





"""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()

SCREAMING_SNAKE_CASE__					=   2

class 		lowercase					:





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





      def __eq__(       self      ,						lowercase )		->     List[str]:
            return self.indices == other.indices





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

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





      def __len__(       self )		->     Dict:
            return len(self.symbols )





      def __contains__(       self      ,						lowercase )		->     str:
            return sym in self.indices





      @classmethod
      def     _snake_case			(       cls      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       cls()
            d.add_from_file(__UpperCAmelCase )
            return d





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

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





      def     _snake_case			(       self      ,						lowercase )		->     Any:
            return 0





      def     _snake_case			(       self      ,						lowercase )		->     Dict:

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

            lowerCAmelCase  						=       f.readlines()
            lowerCAmelCase  						=       self._load_meta(__UpperCAmelCase )

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







def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




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

      # handle various types of models

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

      lowerCAmelCase  						=       chkpt['cfg']['model']

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

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

      lowerCAmelCase  						=       os.path.join(_SCREAMING_SNAKE_CASE							,						VOCAB_FILES_NAMES["""merges_file"""]			)
      shutil.copyfile(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			)

      # model config
      lowerCAmelCase  						=       os.path.join(_SCREAMING_SNAKE_CASE							,						"""config.json"""			)

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

      # tokenizer config
      lowerCAmelCase  						=       os.path.join(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       {
          'bos_token': '<s>',
          'eos_token': '</s>',
          'model_max_length': 10_24,
          'pad_token': '<pad>',
          'special_tokens_map_file': None,
          'tokenizer_class': 'BioGptTokenizer',
          'unk_token': '<unk>',
      }

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

      # model
      lowerCAmelCase  						=       chkpt['model']

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

      lowerCAmelCase  						=       list(model_state_dict.keys()			)
      for layer_name in layer_names:
            if layer_name.endswith("""output_projection.weight"""			):
                  lowerCAmelCase  						=       model_state_dict.pop(_SCREAMING_SNAKE_CASE			)
            else:
                  lowerCAmelCase  						=       model_state_dict.pop(_SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       BioGptConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       BioGptForCausalLM(_SCREAMING_SNAKE_CASE			)

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

      # save
      lowerCAmelCase  						=       os.path.join(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			)
      print(F'Generating {pytorch_weights_dump_path}'			)
      torch.save(_SCREAMING_SNAKE_CASE							,						_SCREAMING_SNAKE_CASE			)

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


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













"""simple docstring"""





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__A											=		logging.get_logger(__name__)






@add_end_docstrings(a       )
class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       def __init__(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         super().__init__(**__UpperCAmelCase      )
         requires_backends(self    , 'vision'      )
         requires_backends(self    , 'torch'      )

         if self.framework != "pt":
           raise ValueError(F"The {self.__class__} is only available in PyTorch."      )

         self.check_model_type(__UpperCAmelCase      )






       def      snake_case						(    self    , **__UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							{}
         lowerCAmelCase__			:Tuple											=							{}
         lowerCAmelCase__			:Any											=							{}
         # preprocess args
         if "points_per_batch" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['points_per_batch']
         if "points_per_crop" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['points_per_crop']
         if "crops_n_layers" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crops_n_layers']
         if "crop_overlap_ratio" in kwargs:
           lowerCAmelCase__			:Any											=							kwargs['crop_overlap_ratio']
         if "crop_n_points_downscale_factor" in kwargs:
           lowerCAmelCase__			:Dict											=							kwargs['crop_n_points_downscale_factor']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
           lowerCAmelCase__			:Tuple											=							kwargs['pred_iou_thresh']
         if "stability_score_offset" in kwargs:
           lowerCAmelCase__			:Optional[int]											=							kwargs['stability_score_offset']
         if "mask_threshold" in kwargs:
           lowerCAmelCase__			:List[Any]											=							kwargs['mask_threshold']
         if "stability_score_thresh" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							kwargs['stability_score_thresh']
         if "crops_nms_thresh" in kwargs:
           lowerCAmelCase__			:int											=							kwargs['crops_nms_thresh']
         if "output_rle_mask" in kwargs:
           lowerCAmelCase__			:Union[str, Any]											=							kwargs['output_rle_mask']
         if "output_bboxes_mask" in kwargs:
           lowerCAmelCase__			:Optional[Any]											=							kwargs['output_bboxes_mask']
         return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(    self    , __UpperCAmelCase    , *__UpperCAmelCase    , __UpperCAmelCase=None    , __UpperCAmelCase=None    , **__UpperCAmelCase      ):



