prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Generate text with language model
  Is there any script that can generate text from trained language model?
  Also calculating perplexity on testing data would be helpful.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `flair/models/language_model.py`
  Content:
  ```
  1 import torch.nn as nn
  2 import torch
  3 import math
  4 from torch.autograd import Variable
  5 from typing import Dict, List
  6 from flair.data import Dictionary
  7 
  8 
  9 class LanguageModel(nn.Module):
  10     """Container module with an encoder, a recurrent module, and a decoder."""
  11 
  12     def __init__(self,
  13                  dictionary: Dictionary,
  14                  is_forward_lm: bool,
  15                  hidden_size: int,
  16                  nlayers: int,
  17                  embedding_size: int = 100,
  18                  nout=None,
  19                  dropout=0.5):
  20 
  21         super(LanguageModel, self).__init__()
  22 
  23         self.dictionary = dictionary
  24         self.is_forward_lm: bool = is_forward_lm
  25 
  26         self.dropout = dropout
  27         self.hidden_size = hidden_size
  28         self.embedding_size = embedding_size
  29         self.nlayers = nlayers
  30 
  31         self.drop = nn.Dropout(dropout)
  32         self.encoder = nn.Embedding(len(dictionary), embedding_size)
  33 
  34         if nlayers == 1:
  35             self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)
  36         else:
  37             self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)
  38 
  39         self.hidden = None
  40 
  41         self.nout = nout
  42         if nout is not None:
  43             self.proj = nn.Linear(hidden_size, nout)
  44             self.initialize(self.proj.weight)
  45             self.decoder = nn.Linear(nout, len(dictionary))
  46         else:
  47             self.proj = None
  48             self.decoder = nn.Linear(hidden_size, len(dictionary))
  49 
  50         self.init_weights()
  51 
  52         # auto-spawn on GPU if available
  53         if torch.cuda.is_available():
  54             self.cuda()
  55 
  56     def init_weights(self):
  57         initrange = 0.1
  58         self.encoder.weight.data.uniform_(-initrange, initrange)
  59         self.decoder.bias.data.fill_(0)
  60         self.decoder.weight.data.uniform_(-initrange, initrange)
  61 
  62     def set_hidden(self, hidden):
  63         self.hidden = hidden
  64 
  65     def forward(self, input, hidden, ordered_sequence_lengths=None):
  66         encoded = self.encoder(input)
  67         emb = self.drop(encoded)
  68 
  69         self.rnn.flatten_parameters()
  70 
  71         output, hidden = self.rnn(emb, hidden)
  72 
  73         if self.proj is not None:
  74             output = self.proj(output)
  75 
  76         output = self.drop(output)
  77 
  78         decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))
  79 
  80         return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden
  81 
  82     def init_hidden(self, bsz):
  83         weight = next(self.parameters()).data
  84         return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),
  85                 Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))
  86 
  87     def get_representation(self, strings: List[str], detach_from_lm=True):
  88 
  89         sequences_as_char_indices: List[List[int]] = []
  90         for string in strings:
  91             char_indices = [self.dictionary.get_idx_for_item(char) for char in string]
  92             sequences_as_char_indices.append(char_indices)
  93 
  94         batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))
  95 
  96         if torch.cuda.is_available():
  97             batch = batch.cuda()
  98 
  99         hidden = self.init_hidden(len(strings))
  100         prediction, rnn_output, hidden = self.forward(batch, hidden)
  101 
  102         if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)
  103 
  104         return rnn_output
  105 
  106     def repackage_hidden(self, h):
  107         """Wraps hidden states in new Variables, to detach them from their history."""
