




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
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.modeling_utils import PreTrainedModel
from transformers.utils import logging


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







@dataclass
class __snake_case     :
   UpperCAmelCase__    :       List[str]		=    4_2
   UpperCAmelCase__    :       Union[str, Any]		=    field(default_factory=__UpperCAmelCase						)
   UpperCAmelCase__    :       Tuple		=    field(default_factory=__UpperCAmelCase						)
   UpperCAmelCase__    :       Union[str, Any]		=    field(default_factory=__UpperCAmelCase						)







   def lowerCamelCase		( self						:		List[str]   ,		_snake_case						:		Optional[int]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				len(list(m.modules())) == 1 or isinstance(UpperCAmelCase_   ,		nn.Convad) or isinstance(UpperCAmelCase_   ,		nn.BatchNormad)
        if has_not_submodules:
             self.traced.append(UpperCAmelCase_)
             UpperCAmelCase_		=				m







   def __call__( self						:		List[Any]   ,		_snake_case						:		Dict):

        """simple docstring"""


        for name, m in self.module.named_modules():
             self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,		name=UpperCAmelCase_)))
        self.module(UpperCAmelCase_)
        [x.remove() for x in self.handles]
        return self







   @property
   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


        return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys())) > 0}







class __snake_case     (     nn.Module						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		Optional[Any]):

        """simple docstring"""


        super().__init__()

        UpperCAmelCase_		=				[]
        # - 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_		=				len(UpperCAmelCase_) + 1
             feature_blocks.append((F"""res{block_index}""", v))

        UpperCAmelCase_		=				nn.ModuleDict(UpperCAmelCase_)







   def lowerCamelCase		( self						:		Any   ,		_snake_case						:		Dict):

        """simple docstring"""


        return get_trunk_forward_outputs(
            UpperCAmelCase_   ,		out_feat_keys=UpperCAmelCase_   ,		feature_blocks=self._feature_blocks   ,		)







class __snake_case     (     __UpperCAmelCase						):







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def   A		(__A  :       Dict			)  -> Any:







     """simple docstring"""


     UpperCAmelCase_		=				RegNetConfig(depths=[2, 7, 17, 1]  ,							hidden_sizes=[8, 8, 8, 8]  ,							groups_width=8			)
     if "in1k" in model_name:
          UpperCAmelCase_		=				RegNetForImageClassification(a_			)
     else:
          UpperCAmelCase_		=				RegNetModel(a_			)
     # create from model (with small weights)
     UpperCAmelCase_		=				FakeRegNetVisslWrapper(
         RegNet(FakeRegNetParams(depth=27  ,							group_width=1010  ,							w_a=1744  ,							w_a=620.83  ,							w_m=2.52			)			)			)

     with torch.no_grad():
          UpperCAmelCase_		=				from_model.eval()
          UpperCAmelCase_		=				our_model.eval()

          UpperCAmelCase_		=				torch.randn((1, 3, 32, 32)			)
          # trace both
          UpperCAmelCase_		=				Tracker(a_			)
          UpperCAmelCase_		=				dest_tracker(a_			).parametrized

          pprint(dest_tracker.nameamodule			)
          UpperCAmelCase_		=				Tracker(a_			)
          UpperCAmelCase_		=				src_tracker(a_			).parametrized

     # convert the keys -> module dict to keys -> params
     def to_params_dict(__A  :       Optional[int]			):
          UpperCAmelCase_		=				OrderedDict()
          for name, module in dict_with_modules.items():
               for param_name, param in module.state_dict().items():
                    UpperCAmelCase_		=				param
          return params_dict

     UpperCAmelCase_		=				{}

     UpperCAmelCase_		=				to_params_dict(a_			)
     UpperCAmelCase_		=				to_params_dict(a_			)

     for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()  ,							dst_state_dict.items()			):
          UpperCAmelCase_		=				dest_key
          logger.info(F"""{src_key} -> {dest_key}"""			)
     # if "in1k" was in the model_name it means it must have a classification head (was finetuned)
     if "in1k" in model_name:
          UpperCAmelCase_		=				'classifier.1.weight'
          UpperCAmelCase_		=				'classifier.1.bias'

     return from_to_ours_keys


def   A		(__A  :       Dict  ,							__A  :       int = None  ,							__A  :       List[Any] = True			)  -> int:







     """simple docstring"""


     UpperCAmelCase_		=				'imagenet-1k-id2label.json'
     UpperCAmelCase_		=				1000

     UpperCAmelCase_		=				'huggingface/label-files'
     UpperCAmelCase_		=				num_labels
     UpperCAmelCase_		=				json.load(open(cached_download(hf_hub_url(a_  ,							a_  ,							repo_type='''dataset'''			)			)  ,							'''r'''			)			)
     UpperCAmelCase_		=				{int(a_			): v for k, v in idalabel.items()}

