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

_lowercase							:      List[Any]      							=logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase_			)
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __init__(				self    :					Dict      ,	**SCREAMING_SNAKE_CASE__    :					Any     )    ->				int:
         super().__init__(**SCREAMING_SNAKE_CASE__     )
         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(SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	**SCREAMING_SNAKE_CASE__    :					Dict     )    ->				Union[str, Any]:
         A						:     Dict         ={}
         A						:     Dict         ={}
         A						:     int         ={}
         # preprocess args
         if "points_per_batch" in kwargs:
                A						:     int         =kwargs['points_per_batch']
         if "points_per_crop" in kwargs:
                A						:     Any         =kwargs['points_per_crop']
         if "crops_n_layers" in kwargs:
                A						:     Any         =kwargs['crops_n_layers']
         if "crop_overlap_ratio" in kwargs:
                A						:     Optional[Any]         =kwargs['crop_overlap_ratio']
         if "crop_n_points_downscale_factor" in kwargs:
                A						:     Tuple         =kwargs['crop_n_points_downscale_factor']
         # postprocess args
         if "pred_iou_thresh" in kwargs:
                A						:     Optional[Any]         =kwargs['pred_iou_thresh']
         if "stability_score_offset" in kwargs:
                A						:     Union[str, Any]         =kwargs['stability_score_offset']
         if "mask_threshold" in kwargs:
                A						:     List[Any]         =kwargs['mask_threshold']
         if "stability_score_thresh" in kwargs:
                A						:     Any         =kwargs['stability_score_thresh']
         if "crops_nms_thresh" in kwargs:
                A						:     Union[str, Any]         =kwargs['crops_nms_thresh']
         if "output_rle_mask" in kwargs:
                A						:     Dict         =kwargs['output_rle_mask']
         if "output_bboxes_mask" in kwargs:
                A						:     Optional[int]         =kwargs['output_bboxes_mask']
         return preprocess_kwargs, forward_params, postprocess_kwargs







  def __call__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	*SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Any=None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=None      ,	**SCREAMING_SNAKE_CASE__    :					Dict     )    ->				int:
         return super().__call__(SCREAMING_SNAKE_CASE__      ,	*SCREAMING_SNAKE_CASE__      ,	num_workers=SCREAMING_SNAKE_CASE__      ,	batch_size=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					List[str]=64      ,	SCREAMING_SNAKE_CASE__    :					int = 0      ,	SCREAMING_SNAKE_CASE__    :					float = 5_12 / 15_00      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = 32      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = 1      ,	)    ->				Optional[int]:
         A						:     Tuple         =load_image(SCREAMING_SNAKE_CASE__     )
         A						:     List[Any]         =self.image_processor.size['longest_edge']
         A  ,  A  ,  A  ,  A						:     str         =self.image_processor.generate_crop_boxes(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )
         A						:     Dict         =self.image_processor(images=SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'     )

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

         A						:     str         =grid_points.shape[1]
         A						:     Tuple         =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      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                A						:     Dict         =grid_points[:, i : i + points_per_batch, :, :]
                A						:     Optional[int]         =input_labels[:, i : i + points_per_batch]
                A						:     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 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.8_8      ,	SCREAMING_SNAKE_CASE__    :					Any=0.9_5      ,	SCREAMING_SNAKE_CASE__    :					int=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=1      ,	)    ->				int:
         A						:     Union[str, Any]         =model_inputs.pop('input_boxes'     )
         A						:     List[str]         =model_inputs.pop('is_last'     )
         A						:     Dict         =model_inputs.pop('original_sizes'     ).tolist()
         A						:     Optional[Any]         =model_inputs.pop('reshaped_input_sizes'     ).tolist()

         A						:     Tuple         =self.model(**SCREAMING_SNAKE_CASE__     )

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







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.7      ,	)    ->				str:
         A						:     Union[str, Any]         =[]
         A						:     Optional[Any]         =[]
         A						:     Any         =[]
         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'     )     )