  108         if type(h) == torch.Tensor:
  109             return Variable(h.data)
  110         else:
  111             return tuple(self.repackage_hidden(v) for v in h)
  112 
  113     def initialize(self, matrix):
  114         in_, out_ = matrix.size()
  115         stdv = math.sqrt(3. / (in_ + out_))
  116         matrix.data.uniform_(-stdv, stdv)
  117 
  118     @classmethod
  119     def load_language_model(cls, model_file):
  120 
  121         if not torch.cuda.is_available():
  122             state = torch.load(model_file, map_location='cpu')
  123         else:
  124             state = torch.load(model_file)
  125 
  126         model = LanguageModel(state['dictionary'],
  127                                              state['is_forward_lm'],
  128                                              state['hidden_size'],
  129                                              state['nlayers'],
  130                                              state['embedding_size'],
  131                                              state['nout'],
  132                                              state['dropout'])
  133         model.load_state_dict(state['state_dict'])
  134         model.eval()
  135         if torch.cuda.is_available():
  136             model.cuda()
  137         return model
  138 
  139     def save(self, file):
  140         model_state = {
  141             'state_dict': self.state_dict(),
  142             'dictionary': self.dictionary,
  143             'is_forward_lm': self.is_forward_lm,
  144             'hidden_size': self.hidden_size,
  145             'nlayers': self.nlayers,
  146             'embedding_size': self.embedding_size,
  147             'nout': self.nout,
  148             'dropout': self.dropout
  149         }
  150         torch.save(model_state, file, pickle_protocol=4)
  151
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/flair/models/language_model.py b/flair/models/language_model.py
  --- a/flair/models/language_model.py
  +++ b/flair/models/language_model.py
  @@ -2,7 +2,7 @@
   import torch
   import math
   from torch.autograd import Variable
  -from typing import Dict, List
  +from typing import List
   from flair.data import Dictionary
   
   
  @@ -148,3 +148,24 @@
               'dropout': self.dropout
           }
           torch.save(model_state, file, pickle_protocol=4)
  +
  +    def generate_text(self, number_of_characters=1000) -> str:
  +        characters = []
  +
  +        idx2item = self.dictionary.idx2item
  +
  +        # initial hidden state
  +        hidden = self.init_hidden(1)
  +        input = torch.rand(1, 1).mul(len(idx2item)).long()
  +        if torch.cuda.is_available():
  +            input = input.cuda()
  +
  +        for i in range(number_of_characters):
  +            prediction, rnn_output, hidden = self.forward(input, hidden)
  +            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()
  +            word_idx = torch.multinomial(word_weights, 1)[0]
  +            input.data.fill_(word_idx)
  +            word = idx2item[word_idx].decode('UTF-8')
  +            characters.append(word)
  +
  +        return ''.join(characters)
  
verification_info: {"golden_diff": "diff --git a/flair/models/language_model.py b/flair/models/language_model.py\n--- a/flair/models/language_model.py\n+++ b/flair/models/language_model.py\n@@ -2,7 +2,7 @@\n import torch\n import math\n from torch.autograd import Variable\n-from typing import Dict, List\n+from typing import List\n from flair.data import Dictionary\n \n \n@@ -148,3 +148,24 @@\n             'dropout': self.dropout\n         }\n         torch.save(model_state, file, pickle_protocol=4)\n+\n+    def generate_text(self, number_of_characters=1000) -> str:\n+        characters = []\n+\n+        idx2item = self.dictionary.idx2item\n+\n+        # initial hidden state\n+        hidden = self.init_hidden(1)\n+        input = torch.rand(1, 1).mul(len(idx2item)).long()\n+        if torch.cuda.is_available():\n+            input = input.cuda()\n+\n+        for i in range(number_of_characters):\n+            prediction, rnn_output, hidden = self.forward(input, hidden)\n+            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n+            word_idx = torch.multinomial(word_weights, 1)[0]\n+            input.data.fill_(word_idx)\n+            word = idx2item[word_idx].decode('UTF-8')\n+            characters.append(word)\n+\n+        return ''.join(characters)\n", "issue": "Generate text with language model\nIs there any script that can generate text from trained language model?\r\nAlso calculating perplexity on testing data would be helpful.\n", "before_files": [{"content": "import torch.nn as nn\nimport torch\nimport math\nfrom torch.autograd import Variable\nfrom typing import Dict, List\nfrom flair.data import Dictionary\n\n\nclass LanguageModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self,\n                 dictionary: Dictionary,\n                 is_forward_lm: bool,\n                 hidden_size: int,\n                 nlayers: int,\n                 embedding_size: int = 100,\n                 nout=None,\n                 dropout=0.5):\n\n        super(LanguageModel, self).