





'''simple docstring'''




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__SCREAMING_SNAKE_CASE     :Optional[int]       =				os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__SCREAMING_SNAKE_CASE     :Any       =				'''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''







class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir	,			"models/bert/"    )    )
      _UpperCAmelCase     							=   self.transformer_dir
      shutil.copy(
          os.path.join(snake_case_	,			"src/transformers/models/bert/modeling_bert.py"    )	,			os.path.join(self.transformer_dir	,			"models/bert/modeling_bert.py"    )	,			)




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   "src/transformers"
      shutil.rmtree(self.transformer_dir    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    int	,			snake_case_       :    Any	,			snake_case_       :    Any	,			snake_case_       :    Union[str, Any]=None    ):
      _UpperCAmelCase     							=   comment + f'\nclass {class_name}(nn.Module):\n' + class_code
      if overwrite_result is not None:
           _UpperCAmelCase     							=   comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
      _UpperCAmelCase     							=   black.Mode(target_versions={black.TargetVersion.PYaa}	,			line_length=1_1_9    )
      _UpperCAmelCase     							=   black.format_str(snake_case_	,			mode=snake_case_    )
      _UpperCAmelCase     							=   os.path.join(self.transformer_dir	,			"new_code.py"    )
      with open(snake_case_	,			"w"	,			newline="\n"    ) as f:
           f.write(snake_case_    )
      if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(snake_case_    )    ) == 0    )
      else:
           check_copies.is_copy_consistent(f.name	,			overwrite=snake_case_    )
           with open(snake_case_	,			"r"    ) as f:
                self.assertTrue(f.read()	,			snake_case_    )




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"    )
      self.assertEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    Any    ):
      # Base copy consistency
      self.check_copy_consistency(
          "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"	,			"BertLMPredictionHead"	,			REFERENCE_CODE + "\n"	,			)

      # With no empty line at the end
      self.check_copy_consistency(
          "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"	,			"BertLMPredictionHead"	,			snake_case_	,			)

      # Copy consistency with rename
      self.check_copy_consistency(
          "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"	,			"TestModelLMPredictionHead"	,			re.sub("Bert"	,			"TestModel"	,			snake_case_    )	,			)

      # Copy consistency with a really long name
      _UpperCAmelCase     							=   "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
      self.check_copy_consistency(
          f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'	,			f'{long_class_name}LMPredictionHead'	,			re.sub("Bert"	,			snake_case_	,			snake_case_    )	,			)

      # Copy consistency with overwrite
      self.check_copy_consistency(
          "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"	,			"TestModelLMPredictionHead"	,			snake_case_	,			overwrite_result=re.sub("Bert"	,			"TestModel"	,			snake_case_    )	,			)




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   check_copies.LOCALIZED_READMES["README_zh-hans.md"]

      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
          " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
          " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
          " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
          " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
          " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
          " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
          " method has been applied to compress GPT2 into"
          " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
          " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
          " Multilingual BERT into"
          " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
          " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
          " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
          " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
          " Luong, Quoc V. Le, Christopher D. Manning."
      )
      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )
      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
          " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
          " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
          " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
          " method has been applied to compress GPT2 into"
          " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
          " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
          " Multilingual BERT into"
          " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
          " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
          " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
          " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
          " Christopher D. Manning 发布。\n"
      )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   check_copies.convert_to_localized_md(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

      self.assertFalse(snake_case_    )
      self.assertEqual(snake_case_	,			snake_case_    )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   check_copies.convert_to_localized_md(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

      # Check whether the number of models is equal to README.md after conversion.
      self.assertTrue(snake_case_    )

      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
          " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
          " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
          " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
      )
      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
          " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )
      _UpperCAmelCase     							=   (
          "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
          " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
          " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
          " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
      )

      _UpperCAmelCase							,  _UpperCAmelCase     							=   check_copies.convert_to_localized_md(
          snake_case_	,			snake_case_	,			localized_readme["format_model_list"]    )

      # Check if the model link is synchronized.
      self.assertEqual(snake_case_	,			snake_case_    )





import torch
from torch import nn



class 				a							(			nn.Module       ):







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_=1		,				lowerCAmelCase_=False							)  ->					Any:
        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(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        =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
                      _A        =  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        =  keep_order



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        if proj is None:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				lowerCAmelCase_		,				bias=lowerCAmelCase_							)
        else:
               # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
               _A        =  nn.functional.linear(lowerCAmelCase_		,				proj.t().contiguous()							)
               _A        =  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      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=None		,				lowerCAmelCase_=False							)  ->					List[Any]:

        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(lowerCAmelCase_		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]							)
               if labels is not None:
                      _A        =  labels != -1_00
                      _A        =  torch.zeros_like(lowerCAmelCase_		,				dtype=hidden.dtype		,				device=hidden.device							)
                      _A        =  (
                          -nn.functional.log_softmax(lowerCAmelCase_		,				dim=-1							)[mask].gather(1		,				labels[mask].unsqueeze(1							)							).squeeze(1							)
                      )
               else:
                      _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				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(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

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

               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
               _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

               _A        =  0
               _A        =  [0] + self.cutoffs
               for i in range(len(lowerCAmelCase_							) - 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		,				lowerCAmelCase_							) - l_idx
                             _A        =  head_logprob.index_select(0		,				lowerCAmelCase_							)
                             _A        =  hidden.index_select(0		,				lowerCAmelCase_							)
                      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(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				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		,				lowerCAmelCase_		,				-logprob_i							)
                             else:
                                    out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
                             offset += logprob_i.size(0							)

        return out



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					int:
        if self.n_clusters == 0:
               _A        =  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        =  [], []
               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(lowerCAmelCase_							)
                      biases.append(lowerCAmelCase_							)

               _A   ,  _A   ,  _A        =  weights[0], biases[0], self.out_projs[0]
               _A        =  self._compute_logit(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

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

               _A        =  [0] + self.cutoffs
               for i in range(len(lowerCAmelCase_							) - 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(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)
                             _A        =  nn.functional.log_softmax(lowerCAmelCase_		,				dim=1							)

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

               return out




