






"""simple docstring"""



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase	:	Tuple				=      logging.get_logger()




@dataclass
class 			lowerCAmelCase__			:
   SCREAMING_SNAKE_CASE_    =42
   SCREAMING_SNAKE_CASE_    =field(default_factory=__magic_name__   )
   SCREAMING_SNAKE_CASE_    =field(default_factory=__magic_name__   )
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Any							,			snake_case__      :	Tensor							,			snake_case__      :	Tensor							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							len(list(m.modules()							)							) == 1 or isinstance(snake_case__							,			nn.Convad							) or isinstance(snake_case__							,			nn.BatchNormad							)
        if has_not_submodules:
             self.traced.append(snake_case__							)
   def __call__(		self      :	Tuple							,			snake_case__      :	Tensor							):


        '''simple docstring'''
        for m in self.module.modules():
             self.handles.append(m.register_forward_hook(self._forward_hook							)							)
        self.module(snake_case__							)
        [x.remove() for x in self.handles]
        return self




   @property
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda snake_case__							: len(list(x.state_dict().keys()							)							) > 0							,			self.traced							)							)




@dataclass
class 			lowerCAmelCase__			:
   SCREAMING_SNAKE_CASE_    =42
   SCREAMING_SNAKE_CASE_    =42
   SCREAMING_SNAKE_CASE_    =1
   SCREAMING_SNAKE_CASE_    =field(default_factory=__magic_name__   )
   SCREAMING_SNAKE_CASE_    =field(default_factory=__magic_name__   )
   SCREAMING_SNAKE_CASE_    =True
   def __call__(		self      :	List[Any]							,			snake_case__      :	Tensor							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							Tracker(self.dest							)(snake_case__							).parametrized
        UpperCAmelCase__						:    int							=							Tracker(self.src							)(snake_case__							).parametrized

        UpperCAmelCase__						:    int							=							list(filter(lambda snake_case__							: type(snake_case__							) not in self.src_skip							,			snake_case__							)							)
        UpperCAmelCase__						:    Any							=							list(filter(lambda snake_case__							: type(snake_case__							) not in self.dest_skip							,			snake_case__							)							)

        if len(snake_case__							) != len(snake_case__							) and self.raise_if_mismatch:
             raise Exception(
                 f'Numbers of operations are different. Source module has {len(snake_case__							)} operations while'
                 f' destination module has {len(snake_case__							)}.'							)

        for dest_m, src_m in zip(snake_case__							,			snake_case__							):
             dest_m.load_state_dict(src_m.state_dict()							)
             if self.verbose == 1:
                  print(f'Transfered from={src_m} to={dest_m}'							)




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Any							,			snake_case__      :	nn.Module							):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    List[Tuple[str, nn.Module]]							=							[]
        # - get the stem
        feature_blocks.append(("conv1", model.stem)							)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
             assert k.startswith("block"							), f'Unexpected layer name {k}'
             UpperCAmelCase__						:    Dict							=							len(snake_case__							) + 1
             feature_blocks.append((f'res{block_index}', v)							)

        UpperCAmelCase__						:    int							=							nn.ModuleDict(snake_case__							)




   def       __a							(		self      :	Dict							,			snake_case__      :	Tensor							):


        '''simple docstring'''
        return get_trunk_forward_outputs(
            snake_case__							,			out_feat_keys=snake_case__							,			feature_blocks=self._feature_blocks							,			)




class 			lowerCAmelCase__			(    __magic_name__   ):
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							x.split("-"							)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]							)




   def __getitem__(		self      :	str							,			snake_case__      :	str							):


        '''simple docstring'''
        # default to timm!
        if x not in self:
             UpperCAmelCase__						:    str							=							self.convert_name_to_timm(snake_case__							)
             UpperCAmelCase__						:    List[Any]							=							partial(lambda: (timm.create_model(snake_case__							,			pretrained=snake_case__							).eval(), None)							)

        else:
             UpperCAmelCase__						:    Dict							=							super().__getitem__(snake_case__							)

        return val




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __getitem__(		self      :	Optional[int]							,			snake_case__      :	str							):


        '''simple docstring'''
        if "seer" in x and "in1k" not in x:
             UpperCAmelCase__						:    List[Any]							=							RegNetModel
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							RegNetForImageClassification
        return val




