

'''simple docstring'''






import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCAmelCase__      		=			2






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  str      ,*,  # begin keyword-only arguments
      lowercase__     :  Optional[int]="<s>"      ,lowercase__     :  Any="<pad>"      ,lowercase__     :  str="</s>"      ,lowercase__     :  Optional[Any]="<unk>"      ,lowercase__     :  Optional[Any]=None      ,):
   __lowercase						,			__lowercase						,			__lowercase						,			__lowercase													=				bos, unk, pad, eos
   __lowercase													=				[]
   __lowercase													=				[]
   __lowercase													=				{}
   __lowercase													=				self.add_symbol(lowercase__    )
   __lowercase													=				self.add_symbol(lowercase__    )
   __lowercase													=				self.add_symbol(lowercase__    )
   __lowercase													=				self.add_symbol(lowercase__    )
   if extra_special_symbols:
    for s in extra_special_symbols:
     self.add_symbol(lowercase__    )
   __lowercase													=				len(self.symbols    )






  def __eq__(   self     :  Dict      ,lowercase__     :  int    ):
   return self.indices == other.indices






  def __getitem__(   self     :  str      ,lowercase__     :  List[Any]    ):
   if idx < len(self.symbols    ):
    return self.symbols[idx]
   return self.unk_word






  def __len__(   self     :  List[str]    ):
   return len(self.symbols    )






  def __contains__(   self     :  List[str]      ,lowercase__     :  Optional[int]    ):
   return sym in self.indices






  @classmethod
  def       SCREAMING_SNAKE_CASE   (   cls     :  str      ,lowercase__     :  Tuple    ):
   __lowercase													=				cls()
   d.add_from_file(lowercase__    )
   return d






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]=1      ,lowercase__     :  Dict=False    ):
   if word in self.indices and not overwrite:
    __lowercase													=				self.indices[word]
    __lowercase													=				self.count[idx] + n
    return idx
   else:
    __lowercase													=				len(self.symbols    )
    __lowercase													=				idx
    self.symbols.append(lowercase__    )
    self.count.append(lowercase__    )
    return idx






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int    ):
   return 0






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  int    ):
   if isinstance(lowercase__      ,lowercase__    ):
    try:
     with open(lowercase__      ,'''r'''      ,encoding='''utf-8'''    ) as fd:
      self.add_from_file(lowercase__    )
    except FileNotFoundError as fnfe:
     raise fnfe
    except UnicodeError:
     raise Exception('''Incorrect encoding detected in {}, please rebuild the dataset'''.format(lowercase__    )    )
    return

   __lowercase													=				f.readlines()
   __lowercase													=				self._load_meta(lowercase__    )

   for line in lines[indices_start_line:]:
    try:
     __lowercase						,			__lowercase													=				line.rstrip().rsplit(''' '''      ,1    )
     if field == "#fairseq:overwrite":
      __lowercase													=				True
      __lowercase						,			__lowercase													=				line.rsplit(''' '''      ,1    )
     else:
      __lowercase													=				False
     __lowercase													=				int(lowercase__    )
     __lowercase													=				line
     if word in self and not overwrite:
      raise RuntimeError(
          '''Duplicate word found when loading Dictionary: \'{}\'. '''
          '''Duplicate words can overwrite earlier ones by adding the '''
          '''#fairseq:overwrite flag at the end of the corresponding row '''
          '''in the dictionary file. If using the Camembert model, please '''
          '''download an updated copy of the model file.'''.format(lowercase__    )    )
     self.add_symbol(lowercase__      ,n=lowercase__      ,overwrite=lowercase__    )
    except ValueError:
     raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''    )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				dict((re.sub(R'''@@$'''  ,       ''''''  ,       A__   ), v) if k.endswith('''@@'''   ) else (re.sub(R'''$'''  ,       '''</w>'''  ,       A__   ), v) for k, v in d.items()   )
 __lowercase													=				'''<s> <pad> </s> <unk>'''.split()
 # restore the special tokens
 for k in keep_keys:
  del da[F"{k}</w>"]
  __lowercase													=				d[k]  # restore
 return da


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 if not os.path.exists(A__   ):
  raise ValueError(F"path {biogpt_checkpoint_path} does not exist!"   )
 os.makedirs(A__  ,       exist_ok=A__   )
 print(F"Writing results to {pytorch_dump_folder_path}"   )

 # handle various types of models

 __lowercase													=				os.path.join(A__  ,       '''checkpoint.pt'''   )
 if not os.path.isfile(A__   ):
  raise ValueError(F"path to the file {checkpoint_file} does not exist!"   )
 __lowercase													=				torch.load(A__  ,       map_location='''cpu'''   )