     UpperCAmelCase_		=				idalabel
     UpperCAmelCase_		=				{v: k for k, v in idalabel.items()}

     UpperCAmelCase_		=				partial(a_  ,							num_labels=a_  ,							idalabel=a_  ,							labelaid=a_			)

     UpperCAmelCase_		=				{
         'regnet-y-10b-seer': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]  ,							hidden_sizes=[2020, 4040, 11110, 28280]  ,							groups_width=1010			),
         # finetuned on imagenet
         'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
             depths=[2, 7, 17, 1]  ,							hidden_sizes=[2020, 4040, 11110, 28280]  ,							groups_width=1010			),
     }

     # add seer weights logic
     def load_using_classy_vision(__A  :       Dict			) -> Tuple[Dict, Dict]:
          UpperCAmelCase_		=				torch.hub.load_state_dict_from_url(a_  ,							model_dir=str(a_			)  ,							map_location='''cpu'''			)
          # check if we have a head, if yes add it
          UpperCAmelCase_		=				files['classy_state_dict']['base_model']['model']
          return model_state_dict["trunk"], model_state_dict["heads"]

     UpperCAmelCase_		=				{
         'regnet-y-10b-seer': partial(
             a_  ,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''  ,							),
         'regnet-y-10b-seer-in1k': partial(
             a_  ,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''  ,							),
     }

     UpperCAmelCase_		=				get_from_to_our_keys(a_			)

     if not (save_directory / F"""{model_name}.pth""").exists():
          logger.info('''Loading original state_dict.'''			)
          UpperCAmelCase_		=				names_to_from_model[model_name]()
          UpperCAmelCase_		=				from_state_dict_trunk
          if "in1k" in model_name:
               # add the head
               UpperCAmelCase_		=				{**from_state_dict_trunk, **from_state_dict_head}
          logger.info('''Done!'''			)

          UpperCAmelCase_		=				{}

          UpperCAmelCase_		=				list(from_state_dict.keys()			)
          UpperCAmelCase_		=				r'\.block.-part.'
          # this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
          for key in from_state_dict.keys():
               # remove the weird "block[0,1]-part" from the key
               UpperCAmelCase_		=				re.sub(a_  ,							''''''  ,							a_			)
               # now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
               UpperCAmelCase_		=				from_to_ours_keys[src_key]
               # store the parameter with our key
               UpperCAmelCase_		=				from_state_dict[key]
               not_used_keys.remove(a_			)
          # check that all keys have been updated
          assert len(a_			) == 0, F"""Some keys where not used {",".join(a_			)}"""

          logger.info(F"""The following keys were not used: {",".join(a_			)}"""			)

          # save our state dict to disk
          torch.save(a_  ,							save_directory / F"""{model_name}.pth"""			)

          del converted_state_dict
     else:
          logger.info('''The state_dict was already stored on disk.'''			)
     if push_to_hub:
          logger.info(F"""Token is {os.environ["HF_TOKEN"]}"""			)
          logger.info('''Loading our model.'''			)
          # create our model
          UpperCAmelCase_		=				names_to_config[model_name]
          UpperCAmelCase_		=				RegNetModel
          if "in1k" in model_name:
               UpperCAmelCase_		=				RegNetForImageClassification
          UpperCAmelCase_		=				our_model_func(a_			)
          # place our model to the meta device (so remove all the weights)
          our_model.to(torch.device('''meta'''			)			)
          logger.info('''Loading state_dict in our model.'''			)
          # load state dict
          UpperCAmelCase_		=				our_model.state_dict().keys()
          PreTrainedModel._load_pretrained_model_low_mem(
              a_  ,							a_  ,							[save_directory / F"""{model_name}.pth"""]			)
          logger.info('''Finally, pushing!'''			)
          # push it to hub
          our_model.push_to_hub(
              repo_path_or_name=save_directory / model_name  ,							commit_message='''Add model'''  ,							output_dir=save_directory / model_name  ,							)
          UpperCAmelCase_		=				384
          # we can use the convnext one
          UpperCAmelCase_		=				AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''  ,							size=a_			)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / model_name  ,							commit_message='''Add image processor'''  ,							output_dir=save_directory / model_name  ,							)


if __name__ == "__main__":
    snake_case_					:					List[Any]       						=			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.",
    )

    snake_case_					:					int       						=			parser.parse_args()

    snake_case_					:					Any       						=			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 os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


_A				=					'block_records.npy'


_A				=					logging.get_logger(__name__)
def 						UpperCAmelCase   (						a_,		a_     ):