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[str]			,					snake_case       : List[Tuple[str, str]]				)->		List[str]:






     '''simple docstring'''



     for from_key, to_key in keys:
          UpperCAmelCase__						:    int							=							from_state_dict[from_key].clone()
          print(f'Copied key={from_key} to={to_key}'				)
     return to_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Callable[[], nn.Module]			,					snake_case       : Callable[[], nn.Module]			,					snake_case       : RegNetConfig			,					snake_case       : Path			,					snake_case       : bool = True			,					)->		List[Any]:






     '''simple docstring'''



     print(f'Converting {name}...'				)
     with torch.no_grad():
          UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							from_model_func()
          UpperCAmelCase__						:    Union[str, Any]							=							our_model_func(snake_case				).eval()
          UpperCAmelCase__						:    str							=							ModuleTransfer(src=snake_case			,					dest=snake_case			,					raise_if_mismatch=snake_case				)
          UpperCAmelCase__						:    str							=							torch.randn((1, 3, 224, 224)				)
          module_transfer(snake_case				)

     if from_state_dict is not None:
          UpperCAmelCase__						:    List[str]							=							[]
          # for seer - in1k finetuned we have to manually copy the head
          if "seer" in name and "in1k" in name:
               UpperCAmelCase__						:    Dict							=							[("0.clf.0.weight", "classifier.1.weight"), ("0.clf.0.bias", "classifier.1.bias")]
          UpperCAmelCase__						:    Optional[int]							=							manually_copy_vissl_head(snake_case			,					our_model.state_dict()			,					snake_case				)
          our_model.load_state_dict(snake_case				)

     UpperCAmelCase__						:    List[str]							=							our_model(snake_case			,					output_hidden_states=snake_case				)
     UpperCAmelCase__						:    List[Any]							=							(
         our_outputs.logits if isinstance(snake_case			,					snake_case				) else our_outputs.last_hidden_state
     )

     UpperCAmelCase__						:    List[str]							=							from_model(snake_case				)
     UpperCAmelCase__						:    Any							=							from_output[-1] if type(snake_case				) is list else from_output

     # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
     if "seer" in name and "in1k" in name:
          UpperCAmelCase__						:    str							=							our_outputs.hidden_states[-1]

     assert torch.allclose(snake_case			,					snake_case				), "The model logits don't match the original one."

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / name			,					commit_message="Add model"			,					use_temp_dir=snake_case			,					)

          UpperCAmelCase__						:    List[str]							=							224 if "seer" not in name else 384
          # we can use the convnext one
          UpperCAmelCase__						:    Dict							=							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			,					size=snake_case				)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / name			,					commit_message="Add image processor"			,					use_temp_dir=snake_case			,					)

          print(f'Pushed {name}'				)


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Path			,					snake_case       : str = None			,					snake_case       : bool = True				)->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__						:    Union[str, Any]							=							"imagenet-1k-id2label.json"
     UpperCAmelCase__						:    Dict							=							1000
     UpperCAmelCase__						:    int							=							(1, num_labels)

     UpperCAmelCase__						:    Optional[Any]							=							"huggingface/label-files"
     UpperCAmelCase__						:    Optional[Any]							=							num_labels
     UpperCAmelCase__						:    str							=							json.load(open(cached_download(hf_hub_url(snake_case			,					snake_case			,					repo_type="dataset"				)				)			,					"r"				)				)
     UpperCAmelCase__						:    List[str]							=							{int(snake_case				): v for k, v in idalabel.items()}

     UpperCAmelCase__						:    Dict							=							idalabel
     UpperCAmelCase__						:    str							=							{v: k for k, v in idalabel.items()}

     UpperCAmelCase__						:    Tuple							=							partial(snake_case			,					num_labels=snake_case			,					idalabel=snake_case			,					labelaid=snake_case				)