 __lowercase													=				chkpt['''cfg''']['''model''']

 # dicts
 __lowercase													=				os.path.join(A__  ,       '''dict.txt'''   )
 if not os.path.isfile(A__   ):
  raise ValueError(F"path to the file {dict_file} does not exist!"   )
 __lowercase													=				Dictionary.load(A__   )
 __lowercase													=				rewrite_dict_keys(src_dict.indices   )
 __lowercase													=				len(A__   )
 __lowercase													=				os.path.join(A__  ,       VOCAB_FILES_NAMES['''vocab_file''']   )
 print(F"Generating {src_vocab_file} of {src_vocab_size} records"   )
 with open(A__  ,       '''w'''  ,       encoding='''utf-8'''   ) as f:
  f.write(json.dumps(A__  ,       ensure_ascii=A__  ,       indent=A__   )   )

 # merges_file (bpecodes)
 __lowercase													=				os.path.join(A__  ,       '''bpecodes'''   )
 if not os.path.isfile(A__   ):
  raise ValueError(F"path to the file {bpecodes_file} does not exist!"   )

 __lowercase													=				os.path.join(A__  ,       VOCAB_FILES_NAMES['''merges_file''']   )
 shutil.copyfile(A__  ,       A__   )

 # model config
 __lowercase													=				os.path.join(A__  ,       '''config.json'''   )

 __lowercase													=				{
     '''activation_dropout''': args['''activation_dropout'''],
     '''architectures''': ['''BioGptForCausalLM'''],
     '''attention_probs_dropout_prob''': args['''attention_dropout'''],
     '''bos_token_id''': 0,
     '''eos_token_id''': 2,
     '''hidden_act''': args['''activation_fn'''],
     '''hidden_dropout_prob''': args['''dropout'''],
     '''hidden_size''': args['''decoder_embed_dim'''],
     '''initializer_range''': 0.0_2,
     '''intermediate_size''': args['''decoder_ffn_embed_dim'''],
     '''layer_norm_eps''': 1e-12,
     '''layerdrop''': args['''decoder_layerdrop'''],
     '''max_position_embeddings''': args['''max_target_positions'''],
     '''model_type''': '''biogpt''',
     '''num_attention_heads''': args['''decoder_attention_heads'''],
     '''num_hidden_layers''': args['''decoder_layers'''],
     '''pad_token_id''': 1,
     '''scale_embedding''': not args['''no_scale_embedding'''],
     '''tie_word_embeddings''': args['''share_decoder_input_output_embed'''],
     '''vocab_size''': src_vocab_size,
 }

 # good hparam defaults to start with

 print(F"Generating {biogpt_model_config_file}"   )
 with open(A__  ,       '''w'''  ,       encoding='''utf-8'''   ) as f:
  f.write(json.dumps(A__  ,       ensure_ascii=A__  ,       indent=A__   )   )

 # tokenizer config
 __lowercase													=				os.path.join(A__  ,       A__   )

 __lowercase													=				{
     '''bos_token''': '''<s>''',
     '''eos_token''': '''</s>''',
     '''model_max_length''': 1024,
     '''pad_token''': '''<pad>''',
     '''special_tokens_map_file''': None,
     '''tokenizer_class''': '''BioGptTokenizer''',
     '''unk_token''': '''<unk>''',
 }

 print(F"Generating {biogpt_tokenizer_config_file}"   )
 with open(A__  ,       '''w'''  ,       encoding='''utf-8'''   ) as f:
  f.write(json.dumps(A__  ,       ensure_ascii=A__  ,       indent=A__   )   )

 # model
 __lowercase													=				chkpt['''model''']

 # remove unneeded keys
 __lowercase													=				[
     '''decoder.version''',
 ]
 for k in ignore_keys:
  model_state_dict.pop(A__  ,       A__   )

 __lowercase													=				list(model_state_dict.keys()   )
 for layer_name in layer_names:
  if layer_name.endswith('''output_projection.weight'''   ):
   __lowercase													=				model_state_dict.pop(A__   )
  else:
   __lowercase													=				model_state_dict.pop(A__   )

 __lowercase													=				BioGptConfig.from_pretrained(A__   )
 __lowercase													=				BioGptForCausalLM(A__   )

 # check that it loads ok
 model_new.load_state_dict(A__   )

 # save
 __lowercase													=				os.path.join(A__  ,       A__   )
 print(F"Generating {pytorch_weights_dump_path}"   )
 torch.save(A__  ,       A__   )

 print('''Conversion is done!'''   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--biogpt_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help=(
         '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
         ''' bpecodes, etc.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

