

"""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()

a					:Optional[int]    = 2

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    *,  # begin keyword-only arguments
         _a="<s>"	,    _a="<pad>"	,    _a="</s>"	,    _a="<unk>"	,    _a=None	,    )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	bos, unk, pad, eos
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[]
           SCREAMING_SNAKE_CASE__   :      str      		=	{}
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.add_symbol(_a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.add_symbol(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.add_symbol(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.add_symbol(_a  )
           if extra_special_symbols:
                 for s in extra_special_symbols:
                       self.add_symbol(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	len(self.symbols  )

     def __eq__(						self	,    _a  )  ->      Union[str, Any]:


           """simple docstring"""


           return self.indices == other.indices

     def __getitem__(						self	,    _a  )  ->      List[str]:


           """simple docstring"""


           if idx < len(self.symbols  ):
                 return self.symbols[idx]
           return self.unk_word

     def __len__(						self  )  ->      str:


           """simple docstring"""


           return len(self.symbols  )

     def __contains__(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           return sym in self.indices

     @classmethod
     def 					_a			(						cls	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	cls()
           d.add_from_file(_a  )
           return d

     def 					_a			(						self	,    _a	,    _a=1	,    _a=False  )  ->      Optional[Any]:


           """simple docstring"""


           if word in self.indices and not overwrite:
                 SCREAMING_SNAKE_CASE__   :      str      		=	self.indices[word]
                 SCREAMING_SNAKE_CASE__   :      Any      		=	self.count[idx] + n
                 return idx
           else:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(self.symbols  )
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	idx
                 self.symbols.append(_a  )
                 self.count.append(_a  )
                 return idx

     def 					_a			(						self	,    _a  )  ->      Tuple:


           """simple docstring"""


           return 0





     def 					_a			(						self	,    _a  )  ->      Tuple:


           """simple docstring"""


           if isinstance(_a	,    _a  ):
                 try:
                       with open(_a	,    """r"""	,    encoding="""utf-8"""  ) as fd:
                             self.add_from_file(_a  )
                 except FileNotFoundError as fnfe:
                       raise fnfe
                 except UnicodeError:
                       raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(_a  )  )
                 return

           SCREAMING_SNAKE_CASE__   :      Any      		=	f.readlines()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self._load_meta(_a  )

           for line in lines[indices_start_line:]:
                 try:
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	line.rstrip().rsplit(""" """	,    1  )
                       if field == "#fairseq:overwrite":
                             SCREAMING_SNAKE_CASE__   :      List[Any]      		=	True
                             SCREAMING_SNAKE_CASE__   :      Any      		=	line.rsplit(""" """	,    1  )
                       else:
                             SCREAMING_SNAKE_CASE__   :      Tuple      		=	False
                       SCREAMING_SNAKE_CASE__   :      str      		=	int(_a  )
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	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(_a  )  )
                       self.add_symbol(_a	,    n=_a	,    overwrite=_a  )
                 except ValueError:
                       raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""  )





def  _lowercase					(    __lowerCAmelCase							)       ->				Dict:
      # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
      # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
      SCREAMING_SNAKE_CASE__   :      int      		=	dict((re.sub(r"""@@$"""  ,					""""""  ,					__lowerCAmelCase							), v) if k.endswith("""@@"""							) else (re.sub(r"""$"""  ,					"""</w>"""  ,					__lowerCAmelCase							), v) for k, v in d.items()							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""<s> <pad> </s> <unk>""".split()
      # restore the special tokens
      for k in keep_keys:
            del da[F'''{k}</w>''']
            SCREAMING_SNAKE_CASE__   :      str      		=	d[k]  # restore
      return da




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Optional[Any]:
      # prep
      if not os.path.exists(__lowerCAmelCase							):
            raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''							)
      os.makedirs(__lowerCAmelCase  ,					exist_ok=__lowerCAmelCase							)
      print(F'''Writing results to {pytorch_dump_folder_path}'''							)

      # handle various types of models

      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	os.path.join(__lowerCAmelCase  ,					"""checkpoint.pt"""							)
      if not os.path.isfile(__lowerCAmelCase							):
            raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)

      SCREAMING_SNAKE_CASE__   :      Dict      		=	chkpt["""cfg"""]["""model"""]

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

      # merges_file (bpecodes)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	os.path.join(__lowerCAmelCase  ,					"""bpecodes"""							)
      if not os.path.isfile(__lowerCAmelCase							):
            raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''							)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	os.path.join(__lowerCAmelCase  ,					VOCAB_FILES_NAMES["""merges_file"""]							)
      shutil.copyfile(__lowerCAmelCase  ,					__lowerCAmelCase							)

      # model config
      SCREAMING_SNAKE_CASE__   :      Any      		=	os.path.join(__lowerCAmelCase  ,					"""config.json"""							)

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{
          """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""": 0.02,
          """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(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # tokenizer config
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      str      		=	{
          """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(__lowerCAmelCase  ,					"""w"""  ,					encoding="""utf-8"""							) as f:
            f.write(json.dumps(__lowerCAmelCase  ,					ensure_ascii=__lowerCAmelCase  ,					indent=__lowerCAmelCase							)							)

      # model
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	chkpt["""model"""]

      # remove unneeded keys
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	[
          """decoder.version""",
      ]
      for k in ignore_keys:
            model_state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	list(model_state_dict.keys()							)
      for layer_name in layer_names:
            if layer_name.endswith("""output_projection.weight"""							):
                  SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_state_dict.pop(__lowerCAmelCase							)
            else:
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_state_dict.pop(__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      str      		=	BioGptConfig.from_pretrained(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	BioGptForCausalLM(__lowerCAmelCase							)

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

      # save
      SCREAMING_SNAKE_CASE__   :      int      		=	os.path.join(__lowerCAmelCase  ,					__lowerCAmelCase							)
      print(F'''Generating {pytorch_weights_dump_path}'''							)
      torch.save(__lowerCAmelCase  ,					__lowerCAmelCase							)

      print("""Conversion is done!"""							)


if __name__ == "__main__":
  a					:Union[str, Any]    = 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."
  )
  a					:Dict    = parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_token_type_ids
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      int      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	type_vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_choices

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_attention_mask:
                 SCREAMING_SNAKE_CASE__   :      int      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	RoFormerConfig(
               vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=_a	,    initializer_range=self.initializer_range	,    )

           return config, input_ids, token_type_ids, attention_mask





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]  )

           self.assertTrue(jnp.allclose(output[:, :3, :3]	,    _a	,    atol=1E-4  )  )