     UpperCAmelCase__						:    List[Any]							=							{
         "regnet-x-002": ImageNetPreTrainedConfig(
             depths=[1, 1, 4, 7]			,					hidden_sizes=[24, 56, 152, 368]			,					groups_width=8			,					layer_type="x"				),
         "regnet-x-004": ImageNetPreTrainedConfig(
             depths=[1, 2, 7, 12]			,					hidden_sizes=[32, 64, 160, 384]			,					groups_width=16			,					layer_type="x"				),
         "regnet-x-006": ImageNetPreTrainedConfig(
             depths=[1, 3, 5, 7]			,					hidden_sizes=[48, 96, 240, 528]			,					groups_width=24			,					layer_type="x"				),
         "regnet-x-008": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 5]			,					hidden_sizes=[64, 128, 288, 672]			,					groups_width=16			,					layer_type="x"				),
         "regnet-x-016": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 2]			,					hidden_sizes=[72, 168, 408, 912]			,					groups_width=24			,					layer_type="x"				),
         "regnet-x-032": ImageNetPreTrainedConfig(
             depths=[2, 6, 15, 2]			,					hidden_sizes=[96, 192, 432, 1008]			,					groups_width=48			,					layer_type="x"				),
         "regnet-x-040": ImageNetPreTrainedConfig(
             depths=[2, 5, 14, 2]			,					hidden_sizes=[80, 240, 560, 1360]			,					groups_width=40			,					layer_type="x"				),
         "regnet-x-064": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]			,					hidden_sizes=[168, 392, 784, 1624]			,					groups_width=56			,					layer_type="x"				),
         "regnet-x-080": ImageNetPreTrainedConfig(
             depths=[2, 5, 15, 1]			,					hidden_sizes=[80, 240, 720, 1920]			,					groups_width=120			,					layer_type="x"				),
         "regnet-x-120": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]			,					hidden_sizes=[224, 448, 896, 2240]			,					groups_width=112			,					layer_type="x"				),
         "regnet-x-160": ImageNetPreTrainedConfig(
             depths=[2, 6, 13, 1]			,					hidden_sizes=[256, 512, 896, 2048]			,					groups_width=128			,					layer_type="x"				),
         "regnet-x-320": ImageNetPreTrainedConfig(
             depths=[2, 7, 13, 1]			,					hidden_sizes=[336, 672, 1344, 2520]			,					groups_width=168			,					layer_type="x"				),
         # y variant
         "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]			,					hidden_sizes=[24, 56, 152, 368]			,					groups_width=8				),
         "regnet-y-004": ImageNetPreTrainedConfig(
             depths=[1, 3, 6, 6]			,					hidden_sizes=[48, 104, 208, 440]			,					groups_width=8				),
         "regnet-y-006": ImageNetPreTrainedConfig(
             depths=[1, 3, 7, 4]			,					hidden_sizes=[48, 112, 256, 608]			,					groups_width=16				),
         "regnet-y-008": ImageNetPreTrainedConfig(
             depths=[1, 3, 8, 2]			,					hidden_sizes=[64, 128, 320, 768]			,					groups_width=16				),
         "regnet-y-016": ImageNetPreTrainedConfig(
             depths=[2, 6, 17, 2]			,					hidden_sizes=[48, 120, 336, 888]			,					groups_width=24				),
         "regnet-y-032": ImageNetPreTrainedConfig(
             depths=[2, 5, 13, 1]			,					hidden_sizes=[72, 216, 576, 1512]			,					groups_width=24				),
         "regnet-y-040": ImageNetPreTrainedConfig(
             depths=[2, 6, 12, 2]			,					hidden_sizes=[128, 192, 512, 1088]			,					groups_width=64				),
         "regnet-y-064": ImageNetPreTrainedConfig(
             depths=[2, 7, 14, 2]			,					hidden_sizes=[144, 288, 576, 1296]			,					groups_width=72				),
         "regnet-y-080": ImageNetPreTrainedConfig(
             depths=[2, 4, 10, 1]			,					hidden_sizes=[168, 448, 896, 2016]			,					groups_width=56				),
         "regnet-y-120": ImageNetPreTrainedConfig(
             depths=[2, 5, 11, 1]			,					hidden_sizes=[224, 448, 896, 2240]			,					groups_width=112				),
         "regnet-y-160": ImageNetPreTrainedConfig(
             depths=[2, 4, 11, 1]			,					hidden_sizes=[224, 448, 1232, 3024]			,					groups_width=112				),
         "regnet-y-320": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]			,					hidden_sizes=[232, 696, 1392, 3712]			,					groups_width=232				),
         # models created by SEER -> https://arxiv.org/abs/2202.08360
         "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 12, 1]			,					hidden_sizes=[232, 696, 1392, 3712]			,					groups_width=232				),
         "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 12, 1]			,					hidden_sizes=[328, 984, 1968, 4920]			,					groups_width=328				),
         "regnet-y-1280-seer": RegNetConfig(
             depths=[2, 7, 17, 1]			,					hidden_sizes=[528, 1056, 2904, 7392]			,					groups_width=264				),
         "regnet-y-2560-seer": RegNetConfig(
             depths=[3, 7, 16, 1]			,					hidden_sizes=[640, 1696, 2544, 5088]			,					groups_width=640				),
         "regnet-y-10b-seer": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]			,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,					groups_width=1010				),
         # finetuned on imagenet
         "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]			,					hidden_sizes=[232, 696, 1392, 3712]			,					groups_width=232				),
         "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 5, 12, 1]			,					hidden_sizes=[328, 984, 1968, 4920]			,					groups_width=328				),
         "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]			,					hidden_sizes=[528, 1056, 2904, 7392]			,					groups_width=264				),
         "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
             depths=[3, 7, 16, 1]			,					hidden_sizes=[640, 1696, 2544, 5088]			,					groups_width=640				),
         "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]			,					hidden_sizes=[2020, 4040, 1_1110, 2_8280]			,					groups_width=1010				),
     }

