



"""simple docstring"""






import json
import os
from typing import Optional, Tuple

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


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

__snake_case							:     Optional[Any]   =					{'vocab_file': 'vocab.json'}

__snake_case							:     str   =					{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

__snake_case							:     str   =					{'mgp-str': 27}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

	def __init__(  self:	int ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str]="[GO]" ,    _SCREAMING_SNAKE_CASE:	int="[GO]" ,    _SCREAMING_SNAKE_CASE:	List[Any]="[s]" ,    _SCREAMING_SNAKE_CASE:	Dict="[GO]" ,    **_SCREAMING_SNAKE_CASE:	str)       -> Tuple:

				"""simple docstring"""

				super().__init__(
				    unk_token=_SCREAMING_SNAKE_CASE ,    bos_token=_SCREAMING_SNAKE_CASE ,    eos_token=_SCREAMING_SNAKE_CASE ,    pad_token=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE ,    )

				with open(_SCREAMING_SNAKE_CASE ,    encoding="utf-8") as vocab_handle:
							__lowerCAmelCase   :						Union[str, Any]				= json.load(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= {v: k for k, v in self.vocab.items()}

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Union[str, Any]:

				"""simple docstring"""

				return len(self.vocab)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> int:

				"""simple docstring"""

				return dict(self.vocab ,    **self.added_tokens_encoder)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Any)       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= []
				for s in text:
							char_tokens.extend(_SCREAMING_SNAKE_CASE)
				return char_tokens

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	int)       -> Union[str, Any]:

				"""simple docstring"""

				return self.vocab.get(_SCREAMING_SNAKE_CASE ,    self.vocab.get(self.unk_token))

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Dict:

				"""simple docstring"""

				return self.decoder.get(_SCREAMING_SNAKE_CASE)







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

				"""simple docstring"""

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

				with open(_SCREAMING_SNAKE_CASE ,    "w" ,    encoding="utf-8") as f:
							f.write(json.dumps(self.vocab ,    indent=2 ,    sort_keys=_SCREAMING_SNAKE_CASE ,    ensure_ascii=_SCREAMING_SNAKE_CASE) + "\n")

				return (vocab_file,)




"""simple docstring"""






import torch
from torch import nn



class     A__     (						nn.Module							):




	'''simple docstring'''





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

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase   :						Optional[int]				= n_token
				__lowerCAmelCase   :						int				= d_embed
				__lowerCAmelCase   :						str				= d_proj

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

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

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

				__lowerCAmelCase   :						List[Any]				= nn.ModuleList()
				__lowerCAmelCase   :						Any				= 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(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)))
										else:
													self.out_projs.append(_SCREAMING_SNAKE_CASE)

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

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

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

				__lowerCAmelCase   :						str				= keep_order

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Dict)       -> int:

				"""simple docstring"""

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

				return logit

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple=None ,    _SCREAMING_SNAKE_CASE:	int=False)       -> List[str]:

				"""simple docstring"""

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

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

										if i == 0:
													__lowerCAmelCase   :						int				= torch.cat([weight_i, self.cluster_weight] ,    dim=0)
													__lowerCAmelCase   :						Union[str, Any]				= torch.cat([bias_i, self.cluster_bias] ,    dim=0)

										weights.append(_SCREAMING_SNAKE_CASE)
										biases.append(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Dict				= weights[0], biases[0], self.out_projs[0]

							__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Optional[Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

							if labels is None:
										__lowerCAmelCase   :						Optional[Any]				= hidden.new_empty((head_logit.size(0), self.n_token))
							else:
										__lowerCAmelCase   :						int				= torch.zeros_like(_SCREAMING_SNAKE_CASE ,    dtype=hidden.dtype ,    device=hidden.device)

							__lowerCAmelCase   :						str				= 0
							__lowerCAmelCase   :						List[str]				= [0] + self.cutoffs
							for i in range(len(_SCREAMING_SNAKE_CASE) - 1):
										__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= cutoff_values[i], cutoff_values[i + 1]

										if labels is not None:
													__lowerCAmelCase   :						List[str]				= (labels >= l_idx) & (labels < r_idx)
													__lowerCAmelCase   :						Any				= mask_i.nonzero().squeeze()

													if indices_i.numel() == 0:
																continue

													__lowerCAmelCase   :						Optional[Any]				= labels.index_select(0 ,    _SCREAMING_SNAKE_CASE) - l_idx
													__lowerCAmelCase   :						List[Any]				= head_logprob.index_select(0 ,    _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase   :						Tuple				= hidden.index_select(0 ,    _SCREAMING_SNAKE_CASE)
										else:
													__lowerCAmelCase   :						List[str]				= hidden

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

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

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

				return out







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

				"""simple docstring"""

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

										if i == 0:
													__lowerCAmelCase   :						int				= torch.cat([weight_i, self.cluster_weight] ,    dim=0)
													__lowerCAmelCase   :						int				= torch.cat([bias_i, self.cluster_bias] ,    dim=0)

										weights.append(_SCREAMING_SNAKE_CASE)
										biases.append(_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= weights[0], biases[0], self.out_projs[0]
							__lowerCAmelCase   :						str				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							__lowerCAmelCase   :						Optional[Any]				= hidden.new_empty((head_logit.size(0), self.n_token))
							__lowerCAmelCase   :						Union[str, Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

							__lowerCAmelCase   :						Dict				= [0] + self.cutoffs
							for i in range(len(_SCREAMING_SNAKE_CASE) - 1):
										__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= cutoff_values[i], cutoff_values[i + 1]

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

													__lowerCAmelCase   :						Any				= self._compute_logit(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase   :						List[Any]				= nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,    dim=1)

													__lowerCAmelCase   :						int				= head_logprob[:, -i] + tail_logprob_i
													__lowerCAmelCase   :						Tuple				= logprob_i

							return out