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()
UpperCAmelCase_    		=				logging.get_logger()





@dataclass
class   lowerCAmelCase    :
     _SCREAMING_SNAKE_CASE  :  nn.Module
     _SCREAMING_SNAKE_CASE  :  List[nn.Module]		=field(default_factory=_a      )
     _SCREAMING_SNAKE_CASE  :  list		=field(default_factory=_a      )
     _SCREAMING_SNAKE_CASE  :  Dict[str, nn.Module]		=field(default_factory=_a      )



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



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





     @property
     def       a__	(       self							):
            # check the len of the state_dict keys to see if we have learnable params
            return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}





class   lowerCAmelCase    (       nn.Module      ):



     def __init__(       self					,   lowerCAmelCase__							):
            super().__init__()

            _A=						[]
            # - 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=						len(lowerCAmelCase__							) + 1
                   feature_blocks.append((f"res{block_index}", v)							)

            _A=						nn.ModuleDict(lowerCAmelCase__							)





     def       a__	(       self					,   lowerCAmelCase__							):
            return get_trunk_forward_outputs(
                lowerCAmelCase__					,   out_feat_keys=lowerCAmelCase__					,   feature_blocks=self._feature_blocks					,   )





class   lowerCAmelCase    (       _a      ):



     def       a__	(       self							):
            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 				UpperCamelCase					(     lowerCAmelCase_ )		->    Dict[str, str]:




       '''simple docstring'''






       _A=						RegNetConfig(depths=[2, 7, 17, 1]							,       hidden_sizes=[8, 8, 8, 8]							,       groups_width=8 )
       if "in1k" in model_name:
              _A=						RegNetForImageClassification(lowerCAmelCase_ )
       else:
              _A=						RegNetModel(lowerCAmelCase_ )
       # create from model (with small weights)
       _A=						FakeRegNetVisslWrapper(
           RegNet(FakeRegNetParams(depth=27							,       group_width=10_10							,       w_a=17_44							,       w_a=620.83							,       w_m=2.52 ) ) )

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

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

              pprint(dest_tracker.nameamodule )
              _A=						Tracker(lowerCAmelCase_ )
              _A=						src_tracker(lowerCAmelCase_ ).parametrized

       # convert the keys -> module dict to keys -> params
       def to_params_dict(lowerCAmelCase_ ):
              _A=						OrderedDict()
              for name, module in dict_with_modules.items():
                     for param_name, param in module.state_dict().items():
                            _A=						param
              return params_dict

       _A=						{}

       _A=						to_params_dict(lowerCAmelCase_ )
       _A=						to_params_dict(lowerCAmelCase_ )

       for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,       dst_state_dict.items() ):
              _A=						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=						'classifier.1.weight'
              _A=						'classifier.1.bias'

       return from_to_ours_keys

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_ = None							,       lowerCAmelCase_ = True )		->    Tuple:




       '''simple docstring'''






       _A=						'imagenet-1k-id2label.json'
       _A=						10_00

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

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

       _A=						partial(lowerCAmelCase_							,       num_labels=lowerCAmelCase_							,       idalabel=lowerCAmelCase_							,       labelaid=lowerCAmelCase_ )

       _A=						{
           'regnet-y-10b-seer': ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]							,       hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,       groups_width=10_10 ),
           # finetuned on imagenet
           'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
               depths=[2, 7, 17, 1]							,       hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]							,       groups_width=10_10 ),
       }

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

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

       _A=						get_from_to_our_keys(lowerCAmelCase_ )

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

              _A=						{}

              _A=						list(from_state_dict.keys() )
              _A=						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=						re.sub(lowerCAmelCase_							,       ''							,       lowerCAmelCase_ )
                     # 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=						from_to_ours_keys[src_key]
                     # store the parameter with our key
                     _A=						from_state_dict[key]
                     not_used_keys.remove(lowerCAmelCase_ )
              # check that all keys have been updated
              assert len(lowerCAmelCase_ ) == 0, F"Some keys where not used {','.join(lowerCAmelCase_ )}"

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

              # save our state dict to disk
              torch.save(lowerCAmelCase_							,       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=						names_to_config[model_name]
              _A=						RegNetModel
              if "in1k" in model_name:
                     _A=						RegNetForImageClassification
              _A=						our_model_func(lowerCAmelCase_ )
              # 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=						our_model.state_dict().keys()
              PreTrainedModel._load_pretrained_model_low_mem(
                  lowerCAmelCase_							,       lowerCAmelCase_							,       [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=						3_84
              # we can use the convnext one
              _A=						AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'							,       size=lowerCAmelCase_ )
              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__":
      UpperCAmelCase_    		=				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.''',
      )

      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				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)
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch





class   lowerCAmelCase    (       unittest.TestCase      ):



     @patch('sys.argv'					,   ['fakeprogrampath', 'env']							)
     def       a__	(       self							):
            # test transformers-cli env
            import transformers.commands.transformers_cli

            with CaptureStd() as cs:
                   transformers.commands.transformers_cli.main()
            self.assertIn('Python version'					,   cs.out							)
            self.assertIn('Platform'					,   cs.out							)
            self.assertIn('Using distributed or parallel set-up in script?'					,   cs.out							)



     @is_pt_tf_cross_test
     @patch(
         'sys.argv'					,   ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)  # cleans potential past runs
            transformers.commands.transformers_cli.main()

            # The original repo has no TF weights -- if they exist, they were created by the CLI
            self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'							)							)



     @require_torch
     @patch('sys.argv'					,   ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'							)							)





     @require_torch
     @patch(
         'sys.argv'					,   [
             'fakeprogrampath',
             'download',
             'hf-internal-testing/test_dynamic_model_with_tokenizer',
             '--trust-remote-code',
             '--cache-dir',
             '/tmp',
         ]					,   )
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'							)							)
            self.assertTrue(
                os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'							)							)