




import torch
from torch import nn




class       a__	( nn.Module				):







     def __init__(     self  ,		A  ,		A  ,		A  ,		A  ,		A=1  ,		A=False			)  ->   int:



          '''simple docstring'''



          super().__init__()

          a     =				n_token
          a     =				d_embed
          a     =				d_proj

          a     =				cutoffs + [n_token]
          a     =				[0] + self.cutoffs
          a     =				div_val

          a     =				self.cutoffs[0]
          a     =				len(self.cutoffs			) - 1
          a     =				self.shortlist_size + self.n_clusters

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

          a     =				nn.ModuleList()
          a     =				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(A  ,		A			)			)			)
                    else:
                         self.out_projs.append(A			)

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

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

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

          a     =				keep_order







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A			)  ->   List[str]:



          '''simple docstring'''



          if proj is None:
               a     =				nn.functional.linear(A  ,		A  ,		bias=A			)
          else:
               # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
               a     =				nn.functional.linear(A  ,		proj.t().contiguous()			)
               a     =				nn.functional.linear(A  ,		A  ,		bias=A			)
               # 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  ,		A  ,		A=None  ,		A=False			)  ->   List[Any]:



          '''simple docstring'''



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

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

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

                    weights.append(A			)
                    biases.append(A			)

               a  ,				a  ,				a     =				weights[0], biases[0], self.out_projs[0]

               a     =				self._compute_logit(A  ,		A  ,		A  ,		A			)
               a     =				nn.functional.log_softmax(A  ,		dim=1			)

               if labels is None:
                    a     =				hidden.new_empty((head_logit.size(0			), self.n_token)			)
               else:
                    a     =				torch.zeros_like(A  ,		dtype=hidden.dtype  ,		device=hidden.device			)

               a     =				0
               a     =				[0] + self.cutoffs
               for i in range(len(A			) - 1			):
                    a  ,				a     =				cutoff_values[i], cutoff_values[i + 1]

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

                         if indices_i.numel() == 0:
                              continue

                         a     =				labels.index_select(0  ,		A			) - l_idx
                         a     =				head_logprob.index_select(0  ,		A			)
                         a     =				hidden.index_select(0  ,		A			)
                    else:
                         a     =				hidden

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

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

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

          return out







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Any:



          '''simple docstring'''



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

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

                    weights.append(A			)
                    biases.append(A			)

               a  ,				a  ,				a     =				weights[0], biases[0], self.out_projs[0]
               a     =				self._compute_logit(A  ,		A  ,		A  ,		A			)

               a     =				hidden.new_empty((head_logit.size(0			), self.n_token)			)
               a     =				nn.functional.log_softmax(A  ,		dim=1			)

               a     =				[0] + self.cutoffs
               for i in range(len(A			) - 1			):
                    a  ,				a     =				cutoff_values[i], cutoff_values[i + 1]

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

                         a     =				self._compute_logit(A  ,		A  ,		A  ,		A			)
                         a     =				nn.functional.log_softmax(A  ,		dim=1			)

                         a     =				head_logprob[:, -i] + tail_logprob_i
                         a     =				logprob_i

               return out







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

