


'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


a_						:       Union[str, Any]      			=				logging.get_logger(__name__)

a_						:       Optional[int]      			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_						:       Any      			=				{
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

a_						:       Any      			=				{
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						VOCAB_FILES_NAMES
  _lowerCamelCase						=						PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase						=						["input_ids", "attention_mask"]
  _lowerCamelCase						=						RobertaTokenizer






  def __init__(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase="replace"    ,						UpperCamelCase="<s>"    ,						UpperCamelCase="</s>"    ,						UpperCamelCase="</s>"    ,						UpperCamelCase="<s>"    ,						UpperCamelCase="<unk>"    ,						UpperCamelCase="<pad>"    ,						UpperCamelCase="<mask>"    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(
       UpperCamelCase    ,						UpperCamelCase    ,						tokenizer_file=UpperCamelCase    ,						errors=UpperCamelCase    ,						bos_token=UpperCamelCase    ,						eos_token=UpperCamelCase    ,						sep_token=UpperCamelCase    ,						cls_token=UpperCamelCase    ,						unk_token=UpperCamelCase    ,						pad_token=UpperCamelCase    ,						mask_token=UpperCamelCase    ,						add_prefix_space=UpperCamelCase    ,						trim_offsets=UpperCamelCase    ,						**UpperCamelCase    ,						)

   lowerCamelCase_  				=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
   if pre_tok_state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
    lowerCamelCase_  				=						getattr(UpperCamelCase    ,						pre_tok_state.pop("type"  )  )
    lowerCamelCase_  				=						add_prefix_space
    lowerCamelCase_  				=						pre_tok_class(**UpperCamelCase  )

   lowerCamelCase_  				=						add_prefix_space

   lowerCamelCase_  				=						"post_processor"
   lowerCamelCase_  				=						getattr(self.backend_tokenizer    ,						UpperCamelCase    ,						UpperCamelCase  )
   if tokenizer_component_instance:
    lowerCamelCase_  				=						json.loads(tokenizer_component_instance.__getstate__()  )

    # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
    if "sep" in state:
     lowerCamelCase_  				=						tuple(state["sep"]  )
    if "cls" in state:
     lowerCamelCase_  				=						tuple(state["cls"]  )

    lowerCamelCase_  				=						False

    if state.get("add_prefix_space"    ,						UpperCamelCase  ) != add_prefix_space:
     lowerCamelCase_  				=						add_prefix_space
     lowerCamelCase_  				=						True

    if state.get("trim_offsets"    ,						UpperCamelCase  ) != trim_offsets:
     lowerCamelCase_  				=						trim_offsets
     lowerCamelCase_  				=						True

    if changes_to_apply:
     lowerCamelCase_  				=						getattr(UpperCamelCase    ,						state.pop("type"  )  )
     lowerCamelCase_  				=						component_class(**UpperCamelCase  )
     setattr(self.backend_tokenizer    ,						UpperCamelCase    ,						UpperCamelCase  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._mask_token is None:
    if self.verbose:
     logger.error("Using mask_token, but it is not set yet."  )
    return None
   return str(self._mask_token  )






  @mask_token.setter
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase    ,						rstrip=UpperCamelCase  ) if isinstance(UpperCamelCase    ,						UpperCamelCase  ) else value
   lowerCamelCase_  				=						value






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						kwargs.get("is_split_into_words"    ,						UpperCamelCase  )
   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._batch_encode_plus(*UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						kwargs.get("is_split_into_words"    ,						UpperCamelCase  )

   assert self.add_prefix_space or not is_split_into_words, (
       f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
       "to use it with pretokenized inputs."
   )

   return super()._encode_plus(*UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   lowerCamelCase_  				=						self._tokenizer.model.save(UpperCamelCase    ,						name=UpperCamelCase  )
   return tuple(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
    return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = None  ):







   """simple docstring"""
   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 + sep + token_ids_a + sep  ) * [0]










'''simple docstring'''

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 __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    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 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    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   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       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():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



