,			):
       snake_case__  :    str							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else bos_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    Any							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else cls_token
       snake_case__  :    Tuple							       =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
       snake_case__  :    Union[str, Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Union[str, Any]							       ={} 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				,			)

       snake_case__  :    Optional[int]							       =vocab_file

       snake_case__  :    List[str]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(a   )
      @property
      def 						lowercase__					(     self   ):
       return self.sp_model.get_piece_size()
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       ={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   ):
       snake_case__  :    List[Any]							       =self.__dict__.copy()
       snake_case__  :    Optional[int]							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[Any]							       ={}

       snake_case__  :    Tuple							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.piece_to_id(a   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Dict							       =self.sp_model.IdToPiece(a   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       =[]
       snake_case__  :    Dict							       =""""""
       snake_case__  :    Dict							       =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
         snake_case__  :    Optional[Any]							       =True
         snake_case__  :    Dict							       =[]
        else:
         current_sub_tokens.append(a   )
         snake_case__  :    List[Any]							       =False
       out_string += self.sp_model.decode(a   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a				,			a = False				,			a = None				,			a = True				,			**a				,			):
       snake_case__  :    List[str]							       =kwargs.pop("""use_source_tokenizer"""				,			a   )

       snake_case__  :    int							       =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
       snake_case__  :    List[Any]							       =[]
       snake_case__  :    int							       =[]
       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   )   )
          snake_case__  :    List[Any]							       =[]
         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:
        snake_case__  :    Dict							       =re.sub(R""" (\[(MASK|SEP)\])"""				,			R"""\1"""				,			""" """.join(a   )   )
       else:
        snake_case__  :    Dict							       ="""""".join(a   )

       snake_case__  :    str							       =(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
        snake_case__  :    Any							       =self.clean_up_tokenization(a   )
        return clean_text
       else:
        return text
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Any							       =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:
         snake_case__  :    Any							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       return (out_vocab_file,)
      def 						lowercase__					(     self				,			a				,			a = None   ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    Union[str, Any]							       =[self.cls_token_id]
       snake_case__  :    Dict							       =[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = 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 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    List[Any]							       =[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]