     UpperCAmelCase__						:    str							=							NameToOurModelFuncMap()
     UpperCAmelCase__						:    Optional[Any]							=							NameToFromModelFuncMap()
     # add seer weights logic

     def load_using_classy_vision(snake_case       : str			,					snake_case       : Callable[[], nn.Module]				) -> Tuple[nn.Module, Dict]:
          UpperCAmelCase__						:    Optional[Any]							=							torch.hub.load_state_dict_from_url(snake_case			,					model_dir=str(snake_case				)			,					map_location="cpu"				)
          UpperCAmelCase__						:    Optional[Any]							=							model_func()
          # check if we have a head, if yes add it
          UpperCAmelCase__						:    str							=							files["classy_state_dict"]["base_model"]["model"]
          UpperCAmelCase__						:    str							=							model_state_dict["trunk"]
          model.load_state_dict(snake_case				)
          return model.eval(), model_state_dict["heads"]

     # pretrained
     UpperCAmelCase__						:    Dict							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)			,					)

     UpperCAmelCase__						:    List[Any]							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)			,					)

     UpperCAmelCase__						:    List[Any]							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)			,					)

     UpperCAmelCase__						:    Dict							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"			,					lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27			,					group_width=1010			,					w_a=1744			,					w_a=620.83			,					w_m=2.52				)				)				)			,					)

     # IN1K finetuned
     UpperCAmelCase__						:    int							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)			,					)

     UpperCAmelCase__						:    Optional[int]							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)			,					)

     UpperCAmelCase__						:    Tuple							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"			,					lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)			,					)

     UpperCAmelCase__						:    str							=							partial(
         snake_case			,					"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"			,					lambda: FakeRegNetVisslWrapper(
             RegNet(RegNetParams(depth=27			,					group_width=1010			,					w_a=1744			,					w_a=620.83			,					w_m=2.52				)				)				)			,					)

     if model_name:
          convert_weight_and_push(
              snake_case			,					names_to_from_model_map[model_name]			,					names_to_ours_model_map[model_name]			,					names_to_config[model_name]			,					snake_case			,					snake_case			,					)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   snake_case			,					names_to_from_model_map[model_name]			,					names_to_ours_model_map[model_name]			,					snake_case			,					snake_case			,					snake_case			,					)
     return config, expected_shape


if __name__ == "__main__":
 _lowerCAmelCase	:	List[str]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help=(
         """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
         """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=Path,
     required=True,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     default=True,
     type=bool,
     required=False,
     help="""If True, push model and image processor to the hub.""",
 )

 _lowerCAmelCase	:	Optional[Any]				=      parser.parse_args()

 _lowerCAmelCase	:	Path				=      args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










"""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()

_lowerCAmelCase	:	Optional[int]				=      2




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


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							bos, unk, pad, eos
        UpperCAmelCase__						:    str							=							[]
        UpperCAmelCase__						:    Tuple							=							[]
        UpperCAmelCase__						:    Optional[int]							=							{}
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Tuple							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    str							=							self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    Optional[int]							=							self.add_symbol(snake_case__							)
        if extra_special_symbols:
             for s in extra_special_symbols:
                  self.add_symbol(snake_case__							)
        UpperCAmelCase__						:    List[str]							=							len(self.symbols							)
   def __eq__(		self      :	List[Any]							,			snake_case__      :	str							):