__init__()\n\n        self.dictionary = dictionary\n        self.is_forward_lm: bool = is_forward_lm\n\n        self.dropout = dropout\n        self.hidden_size = hidden_size\n        self.embedding_size = embedding_size\n        self.nlayers = nlayers\n\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(len(dictionary), embedding_size)\n\n        if nlayers == 1:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)\n        else:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)\n\n        self.hidden = None\n\n        self.nout = nout\n        if nout is not None:\n            self.proj = nn.Linear(hidden_size, nout)\n            self.initialize(self.proj.weight)\n            self.decoder = nn.Linear(nout, len(dictionary))\n        else:\n            self.proj = None\n            self.decoder = nn.Linear(hidden_size, len(dictionary))\n\n        self.init_weights()\n\n        # auto-spawn on GPU if available\n        if torch.cuda.is_available():\n            self.cuda()\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.fill_(0)\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def set_hidden(self, hidden):\n        self.hidden = hidden\n\n    def forward(self, input, hidden, ordered_sequence_lengths=None):\n        encoded = self.encoder(input)\n        emb = self.drop(encoded)\n\n        self.rnn.flatten_parameters()\n\n        output, hidden = self.rnn(emb, hidden)\n\n        if self.proj is not None:\n            output = self.proj(output)\n\n        output = self.drop(output)\n\n        decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))\n\n        return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters()).data\n        return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),\n                Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))\n\n    def get_representation(self, strings: List[str], detach_from_lm=True):\n\n        sequences_as_char_indices: List[List[int]] = []\n        for string in strings:\n            char_indices = [self.dictionary.get_idx_for_item(char) for char in string]\n            sequences_as_char_indices.append(char_indices)\n\n        batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))\n\n        if torch.cuda.is_available():\n            batch = batch.cuda()\n\n        hidden = self.init_hidden(len(strings))\n        prediction, rnn_output, hidden = self.forward(batch, hidden)\n\n        if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)\n\n        return rnn_output\n\n    def repackage_hidden(self, h):\n        \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n        if type(h) == torch.Tensor:\n            return Variable(h.data)\n        else:\n            return tuple(self.repackage_hidden(v) for v in h)\n\n    def initialize(self, matrix):\n        in_, out_ = matrix.size()\n        stdv = math.sqrt(3. / (in_ + out_))\n        matrix.data.uniform_(-stdv, stdv)\n\n    @classmethod\n    def load_language_model(cls, model_file):\n\n        if not torch.cuda.is_available():\n            state = torch.load(model_file, map_location='cpu')\n        else:\n            state = torch.load(model_file)\n\n        model = LanguageModel(state['dictionary'],\n                                             state['is_forward_lm'],\n                                             state['hidden_size'],\n                                             state['nlayers'],\n                                             state['embedding_size'],\n                                             state['nout'],\n                                             state['dropout'])\n        model.load_state_dict(state['state_dict'])\n        model.eval()\n        if torch.cuda.is_available():\n            model.cuda()\n        return model\n\n    def save(self, file):\n        model_state = {\n            'state_dict': self.state_dict(),\n            'dictionary': self.dictionary,\n            'is_forward_lm': self.is_forward_lm,\n            'hidden_size': self.hidden_size,\n            'nlayers': self.nlayers,\n            'embedding_size': self.embedding_size,\n            'nout': self.nout,\n            'dropout': self.dropout\n        }\n        torch.save(model_state, file, pickle_protocol=4)\n", "path": "flair/models/language_model.py"}], "after_files": [{"content": "import torch.nn as nn\nimport torch\nimport math\nfrom torch.autograd import Variable\nfrom typing import List\nfrom flair.data import Dictionary\n\n\nclass LanguageModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self,\n                 dictionary: Dictionary,\n                 is_forward_lm: bool,\n                 hidden_size: int,\n                 nlayers: int,\n                 embedding_size: int = 100,\n                 nout=None,\n                 dropout=0.5):\n\n        super(LanguageModel, self).