         A						:     Optional[Any]         =torch.cat(SCREAMING_SNAKE_CASE__     )
         A						:     str         =torch.cat(SCREAMING_SNAKE_CASE__     )
         A  ,  A  ,  A  ,  A						:     List[Any]         =self.image_processor.post_process_for_mask_generation(
             SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     )

         A						:     int         =defaultdict(SCREAMING_SNAKE_CASE__     )
         for output in model_outputs:
                for k, v in output.items():
                       extra[k].append(SCREAMING_SNAKE_CASE__     )

         A						:     Dict         ={}
         if output_rle_mask:
                A						:     Optional[Any]         =rle_mask

         if output_bboxes_mask:
                A						:     List[str]         =bounding_boxes

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







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							:      int      							=2



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


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







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







  def __getitem__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				List[Any]:
         if idx < len(self.symbols     ):
                return self.symbols[idx]
         return self.unk_word







  def __len__(				self    :					List[Any]     )    ->				Union[str, Any]:
         return len(self.symbols     )







  def __contains__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Tuple:
         return sym in self.indices







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(				cls    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					int     )    ->				Any:
         A						:     Union[str, Any]         =cls()
         d.add_from_file(SCREAMING_SNAKE_CASE__     )
         return d







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					str      ,	SCREAMING_SNAKE_CASE__    :					Any=1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=False     )    ->				Any:
         if word in self.indices and not overwrite:
                A						:     int         =self.indices[word]
                A						:     Union[str, Any]         =self.count[idx] + n
                return idx
         else:
                A						:     Tuple         =len(self.symbols     )
                A						:     str         =idx
                self.symbols.append(SCREAMING_SNAKE_CASE__     )
                self.count.append(SCREAMING_SNAKE_CASE__     )
                return idx







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Optional[Any]:
         return 0







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

         A						:     str         =f.readlines()
         A						:     int         =self._load_meta(SCREAMING_SNAKE_CASE__     )

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







def A__  (			lowercase:     Union[str, Any]					)				->		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}
       A						:     int         =dict((re.sub(r'@@$',		'',		lowercase					), v) if k.endswith('@@'					) else (re.sub(r'$',		'</w>',		lowercase					), v) for k, v in d.items()					)
       A						:     int         ='<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              A						:     List[Any]         =d[k]  # restore
       return da



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

       # handle various types of models

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

       A						:     Any         =chkpt['cfg']['model']

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

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

       A						:     List[str]         =os.path.join(lowercase,		VOCAB_FILES_NAMES['merges_file']					)
       shutil.copyfile(lowercase,		lowercase					)

       # model config
       A						:     Tuple         =os.path.join(lowercase,		'config.json'					)

       A						:     Tuple         ={
           '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-1_2,
           '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(lowercase,		'w',		encoding='utf-8'					) as f:
              f.write(json.dumps(lowercase,		ensure_ascii=lowercase,		indent=lowercase					)					)

       # tokenizer config
       A						:     int         =os.path.join(lowercase,		lowercase					)

       A						:     List[str]         ={
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1_024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

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

       # model
       A						:     List[Any]         =chkpt['model']

       # remove unneeded keys
       A						:     List[Any]         =[
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowercase,		lowercase					)

       A						:     str         =list(model_state_dict.keys()					)
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'					):
                     A						:     Union[str, Any]         =model_state_dict.pop(lowercase					)
              else:
                     A						:     List[str]         =model_state_dict.pop(lowercase					)

       A						:     Any         =BioGptConfig.from_pretrained(lowercase					)
       A						:     str         =BioGptForCausalLM(lowercase					)

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

       # save
       A						:     Tuple         =os.path.join(lowercase,		lowercase					)
       print(F'Generating {pytorch_weights_dump_path}'					)
       torch.save(lowercase,		lowercase					)

       print('Conversion is done!'					)


if __name__ == "__main__":
    _lowercase							:      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.'''
    )
    _lowercase							:      List[Any]      							=parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






