


'''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


__snake_case							   =  logging.get_logger(__name__)


__snake_case							   =  '''▁'''

__snake_case							   =  {'''vocab_file''': '''spiece.model'''}

__snake_case							   =  {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

__snake_case							   =  {
    '''google/reformer-crime-and-punishment''': 524288,
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       VOCAB_FILES_NAMES
		_a									=       PRETRAINED_VOCAB_FILES_MAP
		_a									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a									=       ['input_ids', 'attention_mask']




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_="</s>"					,     UpperCamelCase_="<unk>"					,     UpperCamelCase_=[]					,     UpperCamelCase_ = None					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				{} if sp_model_kwargs is None else sp_model_kwargs

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

							UpperCamelCase__     :List[str]							      =				vocab_file
							UpperCamelCase__     :Optional[Any]							      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(UpperCamelCase_							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return self.sp_model.get_piece_size()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				{self.convert_ids_to_tokens(UpperCamelCase_							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab




		def __getstate__(	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.__dict__.copy()
							UpperCamelCase__     :Dict							      =				None
							return state




		def __setstate__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				d

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

							UpperCamelCase__     :Union[str, Any]							      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return self.sp_model.piece_to_id(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if index < self.sp_model.get_piece_size():
												UpperCamelCase__     :Optional[Any]							      =				self.sp_model.IdToPiece(UpperCamelCase_							)
							return token




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				[]
							UpperCamelCase__     :str							      =				''''''
							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(UpperCamelCase_							) + token
																	UpperCamelCase__     :int							      =				[]
												else:
																	current_sub_tokens.append(UpperCamelCase_							)
							out_string += self.sp_model.decode(UpperCamelCase_							)
							return out_string.strip()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							if not os.path.isdir(UpperCamelCase_							):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
												return
							UpperCamelCase__     :Optional[int]							      =				os.path.join(
							    UpperCamelCase_					,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(UpperCamelCase_							) and os.path.isfile(self.vocab_file							):
												copyfile(self.vocab_file					,     UpperCamelCase_							)
							elif not os.path.isfile(self.vocab_file							):
												with open(UpperCamelCase_					,     '''wb'''							) as fi:
																	UpperCamelCase__     :Union[str, Any]							      =				self.sp_model.serialized_model_proto()
																	fi.write(UpperCamelCase_							)

							return (out_vocab_file,)



'''simple docstring'''
import torch
from torch import nn
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=1					,     UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				n_token
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Dict							      =				d_proj

							UpperCamelCase__     :Dict							      =				cutoffs + [n_token]
							UpperCamelCase__     :Union[str, Any]							      =				[0] + self.cutoffs
							UpperCamelCase__     :Any							      =				div_val

							UpperCamelCase__     :int							      =				self.cutoffs[0]
							UpperCamelCase__     :List[Any]							      =				len(self.cutoffs							) - 1
							UpperCamelCase__     :List[Any]							      =				self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												UpperCamelCase__     :Any							      =				nn.Parameter(torch.zeros(self.n_clusters					,     self.d_embed							)							)
												UpperCamelCase__     :Optional[Any]							      =				nn.Parameter(torch.zeros(self.n_clusters							)							)

							UpperCamelCase__     :Union[str, Any]							      =				nn.ModuleList()
							UpperCamelCase__     :str							      =				nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs							)							):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)
																	else:
																						self.out_projs.append(UpperCamelCase_							)

												self.out_layers.append(nn.Linear(UpperCamelCase_					,     UpperCamelCase_							)							)
							else:
												for i in range(len(self.cutoffs							)							):
																	UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	UpperCamelCase__     :Dict							      =				d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)

																	self.out_layers.append(nn.Linear(UpperCamelCase_					,     r_idx - l_idx							)							)

							UpperCamelCase__     :Tuple							      =				keep_order




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if proj is None:
												UpperCamelCase__     :List[str]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												UpperCamelCase__     :Any							      =				nn.functional.linear(UpperCamelCase_					,     proj.t().contiguous()							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if labels is not None:
												# Shift so that tokens < n predict n
												UpperCamelCase__     :Optional[Any]							      =				hidden[..., :-1, :].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				labels[..., 1:].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				hidden.view(-1					,     hidden.size(-1							)							)
												UpperCamelCase__     :str							      =				labels.view(-1							)
												if hidden.size(0							) != labels.size(0							):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''							)
							else:
												UpperCamelCase__     :int							      =				hidden.view(-1					,     hidden.size(-1							)							)

							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												if labels is not None:
																	UpperCamelCase__     :int							      =				labels != -100
																	UpperCamelCase__     :List[Any]							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)
																	UpperCamelCase__     :str							      =				(
																	    -nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)[mask].gather(1					,     labels[mask].unsqueeze(1							)							).squeeze(1							)
																	)
												else:
																	UpperCamelCase__     :Dict							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :str							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :Optional[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :str							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :List[str]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				weights[0], biases[0], self.out_projs[0]

												UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												if labels is None:
																	UpperCamelCase__     :Union[str, Any]							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												else:
																	UpperCamelCase__     :Any							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)

												UpperCamelCase__     :Any							      =				0
												UpperCamelCase__     :str							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						UpperCamelCase__     :Any							      =				(labels >= l_idx) & (labels < r_idx)
																						UpperCamelCase__     :int							      =				mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						UpperCamelCase__     :Tuple							      =				labels.index_select(0					,     UpperCamelCase_							) - l_idx
																						UpperCamelCase__     :str							      =				head_logprob.index_select(0					,     UpperCamelCase_							)
																						UpperCamelCase__     :int							      =				hidden.index_select(0					,     UpperCamelCase_							)
																	else:
																						UpperCamelCase__     :Dict							      =				hidden

																	if i == 0:
																						if labels is not None:
																											UpperCamelCase__     :Any							      =				head_logprob_i.gather(1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)
																						UpperCamelCase__     :Any							      =				self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											UpperCamelCase__     :List[Any]							      =				head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											UpperCamelCase__     :Dict							      =				logprob_i

																	if labels is not None:
																						if (hasattr(self					,     '''keep_order'''							) and self.keep_order) or keep_order:
																											out.index_copy_(0					,     UpperCamelCase_					,     -logprob_i							)
																						else:
																											out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
																						offset += logprob_i.size(0							)

							return out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[Any]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												return nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :Union[str, Any]							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :str							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :Dict							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :Optional[Any]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				weights[0], biases[0], self.out_projs[0]
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												UpperCamelCase__     :int							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												UpperCamelCase__     :int							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						UpperCamelCase__     :str							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :List[str]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Tuple							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

																						UpperCamelCase__     :Optional[Any]							      =				head_logprob[:, -i] + tail_logprob_i
																						UpperCamelCase__     :Union[str, Any]							      =				logprob_i

												return out