

'''simple docstring'''



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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xlnet import XLNetTokenizer
else:
		__snake_case			:     Tuple										=      None


__snake_case			:     List[str]										=      logging.get_logger(__name__)

__snake_case			:     Union[str, Any]										=      {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case			:     List[str]										=      {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

__snake_case			:     Optional[int]										=      {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

__snake_case			:     int										=      '▁'

# Segments (not really needed)
__snake_case			:     Union[str, Any]										=      0
__snake_case			:     Union[str, Any]										=      1
__snake_case			:     Optional[Any]										=      2
__snake_case			:     Dict										=      3
__snake_case			:     Any										=      4



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	VOCAB_FILES_NAMES
			__snake_case        =	PRETRAINED_VOCAB_FILES_MAP
			__snake_case        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case        =	'left'
			__snake_case        =	XLNetTokenizer


			def __init__(				self	:						List[str]		,      lowerCAmelCase_	:						Tuple=None		,      lowerCAmelCase_	:						Any=None		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						Tuple=True		,      lowerCAmelCase_	:						str=False		,      lowerCAmelCase_	:						List[Any]="<s>"		,      lowerCAmelCase_	:						Optional[Any]="</s>"		,      lowerCAmelCase_	:						int="<unk>"		,      lowerCAmelCase_	:						Optional[Any]="<sep>"		,      lowerCAmelCase_	:						Any="<pad>"		,      lowerCAmelCase_	:						int="<cls>"		,      lowerCAmelCase_	:						List[str]="<mask>"		,      lowerCAmelCase_	:						Optional[Any]=["<eop>", "<eod>"]		,      **lowerCAmelCase_	:						Any		,      )      ->		Union[str, Any]:





									'''simple docstring'''
									# Mask token behave like a normal word, i.e. include the space before it
									A__					:       List[Any]     =AddedToken(lowerCAmelCase_		,      lstrip=lowerCAmelCase_		,      rstrip=lowerCAmelCase_			) if isinstance(lowerCAmelCase_		,      lowerCAmelCase_			) else mask_token

									super().__init__(
									    vocab_file=lowerCAmelCase_		,      tokenizer_file=lowerCAmelCase_		,      do_lower_case=lowerCAmelCase_		,      remove_space=lowerCAmelCase_		,      keep_accents=lowerCAmelCase_		,      bos_token=lowerCAmelCase_		,      eos_token=lowerCAmelCase_		,      unk_token=lowerCAmelCase_		,      sep_token=lowerCAmelCase_		,      pad_token=lowerCAmelCase_		,      cls_token=lowerCAmelCase_		,      mask_token=lowerCAmelCase_		,      additional_special_tokens=lowerCAmelCase_		,      **lowerCAmelCase_		,      )

									A__					:       str     =3
									A__					:       Optional[Any]     =do_lower_case
									A__					:       Union[str, Any]     =remove_space
									A__					:       str     =keep_accents
									A__					:       Dict     =vocab_file
									A__					:       Any     =False if not self.vocab_file else True


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





									'''simple docstring'''
									A__					:       List[Any]     =[self.sep_token_id]
									A__					:       Tuple     =[self.cls_token_id]
									if token_ids_a is None:
															return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls


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





									'''simple docstring'''
									A__					:       int     =[self.sep_token_id]
									A__					:       Union[str, Any]     =[2]

									if token_ids_a is None:
															return len(token_ids_a + sep			) * [0] + cls_segment_id
									return len(token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1] + cls_segment_id



			def lowercase__						(				self	:						List[str]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Optional[str] = None			)      ->		Tuple[str]:





									'''simple docstring'''
									if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""			)

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

									if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCAmelCase_			):
															copyfile(self.vocab_file		,      lowerCAmelCase_			)

									return (out_vocab_file,)




'''simple docstring'''



import torch
from torch import nn



class lowerCamelCase			(  nn.Module						):



			'''simple docstring'''






			def __init__(				self	:						Any		,      lowerCAmelCase_	:						List[str]		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						Dict		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						Optional[int]=1		,      lowerCAmelCase_	:						str=False			)      ->		List[str]:





									'''simple docstring'''
									super().__init__()

									A__					:       Any     =n_token
									A__					:       int     =d_embed
									A__					:       Any     =d_proj

									A__					:       Tuple     =cutoffs + [n_token]
									A__					:       Optional[Any]     =[0] + self.cutoffs
									A__					:       Dict     =div_val

									A__					:       str     =self.cutoffs[0]
									A__					:       Optional[Any]     =len(self.cutoffs			) - 1
									A__					:       List[Any]     =self.shortlist_size + self.n_clusters

									if self.n_clusters > 0:
															A__					:       Any     =nn.Parameter(torch.zeros(self.n_clusters		,      self.d_embed			)			)
															A__					:       str     =nn.Parameter(torch.zeros(self.n_clusters			)			)

									A__					:       Union[str, Any]     =nn.ModuleList()
									A__					:       Optional[int]     =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(lowerCAmelCase_		,      lowerCAmelCase_			)			)			)
																					else:
																											self.out_projs.append(lowerCAmelCase_			)

															self.out_layers.append(nn.Linear(lowerCAmelCase_		,      lowerCAmelCase_			)			)
									else:
															for i in range(len(self.cutoffs			)			):
																					A__   ,						A__					:       Optional[int]     =self.cutoff_ends[i], self.cutoff_ends[i + 1]
																					A__					:       Tuple     =d_embed // (div_val**i)

