
"""simple docstring"""



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()
a_				=    logging.get_logger()






@dataclass
class 	__snake_case      :





 """simple docstring"""


 _lowerCamelCase				 =  42
 _lowerCamelCase				 =  field(default_factory=lowerCAmelCase_  )
 _lowerCamelCase				 =  field(default_factory=lowerCAmelCase_  )
 _lowerCamelCase				 =  field(default_factory=lowerCAmelCase_  )

 def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




     __A					:		str						=  len(list(m.modules()						)						) == 1 or isinstance(__a      ,		nn.Convad						) or isinstance(__a      ,		nn.BatchNormad						)
     if has_not_submodules:
         self.traced.append(__a						)
         __A					:		Optional[Any]						=  m

 def __call__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




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







 @property
 def       UpperCamelCase__(					self						):



     '''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  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




     super().__init__()

     __A					:		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}"""
         __A					:		List[Any]						=  len(__a						) + 1
         feature_blocks.append((F"""res{block_index}""", v)						)

     __A					:		str						=  nn.ModuleDict(__a						)







 def       UpperCamelCase__(					self      ,		__lowerCamelCase						):



     '''simple docstring'''




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






class 	__snake_case      (	lowerCAmelCase_  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''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 	__lowercase  (     snake_case_						:						str		)     ->List[Any]:



    '''simple docstring'''







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

    with torch.no_grad():
        __A					:		int						=  from_model.eval()
        __A					:		List[Any]						=  our_model.eval()

        __A					:		Tuple						=  torch.randn((1, 3, 32, 32)		)
        # trace both
        __A					:		List[Any]						=  Tracker(_SCREAMING_SNAKE_CASE		)
        __A					:		List[str]						=  dest_tracker(_SCREAMING_SNAKE_CASE		).parametrized

        pprint(dest_tracker.nameamodule		)
        __A					:		str						=  Tracker(_SCREAMING_SNAKE_CASE		)
        __A					:		int						=  src_tracker(_SCREAMING_SNAKE_CASE		).parametrized

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

    __A					:		Optional[int]						=  {}

    __A					:		Any						=  to_params_dict(_SCREAMING_SNAKE_CASE		)
    __A					:		int						=  to_params_dict(_SCREAMING_SNAKE_CASE		)

    for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,dst_state_dict.items()		):
        __A					:		List[Any]						=  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:
        __A					:		str						=  'classifier.1.weight'
        __A					:		Dict						=  'classifier.1.bias'

    return from_to_ours_keys







def 	__lowercase  (     snake_case_						:						Path       ,snake_case_						:						str = None       ,snake_case_						:						bool = True		)     ->List[Any]:



    '''simple docstring'''







    __A					:		List[Any]						=  'imagenet-1k-id2label.json'
    __A					:		str						=  1000

    __A					:		List[str]						=  'huggingface/label-files'
    __A					:		Tuple						=  num_labels
    __A					:		Tuple						=  json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,repo_type='''dataset'''		)		)       ,'''r'''		)		)
    __A					:		Union[str, Any]						=  {int(_SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}

    __A					:		List[str]						=  idalabel
    __A					:		str						=  {v: k for k, v in idalabel.items()}

    __A					:		Dict						=  partial(_SCREAMING_SNAKE_CASE       ,num_labels=_SCREAMING_SNAKE_CASE       ,idalabel=_SCREAMING_SNAKE_CASE       ,labelaid=_SCREAMING_SNAKE_CASE		)

    __A					:		List[str]						=  {
        '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(snake_case_						:						str		) -> Tuple[Dict, Dict]:
        __A					:		List[Any]						=  torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE       ,model_dir=str(_SCREAMING_SNAKE_CASE		)       ,map_location='''cpu'''		)
        # check if we have a head, if yes add it
        __A					:		Optional[Any]						=  files['classy_state_dict']['base_model']['model']
        return model_state_dict["trunk"], model_state_dict["heads"]

    __A					:		List[Any]						=  {
        'regnet-y-10b-seer': partial(
            _SCREAMING_SNAKE_CASE       ,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''       ,),
        'regnet-y-10b-seer-in1k': partial(
            _SCREAMING_SNAKE_CASE       ,'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''       ,),
    }

    __A					:		Dict						=  get_from_to_our_keys(_SCREAMING_SNAKE_CASE		)

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

        __A					:		Optional[Any]						=  {}

        __A					:		Optional[int]						=  list(from_state_dict.keys()		)
        __A					:		int						=  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
            __A					:		Any						=  re.sub(_SCREAMING_SNAKE_CASE       ,''''''       ,_SCREAMING_SNAKE_CASE		)
            # 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
            __A					:		Union[str, Any]						=  from_to_ours_keys[src_key]
            # store the parameter with our key
            __A					:		List[str]						=  from_state_dict[key]
            not_used_keys.remove(_SCREAMING_SNAKE_CASE		)
        # check that all keys have been updated
        assert len(_SCREAMING_SNAKE_CASE		) == 0, F"""Some keys where not used {','.join(_SCREAMING_SNAKE_CASE		)}"""

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

        # save our state dict to disk
        torch.save(_SCREAMING_SNAKE_CASE       ,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
        __A					:		Dict						=  names_to_config[model_name]
        __A					:		str						=  RegNetModel
        if "in1k" in model_name:
            __A					:		Any						=  RegNetForImageClassification
        __A					:		List[str]						=  our_model_func(_SCREAMING_SNAKE_CASE		)
        # 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
        __A					:		str						=  our_model.state_dict().keys()
        PreTrainedModel._load_pretrained_model_low_mem(
            _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,[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       ,)
        __A					:		Tuple						=  384
        # we can use the convnext one
        __A					:		Any						=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       ,size=_SCREAMING_SNAKE_CASE		)
        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__":
       a_				=    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.""",
       )

       a_				=    parser.parse_args()

       a_				=    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 unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


a_				=    """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""






class 	__snake_case      (	unittest.TestCase							,							SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[Any]						=  load_tool('''summarization'''						)
     self.tool.setup()
     __A					:		Tuple						=  load_tool('''summarization'''      ,		remote=__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[str]						=  self.tool(__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		int						=  self.remote_tool(__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[Any]						=  self.tool(text=__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.remote_tool(text=__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




