



"""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()

snake_case   					=				2



class     UpperCAmelCase    :



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



     """simple docstring"""
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      bos, unk, pad, eos
     _snake_case   	=      []
     _snake_case   	=      []
     _snake_case   	=      {}
     _snake_case   	=      self.add_symbol(__lowerCamelCase				)
     _snake_case   	=      self.add_symbol(__lowerCamelCase				)
     _snake_case   	=      self.add_symbol(__lowerCamelCase				)
     _snake_case   	=      self.add_symbol(__lowerCamelCase				)
     if extra_special_symbols:
       for s in extra_special_symbols:
         self.add_symbol(__lowerCamelCase				)
     _snake_case   	=      len(self.symbols				)



   def __eq__(       self    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     return self.indices == other.indices



   def __getitem__(       self    :	Dict							,       __lowerCamelCase    :	Tuple				):



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



   def __len__(       self    :	Optional[Any]				):



     """simple docstring"""
     return len(self.symbols				)



   def __contains__(       self    :	Dict							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     return sym in self.indices



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      cls()
     d.add_from_file(__lowerCamelCase				)
     return d



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[str]=1							,       __lowerCamelCase    :	Optional[int]=False				):



     """simple docstring"""
     if word in self.indices and not overwrite:
       _snake_case   	=      self.indices[word]
       _snake_case   	=      self.count[idx] + n
       return idx
     else:
       _snake_case   	=      len(self.symbols				)
       _snake_case   	=      idx
       self.symbols.append(__lowerCamelCase				)
       self.count.append(__lowerCamelCase				)
       return idx



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return 0





   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Optional[int]				):



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

     _snake_case   	=      f.readlines()
     _snake_case   	=      self._load_meta(__lowerCamelCase				)

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




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




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				str:
  # 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

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

  _snake_case   	=      chkpt['''cfg''']['''model''']

  # dicts
  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						'''dict.txt'''	)
  if not os.path.isfile(lowerCAmelCase_	):
    raise ValueError(f"""path to the file {dict_file} does not exist!"""	)
  _snake_case   	=      Dictionary.load(lowerCAmelCase_	)
  _snake_case   	=      rewrite_dict_keys(src_dict.indices	)
  _snake_case   	=      len(lowerCAmelCase_	)
  _snake_case   	=      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)
  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						'''bpecodes'''	)
  if not os.path.isfile(lowerCAmelCase_	):
    raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""	)

  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						VOCAB_FILES_NAMES['''merges_file''']	)
  shutil.copyfile(lowerCAmelCase_  ,						lowerCAmelCase_	)

  # model config
  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						'''config.json'''	)

  _snake_case   	=      {
      '''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
  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						lowerCAmelCase_	)

  _snake_case   	=      {
      '''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
  _snake_case   	=      chkpt['''model''']

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

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

  _snake_case   	=      BioGptConfig.from_pretrained(lowerCAmelCase_	)
  _snake_case   	=      BioGptForCausalLM(lowerCAmelCase_	)

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

  # save
  _snake_case   	=      os.path.join(lowerCAmelCase_  ,						lowerCAmelCase_	)
  print(f"""Generating {pytorch_weights_dump_path}"""	)
  torch.save(lowerCAmelCase_  ,						lowerCAmelCase_	)

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


if __name__ == "__main__":
     snake_case   					=				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.'''
     )
     snake_case   					=				parser.parse_args()
     convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""






import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''tf_padding'''				)				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''depth_multiplier'''				)				)



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	List[Any]=3							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Dict=0.2_5							,       __lowerCamelCase    :	Optional[Any]=8							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[Any]=1_0_2_4							,       __lowerCamelCase    :	Union[str, Any]=3_2							,       __lowerCamelCase    :	int="relu6"							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Tuple=0.0_2							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      depth_multiplier
     _snake_case   	=      min_depth
     _snake_case   	=      tf_padding
     _snake_case   	=      int(last_hidden_size * depth_multiplier				)
     _snake_case   	=      output_stride
     _snake_case   	=      hidden_act
     _snake_case   	=      classifier_dropout_prob
     _snake_case   	=      use_labels
     _snake_case   	=      is_training
     _snake_case   	=      num_labels
     _snake_case   	=      initializer_range
     _snake_case   	=      scope



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     _snake_case   	=      None
     _snake_case   	=      None
     if self.use_labels:
       _snake_case   	=      ids_tensor([self.batch_size]							,       self.num_labels				)
       _snake_case   	=      ids_tensor([self.batch_size, self.image_size, self.image_size]							,       self.num_labels				)

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels, pixel_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return MobileNetVaConfig(
         num_channels=self.num_channels							,       image_size=self.image_size							,       depth_multiplier=self.depth_multiplier							,       min_depth=self.min_depth							,       tf_padding=self.tf_padding							,       hidden_act=self.hidden_act							,       classifier_dropout_prob=self.classifier_dropout_prob							,       initializer_range=self.initializer_range							,       )



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(
         result.last_hidden_state.shape							,       (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,       )



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      MobileNetVaForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[Any]           =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__		:	List[str]           =  False
   A__		:	Tuple           =  False
   A__		:	List[Any]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModelTester(self				)
     _snake_case   	=      MobileNetVaConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not output attentions'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      model_class(__lowerCamelCase				)
       _snake_case   	=      inspect.signature(model.forward				)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _snake_case   	=      [*signature.parameters.keys()]

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     def check_hidden_states_output(__lowerCamelCase    :	Any							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]				):
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()

       with torch.no_grad():
         _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

       _snake_case   	=      outputs.hidden_states

       _snake_case   	=      2_6
       self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)

     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      True
       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _snake_case   	=      True

       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      MobileNetVaModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				int:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







@require_torch
@require_vision
class     UpperCAmelCase    (	unittest.TestCase  ):



   @cached_property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     return (
         MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''				) if is_vision_available() else None
     )





   @slow
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_1)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(__lowerCamelCase				)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]							,       __lowerCamelCase							,       atol=1E-4				)				)





