'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


lowerCAmelCase_	:		List[str]			=     """▁"""

lowerCAmelCase_	:		Tuple			=     {"""vocab_file""": """spiece.model"""}

lowerCAmelCase_	:		Optional[int]			=     {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

lowerCAmelCase_	:		List[Any]			=     {
    """google/reformer-crime-and-punishment""": 52_4288,
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							VOCAB_FILES_NAMES
				UpperCAmelCase__							=							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__							=							['''input_ids''', '''attention_mask''']





				def __init__(						self					:						int       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						Tuple="</s>"       ,		lowercase__					:						List[str]="<unk>"       ,		lowercase__					:						List[str]=[]       ,		lowercase__					:						Optional[Dict[str, Any]] = None       ,		**lowercase__					:						Tuple       ,		)							->None:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=lowercase__       ,		unk_token=lowercase__       ,		additional_special_tokens=lowercase__       ,		sp_model_kwargs=self.sp_model_kwargs       ,		**lowercase__       ,		)

										_UpperCamelCase			:			Optional[int]   			=						vocab_file
										_UpperCamelCase			:			Dict   			=						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(lowercase__					)





				@property
				def  snake_case__				(						self					:						List[str]					)							->Any:


										'''simple docstring'''




										return self.sp_model.get_piece_size()





				def  snake_case__				(						self					:						Optional[Any]					)							->Dict[str, int]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						{self.convert_ids_to_tokens(lowercase__					): i for i in range(self.vocab_size					)}
										vocab.update(self.added_tokens_encoder					)
										return vocab





				def __getstate__(						self					:						List[str]					)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						self.__dict__.copy()
										_UpperCamelCase			:			str   			=						None
										return state





				def __setstate__(						self					:						Tuple       ,		lowercase__					:						List[Any]					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						d

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

										_UpperCamelCase			:			List[str]   			=						spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(self.vocab_file					)





				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						str					)							->List[str]:


										'''simple docstring'''




										return self.sp_model.encode(lowercase__       ,		out_type=lowercase__					)





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						int					)							->Optional[Any]:


										'''simple docstring'''




										return self.sp_model.piece_to_id(lowercase__					)





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						Optional[int]					)							->int:


										'''simple docstring'''




										if index < self.sp_model.get_piece_size():
																_UpperCamelCase			:			Dict   			=						self.sp_model.IdToPiece(lowercase__					)
										return token





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						Optional[int]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						[]
										_UpperCamelCase			:			Dict   			=						""
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(lowercase__					) + token
																						_UpperCamelCase			:			int   			=						[]
																else:
																						current_sub_tokens.append(lowercase__					)
										out_string += self.sp_model.decode(lowercase__					)
										return out_string.strip()







				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						str       ,		lowercase__					:						Optional[str] = None					)							->Tuple[str]:


										'''simple docstring'''




										if not os.path.isdir(lowercase__					):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
																return
										_UpperCamelCase			:			List[str]   			=						os.path.join(
										    lowercase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

										if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase__					) and os.path.isfile(self.vocab_file					):
																copyfile(self.vocab_file       ,		lowercase__					)
										elif not os.path.isfile(self.vocab_file					):
																with open(lowercase__       ,		"wb"					) as fi:
																						_UpperCamelCase			:			List[Any]   			=						self.sp_model.serialized_model_proto()
																						fi.write(lowercase__					)

										return (out_vocab_file,)



'''simple docstring'''





import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


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


lowerCAmelCase_	:		int			=     r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

"""




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						str       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Tuple					)							->bool:


										'''simple docstring'''




										raise NotImplementedError("StoppingCriteria needs to be subclassed"					)





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						int       ,		lowercase__					:						Optional[int] = None					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						max_length
										_UpperCamelCase			:			str   			=						max_position_embeddings







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Optional[int]       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Optional[Any]					)							->bool:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						input_ids.shape[-1]
										_UpperCamelCase			:			Optional[int]   			=						cur_len >= self.max_length
										if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
																logger.warning_once(
																    "This is a friendly reminder - the current text generation call will exceed the model's predefined "
																    f'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
																    "exceptions, performance degradation, or nothing at all."					)
										return is_done





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						List[Any]       ,		lowercase__					:						int       ,		lowercase__					:						int					)							->int:


										'''simple docstring'''




										warnings.warn(
										    "The class `MaxNewTokensCriteria` is deprecated. "
										    f'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
										    "with `max_length = start_length + max_new_tokens` instead."       ,		lowercase__       ,		)
										_UpperCamelCase			:			Dict   			=						start_length
										_UpperCamelCase			:			Optional[Any]   			=						max_new_tokens
										_UpperCamelCase			:			List[Any]   			=						start_length + max_new_tokens







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Dict       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Dict					)							->bool:


										'''simple docstring'''




										return input_ids.shape[-1] >= self.max_length





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Union[str, Any]       ,		lowercase__					:						float       ,		lowercase__					:						Optional[float] = None					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						max_time
										_UpperCamelCase			:			int   			=						time.time() if initial_timestamp is None else initial_timestamp







				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Dict       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						Tuple					)							->bool:


										'''simple docstring'''




										return time.time() - self.initial_timestamp > self.max_time





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				@add_start_docstrings(lowercase__					)
				def __call__(						self					:						Optional[int]       ,		lowercase__					:						torch.LongTensor       ,		lowercase__					:						torch.FloatTensor       ,		**lowercase__					:						List[Any]					)							->bool:


										'''simple docstring'''




										return any(criteria(lowercase__       ,		lowercase__					) for criteria in self					)







				@property
				def  snake_case__				(						self					:						Tuple					)							->Optional[int]:


										'''simple docstring'''




										for stopping_criterium in self:
																if isinstance(lowercase__       ,		lowercase__					):
																						return stopping_criterium.max_length
																elif isinstance(lowercase__       ,		lowercase__					):
																						return stopping_criterium.max_length
										return None





def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			StoppingCriteriaList:




						'''simple docstring'''
						_UpperCamelCase			:			Optional[int]   			=						stopping_criteria.max_length
						_UpperCamelCase			:			Optional[Any]   			=						deepcopy(UpperCAmelCase					)
						if stopping_max_length is not None and stopping_max_length != max_length:
												warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"     ,UpperCAmelCase					)
						elif stopping_max_length is None:
												new_stopping_criteria.append(MaxLengthCriteria(max_length=UpperCAmelCase					)					)
						return new_stopping_criteria


