





'''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()

lowercase__						:			Optional[int]       =				2
class 				__lowerCAmelCase     :






       """simple docstring"""





       def __init__(							self   :		Union[str, Any]       , *,  # begin keyword-only arguments
           lowerCAmelCase__   :		List[str]="<s>"       , lowerCAmelCase__   :		Union[str, Any]="<pad>"       , lowerCAmelCase__   :		Tuple="</s>"       , lowerCAmelCase__   :		Dict="<unk>"       , lowerCAmelCase__   :		Any=None       , )    ->     List[Any]:

            '''simple docstring'''




            _UpperCamelCase    ,				_UpperCamelCase    ,				_UpperCamelCase    ,				_UpperCamelCase				    =       bos, unk, pad, eos
            _UpperCamelCase				    =       []
            _UpperCamelCase				    =       []
            _UpperCamelCase				    =       {}
            _UpperCamelCase				    =       self.add_symbol(_a     )
            _UpperCamelCase				    =       self.add_symbol(_a     )
            _UpperCamelCase				    =       self.add_symbol(_a     )
            _UpperCamelCase				    =       self.add_symbol(_a     )
            if extra_special_symbols:
                 for s in extra_special_symbols:
                      self.add_symbol(_a     )
            _UpperCamelCase				    =       len(self.symbols     )





       def __eq__(							self   :		int       , lowerCAmelCase__   :		Optional[Any]     )    ->     Union[str, Any]:

            '''simple docstring'''




            return self.indices == other.indices





       def __getitem__(							self   :		Union[str, Any]       , lowerCAmelCase__   :		List[str]     )    ->     Optional[int]:

            '''simple docstring'''




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





       def __len__(							self   :		str     )    ->     Any:

            '''simple docstring'''




            return len(self.symbols     )





       def __contains__(							self   :		int       , lowerCAmelCase__   :		Dict     )    ->     List[Any]:

            '''simple docstring'''




            return sym in self.indices





       @classmethod
       def snake_case__						(							cls   :		str       , lowerCAmelCase__   :		Optional[Any]     )    ->     List[str]:

            '''simple docstring'''




            _UpperCamelCase				    =       cls()
            d.add_from_file(_a     )
            return d





       def snake_case__						(							self   :		Optional[int]       , lowerCAmelCase__   :		Dict       , lowerCAmelCase__   :		Any=1       , lowerCAmelCase__   :		Any=False     )    ->     str:

            '''simple docstring'''




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





       def snake_case__						(							self   :		int       , lowerCAmelCase__   :		List[Any]     )    ->     Dict:

            '''simple docstring'''




            return 0







       def snake_case__						(							self   :		Union[str, Any]       , lowerCAmelCase__   :		int     )    ->     Optional[int]:

            '''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

            _UpperCamelCase				    =       f.readlines()
            _UpperCamelCase				    =       self._load_meta(_a     )

            for line in lines[indices_start_line:]:
                 try:
                      _UpperCamelCase    ,				_UpperCamelCase				    =       line.rstrip().rsplit(''' '''       , 1     )
                      if field == "#fairseq:overwrite":
                           _UpperCamelCase				    =       True
                           _UpperCamelCase    ,				_UpperCamelCase				    =       line.rsplit(''' '''       , 1     )
                      else:
                           _UpperCamelCase				    =       False
                      _UpperCamelCase				    =       int(_a     )
                      _UpperCamelCase				    =       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 					a__				(				lowercase					:     Tuple      )		->		Tuple:





     """simple docstring"""

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







def 					a__				(				lowercase					:     Optional[int],					lowercase					:     List[Any]      )		->		Dict:





     """simple docstring"""

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

     # handle various types of models

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

     _UpperCamelCase				    =       chkpt['''cfg''']['''model''']

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

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

     _UpperCamelCase				    =       os.path.join(snake_case__,					VOCAB_FILES_NAMES['''merges_file''']      )
     shutil.copyfile(snake_case__,					snake_case__      )

     # model config
     _UpperCamelCase				    =       os.path.join(snake_case__,					'''config.json'''      )

     _UpperCamelCase				    =       {
         '''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(snake_case__,					'''w''',					encoding='''utf-8'''      ) as f:
          f.write(json.dumps(snake_case__,					ensure_ascii=snake_case__,					indent=snake_case__      )      )

     # tokenizer config
     _UpperCamelCase				    =       os.path.join(snake_case__,					snake_case__      )

     _UpperCamelCase				    =       {
         '''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(snake_case__,					'''w''',					encoding='''utf-8'''      ) as f:
          f.write(json.dumps(snake_case__,					ensure_ascii=snake_case__,					indent=snake_case__      )      )

     # model
     _UpperCamelCase				    =       chkpt['''model''']

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

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

     _UpperCamelCase				    =       BioGptConfig.from_pretrained(snake_case__      )
     _UpperCamelCase				    =       BioGptForCausalLM(snake_case__      )

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

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

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


if __name__ == "__main__":
   lowercase__						:			Optional[int]       =				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.'
   )
   lowercase__						:			Tuple       =				parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch






@is_pipeline_test
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

							lowerCamelCase			      =					generator(["""This is great !""", """Something else"""]		, num_return_sequences=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							lowerCamelCase			      =					generator(
							    ["""This is great !""", """Something else"""]		, num_return_sequences=2		, batch_size=2		, do_sample=_a )
							self.assertEqual(
							    _a		, [
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							        [{"""generated_text""": ANY(_a )}, {"""generated_text""": ANY(_a )}],
							    ]		, )

							with self.assertRaises(_a ):
										generator(4 )





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
							    {"""generated_text""": """"""},
							]
							self.assertEqual(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							        [
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							            {"""generated_token_ids""": ANY(torch.Tensor )},
							        ],
							    ]		, )





				@require_tf
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