		'''simple docstring'''


		import tensorflow.compat.va as tf

		lowerCamelCase	:     Union[str, Any]            =       tf.data.TFRecordDataset(a_,		buffer_size=512 * 1024 * 1024     )
		lowerCamelCase	:     Union[str, Any]            =       blocks_dataset.batch(a_,		drop_remainder=a_     )
		lowerCamelCase	:     Union[str, Any]            =       next(blocks_dataset.take(1     ).as_numpy_iterator()     )

		return np_record






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=2						,					UpperCAmelCase_=1000						,					UpperCAmelCase_=100						,					UpperCAmelCase_=100000						,					)						->				List[Any]:
			from scann.scann_ops.py.scann_ops_pybind import builder as Builder

			lowerCamelCase	:     int            =       Builder(db=UpperCAmelCase_						,					num_neighbors=UpperCAmelCase_						,					distance_measure='dot_product'   )
			lowerCamelCase	:     Optional[int]            =       builder.tree(
			    num_leaves=UpperCAmelCase_						,					num_leaves_to_search=UpperCAmelCase_						,					training_sample_size=UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       builder.score_ah(dimensions_per_block=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       builder.build()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase      ,       lowerCamelCase	:     List[Any]            =       self.searcher.search_batched(question_projection.detach().cpu()   )
			return retrieved_block_ids.astype('int64'   )






class 			_lowercase  :




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Optional[Any]:
			super().__init__()
			lowerCamelCase	:     Tuple            =       block_records
			lowerCamelCase	:     Any            =       tokenizer




	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_="pt"   )						->				Tuple:
			lowerCamelCase	:     List[str]            =       np.take(self.block_records						,					indices=UpperCAmelCase_						,					axis=0   )

			lowerCamelCase	:     Any            =       self.tokenizer.decode(question_input_ids[0]						,					skip_special_tokens=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Optional[int]            =       []
			for retrieved_block in retrieved_blocks:
					text.append(UpperCAmelCase_   )
					text_pair.append(retrieved_block.decode()   )

			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					padding=UpperCAmelCase_						,					truncation=UpperCAmelCase_						,					return_special_tokens_mask=UpperCAmelCase_						,					max_length=UpperCAmelCase_   )
			lowerCamelCase	:     str            =       concat_inputs.convert_to_tensors(UpperCAmelCase_   )

			if answer_ids is not None:
					return self.block_has_answer(UpperCAmelCase_						,					UpperCAmelCase_   ) + (concat_inputs_tensors,)
			else:
					return (None, None, None, concat_inputs_tensors)




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Dict:
			if os.path.isdir(UpperCAmelCase_   ):
					lowerCamelCase	:     str            =       os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )
			else:
					lowerCamelCase	:     Tuple            =       hf_hub_download(
					    repo_id=UpperCAmelCase_						,					filename=_REALM_BLOCK_RECORDS_FILENAME						,					**UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       np.load(UpperCAmelCase_						,					allow_pickle=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       AutoTokenizer.from_pretrained(UpperCAmelCase_						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )

			return cls(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			# save block records
			np.save(os.path.join(UpperCAmelCase_						,					_REALM_BLOCK_RECORDS_FILENAME   )						,					self.block_records   )
			# save tokenizer
			self.tokenizer.save_pretrained(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				Tuple:
			lowerCamelCase	:     List[Any]            =       []
			lowerCamelCase	:     Any            =       []
			lowerCamelCase	:     int            =       []
			lowerCamelCase	:     Union[str, Any]            =       0

			for input_id in concat_inputs.input_ids:
					lowerCamelCase	:     Optional[Any]            =       input_id.tolist()
					# Check answers between two [SEP] tokens
					lowerCamelCase	:     Tuple            =       input_id_list.index(self.tokenizer.sep_token_id   )
					lowerCamelCase	:     Any            =       first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id   )

					start_pos.append([]   )
					end_pos.append([]   )
					for answer in answer_ids:
							for idx in range(first_sep_idx + 1						,					UpperCAmelCase_   ):
									if answer[0] == input_id_list[idx]:
											if input_id_list[idx : idx + len(UpperCAmelCase_   )] == answer:
													start_pos[-1].append(UpperCAmelCase_   )
													end_pos[-1].append(idx + len(UpperCAmelCase_   ) - 1   )

					if len(start_pos[-1]   ) == 0:
							has_answers.append(UpperCAmelCase_   )
					else:
							has_answers.append(UpperCAmelCase_   )
							if len(start_pos[-1]   ) > max_answers:
									lowerCamelCase	:     List[str]            =       len(start_pos[-1]   )

        # Pad -1 to max_answers
			for start_pos_, end_pos_ in zip(UpperCAmelCase_						,					UpperCAmelCase_   ):
					if len(start_pos_   ) < max_answers:
							lowerCamelCase	:     Optional[int]            =       [-1] * (max_answers - len(start_pos_   ))
							start_pos_ += padded
							end_pos_ += padded
			return has_answers, start_pos, end_pos






