


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

       """simple docstring"""


       SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
       SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





       def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
           super().__init__(
               A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
           _SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

           _SCREAMING_SNAKE_CASE      = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
           if pre_tok_state.get('add_prefix_space'				, A_	) != add_prefix_space:
               _SCREAMING_SNAKE_CASE      = getattr(A_				, pre_tok_state.pop('type'	)	)
               _SCREAMING_SNAKE_CASE      = add_prefix_space
               _SCREAMING_SNAKE_CASE      = pre_tok_class(**A_	)

           _SCREAMING_SNAKE_CASE      = add_prefix_space





       @property
       def 		__magic_name__			(    self	)->	str:
           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 		__magic_name__			(    self				, A_	)->	int:
           _SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
           _SCREAMING_SNAKE_CASE      = value





       def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _SCREAMING_SNAKE_CASE      = [self.cls_token_id]
           _SCREAMING_SNAKE_CASE      = [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep





       def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
           _SCREAMING_SNAKE_CASE      = [self.sep_token_id]
           _SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
           _SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)
           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(*A_				, **A_	)





       def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
           _SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)

           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(*A_				, **A_	)





       def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
           _SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
           return tuple(A_	)





       def 		__magic_name__			(    self				, A_	)->	List[int]:
           _SCREAMING_SNAKE_CASE      = []
           for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)

           if len(A_	) > self.model_max_length:
               _SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
           return input_ids




import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
							import torch_xla.core.xla_model as xm


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				if isinstance(UpperCAmelCase__   ,(list, tuple)							):
								return honor_type(UpperCAmelCase__   ,(move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for t in state)							)
				elif isinstance(UpperCAmelCase__   ,UpperCAmelCase__							):
								return type(UpperCAmelCase__							)({k: move_to_device(UpperCAmelCase__   ,UpperCAmelCase__							) for k, v in state.items()}							)
				elif isinstance(UpperCAmelCase__   ,torch.Tensor							):
								return state.to(UpperCAmelCase__							)
				return state



class    __lowercase  (       torch.optim.Optimizer							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=True				, A_=None	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = optimizer
											_SCREAMING_SNAKE_CASE      = scaler
											_SCREAMING_SNAKE_CASE      = AcceleratorState()
											_SCREAMING_SNAKE_CASE      = GradientState()
											_SCREAMING_SNAKE_CASE      = device_placement
											_SCREAMING_SNAKE_CASE      = False
											_SCREAMING_SNAKE_CASE      = None

											# Handle device placement
											if device_placement:
															_SCREAMING_SNAKE_CASE      = self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
															else:
																			_SCREAMING_SNAKE_CASE      = move_to_device(A_				, self.accelerator_state.device	)
															self.optimizer.load_state_dict(A_	)





							@property
							def 		__magic_name__			(    self	)->	Tuple:
											return self.optimizer.state





							@state.setter
							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = state





							@property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											return self.optimizer.param_groups





							@param_groups.setter
							def 		__magic_name__			(    self				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = param_groups





							@property
							def 		__magic_name__			(    self	)->	Any:
											return self.optimizer.defaults





							@defaults.setter
							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = defaults





							def 		__magic_name__			(    self				, A_	)->	Any:
											self.optimizer.add_param_group(A_	)





							def 		__magic_name__			(    self				, A_	)->	int:
											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(A_				, self.accelerator_state.device	)
											self.optimizer.load_state_dict(A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											return self.optimizer.state_dict()





							def 		__magic_name__			(    self				, A_=None	)->	Any:
											if self.gradient_state.sync_gradients:
															_SCREAMING_SNAKE_CASE      = 'set_to_none' in inspect.signature(self.optimizer.zero_grad	).parameters
															if accept_arg:
																			if set_to_none is None:
																							_SCREAMING_SNAKE_CASE      = False
																			self.optimizer.zero_grad(set_to_none=A_	)
															else:
																			if set_to_none is not None:
																							raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'	)
																			self.optimizer.zero_grad()





							def 		__magic_name__			(    self				, A_=None	)->	Optional[int]:
											if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_SCREAMING_SNAKE_CASE      = {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer				, optimizer_args=A_	)
															elif self.scaler is not None:
																			_SCREAMING_SNAKE_CASE      = False
																			if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																							_SCREAMING_SNAKE_CASE      = True
																			self.scaler.step(self.optimizer				, A_	)
																			self.scaler.update()
																			_SCREAMING_SNAKE_CASE      = self.scaler.get_scale()
																			if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							_SCREAMING_SNAKE_CASE      = scale_after < self._last_scale
																			_SCREAMING_SNAKE_CASE      = scale_after

															else:
																			self.optimizer.step(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											for param_group in self.optimizer.param_groups:
															_SCREAMING_SNAKE_CASE      = [parameters_map.get(A_				, A_	) for p in param_group['params']]





							@property
							def 		__magic_name__			(    self	)->	str:
											warnings.warn(
											    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
											    '`optimizer.step_was_skipped` instead.'				, A_				, )
											return self._is_overflow





							@property
							def 		__magic_name__			(    self	)->	int:
											return self._is_overflow





							def __getstate__(    self	)->	Dict:
											return self.__dict__.copy()





							def __setstate__(    self				, A_	)->	List[str]:
											self.__dict__.update(A_	)