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

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

									A__					:       Optional[int]     =keep_order


			def lowercase__						(				self	:						Dict		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						Any			)      ->		Union[str, Any]:





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

									return logit


			def lowercase__						(				self	:						Tuple		,      lowerCAmelCase_	:						List[str]		,      lowerCAmelCase_	:						List[Any]=None		,      lowerCAmelCase_	:						Dict=False			)      ->		Optional[int]:





									'''simple docstring'''

									if labels is not None:
															# Shift so that tokens < n predict n
															A__					:       Optional[Any]     =hidden[..., :-1, :].contiguous()
															A__					:       List[Any]     =labels[..., 1:].contiguous()
															A__					:       Optional[int]     =hidden.view(-1		,      hidden.size(-1			)			)
															A__					:       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:
															A__					:       Optional[int]     =hidden.view(-1		,      hidden.size(-1			)			)

									if self.n_clusters == 0:
															A__					:       Optional[Any]     =self._compute_logit(lowerCAmelCase_		,      self.out_layers[0].weight		,      self.out_layers[0].bias		,      self.out_projs[0]			)
															if labels is not None:
																					A__					:       Tuple     =labels != -1_00
																					A__					:       int     =torch.zeros_like(lowerCAmelCase_		,      dtype=hidden.dtype		,      device=hidden.device			)
																					A__					:       Union[str, Any]     =(
																					    -nn.functional.log_softmax(lowerCAmelCase_		,      dim=-1			)[mask].gather(1		,      labels[mask].unsqueeze(1			)			).squeeze(1			)
																					)
															else:
																					A__					:       List[Any]     =nn.functional.log_softmax(lowerCAmelCase_		,      dim=-1			)
									else:
															# construct weights and biases
															A__   ,						A__					:       Any     =[], []
															for i in range(len(self.cutoffs			)			):
																					if self.div_val == 1:
																											A__   ,						A__					:       Optional[int]     =self.cutoff_ends[i], self.cutoff_ends[i + 1]
																											A__					:       int     =self.out_layers[0].weight[l_idx:r_idx]
																											A__					:       List[str]     =self.out_layers[0].bias[l_idx:r_idx]
																					else:
																											A__					:       List[str]     =self.out_layers[i].weight
																											A__					:       Union[str, Any]     =self.out_layers[i].bias

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

																					weights.append(lowerCAmelCase_			)
																					biases.append(lowerCAmelCase_			)

															A__   ,						A__   ,						A__					:       Tuple     =weights[0], biases[0], self.out_projs[0]

															A__					:       List[Any]     =self._compute_logit(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
															A__					:       int     =nn.functional.log_softmax(lowerCAmelCase_		,      dim=1			)

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

															A__					:       Any     =0
															A__					:       Tuple     =[0] + self.cutoffs
															for i in range(len(lowerCAmelCase_			) - 1			):
																					A__   ,						A__					:       Tuple     =cutoff_values[i], cutoff_values[i + 1]

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

																											if indices_i.numel() == 0:
																																	continue

																											A__					:       int     =labels.index_select(0		,      lowerCAmelCase_			) - l_idx
																											A__					:       List[str]     =head_logprob.index_select(0		,      lowerCAmelCase_			)
																											A__					:       str     =hidden.index_select(0		,      lowerCAmelCase_			)
																					else:
																											A__					:       Optional[Any]     =hidden

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

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

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

									return out



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





									'''simple docstring'''
									if self.n_clusters == 0:
															A__					:       List[str]     =self._compute_logit(lowerCAmelCase_		,      self.out_layers[0].weight		,      self.out_layers[0].bias		,      self.out_projs[0]			)
															return nn.functional.log_softmax(lowerCAmelCase_		,      dim=-1			)
									else:
															# construct weights and biases
															A__   ,						A__					:       List[str]     =[], []
															for i in range(len(self.cutoffs			)			):
																					if self.div_val == 1:
																											A__   ,						A__					:       int     =self.cutoff_ends[i], self.cutoff_ends[i + 1]
																											A__					:       List[str]     =self.out_layers[0].weight[l_idx:r_idx]
																											A__					:       List[Any]     =self.out_layers[0].bias[l_idx:r_idx]
																					else:
																											A__					:       Dict     =self.out_layers[i].weight
																											A__					:       Any     =self.out_layers[i].bias

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

																					weights.append(lowerCAmelCase_			)
																					biases.append(lowerCAmelCase_			)

															A__   ,						A__   ,						A__					:       Optional[int]     =weights[0], biases[0], self.out_projs[0]
															A__					:       Any     =self._compute_logit(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)

															A__					:       Dict     =hidden.new_empty((head_logit.size(0			), self.n_token)			)
															A__					:       Dict     =nn.functional.log_softmax(lowerCAmelCase_		,      dim=1			)

															A__					:       Tuple     =[0] + self.cutoffs
															for i in range(len(lowerCAmelCase_			) - 1			):
																					A__   ,						A__					:       List[Any]     =cutoff_values[i], cutoff_values[i + 1]

																					if i == 0:
																											A__					:       Tuple     =head_logprob[:, : self.cutoffs[0]]
																					else:
																											A__   ,						A__   ,						A__					:       Any     =weights[i], biases[i], self.out_projs[i]

																											A__					:       Dict     =self._compute_logit(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			)
																											A__					:       str     =nn.functional.log_softmax(lowerCAmelCase_		,      dim=1			)

																											A__					:       str     =head_logprob[:, -i] + tail_logprob_i
																											A__					:       List[Any]     =logprob_i

															return out