         '''simple docstring'''

         return super().__call__(__UpperCAmelCase    , *__UpperCAmelCase    , num_workers=__UpperCAmelCase    , batch_size=__UpperCAmelCase    , **__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=6_4    , __UpperCAmelCase = 0    , __UpperCAmelCase = 5_1_2 / 1_5_0_0    , __UpperCAmelCase = 3_2    , __UpperCAmelCase = 1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							load_image(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							self.image_processor.size['longest_edge']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:int											=							self.image_processor.generate_crop_boxes(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							self.image_processor(images=__UpperCAmelCase    , return_tensors='pt'      )

         with self.device_placement():
           if self.framework == "pt":
             lowerCAmelCase__			:Optional[int]											=							self.get_inference_context()
             with inference_context():
               lowerCAmelCase__			:Any											=							self._ensure_tensor_on_device(__UpperCAmelCase    , device=self.device      )
               lowerCAmelCase__			:Tuple											=							self.model.get_image_embeddings(model_inputs.pop('pixel_values'      )      )
               lowerCAmelCase__			:Optional[int]											=							image_embeddings

         lowerCAmelCase__			:List[Any]											=							grid_points.shape[1]
         lowerCAmelCase__			:Union[str, Any]											=							points_per_batch if points_per_batch is not None else n_points

         if points_per_batch <= 0:
           raise ValueError(
               'Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '
               'To return all points at once, set points_per_batch to None'      )

         for i in range(0    , __UpperCAmelCase    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[Any]											=							grid_points[:, i : i + points_per_batch, :, :]
           lowerCAmelCase__			:List[str]											=							input_labels[:, i : i + points_per_batch]
           lowerCAmelCase__			:List[Any]											=							i == n_points - points_per_batch
           yield {
               "input_points": batched_points,
               "input_labels": labels,
               "input_boxes": crop_boxes,
               "is_last": is_last,
               **model_inputs,
           }






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=0.88    , __UpperCAmelCase=0.95    , __UpperCAmelCase=0    , __UpperCAmelCase=1    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Any											=							model_inputs.pop('input_boxes'      )
         lowerCAmelCase__			:Optional[int]											=							model_inputs.pop('is_last'      )
         lowerCAmelCase__			:Dict											=							model_inputs.pop('original_sizes'      ).tolist()
         lowerCAmelCase__			:Dict											=							model_inputs.pop('reshaped_input_sizes'      ).tolist()

         lowerCAmelCase__			:Optional[int]											=							self.model(**__UpperCAmelCase      )

         # post processing happens here in order to avoid CPU GPU copies of ALL the masks
         lowerCAmelCase__			:int											=							model_outputs['pred_masks']
         lowerCAmelCase__			:Optional[Any]											=							self.image_processor.post_process_masks(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , binarize=__UpperCAmelCase      )
         lowerCAmelCase__			:Any											=							model_outputs['iou_scores']
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Tuple											=							self.image_processor.filter_masks(
             masks[0]    , iou_scores[0]    , original_sizes[0]    , input_boxes[0]    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , )
         return {
             "masks": masks,
             "is_last": is_last,
             "boxes": boxes,
             "iou_scores": iou_scores,
         }






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=False    , __UpperCAmelCase=False    , __UpperCAmelCase=0.7    , ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							[]
         lowerCAmelCase__			:Optional[Any]											=							[]
         lowerCAmelCase__			:int											=							[]
         for model_output in model_outputs:
           all_scores.append(model_output.pop('iou_scores'      )      )
           all_masks.extend(model_output.pop('masks'      )      )
           all_boxes.append(model_output.pop('boxes'      )      )

         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__			:Dict											=							torch.cat(__UpperCAmelCase      )
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							self.image_processor.post_process_for_mask_generation(
             __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Tuple											=							defaultdict(__UpperCAmelCase      )
         for output in model_outputs:
           for k, v in output.items():
             extra[k].append(__UpperCAmelCase      )

         lowerCAmelCase__			:Optional[int]											=							{}
         if output_rle_mask:
           lowerCAmelCase__			:str											=							rle_mask

         if output_bboxes_mask:
           lowerCAmelCase__			:Optional[int]											=							bounding_boxes

         return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