__init__()\n\n        self.dictionary = dictionary\n        self.is_forward_lm: bool = is_forward_lm\n\n        self.dropout = dropout\n        self.hidden_size = hidden_size\n        self.embedding_size = embedding_size\n        self.nlayers = nlayers\n\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(len(dictionary), embedding_size)\n\n        if nlayers == 1:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)\n        else:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)\n\n        self.hidden = None\n\n        self.nout = nout\n        if nout is not None:\n            self.proj = nn.Linear(hidden_size, nout)\n            self.initialize(self.proj.weight)\n            self.decoder = nn.Linear(nout, len(dictionary))\n        else:\n            self.proj = None\n            self.decoder = nn.Linear(hidden_size, len(dictionary))\n\n        self.init_weights()\n\n        # auto-spawn on GPU if available\n        if torch.cuda.is_available():\n            self.cuda()\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.fill_(0)\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def set_hidden(self, hidden):\n        self.hidden = hidden\n\n    def forward(self, input, hidden, ordered_sequence_lengths=None):\n        encoded = self.encoder(input)\n        emb = self.drop(encoded)\n\n        self.rnn.flatten_parameters()\n\n        output, hidden = self.rnn(emb, hidden)\n\n        if self.proj is not None:\n            output = self.proj(output)\n\n        output = self.drop(output)\n\n        decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))\n\n        return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters()).data\n        return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),\n                Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))\n\n    def get_representation(self, strings: List[str], detach_from_lm=True):\n\n        sequences_as_char_indices: List[List[int]] = []\n        for string in strings:\n            char_indices = [self.dictionary.get_idx_for_item(char) for char in string]\n            sequences_as_char_indices.append(char_indices)\n\n        batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))\n\n        if torch.cuda.is_available():\n            batch = batch.cuda()\n\n        hidden = self.init_hidden(len(strings))\n        prediction, rnn_output, hidden = self.forward(batch, hidden)\n\n        if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)\n\n        return rnn_output\n\n    def repackage_hidden(self, h):\n        \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n        if type(h) == torch.Tensor:\n            return Variable(h.data)\n        else:\n            return tuple(self.repackage_hidden(v) for v in h)\n\n    def initialize(self, matrix):\n        in_, out_ = matrix.size()\n        stdv = math.sqrt(3. / (in_ + out_))\n        matrix.data.uniform_(-stdv, stdv)\n\n    @classmethod\n    def load_language_model(cls, model_file):\n\n        if not torch.cuda.is_available():\n            state = torch.load(model_file, map_location='cpu')\n        else:\n            state = torch.load(model_file)\n\n        model = LanguageModel(state['dictionary'],\n                                             state['is_forward_lm'],\n                                             state['hidden_size'],\n                                             state['nlayers'],\n                                             state['embedding_size'],\n                                             state['nout'],\n                                             state['dropout'])\n        model.load_state_dict(state['state_dict'])\n        model.eval()\n        if torch.cuda.is_available():\n            model.cuda()\n        return model\n\n    def save(self, file):\n        model_state = {\n            'state_dict': self.state_dict(),\n            'dictionary': self.dictionary,\n            'is_forward_lm': self.is_forward_lm,\n            'hidden_size': self.hidden_size,\n            'nlayers': self.nlayers,\n            'embedding_size': self.embedding_size,\n            'nout': self.nout,\n            'dropout': self.dropout\n        }\n        torch.save(model_state, file, pickle_protocol=4)\n\n    def generate_text(self, number_of_characters=1000) -> str:\n        characters = []\n\n        idx2item = self.dictionary.idx2item\n\n        # initial hidden state\n        hidden = self.init_hidden(1)\n        input = torch.rand(1, 1).mul(len(idx2item)).long()\n        if torch.cuda.is_available():\n            input = input.cuda()\n\n        for i in range(number_of_characters):\n            prediction, rnn_output, hidden = self.forward(input, hidden)\n            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n            word_idx = torch.multinomial(word_weights, 1)[0]\n            input.data.fill_(word_idx)\n            word = idx2item[word_idx].decode('UTF-8')\n            characters.append(word)\n\n        return ''.join(characters)\n", "path": "flair/models/language_model.py"}]}