        '''simple docstring'''
        return self.indices == other.indices
   def __getitem__(		self      :	Dict							,			snake_case__      :	Union[str, Any]							):


        '''simple docstring'''
        if idx < len(self.symbols							):
             return self.symbols[idx]
        return self.unk_word
   def __len__(		self      :	int							):


        '''simple docstring'''
        return len(self.symbols							)
   def __contains__(		self      :	Dict							,			snake_case__      :	Optional[Any]							):


        '''simple docstring'''
        return sym in self.indices
   @classmethod
   def       __a							(		cls      :	Optional[int]							,			snake_case__      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Dict							=							cls()
        d.add_from_file(snake_case__							)
        return d
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	str							,			snake_case__      :	Dict=1							,			snake_case__      :	Dict=False							):


        '''simple docstring'''
        if word in self.indices and not overwrite:
             UpperCAmelCase__						:    List[Any]							=							self.indices[word]
             UpperCAmelCase__						:    Dict							=							self.count[idx] + n
             return idx
        else:
             UpperCAmelCase__						:    Optional[Any]							=							len(self.symbols							)
             UpperCAmelCase__						:    List[str]							=							idx
             self.symbols.append(snake_case__							)
             self.count.append(snake_case__							)
             return idx
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	List[Any]							):


        '''simple docstring'''
        return 0




   def       __a							(		self      :	Any							,			snake_case__      :	Dict							):


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

        UpperCAmelCase__						:    Optional[int]							=							f.readlines()
        UpperCAmelCase__						:    Dict							=							self._load_meta(snake_case__							)

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




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Optional[int]							=							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__						:    Optional[int]							=							"<s> <pad> </s> <unk>".split()
     # restore the special tokens
     for k in keep_keys:
          del da[f'{k}</w>']
          UpperCAmelCase__						:    List[str]							=							d[k]  # restore
     return da


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : int				)->		Union[str, Any]:






     '''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__						:    Dict							=							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__						:    Optional[int]							=							torch.load(snake_case			,					map_location="cpu"				)

     UpperCAmelCase__						:    Optional[int]							=							chkpt["cfg"]["model"]

     # dicts
     UpperCAmelCase__						:    Optional[int]							=							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__						:    Dict							=							Dictionary.load(snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							rewrite_dict_keys(src_dict.indices				)
     UpperCAmelCase__						:    List[str]							=							len(snake_case				)
     UpperCAmelCase__						:    Tuple							=							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__						:    List[Any]							=							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__						:    int							=							os.path.join(snake_case			,					VOCAB_FILES_NAMES["merges_file"]				)
     shutil.copyfile(snake_case			,					snake_case				)

     # model config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					"config.json"				)

     UpperCAmelCase__						:    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-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(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

     # tokenizer config
     UpperCAmelCase__						:    List[Any]							=							os.path.join(snake_case			,					snake_case				)

     UpperCAmelCase__						:    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(snake_case			,					"w"			,					encoding="utf-8"				) as f:
          f.write(json.dumps(snake_case			,					ensure_ascii=snake_case			,					indent=snake_case				)				)

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

     # remove unneeded keys
     UpperCAmelCase__						:    Union[str, Any]							=							[
         "decoder.version",
     ]
     for k in ignore_keys:
          model_state_dict.pop(snake_case			,					snake_case				)

     UpperCAmelCase__						:    Union[str, Any]							=							list(model_state_dict.keys()				)
     for layer_name in layer_names:
          if layer_name.endswith("output_projection.weight"				):
               UpperCAmelCase__						:    int							=							model_state_dict.pop(snake_case				)
          else:
               UpperCAmelCase__						:    Tuple							=							model_state_dict.pop(snake_case				)

     UpperCAmelCase__						:    Any							=							BioGptConfig.from_pretrained(snake_case				)
     UpperCAmelCase__						:    List[Any]							=							BioGptForCausalLM(snake_case				)

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

     # save
     UpperCAmelCase__						:    Union[str, Any]							=							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__":
 _lowerCAmelCase	:	List[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."""
 )
 _lowerCAmelCase	:	List[str]				=      parser.parse_args()
 convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)


